train.json

[
    {
        "arxiv_id":"2101.00183v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00183v1",
        "title":"Early Prediction of Heart Disease Using PCA and Hybrid Genetic Algorithm with k-Means",
        "summary":"Worldwide research shows that millions of lives lost per year because of heart disease. The healthcare sector produces massive volumes of data on heart disease that are sadly not used to locate secret knowledge for successful decision making. One of the most important aspects at this moment is detecting heart disease at an early stage. Researchers have applied distinct techniques to the UCI Machine Learning heart disease dataset. Many researchers have tried to apply some complex techniques to this dataset, where detailed studies are still missing. In this paper, Principal Component Analysis (PCA) has been used to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means used for final clustering. Typically, the k-means method is using for clustering the data. This type of clustering can get stuck in the local optima because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA) for data clustering to avoid this problem. Our proposed method can predict early heart disease with an accuracy of 94.06%.",
        "published":"2021-01-01T00:00:00.000Z",
        "authors":"['Md. Touhidul Islam', 'Sanjida Reza Rafa', 'Md. Golam Kibria']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":4.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2101.00289v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00289v1",
        "title":"Design and Actuator Optimization of Lightweight and Compliant Knee Exoskeleton for Mobility Assistance of Children with Crouch Gait",
        "summary":"Pediatric exoskeletons offer great promise to increase mobility for children with crouch gait caused by cerebral palsy. A lightweight, compliant and user-specific actuator is critical for maximizing the benefits of an exoskeleton to users. To date, pediatric exoskeletons generally use the same actuators as adult exoskeletons, which are heavy and resistive to natural movement. There is yet no easy way for robotic exoskeletons to accommodate the changes in design requirements that occur as a child ages. We developed a lightweight (1.65 kg unilateral mass) and compliant pediatric knee exoskeleton with a bandwidth of 22.6 Hz that can provide torque assistance to children with crouch gait using high torque density motor. Experimental results demonstrated that the robot exhibited low mechanical impedance (1.79 Nm average backdrive torque) under the unpowered condition and 0.32 Nm with zero-torque tracking control. Root mean square (RMS) error of torque tracking result is less than 0.73 Nm (5.7% with respect to 12 Nm torque). To achieve optimal age-specific performance, we proposed the first optimization framework that considered both motor and transmission of the actuator system that can produce optimal settings for children between 3 and 18 years old. The optimization generated an optimal motor air gap radius that monotonically increases with age from 0.011 to 0.033 meters, and optimal gear ratio varies from 2.6 to 11.6 (3-13 years old) and 11.6 to 10.2 (13-18 years old), leading to actuators of minimal mass.",
        "published":"2021-01-01T00:00:00.000Z",
        "authors":"['Sainan Zhang', 'Tzu-Hao Huang', 'Chunhai Jiao', 'Mhairi MacLean', 'Junxi Zhu', 'Shuangyue Yu', 'Hao Su']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.79,
        "actuality":2.0,
        "controversy":1.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.47225
    },
    {
        "arxiv_id":"2101.00531v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00531v1",
        "title":"Context-Aware Safe Reinforcement Learning for Non-Stationary Environments",
        "summary":"Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the agent's performance while avoiding violations of safety constraints. However, few studies have addressed the non-stationary disturbances in the environments, which may cause catastrophic outcomes. In this paper, we propose the context-aware safe reinforcement learning (CASRL) method, a meta-learning framework to realize safe adaptation in non-stationary environments. We use a probabilistic latent variable model to achieve fast inference of the posterior environment transition distribution given the context data. Safety constraints are then evaluated with uncertainty-aware trajectory sampling. The high cost of safety violations leads to the rareness of unsafe records in the dataset. We address this issue by enabling prioritized sampling during model training and formulating prior safety constraints with domain knowledge during constrained planning. The algorithm is evaluated in realistic safety-critical environments with non-stationary disturbances. Results show that the proposed algorithm significantly outperforms existing baselines in terms of safety and robustness.",
        "published":"2021-01-02T00:00:00.000Z",
        "authors":"['Baiming Chen', 'Zuxin Liu', 'Jiacheng Zhu', 'Mengdi Xu', 'Wenhao Ding', 'Ding Zhao']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.83,
        "actuality":4.3333333333,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2101.00585v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00585v1",
        "title":"UPSLAM: Union of Panoramas SLAM",
        "summary":"We present an empirical investigation of a new mapping system based on a graph of panoramic depth images. Panoramic images efficiently capture range measurements taken by a spinning lidar sensor, recording fine detail on the order of a few centimeters within maps of expansive scope on the order of tens of millions of cubic meters. The flexibility of the system is demonstrated by running the same mapping software against data collected by hand-carrying a sensor around a laboratory space at walking pace, moving it outdoors through a campus environment at running pace, driving the sensor on a small wheeled vehicle on- and off-road, flying the sensor through a forest, carrying it on the back of a legged robot navigating an underground coal mine, and mounting it on the roof of a car driven on public roads. The full 3D maps are built online with a median update time of less than ten milliseconds on an embedded NVIDIA Jetson AGX Xavier system.",
        "published":"2021-01-03T00:00:00.000Z",
        "authors":"['Anthony Cowley', 'Ian D. Miller', 'Camillo Jose Taylor']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.89,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.1943333333
    },
    {
        "arxiv_id":"2101.00633v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00633v2",
        "title":"Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making",
        "summary":"The widespread adoption of algorithmic decision-making systems has brought about the necessity to interpret the reasoning behind these decisions. The majority of these systems are complex black box models, and auxiliary models are often used to approximate and then explain their behavior. However, recent research suggests that such explanations are not overly accessible to non-expert users and can lead to incorrect interpretation of the underlying model. In this paper, we show that a predictive and interactive model based on causality is inherently interpretable, does not require any auxiliary model, and allows both expert and non-expert users to understand the model comprehensively. To demonstrate our method we developed Outcome Explorer, a causality guided interactive interface, and evaluated it by conducting think-aloud sessions with three expert users and a user study with 18 non-expert users. All three expert users found our tool to be comprehensive in supporting their explanation needs while the non-expert users were able to understand the inner workings of the model easily.",
        "published":"2021-01-03T00:00:00.000Z",
        "authors":"['Md Naimul Hoque', 'Klaus Mueller']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.91,
        "actuality":4.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2101.00646v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00646v1",
        "title":"AttnMove: History Enhanced Trajectory Recovery via Attentional Network",
        "summary":"A considerable amount of mobility data has been accumulated due to the proliferation of location-based service. Nevertheless, compared with mobility data from transportation systems like the GPS module in taxis, this kind of data is commonly sparse in terms of individual trajectories in the sense that users do not access mobile services and contribute their data all the time. Consequently, the sparsity inevitably weakens the practical value of the data even it has a high user penetration rate. To solve this problem, we propose a novel attentional neural network-based model, named AttnMove, to densify individual trajectories by recovering unobserved locations at a fine-grained spatial-temporal resolution. To tackle the challenges posed by sparsity, we design various intra- and inter- trajectory attention mechanisms to better model the mobility regularity of users and fully exploit the periodical pattern from long-term history. We evaluate our model on two real-world datasets, and extensive results demonstrate the performance gain compared with the state-of-the-art methods. This also shows that, by providing high-quality mobility data, our model can benefit a variety of mobility-oriented down-stream applications.",
        "published":"2021-01-03T00:00:00.000Z",
        "authors":"['Tong Xia', 'Yunhan Qi', 'Jie Feng', 'Fengli Xu', 'Funing Sun', 'Diansheng Guo', 'Yong Li']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":3.0,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2101.00675v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00675v1",
        "title":"Sentiment Analysis for Open Domain Conversational Agent",
        "summary":"The applicability of common sentiment analysis models to open domain human robot interaction is investigated within this paper. The models are used on a dataset specific to user interaction with the Alana system (a Alexa prize system) in order to determine which would be more appropriate for the task of identifying sentiment when a user interacts with a non-human driven socialbot. With the identification of a model, various improvements are attempted and detailed prior to integration into the Alana system. The study showed that a Random Forest Model with 25 trees trained on the dataset specific to user interaction with the Alana system combined with the dataset present in NLTK Vader outperforms other models. The new system (called 'Rob') matches it's output utterance sentiment with the user's utterance sentiment. This method is expected to improve user experience because it builds upon the overall sentiment detection which makes it seem that new system sympathises with user feelings. Furthermore, the results obtained from the user feedback confirms our expectation.",
        "published":"2021-01-03T00:00:00.000Z",
        "authors":"['Mohamad Alissa', 'Issa Haddad', 'Jonathan Meyer', 'Jade Obeid', 'Kostis Vilaetis', 'Nicolas Wiecek', 'Sukrit Wongariyakavee']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2101.00744v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00744v1",
        "title":"Learning to Optimize Under Constraints with Unsupervised Deep Neural Networks",
        "summary":"In this paper, we propose a machine learning (ML) method to learn how to solve a generic constrained continuous optimization problem. To the best of our knowledge, the generic methods that learn to optimize, focus on unconstrained optimization problems and those dealing with constrained problems are not easy-to-generalize. This approach is quite useful in optimization tasks where the problem's parameters constantly change and require resolving the optimization task per parameter update. In such problems, the computational complexity of optimization algorithms such as gradient descent or interior point method preclude near-optimal designs in real-time applications. In this paper, we propose an unsupervised deep learning (DL) solution for solving constrained optimization problems in real-time by relegating the main computation load to offline training phase. This paper's main contribution is proposing a method for enforcing the equality and inequality constraints to the DL-generated solutions for generic optimization tasks.",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Seyedrazieh Bayati', 'Faramarz Jabbarvaziri']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.81,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.8334166667
    },
    {
        "arxiv_id":"2101.00753v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00753v1",
        "title":"Schemes of Propagation Models and Source Estimators for Rumor Source Detection in Online Social Networks: A Short Survey of a Decade of Research",
        "summary":"Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network. Diffusion model is arguably considered as a very important and challengeable factor for source detection in networks but it is less studied. This paper provides an overview of three representative schemes of Independent Cascade-based, Epidemic-based, and Learning-based to model the patterns of rumor propagation as well as three major schemes of estimators for rumor sources since its inception a decade ago.",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Rong Jin', 'Weili Wu']",
        "arxiv_primary_category":"cs.si",
        "readability":0.88,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.7500833333
    },
    {
        "arxiv_id":"2101.00884v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00884v1",
        "title":"Coreference Resolution in Research Papers from Multiple Domains",
        "summary":"Coreference resolution is essential for automatic text understanding to facilitate high-level information retrieval tasks such as text summarisation or question answering. Previous work indicates that the performance of state-of-the-art approaches (e.g. based on BERT) noticeably declines when applied to scientific papers. In this paper, we investigate the task of coreference resolution in research papers and subsequent knowledge graph population. We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55,485 papers in 10 STM domains. Comprehensive experiments show the usefulness of the proposed approach. Our transfer learning approach considerably outperforms state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0), while the evaluation against a gold standard KG shows that coreference resolution improves the quality of the populated KG significantly with an F1 score of 63.5 (+21.8).",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Arthur Brack', 'Daniel Uwe M\u00fcller', 'Anett Hoppe', 'Ralph Ewerth']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.87,
        "actuality":1.0,
        "controversy":1.3333333333,
        "relevance_magnitude":1.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":1.4721666667
    },
    {
        "arxiv_id":"2101.00929v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.00929v1",
        "title":"Donut visualizations for network-level and regional-level overview of Spatial Social Networks",
        "summary":"Spatial Social Networks (SSN) build on the node and edge structure used in Social Network Analysis (SNA) by incorporating spatial information. Thus, SSNs include both topological and spatial data. The geographic embedding of the nodes makes it impossible to move the nodes freely, rendering standard topological algorithms (e.g. force layout algorithms) used in SNA ineffective to visualize SSN sociograms. We propose a new visualization technique for SSNs that utilize the spatial and social information to provide information about the orientation and scale of connections. The donut visualization can be used to summarize the entire network or can be used on a part of the network. We demonstrate the effectiveness of the donut visualization on two standard SSNs used in literature.",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Dipto Sarkar', 'Piyush Yadav']",
        "arxiv_primary_category":"cs.si",
        "readability":0.76,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":2.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2101.01043v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01043v1",
        "title":"Caching at the Edge: Outage Probability",
        "summary":"Caching at the edge of wireless networks is a keytechnology to reduce traffic in the backhaul link. However, aconcentrated amount of requests during peak-periods may causethe outage of the system, meaning that the network is not ableto serve the whole set of demands. The outage probability is afundamental metric to take into account during the networkdesign. In this paper, we derive the analytical expression ofthe outage probability as a function of the total amount ofusers requests, library size, requests distribution, cache size andcapacity constraints on the backhaul resources. In particular, wefocus on a scenario where end-users have no direct connectionto the master node which holds the complete library of contentthat can be requested. A general formulation of the outage isderived and studied for two relevant caching schemes, i.e. therandom caching scheme and the most popular caching schemes.The exact closed form expressions presented in this paper provideuseful insights on how requests, memory and resources can bebalanced when the parameters of a cache-enabled network haveto designed",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Estefan\u00eda Recayte', 'Andrea Munari']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.81,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0000833333
    },
    {
        "arxiv_id":"2101.01078v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01078v1",
        "title":"Tensorizing Subgraph Search in the Supernet",
        "summary":"Recently, a special kind of graph, i.e., supernet, which allows two nodes connected by multi-choice edges, has exhibited its power in neural architecture search (NAS) by searching for better architectures for computer vision (CV) and natural language processing (NLP) tasks. In this paper, we discover that the design of such discrete architectures also appears in many other important learning tasks, e.g., logical chain inference in knowledge graphs (KGs) and meta-path discovery in heterogeneous information networks (HINs). Thus, we are motivated to generalize the supernet search problem on a broader horizon. However, none of the existing works are effective since the supernet topology is highly task-dependent and diverse. To address this issue, we propose to tensorize the supernet, i.e., unify the subgraph search problems by a tensor formulation and encode the topology inside the supernet by a tensor network. We further propose an efficient algorithm that admits both stochastic and deterministic objectives to solve the search problem. Finally, we perform extensive experiments on diverse learning tasks, i.e., architecture design for CV, logic inference for KG, and meta-path discovery for HIN. Empirical results demonstrate that our method leads to better performance and architectures.",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Hansi Yang', 'Quanming Yao', 'James Kwok']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.8,
        "actuality":2.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.8611666667
    },
    {
        "arxiv_id":"2101.01337v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01337v1",
        "title":"Integration of Domain Knowledge using Medical Knowledge Graph Deep Learning for Cancer Phenotyping",
        "summary":"A key component of deep learning (DL) for natural language processing (NLP) is word embeddings. Word embeddings that effectively capture the meaning and context of the word that they represent can significantly improve the performance of downstream DL models for various NLP tasks. Many existing word embeddings techniques capture the context of words based on word co-occurrence in documents and text; however, they often cannot capture broader domain-specific relationships between concepts that may be crucial for the NLP task at hand. In this paper, we propose a method to integrate external knowledge from medical terminology ontologies into the context captured by word embeddings. Specifically, we use a medical knowledge graph, such as the unified medical language system (UMLS), to find connections between clinical terms in cancer pathology reports. This approach aims to minimize the distance between connected clinical concepts. We evaluate the proposed approach using a Multitask Convolutional Neural Network (MT-CNN) to extract six cancer characteristics -- site, subsite, laterality, behavior, histology, and grade -- from a dataset of ~900K cancer pathology reports. The results show that the MT-CNN model which uses our domain informed embeddings outperforms the same MT-CNN using standard word2vec embeddings across all tasks, with an improvement in the overall micro- and macro-F1 scores by 4.97\\%and 22.5\\%, respectively.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Mohammed Alawad', 'Shang Gao', 'Mayanka Chandra Shekar', 'S. M. Shamimul Hasan', 'J. Blair Christian', 'Xiao-Cheng Wu', 'Eric B. Durbin', 'Jennifer Doherty', 'Antoinette Stroup', 'Linda Coyle', 'Lynne Penberthy', 'Georgia Tourassi']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.85,
        "actuality":2.0,
        "controversy":2.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.3611666667
    },
    {
        "arxiv_id":"2101.01637v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01637v1",
        "title":"Theory-based Habit Modeling for Enhancing Behavior Prediction",
        "summary":"Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors (e.g., toothbrushing) is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users' habit strengths can be very useful for behavior change support systems (BCSS), for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, built on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Chao Zhang', 'Joaquin Vanschoren', 'Arlette van Wissen', 'Daniel Lakens', 'Boris de Ruyter', 'Wijnand A. IJsselsteijn']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.87,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2101.01677v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01677v1",
        "title":"Monocular Depth Estimation for Soft Visuotactile Sensors",
        "summary":"Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key challenges for robust manipulation, as they enable reliable grasps along with the ability to obtain high-resolution sensory feedback on contact geometry and forces. Although they are simple in construction, their utility has been limited due to size constraints introduced by enclosed custom IR\/depth imaging sensors to directly measure surface deformations. Towards mitigating this limitation, we investigate the application of state-of-the-art monocular depth estimation to infer dense internal (tactile) depth maps directly from the internal single small IR imaging sensor. Through real-world experiments, we show that deep networks typically used for long-range depth estimation (1-100m) can be effectively trained for precise predictions at a much shorter range (1-100mm) inside a mostly textureless deformable fluid-filled sensor. We propose a simple supervised learning process to train an object-agnostic network requiring less than 10 random poses in contact for less than 10 seconds for a small set of diverse objects (mug, wine glass, box, and fingers in our experiments). We show that our approach is sample-efficient, accurate, and generalizes across different objects and sensor configurations unseen at training time. Finally, we discuss the implications of our approach for the design of soft visuotactile sensors and grippers.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Rares Ambrus', 'Vitor Guizilini', 'Naveen Kuppuswamy', 'Andrew Beaulieu', 'Adrien Gaidon', 'Alex Alspach']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.83,
        "actuality":2.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2101.01686v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01686v1",
        "title":"Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing",
        "summary":"Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain context-dependent semantic parsing has become a new focus of research. Central to the problem is the challenge of leveraging contextual information of both natural language utterance and database schemas in the interaction history. In this paper, we present a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. The framework employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation, which is further enhanced with a powerful reranking model. At the time of writing, we demonstrate that the proposed framework outperforms all existing models by large margins, achieving new state-of-the-art performance on two large-scale benchmarks, the SParC and CoSQL datasets. Specifically, the model attains a 55.8% question-match and 30.8% interaction-match accuracy on SParC, and a 46.8% question-match and 17.0% interaction-match accuracy on CoSQL.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Binyuan Hui', 'Ruiying Geng', 'Qiyu Ren', 'Binhua Li', 'Yongbin Li', 'Jian Sun', 'Fei Huang', 'Luo Si', 'Pengfei Zhu', 'Xiaodan Zhu']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.8,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.2776666667
    },
    {
        "arxiv_id":"2101.01688v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01688v2",
        "title":"What social media told about us in the time of COVID-19: a scoping review",
        "summary":"With the onset of COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak starting in November 2019 until May 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social platforms and COVID-19. These themes focused on: (i) surveying public attitudes, (ii) identifying infodemics, (iii) assessing mental health, (iv) detecting or predicting COVID-19 cases, (v) analyzing government responses to the pandemic, and (vi) evaluating quality of health information in prevention education videos. Furthermore, our review highlights the paucity of studies on the application of machine learning on social media data related to COVID-19 and a lack of studies documenting real-time surveillance developed with social media data on COVID-19. For COVID-19, social media can play a crucial role in disseminating health information as well as tackling infodemics and misinformation.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Shu-Feng Tsao', 'Helen Chen', 'Therese Tisseverasinghe', 'Yang Yang', 'Lianghua Li', 'Zahid A. Butt']",
        "arxiv_primary_category":"cs.si",
        "readability":0.86,
        "actuality":5.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.9445
    },
    {
        "arxiv_id":"2101.01993v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.01993v1",
        "title":"A Survey of Deep RL and IL for Autonomous Driving Policy Learning",
        "summary":"Autonomous driving (AD) agents generate driving policies based on online perception results, which are obtained at multiple levels of abstraction, e.g., behavior planning, motion planning and control. Driving policies are crucial to the realization of safe, efficient and harmonious driving behaviors, where AD agents still face substantial challenges in complex scenarios. Due to their successful application in fields such as robotics and video games, the use of deep reinforcement learning (DRL) and deep imitation learning (DIL) techniques to derive AD policies have witnessed vast research efforts in recent years. This paper is a comprehensive survey of this body of work, which is conducted at three levels: First, a taxonomy of the literature studies is constructed from the system perspective, among which five modes of integration of DRL\/DIL models into an AD architecture are identified. Second, the formulations of DRL\/DIL models for conducting specified AD tasks are comprehensively reviewed, where various designs on the model state and action spaces and the reinforcement learning rewards are covered. Finally, an in-depth review is conducted on how the critical issues of AD applications regarding driving safety, interaction with other traffic participants and uncertainty of the environment are addressed by the DRL\/DIL models. To the best of our knowledge, this is the first survey to focus on AD policy learning using DRL\/DIL, which is addressed simultaneously from the system, task-driven and problem-driven perspectives. We share and discuss findings, which may lead to the investigation of various topics in the future.",
        "published":"2021-01-06T00:00:00.000Z",
        "authors":"['Zeyu Zhu', 'Huijing Zhao']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.91,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.8888333333
    },
    {
        "arxiv_id":"2101.02011v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02011v1",
        "title":"Towards an Abolitionist AI: the role of Historically Black Colleges and Universities",
        "summary":"Abolition is the process of destroying and then rebuilding the structures that impede liberation. This paper addresses the particular case of Black folk in the United States, but is relevant to the global decolonization movement. Using notions of abolition and infrastructures of feeling developed by Ruth Wilson Gilmore, I view Historically Black Colleges and Universities ( HBCUs ) as a particular kind of abolitionist project, created for the explicit purpose of nurturing and sustaining Black excellence particularly within the sciences. I then examine how artificial intelligence (AI) in particular and computing in general have contributed to racial oppression and the further confinement and diminishing of Black existence. I conclude by examining how the space held by HBCUs in computing might contribute to a re-imagining of AI as a technology that enhances the possibility and actualization of Black life.",
        "published":"2021-01-06T00:00:00.000Z",
        "authors":"['Charles C. Earl']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.361
    },
    {
        "arxiv_id":"2101.02018v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02018v1",
        "title":"Abusive Advertising: Scrutinizing socially relevant algorithms in a black box analysis to examine their impact on vulnerable patient groups in the health sector",
        "summary":"The targeted direct-to-customer marketing of unapproved stem cell treatments by a questionable online industry is directed at vulnerable users who search the Internet in the hope of a cure. This behavior especially poses a threat to individuals who find themselves in hopeless and desperate phases in their lives. They might show low reluctance to try therapies that solely promise a cure but are not scientifically proven to do so. In the worst case, they suffer serious side-effects. Therefore, this thesis examines the display of advertisements of unapproved stem cell treatments for Parkinson's Disease, Multiple Sclerosis, Diabetes on Google's results page. The company announced a policy change in September 2019 that was meant to prohibit and ban the practices in question. However, there was evidence that those ads were still being delivered. A browser extension for Firefox and Chrome was developed and distributed to conduct a crowdsourced Black Box analysis. It was delivered to volunteers and virtual machines in Australia, Canada, the USA and the UK. Data on search results, advertisements and top stories was collected and analyzed. The results showed that there still is questionable advertising even though Google announced to purge it from its platform.",
        "published":"2021-01-04T00:00:00.000Z",
        "authors":"['Martin Reber']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.92,
        "actuality":3.6666666667,
        "controversy":4.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3888333333
    },
    {
        "arxiv_id":"2101.02026v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02026v1",
        "title":"Learning about the reduction of food waste using Blockchain technology",
        "summary":"Farmers need to be efficient and dedicate a lot of time in order to sustain the quality of their animals which are in their care. The most convenient and good quality - price ratio should be chosen for the feed of animals. Blockchain is used in a virtual space to store and share information over a network of users. This is done using the open source Hyperledger Fabric platform. The transactions can be viewed by all the other users in real time. These transactions are stored as JSONs inside CouchDB NoSQL database which supports queries on a large volume of data. When using this technology, the farmer can know with whom the supplier for animal feed collaborated with. The history of the transactions are not saved in just one place. In this way, it is more difficult to hack and provide implausible information. An e-learning platform was created where the farm's user can post information, respectively new blocks about the animal's birth, vaccinations, medicines, including the location of the livestock. The same e-learning platform is accessible from the mobile phone. By using the blockchain technology, anyone, including the client from the shop can know a lot about the origin of the products. Fake origins of food are much more difficult to hide. Fraud is also limited. The system monitored the traceability of dairy products inside a Romanian farm. Data about fodder provider and quality, cow productive performances and health and dairy products process were obtained and analyzed by students who will become specialists at all the levels of the food chain. Blockchain is the technology which in case of a dairy products contamination, the origin of the farm is traced in just a couple of seconds. In this way just a batch of dairy products is removed from distribution, leading to the reduction of food waste.",
        "published":"2021-01-03T00:00:00.000Z",
        "authors":"['Monica-Paula Marin', 'Iuliana Marin', 'Livia Vidu']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.93,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2101.02051v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02051v1",
        "title":"Transformer-based approach towards music emotion recognition from lyrics",
        "summary":"The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emotion recognition remains under-appreciated in spite of several studies reporting superior performance of music emotion classifiers based on features extracted from lyrics. In this study, we use the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics. Our proposed approach outperforms existing methods for multiple datasets. We used a robust methodology to enhance web-crawlers' accuracy for extracting lyrics. This study has important implications in improving applications involved in playlist generation of music based on emotions in addition to improving music recommendation systems.",
        "published":"2021-01-06T00:00:00.000Z",
        "authors":"['Yudhik Agrawal', 'Ramaguru Guru Ravi Shanker', 'Vinoo Alluri']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6111666667
    },
    {
        "arxiv_id":"2101.02178v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02178v1",
        "title":"Improving Training Result of Partially Observable Markov Decision Process by Filtering Beliefs",
        "summary":"In this study I proposed a filtering beliefs method for improving performance of Partially Observable Markov Decision Processes(POMDPs), which is a method wildly used in autonomous robot and many other domains concerning control policy. My method search and compare every similar belief pair. Because a similar belief have insignificant influence on control policy, the belief is filtered out for reducing training time. The empirical results show that the proposed method outperforms the point-based approximate POMDPs in terms of the quality of training results as well as the efficiency of the method.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Oscar LiJen Hsu']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.88,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.2778333333
    },
    {
        "arxiv_id":"2101.02231v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02231v1",
        "title":"Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma",
        "summary":"Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior in both natural and artificial systems, provide a conceptually valid common basis, as evidenced by the current efforts towards a standard model of the mind, to generate human-like intelligent behavior for these synthetic characters. Sigma is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis. Sigma leverages an extended form of factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory-of-Mind and that are perceptual, autonomous, interactive, affective, and adaptive. In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities: (1) Distributional reinforcement learning models in; (2) A pair of adaptive and interactive agent models that demonstrate rule-based, probabilistic, and social reasoning; and (3) A knowledge-free exploration model in which an agent leverages only architectural appraisal variables, namely attention and curiosity, to locate an item while building up a map in a Unity environment.",
        "published":"2021-01-06T00:00:00.000Z",
        "authors":"['Volkan Ustun', 'Paul S. Rosenbloom', 'Seyed Sajjadi', 'Jeremy Nuttal']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.84,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3055833333
    },
    {
        "arxiv_id":"2101.02279v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02279v1",
        "title":"On State Estimation for Legged Locomotion over Soft Terrain",
        "summary":"Locomotion over soft terrain remains a challenging problem for legged robots. Most of the work done on state estimation for legged robots is designed for rigid contacts, and does not take into account the physical parameters of the terrain. That said, this letter answers the following questions: how and why does soft terrain affect state estimation for legged robots? To do so, we utilized a state estimator that fuses IMU measurements with leg odometry that is designed with rigid contact assumptions. We experimentally validated the state estimator with the HyQ robot trotting over both soft and rigid terrain. We demonstrate that soft terrain negatively affects state estimation for legged robots, and that the state estimates have a noticeable drift over soft terrain compared to rigid terrain.",
        "published":"2021-01-06T00:00:00.000Z",
        "authors":"['Shamel Fahmi', 'Geoff Fink', 'Claudio Semini']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.8,
        "actuality":1.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.1111666667
    },
    {
        "arxiv_id":"2101.02409v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02409v1",
        "title":"On the Management of Type 1 Diabetes Mellitus with IoT Devices and ML Techniques",
        "summary":"The purpose of this Conference is to present the main lines of base projects that are founded on research already begun in previous years. In this sense, this manuscript will present the main lines of research in Diabetes Mellitus type 1 and Machine Learning techniques in an Internet of Things environment, so that we can summarize the future lines to be developed as follows: data collection through biosensors, massive data processing in the cloud, interconnection of biodevices, local computing vs. cloud computing, and possibilities of machine learning techniques to predict blood glucose values, including both variable selection algorithms and predictive techniques.",
        "published":"2021-01-07T00:00:00.000Z",
        "authors":"['Ignacio Rodriguez']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2101.02434v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02434v1",
        "title":"Integration of IEEE 802.1AS-based Time Synchronization in IEEE 802.11 as an Enabler for Novel Industrial Use Cases",
        "summary":"Industry 4.0 introduces new use cases, with more and more mobile devices appearing in the industrial landscape. These applications require both new technologies and smooth integration into existing brownfield deployments. Emerging mobile use cases can be divided into optional mobile and mandatory mobile, where the first point considers the use of wireless communications due to soft criteria such as cost savings and the second means use cases that cannot be covered by wireline technologies due to their movement, such as AGVs. For most industrial applications, high determinism, E2E latency and synchronicity are most important. Therefore, we provide a common table, based on these requirements, listing both existing and emerging mobile use cases. Since time synchronization is particularly demanding for wireless use cases, we propose a concept for a simple but precise synchronization in IEEE 802.11 WLAN and a suitable integration using TSN in combination with OPC UA technology as examples. Furthermore, the concept is evaluated with the help of a testbed utilizing state-of-the-art hardware. This means that this concept can be directly applied in existing industry solutions. It can be shown that the concept is already suitable for a wide range of the mandatory mobile applications.",
        "published":"2021-01-07T00:00:00.000Z",
        "authors":"['Michael Gundall', 'Christopher Huber', 'Sergiy Melnyk']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.89,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.7221666667
    },
    {
        "arxiv_id":"2101.02436v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02436v1",
        "title":"Feasibility Study on Virtual Process Controllers as Basis for Future Industrial Automation Systems",
        "summary":"Industry 4.0 offers many possibilities for creating highly efficient and flexible manufacturing. To create such advantages, highly automated and thus digitized processes and systems are required. Here, most technologies known from the office floor are basically suitable for these tasks, but cannot meet the high demands of industrial use cases. Therefore, they cannot replace industrial technologies and devices that have performed well over decades \"out of the box\". For this reason, many technologies known from the office floor are being investigated and adapted for industrial environments. An important task is the virtualization of process controls, as more and more devices use computation offloading, e.g. due to limited resources. In this paper we extend the work on our novel architecture that enables numerous use cases and meets industrial requirements by virtualizing process controllers. In addition, a testbed based on a factory scenario is proposed to evaluate the most important features of the presented architecture.",
        "published":"2021-01-07T00:00:00.000Z",
        "authors":"['Michael Gundall', 'Calvin Glas', 'Hans D. Schotten']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.93,
        "actuality":3.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.4721666667
    },
    {
        "arxiv_id":"2101.02483v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02483v1",
        "title":"Robust Text CAPTCHAs Using Adversarial Examples",
        "summary":"CAPTCHA (Completely Automated Public Truing test to tell Computers and Humans Apart) is a widely used technology to distinguish real users and automated users such as bots. However, the advance of AI technologies weakens many CAPTCHA tests and can induce security concerns. In this paper, we propose a user-friendly text-based CAPTCHA generation method named Robust Text CAPTCHA (RTC). At the first stage, the foregrounds and backgrounds are constructed with randomly sampled font and background images, which are then synthesized into identifiable pseudo adversarial CAPTCHAs. At the second stage, we design and apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers. Our experiments cover comprehensive models including shallow models such as KNN, SVM and random forest, various deep neural networks and OCR models. Experiments show that our CAPTCHAs have a failure rate lower than one millionth in general and high usability. They are also robust against various defensive techniques that attackers may employ, including adversarial training, data pre-processing and manual tagging.",
        "published":"2021-01-07T00:00:00.000Z",
        "authors":"['Rulin Shao', 'Zhouxing Shi', 'Jinfeng Yi', 'Pin-Yu Chen', 'Cho-Jui Hsieh']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.82,
        "actuality":3.6666666667,
        "controversy":1.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2101.02648v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02648v2",
        "title":"Argument Schemes and Dialogue for Explainable Planning",
        "summary":"Artificial Intelligence (AI) is being increasingly deployed in practical applications. However, there is a major concern whether AI systems will be trusted by humans. In order to establish trust in AI systems, there is a need for users to understand the reasoning behind their solutions. Therefore, systems should be able to explain and justify their output. In this paper, we propose an argument scheme-based approach to provide explanations in the domain of AI planning. We present novel argument schemes to create arguments that explain a plan and its key elements; and a set of critical questions that allow interaction between the arguments and enable the user to obtain further information regarding the key elements of the plan. Furthermore, we present a novel dialogue system using the argument schemes and critical questions for providing interactive dialectical explanations.",
        "published":"2021-01-07T00:00:00.000Z",
        "authors":"['Quratul-ain Mahesar', 'Simon Parsons']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.96,
        "actuality":4.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.97225
    },
    {
        "arxiv_id":"2101.02722v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02722v1",
        "title":"The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels",
        "summary":"Robots have to face challenging perceptual settings, including changes in viewpoint, lighting, and background. Current simulated reinforcement learning (RL) benchmarks such as DM Control provide visual input without such complexity, which limits the transfer of well-performing methods to the real world. In this paper, we extend DM Control with three kinds of visual distractions (variations in background, color, and camera pose) to produce a new challenging benchmark for vision-based control, and we analyze state of the art RL algorithms in these settings. Our experiments show that current RL methods for vision-based control perform poorly under distractions, and that their performance decreases with increasing distraction complexity, showing that new methods are needed to cope with the visual complexities of the real world. We also find that combinations of multiple distraction types are more difficult than a mere combination of their individual effects.",
        "published":"2021-01-07T00:00:00.000Z",
        "authors":"['Austin Stone', 'Oscar Ramirez', 'Kurt Konolige', 'Rico Jonschkowski']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.86,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2101.02988v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.02988v1",
        "title":"Graph embeddings for Abusive Language Detection",
        "summary":"Abusive behaviors are common on online social networks. The increasing frequency of antisocial behaviors forces the hosts of online platforms to find new solutions to address this problem. Automating the moderation process has thus received a lot of interest in the past few years. Various methods have been proposed, most based on the exchanged content, and one relying on the structure and dynamics of the conversation. It has the advantage of being languageindependent, however it leverages a hand-crafted set of topological measures which are computationally expensive and not necessarily suitable to all situations. In the present paper, we propose to use recent graph embedding approaches to automatically learn representations of conversational graphs depicting message exchanges. We compare two categories: node vs. whole-graph embeddings. We experiment with a total of 8 approaches and apply them to a dataset of online messages. We also study more precisely which aspects of the graph structure are leveraged by each approach. Our study shows that the representation produced by certain embeddings captures the information conveyed by specific topological measures, but misses out other aspects.",
        "published":"2021-01-08T00:00:00.000Z",
        "authors":"['No\u00e9 Cecillon', 'Vincent Labatut', 'Richard Dufour', 'Georges Linares']",
        "arxiv_primary_category":"cs.si",
        "readability":0.86,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6111666667
    },
    {
        "arxiv_id":"2101.03002v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03002v1",
        "title":"Studying Leaders During Times of Crisis Using Online Social Media -- A COVID Case Study",
        "summary":"Online Social media (OSM) has become a primary platform for discussion on diverse topics. Even famous and public figures often express their views on various topics through OSM platforms. Novel Coronavirus officially called COVID-19, which has become a pandemic and has created a crisis in human history, is one such topic that has attracted a lot of attention on Twitter in recent times. In this work, we analyze 29 million tweets spanning across three months, to study highly influential users, which we called as leaders. We identify these leaders using social network analysis and analyze their tweets using text analysis techniques. We group these leaders in four clusters, namely research, news, health and politics. Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to others and, iii) health organizations and politicians try to gain public trust. The text analysis shows that the researchers are more concerned about understanding symptoms and developing vaccination; news and politicians are mainly discussing about travel and hygiene; and health organizations focuses on hygiene. Our descriptive analysis helps us to extract various features that we used to classify tweets among the four clusters with an accuracy of 96% AUC ROC.",
        "published":"2021-01-08T00:00:00.000Z",
        "authors":"['Rahul Goel', 'Rajesh Sharma']",
        "arxiv_primary_category":"cs.si",
        "readability":0.92,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.4445
    },
    {
        "arxiv_id":"2101.03072v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03072v1",
        "title":"A Primer on HIBS -- High Altitude Platform Stations as IMT Base Stations",
        "summary":"Mobile communication via high-altitude platforms operating in the stratosphere is an idea that has been on the table for decades. In the past few years, however, with recent advances in technology and parallel progress in standardization and regulatory bodies like 3GPP and ITU, these ideas have gained considerable momentum. In this article, we present a comprehensive overview of HIBS - High Altitude Platform Stations as IMT Base Stations. We lay out possible use cases and summarize the current status of the development, from a technological point of view as well as from standardization in 3GPP, and regarding spectrum aspects. We then present preliminary system level simulation results to shed light on the performance of HIBS. We conclude with pointing out several directions for future research.",
        "published":"2021-01-08T00:00:00.000Z",
        "authors":"['Sebastian Euler', 'Xingqin Lin', 'Erika Tejedor', 'Evanny Obregon']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.89,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.0555833333
    },
    {
        "arxiv_id":"2101.03270v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03270v1",
        "title":"Investigation by Driving Simulation of Tractor Overturning Accidents Caused by Steering Instability",
        "summary":"Overturning tractors are the leading cause of fatalities on farms. Steering instability contributes significantly to the tractor overturning. This study investigated tractor overturning accidents caused by the steering instability using a driving simulator. The general commercial driving simulator CarSim (Mechanical Simulation Cooperation, MI, USA) was used. Tractor operations on steep passage slopes were simulated to mimic conditions present for a real accident case reported in Japan. Simulations were performed on roads with and without slopes. The tractor overturned only when on the road with the steep slope. The decrease in the vertical force on the front wheel caused the steering instability and the tractor to overturn. The steering instability caused understeer which prevents the operator from being able to control the tractor properly. Subsequently, the tractor overturned in the simulation. The tractor driving simulator was capable of reproducing the steering instability which can lead to the overturning accident.",
        "published":"2021-01-09T00:00:00.000Z",
        "authors":"['Masahisa Watanabe', 'Kenshi Sakai']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.81,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.1943333333
    },
    {
        "arxiv_id":"2101.03327v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03327v1",
        "title":"Selection of Optimal Parameters in the Fast K-Word Proximity Search Based on Multi-component Key Indexes",
        "summary":"Proximity full-text search is commonly implemented in contemporary full-text search systems. Let us assume that the search query is a list of words. It is natural to consider a document as relevant if the queried words are near each other in the document. The proximity factor is even more significant for the case where the query consists of frequently occurring words. Proximity full-text search requires the storage of information for every occurrence in documents of every word that the user can search. For every occurrence of every word in a document, we employ additional indexes to store information about nearby words, that is, the words that occur in the document at distances from the given word of less than or equal to the MaxDistance parameter. We showed in previous works that these indexes can be used to improve the average query execution time by up to 130 times for queries that consist of words occurring with high-frequency. In this paper, we consider how both the search performance and the search quality depend on the value of MaxDistance and other parameters. Well-known GOV2 text collection is used in the experiments for reproducibility of the results. We propose a new index schema after the analysis of the results of the experiments. This is a pre-print of a contribution published in Supplementary Proceedings of the XXII International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID\/RCDL 2020), Voronezh, Russia, October 13-16, 2020, P. 336-350, published by CEUR Workshop Proceedings. The final authenticated version is available online at: http:\/\/ceur-ws.org\/Vol-2790\/",
        "published":"2021-01-09T00:00:00.000Z",
        "authors":"['Alexander B. Veretennikov']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":1.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.0555833333
    },
    {
        "arxiv_id":"2101.03441v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03441v2",
        "title":"Rate Allocation and Content Placement in Cache Networks",
        "summary":"We introduce the problem of optimal congestion control in cache networks, whereby \\emph{both} rate allocations and content placements are optimized \\emph{jointly}. We formulate this as a maximization problem with non-convex constraints, and propose solving this problem via (a) a Lagrangian barrier algorithm and (b) a convex relaxation. We prove different optimality guarantees for each of these two algorithms; our proofs exploit the fact that the non-convex constraints of our problem involve DR-submodular functions.",
        "published":"2021-01-09T00:00:00.000Z",
        "authors":"['Khashayar Kamran', 'Armin Moharrer', 'Stratis Ioannidis', 'Edmund Yeh']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.76,
        "actuality":2.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.8889166667
    },
    {
        "arxiv_id":"2101.03464v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03464v1",
        "title":"SPAGAN: Shortest Path Graph Attention Network",
        "summary":"Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node. In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN). Unlike conventional GCN models that carry out node-based attentions within each layer, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods. We test SPAGAN on the downstream classification task on several standard datasets, and achieve performances superior to the state of the art. Code is publicly available at https:\/\/github.com\/ihollywhy\/SPAGAN.",
        "published":"2021-01-10T00:00:00.000Z",
        "authors":"['Yiding Yang', 'Xinchao Wang', 'Mingli Song', 'Junsong Yuan', 'Dacheng Tao']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.8,
        "actuality":3.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2101.03478v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03478v1",
        "title":"Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging",
        "summary":"Activity recognition computer vision algorithms can be used to detect the presence of autism-related behaviors, including what are termed \"restricted and repetitive behaviors\", or stimming, by diagnostic instruments. The limited data that exist in this domain are usually recorded with a handheld camera which can be shaky or even moving, posing a challenge for traditional feature representation approaches for activity detection which mistakenly capture the camera's motion as a feature. To address these issues, we first document the advantages and limitations of current feature representation techniques for activity recognition when applied to head banging detection. We then propose a feature representation consisting exclusively of head pose keypoints. We create a computer vision classifier for detecting head banging in home videos using a time-distributed convolutional neural network (CNN) in which a single CNN extracts features from each frame in the input sequence, and these extracted features are fed as input to a long short-term memory (LSTM) network. On the binary task of predicting head banging and no head banging within videos from the Self Stimulatory Behaviour Dataset (SSBD), we reach a mean F1-score of 90.77% using 3-fold cross validation (with individual fold F1-scores of 83.3%, 89.0%, and 100.0%) when ensuring that no child who appeared in the train set was in the test set for all folds. This work documents a successful technique for training a computer vision classifier which can detect human motion with few training examples and even when the camera recording the source clips is unstable. The general methods described here can be applied by designers and developers of interactive systems towards other human motion and pose classification problems used in mobile and ubiquitous interactive systems.",
        "published":"2021-01-10T00:00:00.000Z",
        "authors":"['Peter Washington', 'Aaron Kline', 'Onur Cezmi Mutlu', 'Emilie Leblanc', 'Cathy Hou', 'Nate Stockham', 'Kelley Paskov', 'Brianna Chrisman', 'Dennis P. Wall']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2101.03641v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03641v2",
        "title":"Learning Augmented Index Policy for Optimal Service Placement at the Network Edge",
        "summary":"We consider the problem of service placement at the network edge, in which a decision maker has to choose between $N$ services to host at the edge to satisfy the demands of customers. Our goal is to design adaptive algorithms to minimize the average service delivery latency for customers. We pose the problem as a Markov decision process (MDP) in which the system state is given by describing, for each service, the number of customers that are currently waiting at the edge to obtain the service. However, solving this $N$-services MDP is computationally expensive due to the curse of dimensionality. To overcome this challenge, we show that the optimal policy for a single-service MDP has an appealing threshold structure, and derive explicitly the Whittle indices for each service as a function of the number of requests from customers based on the theory of Whittle index policy. Since request arrival and service delivery rates are usually unknown and possibly time-varying, we then develop efficient learning augmented algorithms that fully utilize the structure of optimal policies with a low learning regret. The first of these is UCB-Whittle, and relies upon the principle of optimism in the face of uncertainty. The second algorithm, Q-learning-Whittle, utilizes Q-learning iterations for each service by using a two time scale stochastic approximation. We characterize the non-asymptotic performance of UCB-Whittle by analyzing its learning regret, and also analyze the convergence properties of Q-learning-Whittle. Simulation results show that the proposed policies yield excellent empirical performance.",
        "published":"2021-01-10T00:00:00.000Z",
        "authors":"['Guojun Xiong', 'Rahul Singh', 'Jian Li']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2101.03654v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03654v1",
        "title":"Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction",
        "summary":"Click-through rate (CTR) prediction, which aims to predict the probability that whether of a user will click on an item, is an essential task for many online applications. Due to the nature of data sparsity and high dimensionality in CTR prediction, a key to making effective prediction is to model high-order feature interactions among feature fields. To explicitly model high-order feature interactions, an efficient way is to stack multihead self-attentive neural networks, which has achieved promising performance. However, one problem of the vanilla self-attentive network is that two terms, a whitened pairwise interaction term and a unary term, are coupled in the computation of the self-attention score, where the pairwise term contributes to learning the importance score for each feature interaction, while the unary term models the impact of one feature on all other features. We identify two factors, coupled gradient computation and shared transformations, impede the learning of both terms. To solve this problem, in this paper,we present a novel Disentangled Self-Attentive neural Network (DSAN) model for CTR prediction, which disentangles the two terms for facilitating learning feature interactions. We conduct extensive experiments framework using two real-world benchmark datasets. The results show that DSAN not only retains computational efficiency but obtains performance improvements over state-of-the-art baselines.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Yanqiao Zhu', 'Yichen Xu', 'Feng Yu', 'Qiang Liu', 'Shu Wu', 'Liang Wang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":4.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.7221666667
    },
    {
        "arxiv_id":"2101.03655v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03655v1",
        "title":"Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities",
        "summary":"The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['MohammadNoor Injadat', 'Abdallah Moubayed', 'Ali Bou Nassif', 'Abdallah Shami']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.95,
        "actuality":3.0,
        "controversy":3.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8055833333
    },
    {
        "arxiv_id":"2101.03693v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03693v1",
        "title":"Exploiting a Fleet of UAVs for Monitoring and Data Acquisition of a Distributed Sensor Network",
        "summary":"This study proposes an efficient data collection strategy exploiting a team of Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors' data collection collaboratively while considering existing constraints of the UAV payload and battery capacity. The proposed mission planner system employs the Differential Evolution (DE) optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding the redundant collection of sensors' data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['S. MahmoudZadeh', 'A. Yazdani', 'A. Elmi', 'A. Abbasi', 'P. Ghanooni']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.85,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.8611666667
    },
    {
        "arxiv_id":"2101.03700v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03700v2",
        "title":"AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21",
        "summary":"Acronym identification focuses on finding the acronyms and the phrases that have been abbreviated, which is crucial for scientific document understanding tasks. However, the limited size of manually annotated datasets hinders further improvement for the problem. Recent breakthroughs of language models pre-trained on large corpora clearly show that unsupervised pre-training can vastly improve the performance of downstream tasks. In this paper, we present an Adversarial Training BERT method named AT-BERT, our winning solution to acronym identification task for Scientific Document Understanding (SDU) Challenge of AAAI 2021. Specifically, the pre-trained BERT is adopted to capture better semantic representation. Then we incorporate the FGM adversarial training strategy into the fine-tuning of BERT, which makes the model more robust and generalized. Furthermore, an ensemble mechanism is devised to involve the representations learned from multiple BERT variants. Assembling all these components together, the experimental results on the SciAI dataset show that our proposed approach outperforms all other competitive state-of-the-art methods.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Danqing Zhu', 'Wangli Lin', 'Yang Zhang', 'Qiwei Zhong', 'Guanxiong Zeng', 'Weilin Wu', 'Jiayu Tang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.83,
        "actuality":1.0,
        "controversy":1.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":1.7221666667
    },
    {
        "arxiv_id":"2101.03757v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03757v2",
        "title":"VaccinItaly: monitoring Italian conversations around vaccines on Twitter",
        "summary":"We monitor online conversations of Italian users around vaccines on Twitter, and we provide public access to the on-going data collection which will run continuously throughout the vaccination campaign taking place in Italy. We started collecting tweets matching vaccine-related keywords (in Italian) on December 20th 2020 using Twitter APIs, capturing the Italian vaccine rollout (27th December 2020), and at the time of this writing (13th January 2020) we collected over 1.8 M tweets, with an average number of 30k tweets shared on a daily basis. We report a consistent amount of low-credibility information already circulating on Twitter alongside vaccine-related conversations, whose prevalence is smaller yet comparable to high-credibility information. We believe that our data will allow researchers to understand the interplay between public discourse on online social media and the evolution of the on-going vaccination campaign against SARS-CoV-2 taking place in Italy.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Francesco Pierri', 'Silvio Pavanetto', 'Marco Brambilla', 'Stefano Ceri']",
        "arxiv_primary_category":"cs.si",
        "readability":0.91,
        "actuality":5.0,
        "controversy":4.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":4.2221666667
    },
    {
        "arxiv_id":"2101.03769v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03769v1",
        "title":"A Review of Evaluation Practices of Gesture Generation in Embodied Conversational Agents",
        "summary":"Embodied Conversational Agents (ECA) take on different forms, including virtual avatars or physical agents, such as a humanoid robot. ECAs are often designed to produce nonverbal behaviour to complement or enhance its verbal communication. One form of nonverbal behaviour is co-speech gesturing, which involves movements that the agent makes with its arms and hands that is paired with verbal communication. Co-speech gestures for ECAs can be created using different generation methods, such as rule-based and data-driven processes. However, reports on gesture generation methods use a variety of evaluation measures, which hinders comparison. To address this, we conducted a systematic review on co-speech gesture generation methods for iconic, metaphoric, deictic or beat gestures, including their evaluation methods. We reviewed 22 studies that had an ECA with a human-like upper body that used co-speech gesturing in a social human-agent interaction, including a user study to evaluate its performance. We found most studies used a within-subject design and relied on a form of subjective evaluation, but lacked a systematic approach. Overall, methodological quality was low-to-moderate and few systematic conclusions could be drawn. We argue that the field requires rigorous and uniform tools for the evaluation of co-speech gesture systems. We have proposed recommendations for future empirical evaluation, including standardised phrases and test scenarios to test generative models. We have proposed a research checklist that can be used to report relevant information for the evaluation of generative models as well as to evaluate co-speech gesture use.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Pieter Wolfert', 'Nicole Robinson', 'Tony Belpaeme']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":3.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.4445
    },
    {
        "arxiv_id":"2101.03785v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03785v1",
        "title":"Predictive Analysis of Chikungunya",
        "summary":"Chikungunya is an emerging threat for health security all over the world which is spreading very fast. Researches for proper forecasting of the incidence rate of chikungunya has been going on in many places in which DARPA has done a very extensive summarized result from 2014 to 2017 with the data of suspected cases, confirmed cases, deaths, population and incidence rate in different countries. In this project, we have analysed the dataset from DARPA and extended it to predict the incidence rate using different features of weather like temperature, humidity, dewiness, wind and pressure along with the latitude and longitude of every country. We had to use different APIs to find out these extra features from 2014-2016. After creating a pure dataset, we have used Linear Regression to predict the incidence rate and calculated the accuracy and error rate.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Sayed Erfan Arefin', 'Tasnia Ashrafi Heya', 'Dr Moinul Zaber']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.111
    },
    {
        "arxiv_id":"2101.03787v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.03787v1",
        "title":"WiCV 2020: The Seventh Women In Computer Vision Workshop",
        "summary":"In this paper we present the details of Women in Computer Vision Workshop - WiCV 2020, organized in alongside virtual CVPR 2020. This event aims at encouraging the women researchers in the field of computer vision. It provides a voice to a minority (female) group in computer vision community and focuses on increasingly the visibility of these researchers, both in academia and industry. WiCV believes that such an event can play an important role in lowering the gender imbalance in the field of computer vision. WiCV is organized each year where it provides a.) opportunity for collaboration with between researchers b.) mentorship to female junior researchers c.) financial support to presenters to overcome monetary burden and d.) large and diverse choice of role models, who can serve as examples to younger researchers at the beginning of their careers. In this paper, we present a report on the workshop program, trends over the past years, a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Hazel Doughty', 'Nour Karessli', 'Kathryn Leonard', 'Boyi Li', 'Carianne Martinez', 'Azadeh Mobasher', 'Arsha Nagrani', 'Srishti Yadav']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.93,
        "actuality":5.0,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2101.04167v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04167v1",
        "title":"First-Order Problem Solving through Neural MCTS based Reinforcement Learning",
        "summary":"The formal semantics of an interpreted first-order logic (FOL) statement can be given in Tarskian Semantics or a basically equivalent Game Semantics. The latter maps the statement and the interpretation into a two-player semantic game. Many combinatorial problems can be described using interpreted FOL statements and can be mapped into a semantic game. Therefore, learning to play a semantic game perfectly leads to the solution of a specific instance of a combinatorial problem. We adapt the AlphaZero algorithm so that it becomes better at learning to play semantic games that have different characteristics than Go and Chess. We propose a general framework, Persephone, to map the FOL description of a combinatorial problem to a semantic game so that it can be solved through a neural MCTS based reinforcement learning algorithm. Our goal for Persephone is to make it tabula-rasa, mapping a problem stated in interpreted FOL to a solution without human intervention.",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Ruiyang Xu', 'Prashank Kadam', 'Karl Lieberherr']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.8,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2101.04237v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04237v1",
        "title":"Solving Common-Payoff Games with Approximate Policy Iteration",
        "summary":"For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, decentralized control is difficult -- computing even an epsilon-optimal joint policy is a NEXP complete problem. Nevertheless, a recently rediscovered insight -- that a team of agents can coordinate via common knowledge -- has given rise to algorithms capable of finding optimal joint policies in small common-payoff games. The Bayesian action decoder (BAD) leverages this insight and deep reinforcement learning to scale to games as large as two-player Hanabi. However, the approximations it uses to do so prevent it from discovering optimal joint policies even in games small enough to brute force optimal solutions. This work proposes CAPI, a novel algorithm which, like BAD, combines common knowledge with deep reinforcement learning. However, unlike BAD, CAPI prioritizes the propensity to discover optimal joint policies over scalability. While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so. Code is available at https:\/\/github.com\/ssokota\/capi .",
        "published":"2021-01-11T00:00:00.000Z",
        "authors":"['Samuel Sokota', 'Edward Lockhart', 'Finbarr Timbers', 'Elnaz Davoodi', \"Ryan D'Orazio\", 'Neil Burch', 'Martin Schmid', 'Michael Bowling', 'Marc Lanctot']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.82,
        "actuality":2.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2101.04262v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04262v1",
        "title":"Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces",
        "summary":"Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Upinder Kaur', 'Praveen Abbaraju', 'Harrison McCarty', 'Richard M. Voyles']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.81,
        "actuality":3.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3888333333
    },
    {
        "arxiv_id":"2101.04283v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04283v1",
        "title":"A Brief Survey of Associations Between Meta-Learning and General AI",
        "summary":"This paper briefly reviews the history of meta-learning and describes its contribution to general AI. Meta-learning improves model generalization capacity and devises general algorithms applicable to both in-distribution and out-of-distribution tasks potentially. General AI replaces task-specific models with general algorithmic systems introducing higher level of automation in solving diverse tasks using AI. We summarize main contributions of meta-learning to the developments in general AI, including memory module, meta-learner, coevolution, curiosity, forgetting and AI-generating algorithm. We present connections between meta-learning and general AI and discuss how meta-learning can be used to formulate general AI algorithms.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Huimin Peng']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.84,
        "actuality":4.6666666667,
        "controversy":4.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2101.04449v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04449v1",
        "title":"Streaming VR Games to the Broad Audience: A Comparison of the First-Person and Third-Person Perspectives",
        "summary":"The spectatorship experience for virtual reality (VR) games differs strongly from its non-VR precursor. When watching non-VR games on platforms such as Twitch, spectators just see what the player sees, as the physical interaction is mostly unimportant for the overall impression. In VR, the immersive full-body interaction is a crucial part of the player experience. Hence, content creators, such as streamers, often rely on green screens or similar solutions to offer a mixed-reality third-person view to disclose their full-body actions. Our work compares the most popular realizations of the first-person and the third-person perspective in an online survey (N=217) with three different VR games. Contrary to the current trend to stream in third-person, our key result is that most viewers prefer the first-person version, which they attribute mostly to the better focus on in-game actions and higher involvement. Based on the study insights, we provide design recommendations for both perspectives.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Katharina Emmerich', 'Andrey Krekhov', 'Sebastian Cmentowski', 'Jens Krueger']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.92,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2101.04540v4",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04540v4",
        "title":"Capturing social media expressions during the COVID-19 pandemic in Argentina and forecasting mental health and emotions",
        "summary":"Purpose. We present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on language expressions used in social media. This approach permits anticipating high prevalence periods in short- to medium-term time horizons. Design. Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, we build descriptive timelines for decision makers to monitor the evolution of markers, and their correlation with crisis events. Second, we model the timelines as time series, and support their forecasting, which in turn serve to identify high prevalence points for the estimated markers. Findings. Results showed that different time series forecasting strategies offer different capabilities. In the best scenario, the emergence of high prevalence periods of emotions and mental health disorders can be satisfactorily predicted with a neural network strategy, even when limited data is available in early stages of a crisis (e.g., 7 days). Originality. Although there have been efforts in the literature to predict mental states of individuals, the analysis of mental health at the collective level has received scarce attention. We take a step forward by proposing a forecasting approach for analyzing the mental health of a given population (or group of individuals) at a larger scale. Practical implications. We believe that this work contributes to a better understanding of how psychological processes related to crisis manifest in social media, being a valuable asset for the design, implementation and monitoring of health prevention and communication policies.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Antonela Tommasel', 'Andres Diaz-Pace', 'Juan Manuel Rodriguez', 'Daniela Godoy']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.92,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2101.04547v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04547v3",
        "title":"Of Non-Linearity and Commutativity in BERT",
        "summary":"In this work we provide new insights into the transformer architecture, and in particular, its best-known variant, BERT. First, we propose a method to measure the degree of non-linearity of different elements of transformers. Next, we focus our investigation on the feed-forward networks (FFN) inside transformers, which contain 2\/3 of the model parameters and have so far not received much attention. We find that FFNs are an inefficient yet important architectural element and that they cannot simply be replaced by attention blocks without a degradation in performance. Moreover, we study the interactions between layers in BERT and show that, while the layers exhibit some hierarchical structure, they extract features in a fuzzy manner. Our results suggest that BERT has an inductive bias towards layer commutativity, which we find is mainly due to the skip connections. This provides a justification for the strong performance of recurrent and weight-shared transformer models.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Sumu Zhao', 'Damian Pascual', 'Gino Brunner', 'Roger Wattenhofer']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":1.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.2499166667
    },
    {
        "arxiv_id":"2101.04719v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04719v1",
        "title":"Expanding Explainability: Towards Social Transparency in AI systems",
        "summary":"As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Upol Ehsan', 'Q. Vera Liao', 'Michael Muller', 'Mark O. Riedl', 'Justin D. Weisz']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.82,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2101.04794v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04794v1",
        "title":"The Medical Authority of AI: A Study of AI-enabled Consumer-facing Health Technology",
        "summary":"Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including automated decisions and interaction design patterns of AISC apps, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology.",
        "published":"2021-01-12T00:00:00.000Z",
        "authors":"['Yue You', 'Yubo Kou', 'Xianghua Ding', 'Xinning Gui']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.7778333333
    },
    {
        "arxiv_id":"2101.04817v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04817v1",
        "title":"Discrete Knowledge Graph Embedding based on Discrete Optimization",
        "summary":"This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods. The convergence of DKGE can be guaranteed theoretically. Extensive experiments demonstrate that DKGE achieves superior accuracy than classical hashing functions that map the effective continuous embeddings into discrete codes. Besides, DKGE reaches comparable accuracy with much lower computational complexity and storage compared to many continuous graph embedding methods.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Yunqi Li', 'Shuyuan Xu', 'Bo Liu', 'Zuohui Fu', 'Shuchang Liu', 'Xu Chen', 'Yongfeng Zhang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.68,
        "actuality":2.0,
        "controversy":1.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.1945
    },
    {
        "arxiv_id":"2101.04834v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04834v1",
        "title":"Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows",
        "summary":"Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. To understand how Auto-ML tools are used in practice today, we performed a qualitative study with participants ranging from novice hobbyists to industry researchers who use Auto-ML tools. We present insights into the benefits and deficiencies of existing tools, as well as the respective roles of the human and automation in ML workflows. Finally, we discuss design implications for the future of Auto-ML tool development. We argue that instead of full automation being the ultimate goal of Auto-ML, designers of these tools should focus on supporting a partnership between the user and the Auto-ML tool. This means that a range of Auto-ML tools will need to be developed to support varying user goals such as simplicity, reproducibility, and reliability.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Doris Xin', 'Eva Yiwei Wu', 'Doris Jung-Lin Lee', 'Niloufar Salehi', 'Aditya Parameswaran']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.611
    },
    {
        "arxiv_id":"2101.04849v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04849v1",
        "title":"Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation",
        "summary":"Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information. To tackle this, we develop a distance-based recommendation model with several novel aspects: (i) each user and item are parameterized by Gaussian distributions to capture the learning uncertainties; (ii) an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets; (iii) explicit user-user\/item-item similarity modeling is incorporated in the objective function. The Wasserstein distance is employed to determine preferences because it obeys the triangle inequality and can measure the distance between probabilistic distributions. Via a comparison using five real-world datasets with state-of-the-art methods, the proposed model outperforms the best existing models by 4-22% in terms of recall@K on Top-K recommendation.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Chen Ma', 'Liheng Ma', 'Yingxue Zhang', 'Ruiming Tang', 'Xue Liu', 'Mark Coates']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":3.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.47225
    },
    {
        "arxiv_id":"2101.04852v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04852v2",
        "title":"Knowledge-Enhanced Top-K Recommendation in Poincar\u00e9 Ball",
        "summary":"Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Chen Ma', 'Liheng Ma', 'Yingxue Zhang', 'Haolun Wu', 'Xue Liu', 'Mark Coates']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.84,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.9445
    },
    {
        "arxiv_id":"2101.04899v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04899v2",
        "title":"Experimental Evaluation of Deep Learning models for Marathi Text Classification",
        "summary":"The Marathi language is one of the prominent languages used in India. It is predominantly spoken by the people of Maharashtra. Over the past decade, the usage of language on online platforms has tremendously increased. However, research on Natural Language Processing (NLP) approaches for Marathi text has not received much attention. Marathi is a morphologically rich language and uses a variant of the Devanagari script in the written form. This works aims to provide a comprehensive overview of available resources and models for Marathi text classification. We evaluate CNN, LSTM, ULMFiT, and BERT based models on two publicly available Marathi text classification datasets and present a comparative analysis. The pre-trained Marathi fast text word embeddings by Facebook and IndicNLP are used in conjunction with word-based models. We show that basic single layer models based on CNN and LSTM coupled with FastText embeddings perform on par with the BERT based models on the available datasets. We hope our paper aids focused research and experiments in the area of Marathi NLP.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Atharva Kulkarni', 'Meet Mandhane', 'Manali Likhitkar', 'Gayatri Kshirsagar', 'Jayashree Jagdale', 'Raviraj Joshi']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.83,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.4721666667
    },
    {
        "arxiv_id":"2101.04992v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.04992v1",
        "title":"Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders",
        "summary":"Due to their compliant structure, industrial robots without precision-enhancing measures are only to a limited extent suitable for machining applications. Apart from structural, thermal and bearing deformations, the main cause for compliant structure is backlash of transmission drives. This paper proposes a method to improve trajectory tracking accuracy by using secondary encoders and applying a feedback and a flatness based feed forward control strategy. For this purpose, a novel nonlinear, continuously differentiable dynamical model of a flexible robot joint is presented. The robot joint is modeled as a two-mass oscillator with pose-dependent inertia, nonlinear friction and nonlinear stiffness, including backlash. A flatness based feed forward control is designed to improve the guiding behaviour and a feedback controller, based on secondary encoders, is implemented for disturbance compensation. Using Automatic Differentiation, the nonlinear feed forward controller can be computed in a few microseconds online. Finally, the proposed algorithms are evaluated in simulations and experimentally on a real KUKA Quantec KR300 Ultra SE.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Jonas Weigand', 'Nigora Gafur', 'Martin Ruskowski']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.79,
        "actuality":3.0,
        "controversy":1.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.3611666667
    },
    {
        "arxiv_id":"2101.05004v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05004v1",
        "title":"Is the User Enjoying the Conversation? A Case Study on the Impact on the Reward Function",
        "summary":"The impact of user satisfaction in policy learning task-oriented dialogue systems has long been a subject of research interest. Most current models for estimating the user satisfaction either (i) treat out-of-context short-texts, such as product reviews, or (ii) rely on turn features instead of on distributed semantic representations. In this work we adopt deep neural networks that use distributed semantic representation learning for estimating the user satisfaction in conversations. We evaluate the impact of modelling context length in these networks. Moreover, we show that the proposed hierarchical network outperforms state-of-the-art quality estimators. Furthermore, we show that applying these networks to infer the reward function in a Partial Observable Markov Decision Process (POMDP) yields to a great improvement in the task success rate.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Lina M. Rojas-Barahona']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.87,
        "actuality":4.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2101.05044v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05044v2",
        "title":"Publishing patterns reflect political polarization in news media",
        "summary":"Digital news outlets rely on a variety of outside contributors, from freelance journalists, to political commentators, to executives and politicians. These external dependencies create a network among news outlets, traced along the contributors they share. Using connections between outlets, we demonstrate how contributors' publishing trajectories tend to align with outlet political leanings. We also show how polarized clustering of outlets translates to differences in the topics of news covered and the style and tone of articles published. In addition, we demonstrate how contributors who cross partisan divides tend to focus on less explicitly political topics. This work addresses an important gap in the media polarization literature, by highlighting how structural factors on the production side of news media create an ecosystem shaped by political leanings, independent of the priorities of any one person or organization.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Nick Hagar', 'Johannes Wachs', 'Em\u0151ke-\u00c1gnes Horv\u00e1t']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.5834166667
    },
    {
        "arxiv_id":"2101.05244v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05244v1",
        "title":"Calibration Methods of Touch-Point Ambiguity for Finger-Fitts Law",
        "summary":"Finger-Fitts law (FFitts law) is a model to predict touch-pointing times that was modified from Fitts' law. It considers the absolute touch-point precision, or a finger tremor factor sigma_a, to decrease the admissible target area and thus increase the task difficulty. Among choices such as running an independent task or performing parameter optimization, there is no consensus on the best methodology to measure sigma_a. By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute sigma_a, and we found that there is no optimal choice to maximize the prediction accuracy of FFitts law.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Shota Yamanaka', 'Hiroki Usuba']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.7778333333
    },
    {
        "arxiv_id":"2101.05337v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05337v1",
        "title":"A Survey on Simulators for Testing Self-Driving Cars",
        "summary":"A rigorous and comprehensive testing plays a key role in training self-driving cars to handle variety of situations that they are expected to see on public roads. The physical testing on public roads is unsafe, costly, and not always reproducible. This is where testing in simulation helps fill the gap, however, the problem with simulation testing is that it is only as good as the simulator used for testing and how representative the simulated scenarios are of the real environment. In this paper, we identify key requirements that a good simulator must have. Further, we provide a comparison of commonly used simulators. Our analysis shows that CARLA and LGSVL simulators are the current state-of-the-art simulators for end to end testing of self-driving cars for the reasons mentioned in this paper. Finally, we also present current challenges that simulation testing continues to face as we march towards building fully autonomous cars.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Prabhjot Kaur', 'Samira Taghavi', 'Zhaofeng Tian', 'Weisong Shi']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.93,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.361
    },
    {
        "arxiv_id":"2101.05667v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05667v1",
        "title":"The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models",
        "summary":"We propose a design pattern for tackling text ranking problems, dubbed \"Expando-Mono-Duo\", that has been empirically validated for a number of ad hoc retrieval tasks in different domains. At the core, our design relies on pretrained sequence-to-sequence models within a standard multi-stage ranking architecture. \"Expando\" refers to the use of document expansion techniques to enrich keyword representations of texts prior to inverted indexing. \"Mono\" and \"Duo\" refer to components in a reranking pipeline based on a pointwise model and a pairwise model that rerank initial candidates retrieved using keyword search. We present experimental results from the MS MARCO passage and document ranking tasks, the TREC 2020 Deep Learning Track, and the TREC-COVID challenge that validate our design. In all these tasks, we achieve effectiveness that is at or near the state of the art, in some cases using a zero-shot approach that does not exploit any training data from the target task. To support replicability, implementations of our design pattern are open-sourced in the Pyserini IR toolkit and PyGaggle neural reranking library.",
        "published":"2021-01-14T00:00:00.000Z",
        "authors":"['Ronak Pradeep', 'Rodrigo Nogueira', 'Jimmy Lin']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.79,
        "actuality":2.0,
        "controversy":1.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.3889166667
    },
    {
        "arxiv_id":"2101.05703v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05703v1",
        "title":"Exploring Asymmetric Roles in Mixed-Ability Gaming",
        "summary":"The landscape of digital games is segregated by player ability. For example, sighted players have a multitude of highly visual games at their disposal, while blind players may choose from a variety of audio games. Attempts at improving cross-ability access to any of those are often limited in the experience they provide, or disregard multiplayer experiences. We explore ability-based asymmetric roles as a design approach to create engaging and challenging mixed-ability play. Our team designed and developed two collaborative testbed games exploring asymmetric interdependent roles. In a remote study with 13 mixed-visual-ability pairs we assessed how roles affected perceptions of engagement, competence, and autonomy, using a mixed-methods approach. The games provided an engaging and challenging experience, in which differences in visual ability were not limiting. Our results underline how experiences unequal by design can give rise to an equitable joint experience.",
        "published":"2021-01-14T00:00:00.000Z",
        "authors":"['David Gon\u00e7alves', 'Andr\u00e9 Rodrigues', 'Mike L. Richardson', 'Alexandra A. de Sousa', 'Michael J. Proulx', 'Tiago Guerreiro']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.91,
        "actuality":4.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2101.05730v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05730v1",
        "title":"Towards Understanding and Evaluating Structural Node Embeddings",
        "summary":"While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences, a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles--i.e., similar functions, ties or interactions with nodes in other positions--irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. It relies on small synthetic or real networks with labels that are not perfectly defined, and its connection to sociological equivalences has hitherto been vague and tenuous. With new node embedding methods being developed at a breakneck pace, proper evaluation and systematic characterization of existing approaches will be essential to progress. To fill in this gap, we set out to understand what types of equivalences structural embeddings capture. We are the first to contribute rigorous intrinsic and extrinsic evaluation methodology for structural embeddings, along with carefully-designed, diverse datasets of varying sizes. We observe a number of different evaluation variables that can lead to different results (e.g., choice of similarity measure, classifier, label definitions). We find that degree distributions within nodes' local neighborhoods can lead to simple yet effective baselines in their own right and guide the future development of structural embedding. We hope that our findings can influence the design of further node embedding methods and also pave the way for more comprehensive and fair evaluation of structural embedding methods.",
        "published":"2021-01-14T00:00:00.000Z",
        "authors":"['Junchen Jin', 'Mark Heimann', 'Di Jin', 'Danai Koutra']",
        "arxiv_primary_category":"cs.si",
        "readability":0.82,
        "actuality":2.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.33325
    },
    {
        "arxiv_id":"2101.05768v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05768v1",
        "title":"How to Attack and Defend 5G Radio Access Network Slicing with Reinforcement Learning",
        "summary":"Reinforcement learning (RL) for network slicing is considered in the 5G radio access network, where the base station, gNodeB, allocates resource blocks (RBs) to the requests of user equipments and maximizes the total reward of accepted requests over time. Based on adversarial machine learning, a novel over-the-air attack is introduced to manipulate the RL algorithm and disrupt 5G network slicing. Subject to an energy budget, the adversary observes the spectrum and builds its own RL-based surrogate model that selects which RBs to jam with the objective of maximizing the number of failed network slicing requests due to jammed RBs. By jamming the RBs, the adversary reduces the RL algorithm's reward. As this reward is used as the input to update the RL algorithm, the performance does not recover even after the adversary stops jamming. This attack is evaluated in terms of the recovery time and the (maximum and total) reward loss, and it is shown to be much more effective than benchmark (random and myopic) jamming attacks. Different reactive and proactive defense mechanisms (protecting the RL algorithm's updates or misleading the adversary's learning process) are introduced to show that it is viable to defend 5G network slicing against this attack.",
        "published":"2021-01-14T00:00:00.000Z",
        "authors":"['Yi Shi', 'Yalin E. Sagduyu', 'Tugba Erpek', 'M. Cenk Gursoy']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.85,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.4721666667
    },
    {
        "arxiv_id":"2101.05891v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05891v1",
        "title":"A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data",
        "summary":"Functional near-infrared spectroscopy (fNIRS) is a non-invasive, economical method used to study its blood flow pattern. These patterns can be used to classify tasks a subject is performing. Currently, most of the classification systems use simple machine learning solutions for the classification of tasks. These conventional machine learning methods, which are easier to implement and interpret, usually suffer from low accuracy and undergo a complex preprocessing phase before network training. The proposed method converts the raw fNIRS time series data into an image using Gramian Angular Summation Field. A Deep Convolutional Neural Network (CNN) based architecture is then used for task classification, including mental arithmetic, motor imagery, and idle state. Further, this method can eliminate the feature selection stage, which affects the traditional classifiers' performance. This system obtained 87.14% average classification accuracy higher than any other method for the dataset.",
        "published":"2021-01-14T00:00:00.000Z",
        "authors":"['Sajila D. Wickramaratne', 'Md Shaad Mahmud']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.86,
        "actuality":2.0,
        "controversy":2.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.58325
    },
    {
        "arxiv_id":"2101.05913v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05913v3",
        "title":"Supervised Transfer Learning at Scale for Medical Imaging",
        "summary":"Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear. This is likely due to the large domain mismatch between the usual natural-image pre-training (e.g. ImageNet) and medical images. However, recent advances in transfer learning have shown substantial improvements from scale. We investigate whether modern methods can change the fortune of transfer learning for medical imaging. For this, we study the class of large-scale pre-trained networks presented by Kolesnikov et al. on three diverse imaging tasks: chest radiography, mammography, and dermatology. We study both transfer performance and critical properties for the deployment in the medical domain, including: out-of-distribution generalization, data-efficiency, sub-group fairness, and uncertainty estimation. Interestingly, we find that for some of these properties transfer from natural to medical images is indeed extremely effective, but only when performed at sufficient scale.",
        "published":"2021-01-14T00:00:00.000Z",
        "authors":"['Basil Mustafa', 'Aaron Loh', 'Jan Freyberg', 'Patricia MacWilliams', 'Megan Wilson', 'Scott Mayer McKinney', 'Marcin Sieniek', 'Jim Winkens', 'Yuan Liu', 'Peggy Bui', 'Shruthi Prabhakara', 'Umesh Telang', 'Alan Karthikesalingam', 'Neil Houlsby', 'Vivek Natarajan']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.91,
        "actuality":4.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.9443333333
    },
    {
        "arxiv_id":"2101.05983v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.05983v1",
        "title":"Russian Troll Account Classification with Twitter and Facebook Data",
        "summary":"In this analysis, we work with the data set that was compiled by Darren Linvill and Patrick Warren, along with a representative sample of Facebook ads that were released by the House Intelligence Committee Minority. The goal of this analysis is to use the categories defined by Linvill and Warren in the Twitter data and investigate if these categories exist in Facebook ads. This begin to give us insights to the tactics used between the two social media services. Further, we try to replicate Linvill and Warren's original categorization of the Twitter data. Lastly, we investigate what categories may exist in the Facebook data.",
        "published":"2021-01-15T00:00:00.000Z",
        "authors":"['Dominic Lewinski', 'Md Rashidul Hasan']",
        "arxiv_primary_category":"cs.si",
        "readability":0.94,
        "actuality":3.6666666667,
        "controversy":4.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0000833333
    },
    {
        "arxiv_id":"2101.06060v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06060v2",
        "title":"The Challenge of Value Alignment: from Fairer Algorithms to AI Safety",
        "summary":"This paper addresses the question of how to align AI systems with human values and situates it within a wider body of thought regarding technology and value. Far from existing in a vacuum, there has long been an interest in the ability of technology to 'lock-in' different value systems. There has also been considerable thought about how to align technologies with specific social values, including through participatory design-processes. In this paper we look more closely at the question of AI value alignment and suggest that the power and autonomy of AI systems gives rise to opportunities and challenges in the domain of value that have not been encountered before. Drawing important continuities between the work of the fairness, accountability, transparency and ethics community, and work being done by technical AI safety researchers, we suggest that more attention needs to be paid to the question of 'social value alignment' - that is, how to align AI systems with the plurality of values endorsed by groups of people, especially on the global level.",
        "published":"2021-01-15T00:00:00.000Z",
        "authors":"['Iason Gabriel', 'Vafa Ghazavi']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.94,
        "actuality":5.0,
        "controversy":3.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.3055833333
    },
    {
        "arxiv_id":"2101.06097v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06097v1",
        "title":"Impact of Autonomous Vehicle Technology on Long Distance Travel Behavior",
        "summary":"Although rapid progress in-vehicle automated technology has sped up the possibility of using fully automated technology for public use, little research has been done on the possible influences of autonomous vehicles (AVs) technology on long-distance travel. This technology has the potential to have a significant effect on intercity trips. This study analyzed a travel survey to anticipate the impact of this technology on long-distance trips. We have divided trips into two different categories including trips for pleasure and trips for business. Different hypotheses based on the authors' knowledge and assisted by existing literature have been defined for each type of trip. By using the Pearson method these hypotheses have been tested and the positive or negative responses from respondents have been evaluated. The findings show that using AVs for pleasure trips can increase the number of travelers and stimulate people to choose longer distances for their trips. In addition, people enjoy more and will be interested to travel more frequently. For business trips, AV technology can reduce travel costs and job-related stress. Unlike pleasure trips for which people are not interested in traveling at night, business travelers prefer to travel at night.",
        "published":"2021-01-13T00:00:00.000Z",
        "authors":"['Maryam Maleki', 'Yupo Chan', 'Mohammad Arani']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.94,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2101.06106v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06106v1",
        "title":"Big Data Generated by Connected and Automated Vehicles for Safety Monitoring, Assessment and Improvement, Final Report (Year 3)",
        "summary":"This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. Within this domain, the report systematically compares Big Data initiatives related to transportation initiatives nationally and internationally and provides insights regarding the evolution of Big Data science applications related to CAVs and new challenges. The objectives addressed are: 1-Creating a database of Big Data efforts by acquiring reports, white papers, and journal publications; 2-Applying text analytics tools to extract key concepts, and spot patterns and trends in Big Data initiatives; 3-Understanding the evolution of CAV Big Data in the context of safety by quantifying granular taxonomies and modeling entity relations among contents in CAV Big Data research initiatives, and 4-Developing a foundation for exploring new approaches to tracking and analyzing CAV Big Data and related innovations. The study synthesizes and derives high-quality information from innovative research activities undertaken by various research entities through Big Data initiatives. The results can provide a conceptual foundation for developing new approaches for guiding and tracking the safety implications of Big Data and related innovations.",
        "published":"2021-01-09T00:00:00.000Z",
        "authors":"['Asad J. Khattak', 'Iman Mahdinia', 'Sevin Mohammadi', 'Amin Mohammadnazar', 'Behram Wali']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.85,
        "actuality":3.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2101.06133v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06133v1",
        "title":"Teaming up with information agents",
        "summary":"Despite the intricacies involved in designing a computer as a teampartner, we can observe patterns in team behavior which allow us to describe at a general level how AI systems are to collaborate with humans. Whereas most work on human-machine teaming has focused on physical agents (e.g. robotic systems), our aim is to study how humans can collaborate with information agents. We propose some appropriate team design patterns, and test them using our Collaborative Intelligence Analysis (CIA) tool.",
        "published":"2021-01-15T00:00:00.000Z",
        "authors":"['Jurriaan van Diggelen', 'Wiard Jorritsma', 'Bob van der Vecht']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.93,
        "actuality":4.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2101.06301v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06301v2",
        "title":"Wi-Fi Wardriving Studies Must Account for Important Statistical Issues",
        "summary":"Knowledge of Wi-Fi networks helps to guide future engineering and spectrum policy decisions. However, due to its unlicensed nature, the deployment of Wi-Fi Access Points is undocumented meaning researchers are left making educated guesses as to the prevalence of these assets through remotely collected or passively sensed measurements. One commonly used method is referred to as `wardriving` essentially where a vehicle is used to collect geospatial statistical data on wireless networks to inform mobile computing and networking security research. Surprisingly, there has been very little examination of the statistical issues with wardriving data, despite the vast number of analyses being published in the literature using this approach. In this paper, a sample of publicly collected wardriving data is compared to a predictive model for Wi-Fi Access Points. The results demonstrate several statistical issues which future wardriving studies must account for, including selection bias, sample representativeness and the modifiable areal unit problem.",
        "published":"2021-01-15T00:00:00.000Z",
        "authors":"['Edward J Oughton', 'Julius Kusuma', 'Thibault Peyronel', 'Jon Crowcroft']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":3.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.5000833333
    },
    {
        "arxiv_id":"2101.06311v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06311v1",
        "title":"Boosting performance for software defined networks from traffic engineering perspective",
        "summary":"Paths selection algorithms and rate adaptation objective functions are usually studied separately. In contrast, this paper evaluates some traffic engineering (TE) systems for software defined networking obtained by combining path selection techniques with average delay and load balancing, the two most popular TE objective functions. Based on TE simulation results, the best TE system suitable for software defined networks is a system where the paths are calculated using an oblivious routing model and its adaptation rate calculated using an average delay objective function. Thus, we propose the RACKE+AD system combining path sets computed using Racke's oblivious routing and traffic splitting objective function using average delay. This model outperforms current state-of-the-art models, maximizes throughput, achieves better network resource utilization, and minimizes delay. The proposed system outperformed SMORE and SWAN by 4.2% and 9.6% respectively, achieving 27% better utilization and delivering 34% more traffic with 50% less latency compared with both systems on a GEANT network.",
        "published":"2021-01-15T00:00:00.000Z",
        "authors":"['Mohammed I. Salman', 'Bin Wang']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.81,
        "actuality":2.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2101.06537v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06537v2",
        "title":"PL2: Towards Predictable Low Latency in Rack-Scale Networks",
        "summary":"High performance rack-scale offerings package disaggregated pools of compute, memory and storage hardware in a single rack to run diverse workloads with varying requirements, including applications that need low and predictable latency. The intra-rack network is typically high speed Ethernet, which can suffer from congestion leading to packet drops and may not satisfy the stringent tail latency requirements for some workloads (including remote memory\/storage accesses). In this paper, we design a Predictable Low Latency(PL2) network architecture for rack-scale systems with Ethernet as interconnecting fabric. PL2 leverages programmable Ethernet switches to carefully schedule packets such that they incur no loss with NIC and switch queues maintained at small, near-zero levels. In our 100 Gbps rack-prototype, PL2 keeps 99th-percentile memcached RPC latencies under 60us even when the RPCs compete with extreme offered-loads of 400%, without losing traffic. Network transfers for a machine learning training task complete 30% faster than a receiver-driven scheme implementation modeled after Homa (222ms vs 321ms 99%ile latency per iteration).",
        "published":"2021-01-16T00:00:00.000Z",
        "authors":"['Yanfang Le', 'Radhika Niranjan Mysore', 'Lalith Suresh', 'Gerd Zellweger', 'Sujata Banerjee', 'Aditya Akella', 'Michael Swift']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.76,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.0276666667
    },
    {
        "arxiv_id":"2101.06548v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06548v1",
        "title":"RVE-CV2X: A Scalable Emulation Framework for Real-Time Evaluation of CV2X-Based Connected Vehicle Applications",
        "summary":"Vehicle-to-Everything (V2X) communication has become an integral component of Intelligent Transportation Systems (ITS) due to its ability to connect vehicles, pedestrians, infrastructure, and create situational awareness among vehicles. Cellular-Vehicle-to-Everything (C-V2X), based on 3rd Generation Partnership Project (3GPP) Release 14, is one such communication technology that has recently gained significant attention to cater the needs of V2X communication. However, for a successful deployment of C-V2X, it is of paramount significance to thoroughly test the performance of this technology. It is unfeasible to physically conduct a V2X communication experiment to test the performance of C-V2X by arranging hundreds of real vehicles and their transceiving on-board units. Although multiple simulators based on frameworks such as NS-3, OMNET++ and OPNET have proven to be reliable and economic alternatives to using real vehicles, all these simulators are time-consuming and require several orders of magnitudes longer than the actual simulation time. As opposed to physical field- and simulation-based testing, network emulators can provide more realistic and repeatable results for testing vehicular communication. This paper proposes a real-time, high-fidelity, hardware-in-the-loop network emulator (RVE-CV2X) based on C-V2X mode 4 that can provide scalable, reliable and repeatable testing scenarios for V2X communication. The accuracy of this emulator is verified by comparing it to an already validated C-V2X simulator based on the NS-3 framework.",
        "published":"2021-01-16T00:00:00.000Z",
        "authors":"['Ghayoor Shah', 'MD Saifuddin', 'Yaser P. Fallah', 'Somak Datta Gupta']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2101.06611v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06611v2",
        "title":"Predictive Processing in Cognitive Robotics: a Review",
        "summary":"Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes such as predictive coding, active inference, perceptual inference, free-energy principle, tend to be used interchangeably. In the field of cognitive robotics there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this paper, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related non-robotic models. The analysis suggests that, first, both research in cognitive robotics implementations and non-robotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in non-robotics models it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.",
        "published":"2021-01-17T00:00:00.000Z",
        "authors":"['Alejandra Ciria', 'Guido Schillaci', 'Giovanni Pezzulo', 'Verena V. Hafner', 'Bruno Lara']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.9721666667
    },
    {
        "arxiv_id":"2101.06619v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06619v1",
        "title":"Solving QSAT problems with neural MCTS",
        "summary":"Recent achievements from AlphaZero using self-play has shown remarkable performance on several board games. It is plausible to think that self-play, starting from zero knowledge, can gradually approximate a winning strategy for certain two-player games after an amount of training. In this paper, we try to leverage the computational power of neural Monte Carlo Tree Search (neural MCTS), the core algorithm from AlphaZero, to solve Quantified Boolean Formula Satisfaction (QSAT) problems, which are PSPACE complete. Knowing that every QSAT problem is equivalent to a QSAT game, the game outcome can be used to derive the solutions of the original QSAT problems. We propose a way to encode Quantified Boolean Formulas (QBFs) as graphs and apply a graph neural network (GNN) to embed the QBFs into the neural MCTS. After training, an off-the-shelf QSAT solver is used to evaluate the performance of the algorithm. Our result shows that, for problems within a limited size, the algorithm learns to solve the problem correctly merely from self-play.",
        "published":"2021-01-17T00:00:00.000Z",
        "authors":"['Ruiyang Xu', 'Karl Lieberherr']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.81,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.5555
    },
    {
        "arxiv_id":"2101.06654v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06654v1",
        "title":"Zero-touch Continuous Network Slicing Control via Scalable Actor-Critic Learning",
        "summary":"Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full potential of heterogeneous 5G and beyond 5G (B5G) communication systems and enable the automation of demand-aware resource management and orchestration (MANO). In this paper, we tackle the issue of B5G radio access network (RAN) joint slice admission control and resource allocation according to proposed slice-enabling cell-free massive multiple-input multiple-output (mMIMO) setup by invoking a continuous deep reinforcement learning (DRL) method. We present a novel Actor-Critic-based network slicing approach called, prioritized twin delayed distributional deep deterministic policy gradient (D-TD3)}. The paper defines and corroborates via extensive experimental results a zero-touch network slicing scheme with a multi-objective approach where the central server learns continuously to accumulate the knowledge learned in the past to solve future problems and re-configure computing resources autonomously while minimizing latency, energy consumption, and virtual network function (VNF) instantiation cost for each slice. Moreover, we pursue a state-action return distribution learning approach with the proposed replay policy and reward-penalty mechanisms. Finally, we present numerical results to showcase the gain of the adopted multi-objective strategy and verify the performance in terms of achieved slice admission rate, latency, energy, CPU utilization, and time efficiency.",
        "published":"2021-01-17T00:00:00.000Z",
        "authors":"['Farhad Rezazadeh', 'Hatim Chergui', 'Christos Verikoukis']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2101.06846v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06846v1",
        "title":"Fast and Accurate Multi-Body Simulation with Stiff Viscoelastic Contacts",
        "summary":"The simulation of multi-body systems with frictional contacts is a fundamental tool for many fields, such as robotics, computer graphics, and mechanics. Hard frictional contacts are particularly troublesome to simulate because they make the differential equations stiff, calling for computationally demanding implicit integration schemes. We suggest to tackle this issue by using exponential integrators, a long-standing class of integration schemes (first introduced in the 60's) that in recent years has enjoyed a resurgence of interest. We show that this scheme can be easily applied to multi-body systems subject to stiff viscoelastic contacts, producing accurate results at lower computational cost than classic explicit schemes. In our tests with quadruped and biped robots, our method demonstrated stable behaviors with large time steps (10 ms) and stiff contacts ($10^5$ N\/m). Its excellent properties, especially for fast and coarse simulations, make it a valuable candidate for many applications in robotics, such as simulation, Model Predictive Control, Reinforcement Learning, and controller design.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Bilal Hammoud', 'Luca Olivieri', 'Ludovic Righetti', 'Justin Carpentier', 'Andrea Del Prete']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":2.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.4165833333
    },
    {
        "arxiv_id":"2101.06864v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06864v1",
        "title":"Understanding Patterns of Users Who Repost Censored Posts on Weibo",
        "summary":"In this study, we focus on understanding patterns of users whose repost contents would later be censored on Weibo, a counterpart of Twitter in China as a social media platform. Little is known about the way regulations and censorship work in this indigenous platform and what role it plays in affecting users' expression of ideas. We collect over a million reposts from over 18,000 users and investigate the patterns of users whose reposts contain content that is no longer visible to the public, from the perspective of user location, device, gender, social capital as well as certified status. We find that user characteristics play an important role in affecting behaviors on Weibo.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Yichi Qian', 'Feng Yuan', 'Hanjia Lyu', 'Jiebo Luo']",
        "arxiv_primary_category":"cs.si",
        "readability":0.94,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.7500833333
    },
    {
        "arxiv_id":"2101.06919v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06919v1",
        "title":"Unsupervised Link and Unlink Prediction on Dynamic Networks",
        "summary":"Understanding and characterizing the process deriving the creation and dissolution of social interactions is a fundamental challenge for social network analysis. In the dynamic setting, it is essential to be able to, given the collection of link states of the network in a certain period, accurately predict the link and unlink states of the network in a future time. Addressing this task is more complicated compared to its static counterpart especially for increasingly large networks due to the prohibitive expensiveness of computational complexity. Consequently, mainstreams of current researches in unsupervised settings ignore the temporal information. Additionally, only a few approaches study on unlink prediction, which is also important to understand the evolution of social networks. In this work, we address such mining tasks by unsupervised learning, and propose a model for link and unlink prediction with temporal information (LUPT). Given a sequence of snapshots of network over time, LUPT utilizes the spectral diffusion by variants of local random walks to calculate the probability vector started from each node at each snapshot. Then, it calculates the similarity score for each of the nodes by the probability vectors of all the previous snapshots. Finally, LUPT predicts the link and unlink states by ranking the similarity scores according to the link and unlink tasks, respectively. Experiments on real-world networks demonstrate that LUPT provides superior results compared to the baseline methods in both link and unlink prediction tasks.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Kun He', 'Christina Muro', 'Boyu Li']",
        "arxiv_primary_category":"cs.si",
        "readability":0.83,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3888333333
    },
    {
        "arxiv_id":"2101.06927v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06927v1",
        "title":"Robustness of Meta Matrix Factorization Against Strict Privacy Constraints",
        "summary":"In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction to preserve users' privacy. We reproduce the experiments of Lin et al. on five datasets, i.e., Douban, Hetrec-MovieLens, MovieLens 1M, Ciao, and Jester. Also, we study the impact of meta learning on the accuracy of MetaMF's recommendations. Furthermore, in our work, we acknowledge that users may have different tolerances for revealing information about themselves. Hence, in a second strand of experiments, we investigate the robustness of MetaMF against strict privacy constraints. Our study illustrates that we can reproduce most of Lin et al.'s results. Plus, we provide strong evidence that meta learning is essential for MetaMF's robustness against strict privacy constraints.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Peter M\u00fcllner', 'Dominik Kowald', 'Elisabeth Lex']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.77,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2101.06949v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06949v1",
        "title":"HinFlair: pre-trained contextual string embeddings for pos tagging and text classification in the Hindi language",
        "summary":"Recent advancements in language models based on recurrent neural networks and transformers architecture have achieved state-of-the-art results on a wide range of natural language processing tasks such as pos tagging, named entity recognition, and text classification. However, most of these language models are pre-trained in high resource languages like English, German, Spanish. Multi-lingual language models include Indian languages like Hindi, Telugu, Bengali in their training corpus, but they often fail to represent the linguistic features of these languages as they are not the primary language of the study. We introduce HinFlair, which is a language representation model (contextual string embeddings) pre-trained on a large monolingual Hindi corpus. Experiments were conducted on 6 text classification datasets and a Hindi dependency treebank to analyze the performance of these contextualized string embeddings for the Hindi language. Results show that HinFlair outperforms previous state-of-the-art publicly available pre-trained embeddings for downstream tasks like text classification and pos tagging. Also, HinFlair when combined with FastText embeddings outperforms many transformers-based language models trained particularly for the Hindi language.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Harsh Patel']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.82,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2101.06980v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.06980v1",
        "title":"Mitigating the Position Bias of Transformer Models in Passage Re-Ranking",
        "summary":"Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we observe a bias in the position of the correct answer in the text in two popular Question Answering datasets used for passage re-ranking. The excessive favoring of earlier positions inside passages is an unwanted artefact. This leads to three common Transformer-based re-ranking models to ignore relevant parts in unseen passages. More concerningly, as the evaluation set is taken from the same biased distribution, the models overfitting to that bias overestimate their true effectiveness. In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results. We propose a debiasing method for retrieval datasets. Our results show that a model trained on a position-biased dataset exhibits a significant decrease in re-ranking effectiveness when evaluated on a debiased dataset. We demonstrate that by mitigating the position bias, Transformer-based re-ranking models are equally effective on a biased and debiased dataset, as well as more effective in a transfer-learning setting between two differently biased datasets.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Sebastian Hofst\u00e4tter', 'Aldo Lipani', 'Sophia Althammer', 'Markus Zlabinger', 'Allan Hanbury']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.84,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.5278333333
    },
    {
        "arxiv_id":"2101.07120v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07120v1",
        "title":"Neural Abstractive Text Summarizer for Telugu Language",
        "summary":"Abstractive Text Summarization is the process of constructing semantically relevant shorter sentences which captures the essence of the overall meaning of the source text. It is actually difficult and very time consuming for humans to summarize manually large documents of text. Much of work in abstractive text summarization is being done in English and almost no significant work has been reported in Telugu abstractive text summarization. So, we would like to propose an abstractive text summarization approach for Telugu language using Deep learning. In this paper we are proposing an abstractive text summarization Deep learning model for Telugu language. The proposed architecture is based on encoder-decoder sequential models with attention mechanism. We have applied this model on manually created dataset to generate a one sentence summary of the source text and have got good results measured qualitatively.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Mohan Bharath B', 'Aravindh Gowtham B', 'Akhil M']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.82,
        "actuality":4.0,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.6943333333
    },
    {
        "arxiv_id":"2101.07158v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07158v2",
        "title":"DECT-2020 New Radio: The Next Step Towards 5G Massive Machine-Type Communications",
        "summary":"Massive machine type communications (mMTC) is one of the cornerstone services that have to be supported by 5G systems. 3GPP has already introduced LTE-M and NB-IoT, often referred to as cellular IoT, in 3GPP Releases 13, 14, and 15 and submitted these technologies as part of 3GPP IMT-2020 (i.e., 5G) technology submission to ITU-R. Even though NB-IoT and LTE-M have shown to satisfy 5G mMTC requirements defined by ITU-R, it is expected that these cellular IoT solutions will not address all aspects of IoT and ongoing digitalization, including the support for direct communication between \"things\" with flexible deployments, different business models, as well as support for even higher node densities and enhanced coverage. In this paper, we introduce the DECT-2020 standard recently published by ETSI for mMTC communications. We evaluate its performance and compare it to the existing LPWAN solutions showing that it outperforms those in terms of supported density of nodes while still keeping delay and loss guarantees at the required level.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Roman Kovalchukov', 'Dmitri Moltchanov', 'Juho Pirskanen', 'Joonas Sae', 'Jussi Numminen', 'Yevgeni Koucheryavy', 'Mikko Valkama']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1945
    },
    {
        "arxiv_id":"2101.07196v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07196v2",
        "title":"Impact of COVID-19 on Adoption of IoT in Different Sectors",
        "summary":"COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's impact on the adoption of the Internet of Things (IoT) in various broad sectors namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our analysis of this impact on IoT adoption is based on interviews with many industry experts, a thorough research literature review and a careful examination of reports from leading consulting firms. For each of these sectors, we also provide the details of recent initiatives taken in the wake of COVID-19. We also highlight challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption in these sectors.",
        "published":"2021-01-15T00:00:00.000Z",
        "authors":"['Muhammad Umair', 'Muhammad Aamir Cheema', 'Omer Cheema', 'Huan Li', 'Hua Lu']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.91,
        "actuality":4.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2101.07308v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07308v1",
        "title":"Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains",
        "summary":"Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-world applications. For instance, in person re-identification, videos are captured over a distributed set of cameras with non-overlapping viewpoints. The shift between the source (e.g. lab setting) and target (e.g. cameras) domains may lead to a significant decline in recognition accuracy. Additionally, state-of-the-art CNNs may not be suitable for such real-time applications given their computational requirements. Although several techniques have recently been proposed to address domain shift problems through unsupervised domain adaptation (UDA), or to accelerate\/compress CNNs through knowledge distillation (KD), we seek to simultaneously adapt and compress CNNs to generalize well across multiple target domains. In this paper, we propose a progressive KD approach for unsupervised single-target DA (STDA) and multi-target DA (MTDA) of CNNs. Our method for KD-STDA adapts a CNN to a single target domain by distilling from a larger teacher CNN, trained on both target and source domain data in order to maintain its consistency with a common representation. Our proposed approach is compared against state-of-the-art methods for compression and STDA of CNNs on the Office31 and ImageClef-DA image classification datasets. It is also compared against state-of-the-art methods for MTDA on Digits, Office31, and OfficeHome. In both settings -- KD-STDA and KD-MTDA -- results indicate that our approach can achieve the highest level of accuracy across target domains, while requiring a comparable or lower CNN complexity.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Le Thanh Nguyen-Meidine', 'Atif Belal', 'Madhu Kiran', 'Jose Dolz', 'Louis-Antoine Blais-Morin', 'Eric Granger']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.82,
        "actuality":4.0,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9999166667
    },
    {
        "arxiv_id":"2101.07413v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07413v1",
        "title":"On Dynamic Noise Influence in Differentially Private Learning",
        "summary":"Protecting privacy in learning while maintaining the model performance has become increasingly critical in many applications that involve sensitive data. Private Gradient Descent (PGD) is a commonly used private learning framework, which noises gradients based on the Differential Privacy protocol. Recent studies show that \\emph{dynamic privacy schedules} of decreasing noise magnitudes can improve loss at the final iteration, and yet theoretical understandings of the effectiveness of such schedules and their connections to optimization algorithms remain limited. In this paper, we provide comprehensive analysis of noise influence in dynamic privacy schedules to answer these critical questions. We first present a dynamic noise schedule minimizing the utility upper bound of PGD, and show how the noise influence from each optimization step collectively impacts utility of the final model. Our study also reveals how impacts from dynamic noise influence change when momentum is used. We empirically show the connection exists for general non-convex losses, and the influence is greatly impacted by the loss curvature.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Junyuan Hong', 'Zhangyang Wang', 'Jiayu Zhou']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.86,
        "actuality":4.0,
        "controversy":2.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.6389166667
    },
    {
        "arxiv_id":"2101.07458v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07458v2",
        "title":"Hybrid Trilinear and Bilinear Programming for Aligning Partially Overlapping Point Sets",
        "summary":"Alignment methods which can handle partially overlapping point sets and are invariant to the corresponding transformations are desirable in computer vision, with applications such as providing initial transformation configuration for local search based methods like ICP. To this end, we first show that the objective of the robust point matching (RPM) algorithm is a cubic polynomial. We then utilize the convex envelopes of trilinear and bilinear monomials to develop its lower bounding function. The resulting lower bounding problem can be efficiently solved via linear assignment and low dimensional convex quadratic programming. We next develop a branch-and-bound (BnB) algorithm which only branches over the transformation parameters and converges quickly. Experimental results demonstrated favorable performance of the proposed method over the state-of-the-art methods in terms of robustness and speed.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Wei Lian', 'Wangmeng Zuo', 'Lei Zhang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.8,
        "actuality":3.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2101.07463v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07463v1",
        "title":"Computer Science Communities: Who is Speaking, and Who is Listening to the Women? Using an Ethics of Care to Promote Diverse Voices",
        "summary":"Those working on policy, digital ethics and governance often refer to issues in `computer science', that includes, but is not limited to, common subfields of Artificial Intelligence (AI), Computer Science (CS) Computer Security (InfoSec), Computer Vision (CV), Human Computer Interaction (HCI), Information Systems, (IS), Machine Learning (ML), Natural Language Processing (NLP) and Systems Architecture. Within this framework, this paper is a preliminary exploration of two hypotheses, namely 1) Each community has differing inclusion of minoritised groups (using women as our test case); and 2) Even where women exist in a community, they are not published representatively. Using data from 20,000 research records, totalling 503,318 names, preliminary data supported our hypothesis. We argue that ACM has an ethical duty of care to its community to increase these ratios, and to hold individual computing communities to account in order to do so, by providing incentives and a regular reporting system, in order to uphold its own Code.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Marc Cheong', 'Kobi Leins', 'Simon Coghlan']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8055833333
    },
    {
        "arxiv_id":"2101.07480v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07480v2",
        "title":"How Do Hyperedges Overlap in Real-World Hypergraphs? -- Patterns, Measures, and Generators",
        "summary":"Hypergraphs, a generalization of graphs, naturally represent groupwise relationships among multiple individuals or objects, which are common in many application areas, including web, bioinformatics, and social networks. The flexibility in the number of nodes in each hyperedge, which provides the expressiveness of hypergraphs, brings about structural differences between graphs and hypergraphs. Especially, the overlaps of hyperedges lead to complex high-order relations beyond pairwise relations, raising new questions that have not been considered in graphs: How do hyperedges overlap in real-world hypergraphs? Are there any pervasive characteristics? What underlying process can cause such patterns? In this work, we closely investigate thirteen real-world hypergraphs from various domains and share interesting observations of overlaps of hyperedges. To this end, we define principled measures and statistically compare the overlaps of hyperedges in real-world hypergraphs and those in null models. Additionally, based on the observations, we propose HyperLap, a realistic hypergraph generative model. HyperLap is (a) Realistic: it accurately reproduces overlapping patterns of real-world hypergraphs, (b) Automatically Fittable: its parameters can be tuned automatically using HyperLap+ to generate hypergraphs particularly similar to a given target hypergraph, (c) Scalable: it generates and fits a hypergraph with 0.7 billion hyperedges within a few hours.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Geon Lee', 'Minyoung Choe', 'Kijung Shin']",
        "arxiv_primary_category":"cs.si",
        "readability":0.77,
        "actuality":2.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2101.07542v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07542v1",
        "title":"VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset",
        "summary":"This paper publishes a natural and very complicated dataset of handwritten documents with multiply oriented and curved text lines, namely VML-MOC dataset. These text lines were written as remarks on the page margins by different writers over the years. They appear at different locations within the orientations that range between 0 and 180 or as curvilinear forms. We evaluate a multi-oriented Gaussian based method to segment these handwritten text lines that are skewed or curved in any orientation. It achieves a mean pixel Intersection over Union score of 80.96% on the test documents. The results are compared with the results of a single-oriented Gaussian based text line segmentation method.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Berat Kurar Barakat', 'Rafi Cohen', 'Irina Rabaev', 'Jihad El-Sana']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.84,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6945
    },
    {
        "arxiv_id":"2101.07562v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07562v1",
        "title":"Modelling Downlink Packet Aggregation in Paced 802.11ac WLANs",
        "summary":"We derive an analytic model of packet aggregation on the the downlink of an 802.11ac WLAN when packet arrivals are paced. The model is closed-form and so suitable for both analysis and design of next generation edge architectures that aim to achieve high rate and low delay. The model is validated against both simulations and experimental measurements and found to be remarkably accurate despite its simplicity.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Francesco Gringoli', 'Douglas J. Leith']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":1.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.4999166667
    },
    {
        "arxiv_id":"2101.07681v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07681v2",
        "title":"Hyperspectral Image Restoration via Multi-mode and Double-weighted Tensor Nuclear Norm Minimization",
        "summary":"Tensor nuclear norm (TNN) induced by tensor singular value decomposition plays an important role in hyperspectral image (HSI) restoration tasks. In this letter, we first consider three inconspicuous but crucial phenomenons in TNN. In the Fourier transform domain of HSIs, different frequency components contain different information; different singular values of each frequency component also represent different information. The two physical phenomenons lie not only in the spectral dimension but also in the spatial dimensions. Then, to improve the capability and flexibility of TNN for HSI restoration, we propose a multi-mode and double-weighted TNN based on the above three crucial phenomenons. It can adaptively shrink the frequency components and singular values according to their physical meanings in all modes of HSIs. In the framework of the alternating direction method of multipliers, we design an effective alternating iterative strategy to optimize our proposed model. Restoration experiments on both synthetic and real HSI datasets demonstrate their superiority against related methods.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Sheng Liu', 'Xiaozhen Xie', 'Wenfeng Kong']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.75,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.2778333333
    },
    {
        "arxiv_id":"2101.07902v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07902v2",
        "title":"Integrated Visualization Editing via Parameterized Declarative Templates",
        "summary":"Interfaces for creating visualizations typically embrace one of several common forms. Textual specification enables fine-grained control, shelf building facilitates rapid exploration, while chart choosing promotes immediacy and simplicity. Ideally these approaches could be unified to integrate the user- and usage-dependent benefits found in each modality, yet these forms remain distinct. We propose parameterized declarative templates, a simple abstraction mechanism over JSON-based visualization grammars, as a foundation for multimodal visualization editors. We demonstrate how templates can facilitate organization and reuse by factoring the more than 160 charts that constitute Vega-Lite's example gallery into approximately 40 templates. We exemplify the pliability of abstracting over charting grammars by implementing -- as a template -- the functionality of the shelf builder Polestar (a simulacra of Tableau) and a set of templates that emulate the Google Sheets chart chooser. We show how templates support multimodal visualization editing by implementing a prototype and evaluating it through an approachability study.",
        "published":"2021-01-19T00:00:00.000Z",
        "authors":"['Andrew McNutt', 'Ravi Chugh']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.69,
        "actuality":2.3333333333,
        "controversy":1.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.3611666667
    },
    {
        "arxiv_id":"2101.07959v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07959v1",
        "title":"Class balanced underwater object detection dataset generated by class-wise style augmentation",
        "summary":"Underwater object detection technique is of great significance for various applications in underwater the scenes. However, class imbalance issue is still an unsolved bottleneck for current underwater object detection algorithms. It leads to large precision discrepancies among different classes that the dominant classes with more training data achieve higher detection precisions while the minority classes with fewer training data achieves much lower detection precisions. In this paper, we propose a novel class-wise style augmentation (CWSA) algorithm to generate a class-balanced underwater dataset Balance18 from the public contest underwater dataset URPC2018. CWSA is a new kind of data augmentation technique which augments the training data for the minority classes by generating various colors, textures and contrasts for the minority classes. Compare with previous data augmentation algorithms such flipping, cropping and rotations, CWSA is able to generate a class balanced underwater dataset with diverse color distortions and haze-effects.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Long Chen', 'Junyu Dong', 'Huiyu Zhou']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.81,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":1.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2101.07996v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.07996v1",
        "title":"SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices",
        "summary":"Super-resolution (SR) is a coveted image processing technique for mobile apps ranging from the basic camera apps to mobile health. Existing SR algorithms rely on deep learning models with significant memory requirements, so they have yet to be deployed on mobile devices and instead operate in the cloud to achieve feasible inference time. This shortcoming prevents existing SR methods from being used in applications that require near real-time latency. In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock. The SplitSRBlock supports channel-splitting, allowing the residual blocks to retain spatial information while reducing the computation in the channel dimension. SplitSR has a hybrid design consisting of standard convolutional blocks and lightweight residual blocks, allowing people to tune SplitSR for their computational budget. We evaluate our system on a low-end ARM CPU, demonstrating both higher accuracy and up to 5 times faster inference than previous approaches. We then deploy our model onto a smartphone in an app called ZoomSR to demonstrate the first-ever instance of on-device, deep learning-based SR. We conducted a user study with 15 participants to have them assess the perceived quality of images that were post-processed by SplitSR. Relative to bilinear interpolation -- the existing standard for on-device SR -- participants showed a statistically significant preference when looking at both images (Z=-9.270, p<0.01) and text (Z=-6.486, p<0.01).",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Xin Liu', 'Yuang Li', 'Josh Fromm', 'Yuntao Wang', 'Ziheng Jiang', 'Alex Mariakakis', 'Shwetak Patel']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2101.08021v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08021v1",
        "title":"Bolder is Better: Raising User Awareness through Salient and Concise Privacy Notices",
        "summary":"This paper addresses the question whether the recently proposed approach of concise privacy notices in apps and on websites is effective in raising user awareness. To assess the effectiveness in a realistic setting, we included concise notices in a fictitious but realistic fitness tracking app and asked participants recruited from an online panel to provide their feedback on the usability of the app as a cover story. Importantly, after giving feedback, users were also asked to recall the data practices described in the notices. The experimental setup included the variation of different levels of saliency and riskiness of the privacy notices. Based on a total sample of 2,274 participants, our findings indicate that concise privacy notices are indeed a promising approach to raise user awareness for privacy information when displayed in a salient way, especially in case the notices describe risky data practices. Our results may be helpful for regulators, user advocates and transparency-oriented companies in creating or enforcing better privacy transparency towards average users that do not read traditional privacy policies.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Nico Ebert', 'Kurt Alexander Ackermann', 'Bj\u00f6rn Scheppler']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.9,
        "actuality":4.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2101.08153v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08153v2",
        "title":"Shielding Atari Games with Bounded Prescience",
        "summary":"Deep reinforcement learning (DRL) is applied in safety-critical domains such as robotics and autonomous driving. It achieves superhuman abilities in many tasks, however whether DRL agents can be shown to act safely is an open problem. Atari games are a simple yet challenging exemplar for evaluating the safety of DRL agents and feature a diverse portfolio of game mechanics. The safety of neural agents has been studied before using methods that either require a model of the system dynamics or an abstraction; unfortunately, these are unsuitable to Atari games because their low-level dynamics are complex and hidden inside their emulator. We present the first exact method for analysing and ensuring the safety of DRL agents for Atari games. Our method only requires access to the emulator. First, we give a set of 43 properties that characterise \"safe behaviour\" for 30 games. Second, we develop a method for exploring all traces induced by an agent and a game and consider a variety of sources of game non-determinism. We observe that the best available DRL agents reliably satisfy only very few properties; several critical properties are violated by all agents. Finally, we propose a countermeasure that combines a bounded explicit-state exploration with shielding. We demonstrate that our method improves the safety of all agents over multiple properties.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Mirco Giacobbe', 'Mohammadhosein Hasanbeig', 'Daniel Kroening', 'Hjalmar Wijk']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.87,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8055833333
    },
    {
        "arxiv_id":"2101.08210v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08210v1",
        "title":"VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter",
        "summary":"The wide spread of unfounded election fraud claims surrounding the U.S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U.S. capitol. Under these circumstances, it is critical to understand discussions surrounding these claims on Twitter, a major platform where the claims disseminate. To this end, we collected and release the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and 25.6M retweets from 2.6M users related to voter fraud claims. To make this data immediately useful for a wide area of researchers, we further enhance the data with cluster labels computed from the retweet graph, user suspension status, and perceptual hashes of tweeted images. We also include in the dataset aggregated information for all external links and YouTube videos that appear in the tweets. Preliminary analyses of the data show that Twitter's ban actions mostly affected a specific community of voter fraud claim promoters, and exposes the most common URLs, images and YouTube videos shared in the data.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Anton Abilov', 'Yiqing Hua', 'Hana Matatov', 'Ofra Amir', 'Mor Naaman']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.8611666667
    },
    {
        "arxiv_id":"2101.08248v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08248v2",
        "title":"Generating (Formulaic) Text by Splicing Together Nearest Neighbors",
        "summary":"We propose to tackle conditional text generation tasks, especially those which require generating formulaic text, by splicing together segments of text from retrieved \"neighbor\" source-target pairs. Unlike recent work that conditions on retrieved neighbors in an encoder-decoder setting but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor text (i.e., by inserting or replacing them) to form an output. Standard techniques for training such a policy require an oracle derivation for each generation, and we prove that finding the shortest such derivation can be reduced to parsing under a particular weighted context-free grammar. We find that policies learned in this way allow for interpretable table-to-text or headline generation that is competitive with neighbor-based token-level policies on automatic metrics, though on all but one dataset neighbor-based policies underperform a strong neighborless baseline. In all cases, however, generating by splicing is faster.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Sam Wiseman', 'Arturs Backurs', 'Karl Stratos']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2101.08325v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08325v3",
        "title":"\"This Whole Thing Smacks of Gender\": Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis",
        "summary":"Smart weight scales offer bioimpedance-based body composition analysis as a supplement to pure body weight measurement. Companies such as Withings and Fitbit tout composition analysis as providing self-knowledge and the ability to make more informed decisions. However, these aspirational statements elide the reality that these numbers are a product of proprietary regression equations that require a binary sex\/gender as their input. Our paper combines transgender studies-influenced personal narrative with an analysis of the scientific basis of bioimpedance technology used as part of the Withings smart scale. Attempting to include nonbinary people reveals that bioelectrical impedance analysis has always rested on physiologically shaky ground. White nonbinary people are merely the tip of the iceberg of those who may find that their smart scale is not so intelligent when it comes to their bodies. Using body composition analysis as an example, we explore how the problem of trans and nonbinary inclusion in personal health tech goes beyond the issues of adding a third \"gender\" box or slapping a rainbow flag on the packaging. We also provide recommendations as to how to approach creating more inclusive technologies even while still relying on exclusionary data.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Kendra Albert', 'Maggie Delano']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.86,
        "actuality":4.0,
        "controversy":4.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.8611666667
    },
    {
        "arxiv_id":"2101.08348v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08348v1",
        "title":"Physical Reservoir Computing with Origami and its Application to Robotic Crawling",
        "summary":"A new paradigm called physical reservoir computing has recently emerged, where the nonlinear dynamics of high-dimensional and fixed physical systems are harnessed as a computational resource to achieve complex tasks. Via extensive simulations based on a dynamic truss-frame model, this study shows that an origami structure can perform as a dynamic reservoir with sufficient computing power to emulate high-order nonlinear systems, generate stable limit cycles, and modulate outputs according to dynamic inputs. This study also uncovers the linkages between the origami reservoir's physical designs and its computing power, offering a guideline to optimize the computing performance. Comprehensive parametric studies show that selecting optimal feedback crease distribution and fine-tuning the underlying origami folding designs are the most effective approach to improve computing performance. Furthermore, this study shows how origami's physical reservoir computing power can apply to soft robotic control problems by a case study of earthworm-like peristaltic crawling without traditional controllers. These results can pave the way for origami-based robots with embodied mechanical intelligence.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Priyanka Bhovad', 'Suyi Li']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.79,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2101.08459v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08459v1",
        "title":"Fire Threat Detection From Videos with Q-Rough Sets",
        "summary":"This article defines new methods for unsupervised fire region segmentation and fire threat detection from video stream. Fire in control serves a number of purposes to human civilization, but it could simultaneously be a threat once its spread becomes uncontrolled. There exists many methods on fire region segmentation and fire non-fire classification. But the approaches to determine the threat associated with fire is relatively scare, and no such unsupervised method has been formulated yet. Here we focus on developing an unsupervised method with which the threat of fire can be quantified and accordingly generate an alarm in automated surveillance systems in indoor as well as in outdoors. Fire region segmentation without any manual intervention\/ labelled data set is a major challenge while formulating such a method. Here we have used rough approximations to approximate the fire region, and to manage the incompleteness of the knowledge base, due to absence of any prior information. Utility maximization of Q-learning has been used to minimize ambiguities in the rough approximations. The new set approximation method, thus developed here, is named as Q-rough set. It is used for fire region segmentation from video frames. The threat index of fire flame over the input video stream has been defined in sync with the relative growth in the fire segments on the recent frames. All theories and indices defined here have been experimentally validated with different types of fire videos, through demonstrations and comparisons, as superior to the state of the art.",
        "published":"2021-01-21T00:00:00.000Z",
        "authors":"['Debarati B. Chakrabortya', 'Vinay Detania', 'Shah Parshv Jigneshkumar']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.2778333333
    },
    {
        "arxiv_id":"2101.08676v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08676v1",
        "title":"Conceptualization and cases of study on cyber operations against the sustainability of the tactical edge",
        "summary":"The last decade consolidated the cyberspace as fifth domain of operations, which extends its preliminarily intelligence and information exchange purposes towards enabling complex offensive and defensive operations supported\/supportively of parallel kinetic domain actuations. Although there is a plethora of well documented cases on strategic and operational interventions of cyber commands, the cyber tactical military edge is still a challenge, where cyber fires barely integrate to the traditional joint targeting cycle due among others to long planning\/development times, asymmetric effects, strict target reachability requirements, or the fast propagation of collateral damage; the latter rapidly deriving on hybrid impacts (political, economic, social, etc.) and evidencing significant socio-technical gaps. In this context, it is expected that tactical clouds disruptively facilitate cyber operations at the edge while exposing the rest of the digital assets of the operation to them. On these grounds, the main purpose of the conducted research is to review and in depth analyze the risks and opportunities of jeopardizing the sustainability of the military tactical clouds at the edge by cyber operations. Along with a 1) comprehensively formulation of the researched problematic, the study 2) formalizes the Tactical Denial of Sustainability (TDoS) concept; 3) introduces the phasing, potential attack surfaces, terrains and impact of TDoS attacks; 4) emphasizes the related human and socio-technical aspects; 5) analyzes the threats\/opportunities inherent to their impact on the cloud energy efficiency; 6) reviews their implications at the military cyber thinking for tactical operations; 7) illustrates five extensive CONOPS that facilitate the understanding of the TDoS concept; and given the high novelty of the discussed topics, it 8) paves the way for further research and development actions.",
        "published":"2021-01-21T00:00:00.000Z",
        "authors":"['Marco Antonio Sotelo Monge', 'Jorge Maestre Vidal']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.85,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.4721666667
    },
    {
        "arxiv_id":"2101.08846v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.08846v1",
        "title":"Soloist: Generating Mixed-Initiative Tutorials from Existing Guitar Instructional Videos Through Audio Processing",
        "summary":"Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, existing video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons. Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user's performance, creating a guided learning experience. We demonstrate the capabilities and specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos.",
        "published":"2021-01-21T00:00:00.000Z",
        "authors":"['Bryan Wang', 'Mengyu Yang', 'Tovi Grossman']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.84,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.58325
    },
    {
        "arxiv_id":"2101.09004v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09004v1",
        "title":"CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text",
        "summary":"Code-mixing(CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. CM is mostly practiced on various social media platforms and in informal conversations. Sentiment analysis (SA) is a fundamental step in NLP and is well studied in the monolingual text. Code-mixing adds a challenge to sentiment analysis due to its non-standard representations. This paper proposes a meta embedding with a transformer method for sentiment analysis on the Dravidian code-mixed dataset. In our method, we used meta embeddings to capture rich text representations. We used the proposed method for the Task: \"Sentiment Analysis for Dravidian Languages in Code-Mixed Text\", and it achieved an F1 score of $0.58$ and $0.66$ for the given Dravidian code mixed data sets. The code is provided in the Github https:\/\/github.com\/suman101112\/fire-2020-Dravidian-CodeMix.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Suman Dowlagar', 'Radhika Mamidi']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.85,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2101.09018v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09018v1",
        "title":"Network Clustering for Multi-task Learning",
        "summary":"The Multi-Task Learning (MTL) technique has been widely studied by word-wide researchers. The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task. Since the specific layers directly follow the hard layers, the MTL model needs to estimate this direct change (from general to specific) as well. To alleviate this problem, we introduce the novel cluster layer, which groups tasks into clusters during training procedures. In a cluster layer, the tasks in the same cluster are further required to share the same network. By this way, the cluster layer produces the general presentation for the same cluster, while produces relatively specific presentations for different clusters. As transitions the cluster layers are used between the hard layers and the specific layers. The MTL model thus learns general representations to specific representations gradually. We evaluate our model with MTL document classification and the results demonstrate the cluster layer is quite efficient in MTL.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Dehong Gao', 'Wenjing Yang', 'Huiling Zhou', 'Yi Wei', 'Yi Hu', 'Hao Wang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":2.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.58325
    },
    {
        "arxiv_id":"2101.09167v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09167v1",
        "title":"Improved Sensitivity of Base Layer on the Performance of Rigid Pavement",
        "summary":"The performance of rigid pavement is greatly affected by the properties of base\/subbase as well as subgrade layer. However, the performance predicted by the AASHTOWare Pavement ME design shows low sensitivity to the properties of base and subgrade layers. To improve the sensitivity and better reflect the influence of unbound layers a new set of improved models i.e., resilient modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this study. An Artificial Neural Network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation datasets in the ANN model consist of 27000 simulation cases with different combinations of pavement layer thickness, layer modulus and slab-base interface bond ratio. To examine the sensitivity of modified MR and k-values on pavement response, eight pavement sections data are collected from the Long-Term Pavement performance (LTPP) database and modeled by using the FE software ISLAB2000. The computational results indicate that the modified MR values have higher sensitivity to water content in base layer on critical stress and deflection response of rigid pavements compared to the results using the Pavement ME design model. It is also observed that the k-values using ANN model has the capability of predicting critical pavement response at any partially bonded conditions whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonding and no bonding).",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Sajib Saha', 'Fan Gu', 'Xue Luo', 'Robert L. Lytton']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.83,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.47225
    },
    {
        "arxiv_id":"2101.09244v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09244v2",
        "title":"Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision",
        "summary":"Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle factor changes and interventions. Analyzing electronic health records (EHR) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to demonstrate the feasibility of natural language processing (NLP) models to classify lifestyle factors (e.g., physical activity and excessive diet) from clinical texts. We automatically generated labels for the training data by using a rule-based NLP algorithm. We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models on the weakly labeled training corpus. These models include the BERT base model, PubMedBERT(abstracts + full text), PubMedBERT(only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, and Bio-clinical BERT. We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD. These models were compared on the developed Gold Standard Corpus (GSC) on the two case studies. The PubmedBERT(Abs) model achieved the best performance for physical activity, with its precision, recall, and F-1 scores of 0.96, 0.96, and 0.96, respectively. Regarding classifying excessive diet, the Bio BERT model showed the highest performance with perfect precision, recall, and F-1 scores. The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. The study also demonstrates the effectiveness of BERT models for extracting lifestyle factors for Alzheimer's disease from clinical notes.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Zitao Shen', 'Yoonkwon Yi', 'Anusha Bompelli', 'Fang Yu', 'Yanshan Wang', 'Rui Zhang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":4.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2101.09318v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09318v1",
        "title":"Machine Learning in LiDAR 3D point clouds",
        "summary":"LiDAR point clouds contain measurements of complicated natural scenes and can be used to update digital elevation models, glacial monitoring, detecting faults and measuring uplift detecting, forest inventory, detect shoreline and beach volume changes, landslide risk analysis, habitat mapping, and urban development, among others. A very important application is the classification of the 3D cloud into elementary classes. For example, it can be used to differentiate between vegetation, man-made structures, and water. Our goal is to present a preliminary comparison study for the classification of 3D point cloud LiDAR data that includes several types of feature engineering. In particular, we demonstrate that providing context by augmenting each point in the LiDAR point cloud with information about its neighboring points can improve the performance of downstream learning algorithms. We also experiment with several dimension reduction strategies, ranging from Principal Component Analysis (PCA) to neural network-based auto-encoders, and demonstrate how they affect classification performance in LiDAR point clouds. For instance, we observe that combining feature engineering with a dimension reduction a method such as PCA, there is an improvement in the accuracy of the classification with respect to doing a straightforward classification with the raw data.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['F. Patricia Medina', 'Randy Paffenroth']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2101.09345v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09345v1",
        "title":"BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets",
        "summary":"During the last two decades, we have progressively turned to the Internet and social media to find news, entertain conversations and share opinion. Recently, OpenAI has developed a ma-chine learning system called GPT-2 for Generative Pre-trained Transformer-2, which can pro-duce deepfake texts. It can generate blocks of text based on brief writing prompts that look like they were written by humans, facilitating the spread false or auto-generated text. In line with this progress, and in order to counteract potential dangers, several methods have been pro-posed for detecting text written by these language models. In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots. Our dataset is based on tweets from a previous work, which we have crawled and extended using the Twitter API. We used GPT2-Small-Arabic to generate fake Arabic Sentences. For evaluation, we compared different recurrent neural network (RNN) word embeddings based baseline models, namely: LSTM, BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new transfer-learning model has obtained an accuracy up to 98%. To the best of our knowledge, this work is the first study where ARABERT and GPT2 were combined to detect and classify the Arabic auto-generated texts.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Fouzi Harrag', 'Maria Debbah', 'Kareem Darwish', 'Ahmed Abdelali']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3889166667
    },
    {
        "arxiv_id":"2101.09547v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09547v1",
        "title":"A 3D Modeling Approach to Tractable Analysis in UAV-Enabled Cellular Networks",
        "summary":"This paper aims to propose a three-dimensional (3D) point process that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale cellular network and tractably analyze the fundamental network-wide performances of the network. This 3D point process is devised based on a 2D marked Poisson point process in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We elaborate on some important statistical properties of the proposed 3D point process and use them to tractably analyze the coverage performances of a UAV-enabled cellular network wherein all the UAVs equipped with multiple antennas are served as aerial base stations. The downlink coverage of the UAV-enabled cellular network is found and its closed-form results for some special cases are explicitly derived as well. Furthermore, the fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. These findings are validated by numerical simulation.",
        "published":"2021-01-23T00:00:00.000Z",
        "authors":"['Chun-Hung Liu', 'Di-Chun Liang', 'Rung-Hung Gau']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2101.09562v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09562v1",
        "title":"Deep Learning for General Game Playing with Ludii and Polygames",
        "summary":"Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game's rules, and constructing the neural network's architecture -- in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 500 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.",
        "published":"2021-01-23T00:00:00.000Z",
        "authors":"['Dennis J. N. J. Soemers', 'Vegard Mella', 'Cameron Browne', 'Olivier Teytaud']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.89,
        "actuality":2.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.8611666667
    },
    {
        "arxiv_id":"2101.09588v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09588v2",
        "title":"3D Underactuated Bipedal Walking via H-LIP based Gait Synthesis and Stepping Stabilization",
        "summary":"In this paper, we present a Hybrid-Linear Inverted Pendulum (H-LIP) based approach for synthesizing and stabilizing 3D underactuated bipedal walking. The H-LIP model is proposed to capture the essential components of the underactuated part and actuated part of the robotic walking. The walking gait of the robot is then synthesized based on the H-LIP. We comprehensively characterize the periodic orbits of the H-LIP and provably derive their stepping stabilization. The step-to-step (S2S) dynamics of the H-LIP is then utilized to approximate the S2S dynamics of the horizontal state of the center of mass (COM) of the robotic walking, which results in a H-LIP based stepping controller to provide desired step sizes to stabilize the robotic walking. By realizing the desired step sizes, the robot achieves dynamic and stable walking. The approach is evaluated in both simulation and experiment on the 3D underactuated bipedal robot Cassie, which demonstrate dynamic walking behaviors with both versatility and robustness.",
        "published":"2021-01-23T00:00:00.000Z",
        "authors":"['Xiaobin Xiong', 'Aaron Ames']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.8,
        "actuality":4.0,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2101.09643v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09643v1",
        "title":"A Dual-branch Network for Infrared and Visible Image Fusion",
        "summary":"Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and we directly insert the input image-visible light image in each layer of the entire network. We use SSIM and gradient loss functions that are more consistent with perception instead of mean square error loss. After the adversarial training between the generator and the discriminator, we show that a trained end-to-end fusion network -- the generator network -- is finally obtained. Our experiments show that the fused images obtained by our approach achieve good score based on multiple evaluation indicators. Further, our fused images have better visual effects in multiple sets of contrasts, which are more satisfying to human visual perception.",
        "published":"2021-01-24T00:00:00.000Z",
        "authors":"['Yu Fu', 'Xiao-Jun Wu']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.87,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.22225
    },
    {
        "arxiv_id":"2101.09656v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09656v1",
        "title":"Explanation as a Defense of Recommendation",
        "summary":"Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.",
        "published":"2021-01-24T00:00:00.000Z",
        "authors":"['Aobo Yang', 'Nan Wang', 'Hongbo Deng', 'Hongning Wang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.91,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2101.09667v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09667v2",
        "title":"Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh",
        "summary":"Newspapers are trustworthy media where people get the most reliable and credible information compared with other sources. On the other hand, social media often spread rumors and misleading news to get more traffic and attention. Careful characterization, evaluation, and interpretation of newspaper data can provide insight into intrigue and passionate social issues to monitor any big social incidence. This study analyzed a large set of spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic. The methodology included volume analysis, topic analysis, automated classification, and sentiment analysis of news articles to get insight into the COVID-19 pandemic in different sectors and regions in Bangladesh over a period of time. This analysis will help the government and other organizations to figure out the challenges that have arisen in society due to this pandemic, what steps should be taken immediately and in the post-pandemic period, how the government and its allies can come together to address the crisis in the future, keeping these problems in mind.",
        "published":"2021-01-24T00:00:00.000Z",
        "authors":"['Fahim Shahriar', 'Md Abul Bashar']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.93,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.4445
    },
    {
        "arxiv_id":"2101.09755v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09755v1",
        "title":"RomeBERT: Robust Training of Multi-Exit BERT",
        "summary":"BERT has achieved superior performances on Natural Language Understanding (NLU) tasks. However, BERT possesses a large number of parameters and demands certain resources to deploy. For acceleration, Dynamic Early Exiting for BERT (DeeBERT) has been proposed recently, which incorporates multiple exits and adopts a dynamic early-exit mechanism to ensure efficient inference. While obtaining an efficiency-performance tradeoff, the performances of early exits in multi-exit BERT are significantly worse than late exits. In this paper, we leverage gradient regularized self-distillation for RObust training of Multi-Exit BERT (RomeBERT), which can effectively solve the performance imbalance problem between early and late exits. Moreover, the proposed RomeBERT adopts a one-stage joint training strategy for multi-exits and the BERT backbone while DeeBERT needs two stages that require more training time. Extensive experiments on GLUE datasets are performed to demonstrate the superiority of our approach. Our code is available at https:\/\/github.com\/romebert\/RomeBERT.",
        "published":"2021-01-24T00:00:00.000Z",
        "authors":"['Shijie Geng', 'Peng Gao', 'Zuohui Fu', 'Yongfeng Zhang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.81,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6945
    },
    {
        "arxiv_id":"2101.09765v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09765v2",
        "title":"Belief-based Generation of Argumentative Claims",
        "summary":"When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.",
        "published":"2021-01-24T00:00:00.000Z",
        "authors":"['Milad Alshomary', 'Wei-Fan Chen', 'Timon Gurcke', 'Henning Wachsmuth']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.88,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.9445
    },
    {
        "arxiv_id":"2101.09961v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.09961v1",
        "title":"Scaffolded Gait Learning of a Quadruped Robot with Bayesian Optimization",
        "summary":"During learning trials, systems are exposed to different failure conditions which may break robotic parts before a safe behavior is discovered. Humans contour this problem by grounding their learning to a safer structure\/control first and gradually increasing its difficulty. This paper presents the impact of a similar supports in the learning of a stable gait on a quadruped robot. Based on the psychological theory of instructional scaffolding, we provide different support settings to our robot, evaluated with strain gauges, and use Bayesian Optimization to conduct a parametric search towards a stable Raibert controller. We perform several experiments to measure the relation between constant supports and gradually reduced supports during gait learning, and our results show that a gradually reduced support is capable of creating a more stable gait than a support at a fixed height. Although gaps between simulation and reality can lead robots to catastrophic failures, our proposed method combines speed and safety when learning a new behavior.",
        "published":"2021-01-25T00:00:00.000Z",
        "authors":"['Keyan Zhai', \"Chu'an Li\", 'Andre Rosendo']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.89,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6945
    },
    {
        "arxiv_id":"2101.10091v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10091v3",
        "title":"JTrack: A Digital Biomarker Platform for Remote Monitoring in Neurological and Psychiatric Diseases",
        "summary":"Objective: Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital phenotyping. Method: JTrack consists of an Android-based smartphone application and a web-based project management dashboard. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes. The dashboard also provides management tools to monitor and manage data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. JTrack was developed to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. Results: JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. The main components of the JTrack platform and examples of data being collected using JTrack are presented here. Conclusion: Smartphone-based Digital Biomarker data may provide valuable insight into daily life behaviour in health and disease. JTrack provides an easy and reliable open-source solution for collection of such data.",
        "published":"2021-01-18T00:00:00.000Z",
        "authors":"['Mehran Sahandi Far', 'Michael Stolz', 'Jona M. Fischer', 'Simon B. Eickhoff', 'Juergen Dukart']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.88,
        "actuality":4.0,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.7221666667
    },
    {
        "arxiv_id":"2101.10112v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10112v1",
        "title":"Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election",
        "summary":"The growing political polarization of the American electorate over the last several decades has been widely studied and documented. During the administration of President Donald Trump, charges of \"fake news\" made social and news media not only the means but, to an unprecedented extent, the topic of political communication. Using data from before the November 3rd, 2020 US Presidential election, recent work has demonstrated the viability of using YouTube's social media ecosystem to obtain insights into the extent of US political polarization as well as the relationship between this polarization and the nature of the content and commentary provided by different US news networks. With that work as background, this paper looks at the sharp transformation of the relationship between news consumers and here-to-fore \"fringe\" news media channels in the 64 days between the US presidential election and the violence that took place at US Capitol on January 6th. This paper makes two distinct types of contributions. The first is to introduce a novel methodology to analyze large social media data to study the dynamics of social political news networks and their viewers. The second is to provide insights into what actually happened regarding US political social media channels and their viewerships during this volatile 64 day period.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Ashiqur R. KhudaBukhsh', 'Rupak Sarkar', 'Mark S. Kamlet', 'Tom M. Mitchell']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.94,
        "actuality":4.0,
        "controversy":4.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.5555
    },
    {
        "arxiv_id":"2101.10124v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10124v1",
        "title":"An open-source tool to assess the carbon footprint of research",
        "summary":"Research institutions are bound to contribute to greenhouse gas emission (GHG) reduction efforts for several reasons. First, part of the scientific community's research deals with climate change issues. Second, scientists contribute to students' education: they must be consistent and role models. Third the literature on the carbon footprint of researchers points to the high level of some individual footprints. In a quest for consistency and role models, scientists, teams of scientists or universities have started to quantify their carbon footprints and debate on reduction options. Indeed, measuring the carbon footprint of research activities requires tools designed to tackle its specific features. In this paper, we present an open-source web application, GES 1point5, developed by an interdisciplinary team of scientists from several research labs in France. GES 1point5 is specifically designed to estimate the carbon footprint of research activities in France. It operates at the scale of research labs, i.e. laboratoires, which are the social structures around which research is organized in France and the smallest decision making entities in the French research system. The application allows French research labs to compute their own carbon footprint along a standardized, open protocol. The data collected in a rapidly growing network of labs will be used as part of the Labos 1point5 project to estimate France's research carbon footprint. At the time of submitting this manuscript, 89 research labs had engaged with GES 1point5 to estimate their greenhouse gas emissions. We expect that an international adoption of GES 1point5 (adapted to fit domestic specifics) could contribute to establishing a global understanding of the drivers of the research carbon footprint worldwide and the levers to decrease it.",
        "published":"2021-01-21T00:00:00.000Z",
        "authors":"['J\u00e9r\u00f4me Mariette', 'Odile Blanchard', 'Olivier Bern\u00e9', 'Tamara Ben Ari']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.91,
        "actuality":5.0,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":5.0,
        "newsworthiness_crowd_sum":4.6666666667
    },
    {
        "arxiv_id":"2101.10132v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10132v1",
        "title":"Obsolete Personal Information Update System for the Prevention of Falls among Elderly Patients",
        "summary":"Falls are a common problem affecting the older adults and a major public health issue. Centers for Disease Control and Prevention, and World Health Organization report that one in three adults over the age of 65 and half of the adults over 80 fall each year. In recent years, an ever-increasing range of applications have been developed to help deliver more effective falls prevention interventions. All these applications rely on a huge elderly personal database collected from hospitals, mutual health, and other organizations in caring for elderly. The information describing an elderly is continually evolving and may become obsolete at a given moment and contradict what we already know on the same person. So, it needs to be continuously checked and updated in order to restore the database consistency and then provide better service. This paper provides an outline of an Obsolete personal Information Update System (OIUS) designed in the context of the elderly-fall prevention project. Our OIUS aims to control and update in real-time the information acquired about each older adult, provide on-demand consistent information and supply tailored interventions to caregivers and fall-risk patients. The approach outlined for this purpose is based on a polynomial-time algorithm build on top of a causal Bayesian network representing the elderly data. The result is given as a recommendation tree with some accuracy level. We conduct a thorough empirical study for such a model on an elderly personal information base. Experiments confirm the viability and effectiveness of our OIUS.",
        "published":"2021-01-20T00:00:00.000Z",
        "authors":"['Salma Chaieb', 'Brahim Hnich', 'Ali Ben Mrad']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.93,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2101.10140v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10140v1",
        "title":"Predicting Exercise Adherence and Physical Activity in Older Adults Based on Tablet Engagement: A Post-hoc Study",
        "summary":"Sufficient physical activity can prolong the ability of older adults to live inde-pendently. Community-based exercise programs can be enhanced by regularly performing exercises at home. To support such a home-based exercise program, a blended intervention was developed that combined the use of a tablet application with a personal coach. The purpose of the current study was to explore to which extent tablet engagement predicted exercise adherence and physical activity. The results show that older adults (n=133; M=71 years of age) that participated 6 months in a randomized controlled trial, performed at average 12 home-based ex-ercised per week and exercised on average 3 days per week, thereby meeting WHO guidelines. They used the tablet app on average 7 times per week. Multiple linear regressions revealed that the use of the app statistically predicted the num-ber of exercises that were performed and the number of exercise days. Physical activity, however, did not increase and also could not be predicted by exercise frequency or app use. We conclude that engagement with a tablet can contribute to sustained exercise behavior.",
        "published":"2021-01-25T00:00:00.000Z",
        "authors":"['Sumit Mehra', 'Jantine van den Helder', 'Ben J. A. Kr\u00f6se', 'Raoul H. H. Engelbert', 'Peter J. M. Weijs', 'Bart Visser']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.91,
        "actuality":4.0,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2101.10186v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10186v1",
        "title":"Neurocognitive and traffic based handover strategies",
        "summary":"The level of automation in vehicles will significantly increase over the next decade. As automation will become more and more common, vehicles will not be able to master all traffic related situations for a long time by themselves. In such situations, the driver must take over and steer the vehicle through the situation. One of the important questions is when the takeover should be performed. Many decisive factors must be considered. On the one hand, the current traffic situation including roads, traffic light and other road users, especially vulnerable road users, and on the other hand, the state of the driver must be considered. The goal is to combine neurocognitive measurement of the drivers state and the static and dynamic traffic related data to develop an interpretation of the current situation. This situation analysis should be the basis for the determination of the best takeover point.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Andreas Otte', 'Jonas Vogt', 'Jens Staub', 'Niclas Wolniak', 'Horst Wieker']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.97,
        "actuality":4.6666666667,
        "controversy":4.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.33325
    },
    {
        "arxiv_id":"2101.10257v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10257v1",
        "title":"Regions of Attraction Estimation using Level SetMethod for Complex Network System",
        "summary":"Many complex engineering systems network together functional elements and balance demand loads (e.g.information on data networks, electric power on grids). This allows load spikes to be shifted and avoid a local overload. In mobile wireless networks, base stations(BSs) receive data demand and shift high loads to neighbouring BSs to avoid the outage. The stability of cascade load balancing is important because unstable networks can cause high inefficiency. The research challenge is to prove the stability conditions for any arbitrarily large, complex, and dynamic network topology, and for any balancing dynamic function. Our previous work has proven the conditions for stability for stationary networks near equilibrium for any load balancing dynamic and topology. Most current analyses in dynamic complex networks linearize the system around the fixed equilibrium solutions. This approach is insufficient for dynamic networks with changing equilibrium and estimating the Region of Attraction(ROA) is needed. The novelty of this paper is that we compress this high-dimensional system and use Level Set Methods (LSM) to estimate the ROA. Our results show how we can control the ROA via network topology (local degree control) as a way to configure the mobility of transceivers to ensure the preservation of stable load balancing.",
        "published":"2021-01-25T00:00:00.000Z",
        "authors":"['Mengbang Zou', 'Yu Huang', 'Weisi Guo']",
        "arxiv_primary_category":"cs.si",
        "readability":0.84,
        "actuality":2.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.58325
    },
    {
        "arxiv_id":"2101.10399v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10399v2",
        "title":"Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot",
        "summary":"Visual perception of the objects in a 3D environment is a key to successful performance in autonomous driving and simultaneous localization and mapping (SLAM). In this paper, we present a real time approach for estimating the distances to multiple objects in a scene using only a single-shot image. Given a 2D Bounding Box (BBox) and object parameters, a 3D distance to the object can be calculated directly using 3D reprojection; however, such methods are prone to significant errors because an error from the 2D detection can be amplified in 3D. In addition, it is also challenging to apply such methods to a real-time system due to the computational burden. In the case of the traditional multi-object detection methods, %they mostly pay attention to existing works have been developed for specific tasks such as object segmentation or 2D BBox regression. These methods introduce the concept of anchor BBox for elaborate 2D BBox estimation, and predictors are specialized and trained for specific 2D BBoxes. In order to estimate the distances to the 3D objects from a single 2D image, we introduce the notion of \\textit{anchor distance} based on an object's location and propose a method that applies the anchor distance to the multi-object detector structure. We let the predictors catch the distance prior using anchor distance and train the network based on the distance. The predictors can be characterized to the objects located in a specific distance range. By propagating the distance prior using a distance anchor to the predictors, it is feasible to perform the precise distance estimation and real-time execution simultaneously. The proposed method achieves about 30 FPS speed, and shows the lowest RMSE compared to the existing methods.",
        "published":"2021-01-25T00:00:00.000Z",
        "authors":"['Hyeonwoo Yu', 'Jean Oh']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":4.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2101.10423v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10423v1",
        "title":"Online Continual Learning in Image Classification: An Empirical Survey",
        "summary":"Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (class incremental) or data nonstationarity (domain incremental). One of the key challenges of continual learning is to avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence of more recent tasks. Over the past few years, many methods and tricks have been introduced to address this problem, but many have not been fairly and systematically compared under a variety of realistic and practical settings. To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as MIR, iCARL, and GDumb and determine which works best at different experimental settings; (2) determine if the best class incremental methods are also competitive in domain incremental setting; (3) evaluate the performance of 7 simple but effective trick such as \"review\" trick and nearest class mean (NCM) classifier to assess their relative impact. Regarding (1), we observe earlier proposed iCaRL remains competitive when the memory buffer is small; GDumb outperforms many recently proposed methods in medium-size datasets and MIR performs the best in larger-scale datasets. For (2), we note that GDumb performs quite poorly while MIR -- already competitive for (1) -- is also strongly competitive in this very different but important setting. Overall, this allows us to conclude that MIR is overall a strong and versatile method across a wide variety of settings. For (3), we find that all 7 tricks are beneficial, and when augmented with the \"review\" trick and NCM classifier, MIR produces performance levels that bring online continual learning much closer to its ultimate goal of matching offline training.",
        "published":"2021-01-25T00:00:00.000Z",
        "authors":"['Zheda Mai', 'Ruiwen Li', 'Jihwan Jeong', 'David Quispe', 'Hyunwoo Kim', 'Scott Sanner']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2101.10670v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10670v1",
        "title":"Ordinal Monte Carlo Tree Search",
        "summary":"In many problem settings, most notably in game playing, an agent receives a possibly delayed reward for its actions. Often, those rewards are handcrafted and not naturally given. Even simple terminal-only rewards, like winning equals one and losing equals minus one, can not be seen as an unbiased statement, since these values are chosen arbitrarily, and the behavior of the learner may change with different encodings. It is hard to argue about good rewards and the performance of an agent often depends on the design of the reward signal. In particular, in domains where states by nature only have an ordinal ranking and where meaningful distance information between game state values is not available, a numerical reward signal is necessarily biased. In this paper we take a look at MCTS, a popular algorithm to solve MDPs, highlight a reoccurring problem concerning its use of rewards, and show that an ordinal treatment of the rewards overcomes this problem. Using the General Video Game Playing framework we show dominance of our newly proposed ordinal MCTS algorithm over other MCTS variants, based on a novel bandit algorithm that we also introduce and test versus UCB.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Tobias Joppen', 'Johannes F\u00fcrnkranz']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.88,
        "actuality":2.0,
        "controversy":3.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.5000833333
    },
    {
        "arxiv_id":"2101.10698v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10698v1",
        "title":"A fast algorithm for complex discord searches in time series: HOT SAX Time",
        "summary":"Time series analysis is quickly proceeding towards long and complex tasks. In recent years, fast approximate algorithms for discord search have been proposed in order to compensate for the increasing size of the time series. It is more interesting, however, to find quick exact solutions. In this research, we improved HOT SAX by exploiting two main ideas: the warm-up process, and the similarity between sequences close in time. The resulting algorithm, called HOT SAX Time (HST), has been validated with real and synthetic time series, and successfully compared with HOT SAX, RRA, SCAMP, and DADD. The complexity of a discord search has been evaluated with a new indicator, the cost per sequence (cps), which allows one to compare searches on time series of different lengths. Numerical evidence suggests that two conditions are involved in determining the complexity of a discord search in a non-trivial way: the length of the discords, and the noise\/signal ratio. In the case of complex searches, HST can be more than 100 times faster than HOT SAX, thus being at the forefront of the exact discord search.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Paolo Avogadro', 'Matteo Alessandro Dominoni']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.87,
        "actuality":2.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2101.10737v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10737v2",
        "title":"Mining the Stars: Learning Quality Ratings with User-facing Explanations for Vacation Rentals",
        "summary":"Online Travel Platforms are virtual two-sided marketplaces where guests search for accommodations and accommodation providers list their properties such as hotels and vacation rentals. The large majority of hotels are rated by official institutions with a number of stars indicating the quality of service they provide. It is a simple and effective mechanism that contributes to match supply with demand by helping guests to find options meeting their criteria and accommodation suppliers to market their product to the right segment directly impacting the number of transactions on the platform. Unfortunately, no similar rating system exists for the large majority of vacation rentals, making it difficult for guests to search and compare options and hard for vacation rentals suppliers to market their product effectively. In this work we describe a machine learned quality rating system for vacation rentals. The problem is challenging, mainly due to explainability requirements and the lack of ground truth. We present techniques to address these challenges and empirical evidence of their efficacy. Our system was successfully deployed and validated through Online Controlled Experiments performed in Booking. com, a large Online Travel Platform, and running for more than one year, impacting more than a million accommodations and millions of guests.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Anastasiia Kornilova', 'Lucas Bernardi']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.93,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2101.10772v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10772v1",
        "title":"LIGHTS: LIGHT Specularity Dataset for specular detection in Multi-view",
        "summary":"Specular highlights are commonplace in images, however, methods for detecting them and in turn removing the phenomenon are particularly challenging. A reason for this, is due to the difficulty of creating a dataset for training or evaluation, as in the real-world we lack the necessary control over the environment. Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Our dataset consists of 18 high quality architectural scenes, where each scene is rendered with multiple views. In total we have 2,603 views with an average of 145 views per scene. Additionally we propose a simple aggregation based method for specular highlight detection that outperforms prior work by 3.6% in two orders of magnitude less time on our dataset.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Mohamed Dahy Elkhouly', 'Theodore Tsesmelis', 'Alessio Del Bue', 'Stuart James']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":2.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6111666667
    },
    {
        "arxiv_id":"2101.10777v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10777v1",
        "title":"Joint Forecasting of Features and Feature Motion for Dense Semantic Future Prediction",
        "summary":"We present a novel dense semantic forecasting approach which is applicable to a variety of architectures and tasks. The approach consists of two modules. Feature-to-motion (F2M) module forecasts a dense deformation field which warps past features into their future positions. Feature-to-feature (F2F) module regresses the future features directly and is therefore able to account for emergent scenery. The compound F2MF approach decouples effects of motion from the effects of novelty in a task-agnostic manner. We aim to apply F2MF forecasting to the most subsampled and the most abstract representation of a desired single-frame model. Our implementations take advantage of deformable convolutions and pairwise correlation coefficients across neighbouring time instants. We perform experiments on three dense prediction tasks: semantic segmentation, instance-level segmentation, and panoptic segmentation. The results reveal state-of-the-art forecasting accuracy across all three modalities on the Cityscapes dataset.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Josip \u0160ari\u0107', 'Sacha Vra\u017ei\u0107', 'Sini\u0161a \u0160egvi\u0107']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.77,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.5555
    },
    {
        "arxiv_id":"2101.10782v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10782v1",
        "title":"A Behavioural Analysis of Credulous Twitter Users",
        "summary":"Thanks to platforms such as Twitter and Facebook, people can know facts and events that otherwise would have been silenced. However, social media significantly contribute also to fast spreading biased and false news while targeting specific segments of the population. We have seen how false information can be spread using automated accounts, known as bots. Using Twitter as a benchmark, we investigate behavioural attitudes of so called `credulous' users, i.e., genuine accounts following many bots. Leveraging our previous work, where supervised learning is successfully applied to single out credulous users, we improve the classification task with a detailed features' analysis and provide evidence that simple and lightweight features are crucial to detect such users. Furthermore, we study the differences in the way credulous and not credulous users interact with bots and discover that credulous users tend to amplify more the content posted by bots and argue that their detection can be instrumental to get useful information on possible dissemination of spam content, propaganda, and, in general, little or no reliable information.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Alessandro Balestrucci', 'Rocco De Nicola', 'Marinella Petrocchi', 'Catia Trubiani']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":5.0,
        "controversy":4.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.2499166667
    },
    {
        "arxiv_id":"2101.10892v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10892v1",
        "title":"Online Body Schema Adaptation through Cost-Sensitive Active Learning",
        "summary":"Humanoid robots have complex bodies and kinematic chains with several Degrees-of-Freedom (DoF) which are difficult to model. Learning the parameters of a kinematic model can be achieved by observing the position of the robot links during prospective motions and minimising the prediction errors. This work proposes a movement efficient approach for estimating online the body-schema of a humanoid robot arm in the form of Denavit-Hartenberg (DH) parameters. A cost-sensitive active learning approach based on the A-Optimality criterion is used to select optimal joint configurations. The chosen joint configurations simultaneously minimise the error in the estimation of the body schema and minimise the movement between samples. This reduces energy consumption, along with mechanical fatigue and wear, while not compromising the learning accuracy. The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. The hand pose is measured with a single camera via markers placed in the palm and back of the robot's hand. A non-parametric occlusion model is proposed to avoid choosing joint configurations where the markers are not visible, thus preventing worthless attempts. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Gon\u00e7alo Cunha', 'Pedro Vicente', 'Alexandre Bernardino', 'Ricardo Ribeiro', 'Pl\u00ednio Moreno']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2101.10964v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.10964v1",
        "title":"Investment vs. reward in a competitive knapsack problem",
        "summary":"Natural selection drives species to develop brains, with sizes that increase with the complexity of the tasks to be tackled. Our goal is to investigate the balance between the metabolic costs of larger brains compared to the advantage they provide in solving general and combinatorial problems. Defining advantage as the performance relative to competitors, a two-player game based on the knapsack problem is used. Within this framework, two opponents compete over shared resources, with the goal of collecting more resources than the opponent. Neural nets of varying sizes are trained using a variant of the AlphaGo Zero algorithm. A surprisingly simple relation, $N_A\/(N_A+N_B)$, is found for the relative win rate of a net with $N_A$ neurons against one with $N_B$. Success increases linearly with investments in additional resources when the networks sizes are very different, i.e. when $N_A \\ll N_B$, with returns diminishing when both networks become comparable in size.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Oren Neumann', 'Claudius Gros']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.89,
        "actuality":2.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.7221666667
    },
    {
        "arxiv_id":"2101.11054v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11054v2",
        "title":"Remote Learners, Home Makers: How Digital Fabrication Was Taught Online During a Pandemic",
        "summary":"Digital fabrication courses that relied on physical makerspaces were severely disrupted by COVID-19. As universities shut down in Spring 2020, instructors developed new models for digital fabrication at a distance. Through interviews with faculty and students and examination of course materials, we recount the experiences of eight remote digital fabrication courses. We found that learning with hobbyist equipment and online social networks could emulate using industrial equipment in shared workshops. Furthermore, at-home digital fabrication offered unique learning opportunities including more iteration, machine tuning, and maintenance. These opportunities depended on new forms of labor and varied based on student living situations. Our findings have implications for remote and in-person digital fabrication instruction. They indicate how access to tools was important, but not as critical as providing opportunities for iteration; they show how remote fabrication exacerbated student inequities; and they suggest strategies for evaluating trade-offs in remote fabrication models with respect to learning objectives.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Gabrielle Benabdallah', 'Samuelle Bourgault', 'Nadya Peek', 'Jennifer Jacobs']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.0555833333
    },
    {
        "arxiv_id":"2101.11102v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11102v1",
        "title":"Data driven Decision Support on Students Behavior using Fuzzy Based Approach",
        "summary":"Monitoring of students behavior in school needs further consideration in order to lessen the number of casualties in every term. The study designs a data driven decision support on students behavior utilizing Fuzzy Based Approach. The study successfully produces common behavioral problems of the student and able to give interventions for the improvement of students behavior. Student behavioral problems identified were absenteeism, tardiness and poor academic performance.",
        "published":"2021-01-05T00:00:00.000Z",
        "authors":"['Jerry M. Lumasag', 'Hidear Talirongan', 'Florence Jean B. Talirongan', 'Charies L. Labanza']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.91,
        "actuality":4.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.5555
    },
    {
        "arxiv_id":"2101.11112v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11112v1",
        "title":"Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment",
        "summary":"We propose a novel approach for cross-lingual Named Entity Recognition (NER) zero-shot transfer using parallel corpora. We built an entity alignment model on top of XLM-RoBERTa to project the entities detected on the English part of the parallel data to the target language sentences, whose accuracy surpasses all previous unsupervised models. With the alignment model we can get pseudo-labeled NER data set in the target language to train task-specific model. Unlike using translation methods, this approach benefits from natural fluency and nuances in target-language original corpus. We also propose a modified loss function similar to focal loss but assigns weights in the opposite direction to further improve the model training on noisy pseudo-labeled data set. We evaluated this proposed approach over 4 target languages on benchmark data sets and got competitive F1 scores compared to most recent SOTA models. We also gave extra discussions about the impact of parallel corpus size and domain on the final transfer performance.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Bing Li', 'Yujie He', 'Wenjin Xu']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.87,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.7778333333
    },
    {
        "arxiv_id":"2101.11216v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11216v1",
        "title":"PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation",
        "summary":"Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision. Simple `direct transfer' of a learned model based on a multilingual input encoding has provided a strong benchmark. This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language. The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer. With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach. We provide analyses of the choice of source languages for multi-source transfer, and the advantage of non-projective parsing. Our code is available online.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Kemal Kurniawan', 'Lea Frermann', 'Philip Schulz', 'Trevor Cohn']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.2778333333
    },
    {
        "arxiv_id":"2101.11218v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11218v1",
        "title":"What We Can Learn From Visual Artists About Software Development",
        "summary":"This paper explores software's role in visual art production by examining how artists use and develop software. We conducted interviews with professional artists who were collaborating with software developers, learning software development, and building and maintaining software. We found artists were motivated to learn software development for intellectual growth and access to technical communities. Artists valued efficient workflows through skilled manual execution and personal software development, but avoided high-level forms of software automation. Artists identified conflicts between their priorities and those of professional developers and computational art communities, which influenced how they used computational aesthetics in their work. These findings contribute to efforts in systems engineering research to integrate end-user programming and creativity support across software and physical media, suggesting opportunities for artists as collaborators. Artists' experiences writing software can guide technical implementations of domain-specific representations, and their experiences in interdisciplinary production can aid inclusive community building around computational tools.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Jingyi Li', 'Sonia Hashim', 'Jennifer Jacobs']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.92,
        "actuality":2.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.5555
    },
    {
        "arxiv_id":"2101.11231v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11231v1",
        "title":"Pano: Engaging with News using Moral Framing towards Bridging Ideological Divides",
        "summary":"Society is showing signs of strong ideological polarization. When pushed to seek perspectives different from their own, people often reject diverse ideas or find them unfathomable. Work has shown that framing controversial issues using the values of the audience can improve reception of opposing views. In this paper, we present Pano, an interactive system motivated by moral framing theory that educates news consumers to think in a framework of fundamental human values. Pano encourages users to challenge their understanding of opposing views through collaborative annotation and discussion of moral framing in articles and comments. We describe two iterations of Pano -- the first covering a suite of ways to interact with news, and the second tailored toward annotation and discussion. We find that compared to a control, Pano users more often empathize with and re-frame arguments in the moral values of the opposing side, showing promise toward the ultimate goal of bridging the ideological divide.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Jessica Wang', 'Amy Zhang', 'David Karger']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.88,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.97225
    },
    {
        "arxiv_id":"2101.11246v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11246v1",
        "title":"A Survey on 5G Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning",
        "summary":"Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail -- from a theoretical and a practical viewpoint -- the main energy efficiency enabling features that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key technology features, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of -- and the status on -- green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['David Lopez-Perez', 'Antonio De Domenico', 'Nicola Piovesan', 'Harvey Baohongqiang', 'Geng Xinli', 'Song Qitao', 'Merouane Debbah']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.91,
        "actuality":5.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.97225
    },
    {
        "arxiv_id":"2101.11260v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11260v1",
        "title":"Modeling opinion leader's role in the diffusion of innovation",
        "summary":"The diffusion of innovations is an important topic for the consumer markets. Early research focused on how innovations spread on the level of the whole society. To get closer to the real world scenarios agent based models (ABM) started focusing on individual-level agents. In our work we will translate an existing ABM that investigates the role of opinion leaders in the process of diffusion of innovations to a new, more expressive platform designed for agent based modeling, GAMA. We will do it to show that taking advantage of new features of the chosen platform should be encouraged when making models in the field of social sciences in the future, because it can be beneficial for the explanatory power of simulation results.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Natasa Vodopivec', 'Carole Adam', 'Jean-Pierre Chanteau']",
        "arxiv_primary_category":"cs.si",
        "readability":0.91,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.9721666667
    },
    {
        "arxiv_id":"2101.11435v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11435v1",
        "title":"Online LDA based brain-computer interface system to aid disabled people",
        "summary":"This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance",
        "published":"2021-01-21T00:00:00.000Z",
        "authors":"['Apdullah Yayik', 'Yakup Kutlu']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.86,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2101.11525v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11525v1",
        "title":"Improving Graph Representation Learning by Contrastive Regularization",
        "summary":"Graph representation learning is an important task with applications in various areas such as online social networks, e-commerce networks, WWW, and semantic webs. For unsupervised graph representation learning, many algorithms such as Node2Vec and Graph-SAGE make use of \"negative sampling\" and\/or noise contrastive estimation loss. This bears similar ideas to contrastive learning, which \"contrasts\" the node representation similarities of semantically similar (positive) pairs against those of negative pairs. However, despite the success of contrastive learning, we found that directly applying this technique to graph representation learning models (e.g., graph convolutional networks) does not always work. We theoretically analyze the generalization performance and propose a light-weight regularization term that avoids the high scales of node representations' norms and the high variance among them to improve the generalization performance. Our experimental results further validate that this regularization term significantly improves the representation quality across different node similarity definitions and outperforms the state-of-the-art methods.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Kaili Ma', 'Haochen Yang', 'Han Yang', 'Tatiana Jin', 'Pengfei Chen', 'Yongqiang Chen', 'Barakeel Fanseu Kamhoua', 'James Cheng']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.8,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2101.11556v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11556v1",
        "title":"Powering COVID-19 community Q&A with Curated Side Information",
        "summary":"Community question answering and discussion platforms such as Reddit, Yahoo! answers or Quora provide users the flexibility of asking open ended questions to a large audience, and replies to such questions maybe useful both to the user and the community on certain topics such as health, sports or finance. Given the recent events around COVID-19, some of these platforms have attracted 2000+ questions from users about several aspects associated with the disease. Given the impact of this disease on general public, in this work we investigate ways to improve the ranking of user generated answers on COVID-19. We specifically explore the utility of external technical sources of side information (such as CDC guidelines or WHO FAQs) in improving answer ranking on such platforms. We found that ranking user answers based on question-answer similarity is not sufficient, and existing models cannot effectively exploit external (side) information. In this work, we demonstrate the effectiveness of different attention based neural models that can directly exploit side information available in technical documents or verified forums (e.g., research publications on COVID-19 or WHO website). Augmented with a temperature mechanism, the attention based neural models can selectively determine the relevance of side information for a given user question, while ranking answers.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Manisha Verma', 'Kapil Thadani', 'Shaunak Mishra']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.93,
        "actuality":4.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.9443333333
    },
    {
        "arxiv_id":"2101.11753v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11753v1",
        "title":"ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification",
        "summary":"Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this problem have focused on transfer learning using embeddings pre-trained on often unrelated tasks, for instance, language modeling. We adopt an alternative approach by transfer learning on an ensemble of related tasks using prototypical networks under the meta-learning paradigm. Using intent classification as a case study, we demonstrate that increasing variability in training tasks can significantly improve classification performance. Further, we apply data augmentation in conjunction with meta-learning to reduce sampling bias. We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task. We explore augmentation in the sentence embedding space as well as prototypical embedding space. Combining meta-learning with augmentation provides upto 6.49% and 8.53% relative F1-score improvements over the best performing systems in the 5-shot and 10-shot learning, respectively.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Manoj Kumar', 'Varun Kumar', 'Hadrien Glaude', 'Cyprien delichy', 'Aman Alok', 'Rahul Gupta']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":3.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2101.11775v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11775v2",
        "title":"Moral and Social Ramifications of Autonomous Vehicles",
        "summary":"Autonomous Vehicles (AVs) raise important social and ethical concerns, especially about accountability, dignity, and justice. We focus on the specific concerns arising from how AV technology will affect the lives and livelihoods of professional and semi-professional drivers. Whereas previous studies of such concerns have focused on the opinions of experts, we seek to understand these ethical and societal challenges from the perspectives of the drivers themselves. To this end, we adopted a qualitative research methodology based on semi-structured interviews. This is an established social science methodology that helps understand the core concerns of stakeholders in depth by avoiding the biases of superficial methods such as surveys. We find that whereas drivers agree with the experts that AVs will significantly impact transportation systems, they are apprehensive about the prospects for their livelihoods and dismiss the suggestions that driving jobs are unsatisfying and their profession does not merit protection. By showing how drivers differ from the experts, our study has ramifications beyond AVs to AI and other advanced technologies. Our findings suggest that qualitative research applied to the relevant, especially disempowered, stakeholders is essential to ensuring that new technologies are introduced ethically.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Veljko Dubljevi\u0107', 'Sean Douglas', 'Jovan Milojevich', 'Nirav Ajmeri', 'William A. Bauer', 'George F. List', 'Munindar P. Singh']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.9,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0000833333
    },
    {
        "arxiv_id":"2101.11870v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11870v1",
        "title":"Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee",
        "summary":"Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Emmanuel Hadoux', 'Anthony Hunter', 'Sylwia Polberg']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.9,
        "actuality":3.0,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.8334166667
    },
    {
        "arxiv_id":"2101.11974v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.11974v1",
        "title":"Disembodied Machine Learning: On the Illusion of Objectivity in NLP",
        "summary":"Machine Learning seeks to identify and encode bodies of knowledge within provided datasets. However, data encodes subjective content, which determines the possible outcomes of the models trained on it. Because such subjectivity enables marginalisation of parts of society, it is termed (social) `bias' and sought to be removed. In this paper, we contextualise this discourse of bias in the ML community against the subjective choices in the development process. Through a consideration of how choices in data and model development construct subjectivity, or biases that are represented in a model, we argue that addressing and mitigating biases is near-impossible. This is because both data and ML models are objects for which meaning is made in each step of the development pipeline, from data selection over annotation to model training and analysis. Accordingly, we find the prevalent discourse of bias limiting in its ability to address social marginalisation. We recommend to be conscientious of this, and to accept that de-biasing methods only correct for a fraction of biases.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Zeerak Waseem', 'Smarika Lulz', 'Joachim Bingel', 'Isabelle Augenstein']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.88,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.4445
    },
    {
        "arxiv_id":"2101.12038v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.12038v1",
        "title":"From pixels to notes: a computational implementation of synaesthesia for cultural artefacts",
        "summary":"Synaesthesia is a condition that enables people to sense information in the form of several senses at once. This work describes a Python implementation of a simulation of synaesthesia between listening to music and viewing a painting. Based on Scriabin's definition, we developed a deterministic process to produce a melody after processing a painting, mimicking the production of notes from colours in the field of view of persons experiencing synaesthesia.",
        "published":"2021-01-27T00:00:00.000Z",
        "authors":"['Dimitris Kritikos', 'Kostas Karpouzis']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2101.12246v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.12246v1",
        "title":"Revisiting Non-Specific Syndromic Surveillance",
        "summary":"Infectious disease surveillance is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as early as possible by monitoring data sources which allow to capture the occurrences of a certain disease. Recent research mainly focuses on the surveillance of specific, known diseases, putting the focus on the definition of the disease pattern under surveillance. Until now, only little effort has been devoted to what we call non-specific syndromic surveillance, i.e., the use of all available data for detecting any kind of outbreaks, including infectious diseases which are unknown beforehand. In this work, we revisit published approaches for non-specific syndromic surveillance and present a set of simple statistical modeling techniques which can serve as benchmarks for more elaborate machine learning approaches. Our experimental comparison on established synthetic data and real data in which we injected synthetic outbreaks shows that these benchmarks already achieve very competitive results and often outperform more elaborate algorithms.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Moritz Kulessa', 'Eneldo Loza Menc\u00eda', 'Johannes F\u00fcrnkranz']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.5278333333
    },
    {
        "arxiv_id":"2101.12446v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.12446v1",
        "title":"Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning",
        "summary":"Counterfactual explanations, which deal with \"why not?\" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action. We also evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our first user study investigates if humans can discern if the counterfactual state explanations are produced by the actual game or produced by a generative deep learning approach. Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game. Our results indicate that counterfactual state explanations have sufficient fidelity to the actual game images to enable non-experts to more effectively identify a flawed RL agent compared to the nearest neighbor baseline and to having no explanation at all.",
        "published":"2021-01-29T00:00:00.000Z",
        "authors":"['Matthew L. Olson', 'Roli Khanna', 'Lawrence Neal', 'Fuxin Li', 'Weng-Keen Wong']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.85,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2101.12465v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.12465v1",
        "title":"AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting",
        "summary":"Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems. While recent advances exploit graph neural networks (GNN) to better learn spatial and temporal dependencies between sensors, they cannot model time-evolving spatio-temporal correlation (STC) between sensors, and require pre-defined graphs, which are neither always available nor totally reliable, and target at only a specific type of sensor data at one time. Moreover, since the form of time-series fluctuation is varied across sensors, a model needs to learn fluctuation modulation. To tackle these issues, in this work, we propose a novel GNN-based model, Attention-adjusted Graph Spatio-Temporal Network (AGSTN). In AGSTN, multi-graph convolution with sequential learning is developed to learn time-evolving STC. Fluctuation modulation is realized by a proposed attention adjustment mechanism. Experiments on three sensor data, air quality, bike demand, and traffic flow, exhibit that AGSTN outperforms the state-of-the-art methods.",
        "published":"2021-01-29T00:00:00.000Z",
        "authors":"['Yi-Ju Lu', 'Cheng-Te Li']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.77,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.0555
    },
    {
        "arxiv_id":"2101.12639v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2101.12639v1",
        "title":"Optimizing $\u03b1\u03bc$",
        "summary":"Alpha Mu is a search algorithm which repairs two defaults of Perfect Information Monte Carlo search: strategy fusion and non locality. In this paper we optimize $\\alpha\\mu$ for the game of Bridge, avoiding useless computations. The proposed optimizations are general and apply to other imperfect information turn-based games. We define multiple optimizations involving Pareto fronts, and show that these optimizations speed up the search. Some of these optimizations are cuts that stop the search at a node, while others keep track of which possible worlds have become redundant, avoiding unnecessary, costly evaluations. We also measure the benefits of parallelizing the double dummy searches at the leaves of the $\\alpha\\mu$ search tree.",
        "published":"2021-01-29T00:00:00.000Z",
        "authors":"['Tristan Cazenave', 'Swann Legras', 'V\u00e9ronique Ventos']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.81,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.5278333333
    },
    {
        "arxiv_id":"2102.00145v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00145v1",
        "title":"Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks",
        "summary":"The flexibility offered by reconfigurable wireless networks, provide new opportunities for various applications such as online AR\/VR gaming, high-quality video streaming and autonomous vehicles, that desire high-bandwidth, reliable and low-latency communications. These applications come with very stringent Quality of Service (QoS) requirements and increase the burden over mobile networks. Currently, there is a huge spectrum scarcity due to the massive data explosion and this problem can be solved by helps of Reconfigurable Wireless Networks (RWNs) where nodes have reconfiguration and perception capabilities. Therefore, a necessity of AI-assisted algorithms for resource block allocation is observed. To tackle this challenge, in this paper, we propose an actor-critic learning-based scheduler for allocating resource blocks in a RWN. Various traffic types with different QoS levels are assigned to our agents to provide more realistic results. We also include mobility in our simulations to increase the dynamicity of networks. The proposed model is compared with another actor-critic model and with other traditional schedulers; proportional fair (PF) and Channel and QoS Aware (CQA) techniques. The proposed models are evaluated by considering the delay experienced by user equipment (UEs), successful transmissions and head-of-the-line delays. The results show that the proposed model noticeably outperforms other techniques in different aspects.",
        "published":"2021-01-30T00:00:00.000Z",
        "authors":"['Shahram Mollahasani', 'Melike Erol-Kantarci', 'Mahdi Hirab', 'Hoda Dehghan', 'Rodney Wilson']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.87,
        "actuality":2.0,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.6389166667
    },
    {
        "arxiv_id":"2102.00205v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00205v1",
        "title":"A self-supervised learning-based 6-DOF grasp planning method for manipulator",
        "summary":"To realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required, the sizes of which directly affect the rate of successful grasps. To reduce the time cost of data acquisition and labeling and increase the rate of successful grasps, we developed a self-supervised learning mechanism to control grasp tasks performed by manipulators. First, a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data acquisition. The complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator, and the TSDF algorithm. Then, the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses, and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp data. Finally, the point cloud in the gripper closing area corresponding to each grasp pose is obtained; it is then used to train the grasp-quality classification model for the manipulator. The results of data acquisition experiments demonstrate that the proposed method allows high-quality data to be obtained. The simulated results prove the effectiveness of the proposed grasp-data acquisition method. The results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator.",
        "published":"2021-01-30T00:00:00.000Z",
        "authors":"['Gang Peng', 'Zhenyu Ren', 'Hao Wang', 'Xinde Li']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.89,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2102.00240v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00240v1",
        "title":"SA-Net: Shuffle Attention for Deep Convolutional Neural Networks",
        "summary":"Attention mechanisms, which enable a neural network to accurately focus on all the relevant elements of the input, have become an essential component to improve the performance of deep neural networks. There are mainly two attention mechanisms widely used in computer vision studies, \\textit{spatial attention} and \\textit{channel attention}, which aim to capture the pixel-level pairwise relationship and channel dependency, respectively. Although fusing them together may achieve better performance than their individual implementations, it will inevitably increase the computational overhead. In this paper, we propose an efficient Shuffle Attention (SA) module to address this issue, which adopts Shuffle Units to combine two types of attention mechanisms effectively. Specifically, SA first groups channel dimensions into multiple sub-features before processing them in parallel. Then, for each sub-feature, SA utilizes a Shuffle Unit to depict feature dependencies in both spatial and channel dimensions. After that, all sub-features are aggregated and a \"channel shuffle\" operator is adopted to enable information communication between different sub-features. The proposed SA module is efficient yet effective, e.g., the parameters and computations of SA against the backbone ResNet50 are 300 vs. 25.56M and 2.76e-3 GFLOPs vs. 4.12 GFLOPs, respectively, and the performance boost is more than 1.34% in terms of Top-1 accuracy. Extensive experimental results on common-used benchmarks, including ImageNet-1k for classification, MS COCO for object detection, and instance segmentation, demonstrate that the proposed SA outperforms the current SOTA methods significantly by achieving higher accuracy while having lower model complexity. The code and models are available at https:\/\/github.com\/wofmanaf\/SA-Net.",
        "published":"2021-01-30T00:00:00.000Z",
        "authors":"['Qing-Long Zhang Yu-Bin Yang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.84,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.91675
    },
    {
        "arxiv_id":"2102.00364v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00364v1",
        "title":"OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow",
        "summary":"Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key process is to adopt warped target feature based on previous flow prediction to correlate with source feature for building 3D matching cost volume. However, the warping operation can lead to troublesome ghosting problem that results in ambiguity. Moreover, occluded areas are treated equally with non occluded regions in most existing works, which may cause performance degradation. To deal with these challenges, we propose a lightweight yet efficient optical flow network, named OAS-Net (occlusion aware sampling network) for accurate optical flow. First, a new sampling based correlation layer is employed without noisy warping operation. Second, a novel occlusion aware module is presented to make raw cost volume conscious of occluded regions. Third, a shared flow and occlusion awareness decoder is adopted for structure compactness. Experiments on Sintel and KITTI datasets demonstrate the effectiveness of proposed approaches.",
        "published":"2021-01-31T00:00:00.000Z",
        "authors":"['Lingtong Kong', 'Xiaohang Yang', 'Jie Yang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.82,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3055833333
    },
    {
        "arxiv_id":"2102.00405v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00405v1",
        "title":"BNLP: Natural language processing toolkit for Bengali language",
        "summary":"BNLP is an open source language processing toolkit for Bengali language consisting with tokenization, word embedding, POS tagging, NER tagging facilities. BNLP provides pre-trained model with high accuracy to do model based tokenization, embedding, POS tagging, NER tagging task for Bengali language. BNLP pre-trained model achieves significant results in Bengali text tokenization, word embedding, POS tagging and NER tagging task. BNLP is using widely in the Bengali research communities with 16K downloads, 119 stars and 31 forks. BNLP is available at https:\/\/github.com\/sagorbrur\/bnlp.",
        "published":"2021-01-31T00:00:00.000Z",
        "authors":"['Sagor Sarker']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.66,
        "actuality":2.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2102.00426v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00426v1",
        "title":"A Simple yet Brisk and Efficient Active Learning Platform for Text Classification",
        "summary":"In this work, we propose the use of a fully managed machine learning service, which utilizes active learning to directly build models from unstructured data. With this tool, business users can quickly and easily build machine learning models and then directly deploy them into a production ready hosted environment without much involvement from data scientists. Our approach leverages state-of-the-art text representation like OpenAI's GPT2 and a fast implementation of the active learning workflow that relies on a simple construction of incremental learning using linear models, thus providing a brisk and efficient labeling experience for the users. Experiments on both publicly available and real-life insurance datasets empirically show why our choices of simple and fast classification algorithms are ideal for the task at hand.",
        "published":"2021-01-31T00:00:00.000Z",
        "authors":"['Teja Kanchinadam', 'Qian You', 'Keith Westpfahl', 'James Kim', 'Siva Gunda', 'Sebastian Seith', 'Glenn Fung']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":3.6666666667,
        "controversy":4.0,
        "relevance_magnitude":5.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.8611666667
    },
    {
        "arxiv_id":"2102.00460v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00460v1",
        "title":"A SDN\/OpenFlow Framework for Dynamic Resource Allocation based on Bandwidth Allocation Model",
        "summary":"The communication network context in actual systems like 5G, cloud and IoT (Internet of Things), presents an ever-increasing number of users, applications, and services that are highly distributed with distinct and heterogeneous communications requirements. Resource allocation in this context requires dynamic, efficient, and customized solutions and Bandwidth Allocation Models (BAMs) are an alternative to support this new trend. This paper proposes the BAMSDN (Bandwidth Allocation Model through Software-Defined Networking) framework that dynamically allocates resources (bandwidth) for a MPLS (MultiProtocol Label Switching) network using a SDN (Software-Defined Networking)\/OpenFlow strategy with BAM. The framework adopts an innovative implementation approach for BAM systems by controlling the MPLS network using SDN with OpenFlow. Experimental results suggest that using SDN\/OpenFlow with BAM for bandwidth allocation does have effective advantages for MPLS networks requiring flexible resource sharing among applications and facilitates the migration path to a SDN\/OpenFlow network.",
        "published":"2021-01-31T00:00:00.000Z",
        "authors":"['Eliseu Silva Torres', 'Rafael F. Reale', 'Leobino N. Sampaio', 'Joberto S. B. Martins']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.79,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2102.00509v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00509v1",
        "title":"TruthBot: An Automated Conversational Tool for Intent Learning, Curated Information Presenting, and Fake News Alerting",
        "summary":"We present TruthBot, an all-in-one multilingual conversational chatbot designed for seeking truth (trustworthy and verified information) on specific topics. It helps users to obtain information specific to certain topics, fact-check information, and get recent news. The chatbot learns the intent of a query by training a deep neural network from the data of the previous intents and responds appropriately when it classifies the intent in one of the classes above. Each class is implemented as a separate module that uses either its own curated knowledge-base or searches the web to obtain the correct information. The topic of the chatbot is currently set to COVID-19. However, the bot can be easily customized to any topic-specific responses. Our experimental results show that each module performs significantly better than its closest competitor, which is verified both quantitatively and through several user-based surveys in multiple languages. TruthBot has been deployed in June 2020 and is currently running.",
        "published":"2021-01-31T00:00:00.000Z",
        "authors":"['Ankur Gupta', 'Yash Varun', 'Prarthana Das', 'Nithya Muttineni', 'Parth Srivastava', 'Hamim Zafar', 'Tanmoy Chakraborty', 'Swaprava Nath']",
        "arxiv_primary_category":"cs.si",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":4.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6945
    },
    {
        "arxiv_id":"2102.00572v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00572v1",
        "title":"Interpretable Reinforcement Learning Inspired by Piaget's Theory of Cognitive Development",
        "summary":"Endeavors for designing robots with human-level cognitive abilities have led to different categories of learning machines. According to Skinner's theory, reinforcement learning (RL) plays a key role in human intuition and cognition. Majority of the state-of-the-art methods including deep RL algorithms are strongly influenced by the connectionist viewpoint. Such algorithms can significantly benefit from theories of mind and learning in other disciplines. This paper entertains the idea that theories such as language of thought hypothesis (LOTH), script theory, and Piaget's cognitive development theory provide complementary approaches, which will enrich the RL field. Following this line of thinking, a general computational building block is proposed for Piaget's schema theory that supports the notions of productivity, systematicity, and inferential coherence as described by Fodor in contrast with the connectionism theory. Abstraction in the proposed method is completely upon the system itself and is not externally constrained by any predefined architecture. The whole process matches the Neisser's perceptual cycle model. Performed experiments on three typical control problems followed by behavioral analysis confirm the interpretability of the proposed method and its competitiveness compared to the state-of-the-art algorithms. Hence, the proposed framework can be viewed as a step towards achieving human-like cognition in artificial intelligent systems.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Aref Hakimzadeh', 'Yanbo Xue', 'Peyman Setoodeh']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.83,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.22225
    },
    {
        "arxiv_id":"2102.00576v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00576v1",
        "title":"Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users",
        "summary":"Online shopping has become a valuable modern convenience, but blind or low vision (BLV) users still face significant challenges using it, because of: 1) inadequate image descriptions and 2) the inability to filter large amounts of information using screen readers. To address those challenges, we propose Revamp, a system that leverages customer reviews for interactive information retrieval. Revamp is a browser integration that supports review-based question-answering interactions on a reconstructed product page. From our interview, we identified four main aspects (color, logo, shape, and size) that are vital for BLV users to understand the visual appearance of a product. Based on the findings, we formulated syntactic rules to extract review snippets, which were used to generate image descriptions and responses to users' queries. Evaluations with eight BLV users showed that Revamp 1) provided useful descriptive information for understanding product appearance and 2) helped the participants locate key information efficiently.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Ruolin Wang', 'Zixuan Chen', 'Mingrui \"Ray\" Zhang', 'Zhaoheng Li', 'Zhixiu Liu', 'Zihan Dang', 'Chun Yu', 'Xiang \"Anthony\" Chen']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":1.0,
        "relevance_magnitude":4.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.4443333333
    },
    {
        "arxiv_id":"2102.00627v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00627v1",
        "title":"Learning to Explain Recommendations",
        "summary":"Explaining to users why some items are recommended is critical, as it helps users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanations as side outputs of the recommendation model, which has two problems: (1) it is difficult to evaluate the produced explanations because they are usually model-dependent, and (2) as a result, the possible impacts of those explanations are less investigated. To address the evaluation problem, we propose learning to explain for explainable recommendation. The basic idea is to train a model that selects explanations from a collection as a ranking-oriented task. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be severer than that in traditional user-item relation data, since not every user-item pair can associate with multiple explanations. To mitigate this issue, we propose to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. To further investigate the impacts of explanations, we extend the traditional item ranking of recommendation to an item-explanation joint-ranking formalization. We study if purposely selecting explanations could achieve certain learning goals, e.g., in this paper, improving the recommendation performance. Experiments on three large datasets verify our solution's effectiveness on both item recommendation and explanation ranking. In addition, our user-item-explanation datasets open up new ways of modeling and evaluating recommendation explanations. To facilitate the development of explainable RS, we will make our datasets and code publicly available.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Lei Li', 'Yongfeng Zhang', 'Li Chen']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.6388333333
    },
    {
        "arxiv_id":"2102.00643v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00643v1",
        "title":"An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends",
        "summary":"Traditionally, the data plane has been designed with fixed functions to forward packets using a small set of protocols. This closed-design paradigm has limited the capability of the switches to proprietary implementations which are hardcoded by vendors, inducing a lengthy, costly, and inflexible process. Recently, data plane programmability has attracted significant attention from both the research community and the industry, permitting operators and programmers in general to run customized packet processing function. This open-design paradigm is paving the way for an unprecedented wave of innovation and experimentation by reducing the time of designing, testing, and adopting new protocols; enabling a customized, top-down approach to develop network applications; providing granular visibility of packet events defined by the programmer; reducing complexity and enhancing resource utilization of the programmable switches; and drastically improving the performance of applications that are offloaded to the data plane. Despite the impressive advantages of programmable data plane switches and their importance in modern networks, the literature has been missing a comprehensive survey. To this end, this paper provides a background encompassing an overview of the evolution of networks from legacy to programmable, describing the essentials of programmable switches, and summarizing their advantages over Software-defined Networking (SDN) and legacy devices. The paper then presents a unique, comprehensive taxonomy of applications developed with P4 language; surveying, classifying, and analyzing more than 150 articles; discussing challenges and considerations; and presenting future perspectives and open research issues.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Elie F. Kfoury', 'Jorge Crichigno', 'Elias Bou-Harb']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":2.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2102.00649v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00649v1",
        "title":"Forecasting Action through Contact Representations from First Person Video",
        "summary":"Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We annotate a subset of the EPIC Kitchens dataset to include time-to-contact between hands and objects, as well as segmentations of hands and objects. Using these annotations we train the Anticipation Module, a module producing Contact Anticipation Maps and Next Active Object Segmentations - novel low-level representations providing temporal and spatial characteristics of anticipated near future action. On top of the Anticipation Module we apply Egocentric Object Manipulation Graphs (Ego-OMG), a framework for action anticipation and prediction. Ego-OMG models longer term temporal semantic relations through the use of a graph modeling transitions between contact delineated action states. Use of the Anticipation Module within Ego-OMG produces state-of-the-art results, achieving 1st and 2nd place on the unseen and seen test sets, respectively, of the EPIC Kitchens Action Anticipation Challenge, and achieving state-of-the-art results on the tasks of action anticipation and action prediction over EPIC Kitchens. We perform ablation studies over characteristics of the Anticipation Module to evaluate their utility.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Eadom Dessalene', 'Chinmaya Devaraj', 'Michael Maynord', 'Cornelia Fermuller', 'Yiannis Aloimonos']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.83,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":5.0,
        "newsworthiness_crowd_sum":4.5833333333
    },
    {
        "arxiv_id":"2102.00845v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00845v2",
        "title":"LSTM-SAKT: LSTM-Encoded SAKT-like Transformer for Knowledge Tracing",
        "summary":"This paper introduces the 2nd place solution for the Riiid! Answer Correctness Prediction in Kaggle, the world's largest data science competition website. This competition was held from October 16, 2020, to January 7, 2021, with 3395 teams and 4387 competitors. The main insights and contributions of this paper are as follows. (i) We pointed out existing Transformer-based models are suffering from a problem that the information which their query\/key\/value can contain is limited. To solve this problem, we proposed a method that uses LSTM to obtain query\/key\/value and verified its effectiveness. (ii) We pointed out 'inter-container' leakage problem, which happens in datasets where questions are sometimes served together. To solve this problem, we showed special indexing\/masking techniques that are useful when using RNN-variants and Transformer. (iii) We found additional hand-crafted features are effective to overcome the limits of Transformer, which can never consider the samples older than the sequence length.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Takashi Oya', 'Shigeo Morishima']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.89,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.2221666667
    },
    {
        "arxiv_id":"2102.00917v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.00917v1",
        "title":"Counting Protests in News Articles: A Dataset and Semi-Automated Data Collection Pipeline",
        "summary":"Between January 2017 and January 2021, thousands of local news sources in the United States reported on over 42,000 protests about topics such as civil rights, immigration, guns, and the environment. Given the vast number of local journalists that report on protests daily, extracting these events as structured data to understand temporal and geographic trends can empower civic decision-making. However, the task of extracting events from news articles presents well known challenges to the NLP community in the fields of domain detection, slot filling, and coreference resolution. To help improve the resources available for extracting structured data from news stories, our contribution is three-fold. We 1) release a manually labeled dataset of news article URLs, dates, locations, crowd size estimates, and 494 discrete descriptive tags corresponding to 42,347 reported protest events in the United States between January 2017 and January 2021; 2) describe the semi-automated data collection pipeline used to discover, sort, and review the 144,568 English articles that comprise the dataset; and 3) benchmark a long-short term memory (LSTM) low dimensional classifier that demonstrates the utility of processing news articles based on syntactic structures, such as paragraphs and sentences, to count the number of reported protest events.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Tommy Leung', 'L. Nathan Perkins']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.88,
        "actuality":5.0,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2102.01020v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01020v1",
        "title":"Relational Consensus-Based Cooperative Task Allocation Management for IIoT-Health Networks",
        "summary":"IIoT services focused on industry-oriented services often require objects run more than one task. IIoT objects poses the challenge of distributing and managing task allocation among them. The fairness of task allocation brings flexible network reconfiguration and maximizes the tasks to be performed. Although existing approaches optimize and manage the dynamics of objects, not all them consider both co-relationship between tasks and object capabilities and the distributed allocation over the cluster service. This paper introduces the ACADIA mechanism for task allocation in IIoT networks in order to distribute task among objects. It relies on relational consensus strategies to allocate tasks and similarity capabilities to determine which objects can play in accomplishing those tasks. Evaluation on NS-3 showed that ACADIA achieved 98% of allocated tasks in an IIoT-Health considering all scenarios, average more than 95% of clusters apt to performed tasks in a low response time, and achieved 50% more effectiveness in task allocation compared to the literature solution CONTASKI.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Carlos Pedroso', 'Yan Uehara de Moraes', 'Michele Nogueira', 'Aldri Santos']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":2.3333333333,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2102.01065v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01065v1",
        "title":"Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches",
        "summary":"Datasets are not only resources for training accurate, deployable systems, but are also benchmarks for developing new modeling approaches. While large, natural datasets are necessary for training accurate systems, are they necessary for driving modeling innovation? For example, while the popular SQuAD question answering benchmark has driven the development of new modeling approaches, could synthetic or smaller benchmarks have led to similar innovations? This counterfactual question is impossible to answer, but we can study a necessary condition: the ability for a benchmark to recapitulate findings made on SQuAD. We conduct a retrospective study of 20 SQuAD modeling approaches, investigating how well 32 existing and synthesized benchmarks concur with SQuAD -- i.e., do they rank the approaches similarly? We carefully construct small, targeted synthetic benchmarks that do not resemble natural language, yet have high concurrence with SQuAD, demonstrating that naturalness and size are not necessary for reflecting historical modeling improvements on SQuAD. Our results raise the intriguing possibility that small and carefully designed synthetic benchmarks may be useful for driving the development of new modeling approaches.",
        "published":"2021-02-01T00:00:00.000Z",
        "authors":"['Nelson F. Liu', 'Tony Lee', 'Robin Jia', 'Percy Liang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2102.01353v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01353v1",
        "title":"Subdimensional Expansion for Multi-objective Multi-agent Path Finding",
        "summary":"Conventional multi-agent path planners typically determine a path that optimizes a single objective, such as path length. Many applications, however, may require multiple objectives, say time-to-completion and fuel use, to be simultaneously optimized in the planning process. Often, these criteria may not be readily compared and sometimes lie in competition with each other. Simply applying standard multi-objective search algorithms to multi-agent path finding may prove to be inefficient because the size of the space of possible solutions, i.e., the Pareto-optimal set, can grow exponentially with the number of agents (the dimension of the search space). This paper presents an approach that bypasses this so-called curse of dimensionality by leveraging our prior multi-agent work with a framework called subdimensional expansion. One example of subdimensional expansion, when applied to A*, is called M* and M* was limited to a single objective function. We combine principles of dominance and subdimensional expansion to create a new algorithm named multi-objective M* (MOM*), which dynamically couples agents for planning only when those agents have to \"interact\" with each other. MOM* computes the complete Pareto-optimal set for multiple agents efficiently and naturally trades off sub-optimal approximations of the Pareto-optimal set and computational efficiency. Our approach is able to find the complete Pareto-optimal set for problem instances with hundreds of solutions which the standard multi-objective A* algorithms could not find within a bounded time.",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Zhongqiang Ren', 'Sivakumar Rathinam', 'Howie Choset']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.8888333333
    },
    {
        "arxiv_id":"2102.01429v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01429v1",
        "title":"Drone Control based on Mental Commands and Facial Expressions",
        "summary":"When it is tried to control drones, there are many different ways through various devices, using either motions like facial motion, special gloves with sensors, red, green, blue cameras on the laptop or even using smartwatches by performing gestures that are picked up by motion sensors. The paper proposes a work on how drones could be controlled using brainwaves without any of those devices. The drone control system of the current research was developed using electroencephalogram signals took by an Emotiv Insight headset. The electroencephalogram signals are collected from the users brain. The processed signal is then sent to the computer via Bluetooth. The headset employs Bluetooth Low Energy for wireless transmission. The brain of the user is trained in order to use the generated electroencephalogram data. The final signal is transmitted to Raspberry Pi zero via the MQTT messaging protocol. The Raspberry Pi controls the movement of the drone through the incoming signal from the headset. After years, brain control can replace many normal input sources like keyboards, touch screens or other traditional ways, so it enhances interactive experiences and provides new ways for disabled people to engage with their surroundings.",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Iuliana Marin', 'Myssar Jabbar Hammood Al-Battbootti', 'Nicolae Goga']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":3.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0834166667
    },
    {
        "arxiv_id":"2102.01436v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01436v1",
        "title":"Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulations",
        "summary":"Recent developments in surgical robotics have led to new advancements in the automation of surgical sub-tasks such as suturing, soft tissue manipulation, tissue tensioning and cutting. However, integration of dynamics to optimize these control policies for the variety of scenes encountered in surgery remains unsolved. Towards this effort, we investigate the integration of differentiable fluid dynamics to optimizing a suction tool's trajectory to clear the surgical field from blood as fast as possible. The fully differentiable fluid dynamics is integrated with a novel suction model for effective model predictive control of the tool. The differentiability of the fluid model is crucial because we utilize the gradients of the fluid states with respect to the suction tool position to optimize the trajectory. Through a series of experiments, we demonstrate how, by incorporating fluid models, the trajectories generated by our method can perform as good as or better than handcrafted human-intuitive suction policies. We also show that our method is adaptable and can work in different cavity conditions while using a single handcrafted strategy fails.",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Jingbin Huang', 'Fei Liu', 'Florian Richter', 'Michael C. Yip']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3055833333
    },
    {
        "arxiv_id":"2102.01538v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01538v1",
        "title":"A new distance measure of Pythagorean fuzzy sets based on matrix and and its application in medical diagnosis",
        "summary":"The pythagorean fuzzy set (PFS) which is developed based on intuitionistic fuzzy set, is more efficient in elaborating and disposing uncertainties in indeterminate situations, which is a very reason of that PFS is applied in various kinds of fields. How to measure the distance between two pythagorean fuzzy sets is still an open issue. Mnay kinds of methods have been proposed to present the of the question in former reaserches. However, not all of existing methods can accurately manifest differences among pythagorean fuzzy sets and satisfy the property of similarity. And some other kinds of methods neglect the relationship among three variables of pythagorean fuzzy set. To addrees the proplem, a new method of measuring distance is proposed which meets the requirements of axiom of distance measurement and is able to indicate the degree of distinction of PFSs well. Then some numerical examples are offered to to verify that the method of measuring distances can avoid the situation that some counter? intuitive and irrational results are produced and is more effective, reasonable and advanced than other similar methods. Besides, the proposed method of measuring distances between PFSs is applied in a real environment of application which is the medical diagnosis and is compared with other previous methods to demonstrate its superiority and efficiency. And the feasibility of the proposed method in handling uncertainties in practice is also proved at the same time.",
        "published":"2021-01-31T00:00:00.000Z",
        "authors":"['Yuanpeng He']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.87,
        "actuality":3.0,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2102.01586v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01586v1",
        "title":"U-LanD: Uncertainty-Driven Video Landmark Detection",
        "summary":"This paper presents U-LanD, a framework for joint detection of key frames and landmarks in videos. We tackle a specifically challenging problem, where training labels are noisy and highly sparse. U-LanD builds upon a pivotal observation: a deep Bayesian landmark detector solely trained on key video frames, has significantly lower predictive uncertainty on those frames vs. other frames in videos. We use this observation as an unsupervised signal to automatically recognize key frames on which we detect landmarks. As a test-bed for our framework, we use ultrasound imaging videos of the heart, where sparse and noisy clinical labels are only available for a single frame in each video. Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly outperform the state-of-the-art non-Bayesian counterpart by a noticeable absolute margin of 42% in R2 score, with almost no overhead imposed on the model size. Our approach is generic and can be potentially applied to other challenging data with noisy and sparse training labels.",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Mohammad H. Jafari', 'Christina Luong', 'Michael Tsang', 'Ang Nan Gu', 'Nathan Van Woudenberg', 'Robert Rohling', 'Teresa Tsang', 'Purang Abolmaesumi']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.89,
        "actuality":2.3333333333,
        "controversy":2.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.5278333333
    },
    {
        "arxiv_id":"2102.01649v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01649v3",
        "title":"Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction",
        "summary":"Multi-scale biomedical knowledge networks are expanding with emerging experimental technologies that generates multi-scale biomedical big data. Link prediction is increasingly used especially in bipartite biomedical networks to identify hidden biological interactions and relationshipts between key entities such as compounds, targets, gene and diseases. We propose a Graph Neural Networks (GNN) method, namely Graph Pair based Link Prediction model (GPLP), for predicting biomedical network links simply based on their topological interaction information. In GPLP, 1-hop subgraphs extracted from known network interaction matrix is learnt to predict missing links. To evaluate our method, three heterogeneous biomedical networks were used, i.e. Drug-Target Interaction network (DTI), Compound-Protein Interaction network (CPI) from NIH Tox21, and Compound-Virus Inhibition network (CVI). Our proposed GPLP method significantly outperforms over the state-of-the-art baselines. In addition, different network incompleteness is analysed with our devised protocol, and we also design an effective approach to improve the model robustness towards incomplete networks. Our method demonstrates the potential applications in other biomedical networks.",
        "published":"2021-01-28T00:00:00.000Z",
        "authors":"['Jinjiang Guo', 'Jie Li', 'Dawei Leng', 'Lurong Pan']",
        "arxiv_primary_category":"cs.si",
        "readability":0.79,
        "actuality":2.6666666667,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2102.01724v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01724v1",
        "title":"On the Power of False Negative Awareness in Indicator-based Caching Systems",
        "summary":"Distributed caching systems such as content distribution networks often advertise their content via lightweight approximate indicators (e.g., Bloom filters) to efficiently inform clients where each datum is likely cached. While false-positive indications are necessary and well understood, most existing works assume no false-negative indications. Our work illustrates practical scenarios where false-negatives are unavoidable and ignoring them has a significant impact on system performance. Specifically, we focus on false-negatives induced by indicator staleness, which arises whenever the system advertises the indicator only periodically, rather than immediately reporting every change in the cache. Such scenarios naturally occur, e.g., in bandwidth-constraint environments or when latency impedes the ability of each client to obtain an updated indicator. Our work introduces novel false-negative aware access policies that continuously estimate the false-negative ratio and sometimes access caches despite negative indications. We present optimal policies for homogeneous settings and provide approximation guarantees for our algorithms in heterogeneous environments. We further perform an extensive simulation study with multiple real system traces. We show that our false-negative aware algorithms incur a significantly lower access cost than existing approaches or match the cost of these approaches while requiring an order of magnitude fewer resources (e.g., caching capacity or bandwidth).",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Itamar Cohen', 'Gil Einziger', 'Gabriel Scalosub']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2102.01752v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01752v1",
        "title":"Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization",
        "summary":"Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches rely on entropic or quadratic regularization, we employ input convex neural networks and cycle-consistency regularization to avoid introducing bias. As a result, our approach does not resort to minimax optimization. We provide theoretical analysis on error bounds as well as empirical evidence of the effectiveness of the proposed approach in low-dimensional qualitative scenarios and high-dimensional quantitative experiments.",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Alexander Korotin', 'Lingxiao Li', 'Justin Solomon', 'Evgeny Burnaev']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.74,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.97225
    },
    {
        "arxiv_id":"2102.01868v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01868v3",
        "title":"Causal Collaborative Filtering",
        "summary":"Recommender systems are important and valuable tools for many personalized services. Collaborative Filtering (CF) algorithms -- among others -- are fundamental algorithms driving the underlying mechanism of personalized recommendation. Many of the traditional CF algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data for matching, including memory-based methods such as user\/item-based CF as well as learning-based methods such as matrix factorization and deep learning models. However, advancing from correlative learning to causal learning is an important problem, because causal\/counterfactual modeling can help us to think outside of the observational data for user modeling and personalization. In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation. We first provide a unified causal view of CF and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We then propose a conditional intervention approach for $do$-calculus so that we can estimate the causal relations based on observational data. Finally, we further propose a general counterfactual constrained learning framework for estimating the user-item preferences. Experiments are conducted on two types of real-world datasets -- traditional and randomized trial data -- and results show that our framework can improve the recommendation performance of many CF algorithms.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Shuyuan Xu', 'Yingqiang Ge', 'Yunqi Li', 'Zuohui Fu', 'Xu Chen', 'Yongfeng Zhang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.84,
        "actuality":3.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2102.01900v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01900v1",
        "title":"Temporal Motifs in Smart Grid",
        "summary":"A complex network can be characterized by patterns. Such frequently occurring significant patterns are called motifs and in a time dependent network, they are called temporal motifs. One of the temporal networks where temporal motifs are observed and play a major role; is the Smart Grid. The energy consumption pattern across the appliances, houses, communities and entire cities help energy utility companies and consumers plan their electricity generation and consumption. The temporal motifs for the smart grid constitutes of the consumers and producers and the edge or connection represents energy flow between two participants of the network, these connections last till the power is being consumed\/generated. This paper formally defines the temporal motifs for smart grid network and proposes a way to create such temporal motifs in the network. We also discuss how the temporal motifs fit into the hierarchical structure of power distribution system of Smart Grid.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Rucha Bhalchandra Joshi', 'Annada Prasad Behera', 'Subhankar Mishra']",
        "arxiv_primary_category":"cs.si",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2102.01932v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01932v1",
        "title":"Roughly Collected Dataset for Contact Force Sensing Catheter",
        "summary":"With rise of interventional cardiology, Catheter Ablation Therapy (CAT) has established itself as a first-line solution to treat cardiac arrhythmia. Although CAT is a promising technique, cardiologist lacks vision inside the body during the procedure, which may cause serious clinical syndromes. To support accurate clinical procedure, Contact Force Sensing (CFS) system is developed to find a position of the catheter tip through the measure of contact force between catheter and heart tissue. However, the practical usability of commercialized CFS systems is not fully understood due to inaccuracy in the measurement. To support the development of more accurate system, we develop a full pipeline of CFS system with newly collected benchmark dataset through a contact force sensing catheter in simplest hardware form. Our dataset was roughly collected with human noise to increase data diversity. Through the analysis of the dataset, we identify a problem defined as Shift of Reference (SoR), which prevents accurate measurement of contact force. To overcome the problem, we conduct the contact force estimation via standard deep neural networks including for Recurrent Neural Network (RNN), Fully Convolutional Network (FCN) and Transformer. An average error in measurement for RNN, FCN and Transformer are, respectively, 2.46g, 3.03g and 3.01g. Through these studies, we try to lay a groundwork, serve a performance criteria for future CFS system research and open a publicly available dataset to public.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Seunghyuk Cho', 'Minsoo Koo', 'Dongwoo Kim', 'Juyong Lee', 'Yeonwoo Jung', 'Kibyung Nam', 'Changmo Hwang']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2102.01974v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.01974v1",
        "title":"AttentionFlow: Visualising Influence in Networks of Time Series",
        "summary":"The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Minjeong Shin', 'Alasdair Tran', 'Siqi Wu', 'Alexander Mathews', 'Rong Wang', 'Georgiana Lyall', 'Lexing Xie']",
        "arxiv_primary_category":"cs.si",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9999166667
    },
    {
        "arxiv_id":"2102.02267v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02267v1",
        "title":"DEFT: Detection Embeddings for Tracking",
        "summary":"Most modern multiple object tracking (MOT) systems follow the tracking-by-detection paradigm, consisting of a detector followed by a method for associating detections into tracks. There is a long history in tracking of combining motion and appearance features to provide robustness to occlusions and other challenges, but typically this comes with the trade-off of a more complex and slower implementation. Recent successes on popular 2D tracking benchmarks indicate that top-scores can be achieved using a state-of-the-art detector and relatively simple associations relying on single-frame spatial offsets -- notably outperforming contemporary methods that leverage learned appearance features to help re-identify lost tracks. In this paper, we propose an efficient joint detection and tracking model named DEFT, or \"Detection Embeddings for Tracking.\" Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network. An LSTM is also added to capture motion constraints. DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards while having significant advantages in robustness when applied to more challenging tracking data. DEFT raises the bar on the nuScenes monocular 3D tracking challenge, more than doubling the performance of the previous top method. Code is publicly available.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Mohamed Chaabane', 'Peter Zhang', 'J. Ross Beveridge', \"Stephen O'Hara\"]",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2102.02311v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02311v1",
        "title":"Causal Sufficiency and Actual Causation",
        "summary":"Pearl opened the door to formally defining actual causation using causal models. His approach rests on two strategies: first, capturing the widespread intuition that X=x causes Y=y iff X=x is a Necessary Element of a Sufficient Set for Y=y, and second, showing that his definition gives intuitive answers on a wide set of problem cases. This inspired dozens of variations of his definition of actual causation, the most prominent of which are due to Halpern & Pearl. Yet all of them ignore Pearl's first strategy, and the second strategy taken by itself is unable to deliver a consensus. This paper offers a way out by going back to the first strategy: it offers six formal definitions of causal sufficiency and two interpretations of necessity. Combining the two gives twelve new definitions of actual causation. Several interesting results about these definitions and their relation to the various Halpern & Pearl definitions are presented. Afterwards the second strategy is evaluated as well. In order to maximize neutrality, the paper relies mostly on the examples and intuitions of Halpern & Pearl. One definition comes out as being superior to all others, and is therefore suggested as a new definition of actual causation.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Sander Beckers']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.88,
        "actuality":1.6666666667,
        "controversy":3.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.6665833333
    },
    {
        "arxiv_id":"2102.02326v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02326v1",
        "title":"Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy",
        "summary":"Inspired by the progress of the End-to-End approach [1], this paper systematically studies the effects of Number of Filters of convolutional layers on the model prediction accuracy of CNN+RNN (Convolutional Neural Networks adding to Recurrent Neural Networks) for ASR Models (Automatic Speech Recognition). Experimental results show that only when the CNN Number of Filters exceeds a certain threshold value is adding CNN to RNN able to improve the performance of the CNN+RNN speech recognition model, otherwise some parameter ranges of CNN can render it useless to add the CNN to the RNN model. Our results show a strong dependency of word accuracy on the Number of Filters of convolutional layers. Based on the experimental results, the paper suggests a possible hypothesis of Sound-2-Vector Embedding (Convolutional Embedding) to explain the above observations. Based on this Embedding hypothesis and the optimization of parameters, the paper develops an End-to-End speech recognition system which has a high word accuracy but also has a light model-weight. The developed LVCSR (Large Vocabulary Continuous Speech Recognition) model has achieved quite a high word accuracy of 90.2% only by its Acoustic Model alone, without any assistance from intermediate phonetic representation and any Language Model. Its acoustic model contains only 4.4 million weight parameters, compared to the 35~68 million acoustic-model weight parameters in DeepSpeech2 [2] (one of the top state-of-the-art LVCSR models) which can achieve a word accuracy of 91.5%. The light-weighted model is good for improving the transcribing computing efficiency and also useful for mobile devices, Driverless Vehicles, etc. Our model weight is reduced to ~10% the size of DeepSpeech2, but our model accuracy remains close to that of DeepSpeech2. If combined with a Language Model, our LVCSR system is able to achieve 91.5% word accuracy.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['James Mou', 'Jun Li']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.85,
        "actuality":4.0,
        "controversy":1.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2102.02434v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02434v1",
        "title":"Assessing Individual and Community Vulnerability to Fake News in Social Networks",
        "summary":"The plague of false information, popularly called fake news has affected lives of news consumers ever since the prevalence of social media. Thus understanding the spread of false information in social networks has gained a lot of attention in the literature. While most proposed models do content analysis of the information, no much work has been done by exploring the community structures that also play an important role in determining how people get exposed to it. In this paper we base our idea on Computational Trust in social networks to propose a novel Community Health Assessment model against fake news. Based on the concepts of neighbor, boundary and core nodes of a community, we propose novel evaluation metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading false information. Our model hypothesizes that if the boundary nodes trust the neighbor nodes of a community who are spreaders, the densely-connected core nodes of the community are highly likely to become spreaders. We test our model with communities generated using three popular community detection algorithms based on two new datasets of information spreading networks collected from Twitter. Our experimental results show that the proposed metrics perform clearly better on the networks spreading false information than on those spreading true ones, indicating our community health assessment model is effective.",
        "published":"2021-02-04T00:00:00.000Z",
        "authors":"['Bhavtosh Rath', 'Wei Gao', 'Jaideep Srivastava']",
        "arxiv_primary_category":"cs.si",
        "readability":0.92,
        "actuality":5.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3055
    },
    {
        "arxiv_id":"2102.02435v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02435v1",
        "title":"Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue",
        "summary":"We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark progress, we introduce a new dataset of GuessMovie, which contains 16,881 documents, each describing a movie, and associated 13,434 dialogues. Further, we propose the MD3 model. Keeping guessing the target document in mind, it converses with the user conditioned on both document engagement and user feedback. In order to incorporate large-scale external documents into the dialogue, it pretrains a document representation which is sensitive to attributes it talks about an object. Then it tracks dialogue state by detecting evolvement of document belief and attribute belief, and finally optimizes dialogue policy in principle of entropy decreasing and reward increasing, which is expected to successfully guess the user's target in a minimum number of turns. Experiments show that our method significantly outperforms several strong baseline methods and is very close to human's performance.",
        "published":"2021-02-04T00:00:00.000Z",
        "authors":"['Han Liu', 'Caixia Yuan', 'Xiaojie Wang', 'Yushu Yang', 'Huixing Jiang', 'Zhongyuan Wang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.9,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4999166667
    },
    {
        "arxiv_id":"2102.02638v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02638v1",
        "title":"Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning",
        "summary":"Recent breakthroughs in deep learning (DL) have led to the emergence of many intelligent mobile applications and services, but in the meanwhile also pose unprecedented computing challenges on resource-constrained mobile devices. This paper builds a collaborative deep inference system between a resource-constrained mobile device and a powerful edge server, aiming at joining the power of both on-device processing and computation offloading. The basic idea of this system is to partition a deep neural network (DNN) into a front-end part running on the mobile device and a back-end part running on the edge server, with the key challenge being how to locate the optimal partition point to minimize the end-to-end inference delay. Unlike existing efforts on DNN partitioning that rely heavily on a dedicated offline profiling stage to search for the optimal partition point, our system has a built-in online learning module, called Autodidactic Neurosurgeon (ANS), to automatically learn the optimal partition point on-the-fly. Therefore, ANS is able to closely follow the changes of the system environment by generating new knowledge for adaptive decision making. The core of ANS is a novel contextual bandit learning algorithm, called $\\mu$LinUCB, which not only has provable theoretical learning performance guarantee but also is ultra-lightweight for easy real-world implementation. We implement our system on a video stream object detection testbed to validate the design of ANS and evaluate its performance. The experiments show that ANS significantly outperforms state-of-the-art benchmarks in terms of tracking system changes and reducing the end-to-end inference delay.",
        "published":"2021-02-02T00:00:00.000Z",
        "authors":"['Letian Zhang', 'Lixing Chen', 'Jie Xu']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.85,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2102.02654v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02654v1",
        "title":"Triadic Exploration and Exploration with Multiple Experts",
        "summary":"Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.",
        "published":"2021-02-04T00:00:00.000Z",
        "authors":"['Maximilian Felde', 'Gerd Stumme']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.86,
        "actuality":2.0,
        "controversy":1.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.47225
    },
    {
        "arxiv_id":"2102.02729v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02729v3",
        "title":"Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review",
        "summary":"Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and\/or test examples to hijack the machine learning algorithm output, leading to possibly user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to it. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure.",
        "published":"2021-02-04T00:00:00.000Z",
        "authors":"['Dongrui Wu', 'Weili Fang', 'Yi Zhang', 'Liuqing Yang', 'Xiaodong Xu', 'Hanbin Luo', 'Xiang Yu']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2102.02779v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02779v1",
        "title":"Unifying Vision-and-Language Tasks via Text Generation",
        "summary":"Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To alleviate these hassles, in this work, we propose a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation, where our models learn to generate labels in text based on the visual and textual inputs. On 7 popular vision-and-language benchmarks, including visual question answering, referring expression comprehension, visual commonsense reasoning, most of which have been previously modeled as discriminative tasks, our generative approach (with a single unified architecture) reaches comparable performance to recent task-specific state-of-the-art vision-and-language models. Moreover, our generative approach shows better generalization ability on answering questions that have rare answers. In addition, we show that our framework allows multi-task learning in a single architecture with a single set of parameters, which achieves similar performance to separately optimized single-task models. Our code will be publicly available at: https:\/\/github.com\/j-min\/VL-T5",
        "published":"2021-02-04T00:00:00.000Z",
        "authors":"['Jaemin Cho', 'Jie Lei', 'Hao Tan', 'Mohit Bansal']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.84,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.7778333333
    },
    {
        "arxiv_id":"2102.02959v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02959v2",
        "title":"Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning",
        "summary":"To develop a high throughput multi-label annotator for body Computed Tomography (CT) reports that can be applied to a variety of diseases, organs, and cases. First, we used a dictionary approach to develop a rule-based algorithm (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs\/pleura, liver\/gallbladder, kidneys\/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithm beyond pre-defined keywords, an attention-guided recurrent neural network (RNN) was trained using the RBA-extracted labels to classify the reports as being positive for one or more diseases or normal for each organ system. Confounding effects on model performance were evaluated using random or pre-trained embedding as well as different sizes of training datasets. Performance was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC) against 2,158 manually obtained labels. Our model extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random embedding across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with relatively small number of cases. Pre-trained Classification AUCs achieved > 0.95 for all five disease outcomes across all three organ systems. Our label-extracting pipeline was able to encompass a variety of cases and diseases by generalizing beyond strict rules with exceptional accuracy. As a framework, this model can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"[\"Vincent M. D'Anniballe\", 'Fakrul I. Tushar', 'Khrystyna Faryna', 'Songyue Han', 'Maciej A. Mazurowski', 'Geoffrey D. Rubin', 'Joseph Y. Lo']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.84,
        "actuality":4.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8889166667
    },
    {
        "arxiv_id":"2102.02995v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.02995v1",
        "title":"Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images",
        "summary":"In eDiscovery, it is critical to ensure that each page produced in legal proceedings conforms with the requirements of court or government agency production requests. Errors in productions could have severe consequences in a case, putting a party in an adverse position. The volume of pages produced continues to increase, and tremendous time and effort has been taken to ensure quality control of document productions. This has historically been a manual and laborious process. This paper demonstrates a novel automated production quality control application which leverages deep learning-based image recognition technology to extract Bates Number and Confidentiality Stamping from legal case production images and validate their correctness. Effectiveness of the method is verified with an experiment using a real-world production data.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Christian J. Mahoney', 'Katie Jensen', 'Fusheng Wei', 'Haozhen Zhao', 'Han Qin', 'Shi Ye']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.91,
        "actuality":3.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.3611666667
    },
    {
        "arxiv_id":"2102.03052v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03052v2",
        "title":"How Information Diffuse in a Nomination Network",
        "summary":"During the special period of the COVID-19 outbreak, this project investigated the driving factors in different information diffusion modes (i.e. broadcasting mode, contagion mode) based on the nomination relations in a social welfare campaign on Weibo. Specifically, we mapped a nomination social network and tracked the core communicators in both modes. Besides, we also observed the network from perspectives such as relationships between core communicators and modularity of the whole network. We extracted 6 homophily factors and tested them on 2 representative communities within the largest component of the network. We found that some core communicators distributed in a co-dependent way. At last, we supposed several explanations to the phenomenon which can be explored in further research.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Minghao Wang', 'Keyu Xu', 'Xiaohui Wang', 'Paolo Mengoni']",
        "arxiv_primary_category":"cs.si",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2102.03089v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03089v1",
        "title":"Leveraging Review Properties for Effective Recommendation",
        "summary":"Many state-of-the-art recommendation systems leverage explicit item reviews posted by users by considering their usefulness in representing the users' preferences and describing the items' attributes. These posted reviews may have various associated properties, such as their length, their age since they were posted, or their item rating. However, it remains unclear how these different review properties contribute to the usefulness of their corresponding reviews in addressing the recommendation task. In particular, users show distinct preferences when considering different aspects of the reviews (i.e. properties) for making decisions about the items. Hence, it is important to model the relationship between the reviews' properties and the usefulness of reviews while learning the users' preferences and the items' attributes. Therefore, we propose to model the reviews with their associated available properties. We introduce a novel review properties-based recommendation model (RPRM) that learns which review properties are more important than others in capturing the usefulness of reviews, thereby enhancing the recommendation results. Furthermore, inspired by the users' information adoption framework, we integrate two loss functions and a negative sampling strategy into our proposed RPRM model, to ensure that the properties of reviews are correlated with the users' preferences. We examine the effectiveness of RPRM using the well-known Yelp and Amazon datasets. Our results show that RPRM significantly outperforms a classical and five state-of-the-art baselines. Moreover, we experimentally show the advantages of using our proposed loss functions and negative sampling strategy, which further enhance the recommendation performances of RPRM.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Xi Wang', 'Iadh Ounis', 'Craig Macdonald']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":3.0,
        "controversy":1.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.97225
    },
    {
        "arxiv_id":"2102.03135v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03135v1",
        "title":"Graph Attention Collaborative Similarity Embedding for Recommender System",
        "summary":"We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our framework consists of two parts: the first part is to learn explicit graph collaborative filtering information such as user-item association through embedding propagation with attention mechanism, and the second part is to learn implicit graph collaborative information such as user-user similarities and item-item similarities through auxiliary loss. We design a new loss function that combines BPR loss with adaptive margin and similarity loss for the similarities learning. Extensive experiments on three benchmarks show that our model is consistently better than the latest state-of-the-art models.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Jinbo Song', 'Chao Chang', 'Fei Sun', 'Zhenyang Chen', 'Guoyong Hu', 'Peng Jiang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.84,
        "actuality":1.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.1388333333
    },
    {
        "arxiv_id":"2102.03179v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03179v1",
        "title":"The Plausibility Paradox for Resized Users in Virtual Environments",
        "summary":"This paper identifies and confirms a perceptual phenomenon: when users interact with simulated objects in a virtual environment where the users' scale deviates greatly from normal, there is a mismatch between the object physics they consider realistic and the object physics that would be correct at that scale. We report the findings of two studies investigating the relationship between perceived realism and a physically accurate approximation of reality in a virtual reality experience in which the user has been scaled by a factor of ten. Study 1 investigated perception of physics when scaled-down by a factor of ten, whereas Study 2 focused on enlargement by a similar amount. Studies were carried out as within-subjects experiments in which a total of 84 subjects performed simple interaction tasks with objects under two different physics simulation conditions. In the true physics condition, the objects, when dropped and thrown, behaved accurately according to the physics that would be correct at that either reduced or enlarged scale in the real world. In the movie physics condition, the objects behaved in a similar manner as they would if no scaling of the user had occurred. We found that a significant majority of the users considered the movie physics condition to be the more realistic one. However, at enlarged scale, many users considered true physics to match their expectations even if they ultimately believed movie physics to be the realistic condition. We argue that our findings have implications for many virtual reality and telepresence applications involving operation with simulated or physical objects in abnormal and especially small scales.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Matti Pouke', 'Katherine J. Mimnaugh', 'Alexis Chambers', 'Timo Ojala', 'Steven M. LaValle']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.92,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6111666667
    },
    {
        "arxiv_id":"2102.03258v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03258v1",
        "title":"Formational bounds of link prediction in collaboration networks",
        "summary":"Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this approach's success is capped by the proportion of links that are formed among existing nodes embedded in these properties. Consequentially, sustained ties as well as links that involve one or two new network participants are typically not predicted. The purpose of this study is to highlight formational constraints that need to be considered to increase the practical value of link prediction methods for collaboration networks. In this study, we identify the distribution of basic link formation types based on four large-scale, over-time collaboration networks, showing that current link predictors can maximally anticipate around 25% of links that involve at least one prior network member. This implies that for collaboration networks, increasing the accuracy of computational link prediction solutions may not be a reasonable goal when the ratio of collaboration ties that are eligible to the classic link prediction process is low.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Jinseok Kim', 'Jana Diesner']",
        "arxiv_primary_category":"cs.si",
        "readability":0.93,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.8334166667
    },
    {
        "arxiv_id":"2102.03265v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03265v1",
        "title":"Diversification in Session-based News Recommender Systems",
        "summary":"Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the active session. Neighborhood-based session-based recommenders has been shown to be highly effective compared to more sophisticated approaches. In this study we propose scenarios to make these session-based recommender systems diversity-aware and to address the filter bubble phenomenon. The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information. The results of applying the proposed scenarios show that these diversification scenarios improve the diversity measures in these session-based recommender systems based on four news datasets.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Alireza Gharahighehi', 'Celine Vens']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":5.0,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.9721666667
    },
    {
        "arxiv_id":"2102.03286v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03286v1",
        "title":"Stable Online Computation Offloading via Lyapunov-guided Deep Reinforcement Learning",
        "summary":"In this paper, we consider a multi-user mobile-edge computing (MEC) network with time-varying wireless channels and stochastic user task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability subject to the long-term data queue stability and average power constraints. The online algorithm is practical in the sense that the decisions for each time frame are made without the assumption of knowing future channel conditions and data arrivals. We formulate the problem as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem that jointly determines the binary offloading (each user computes the task either locally or at the edge server) and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework, named LyDROO, that combines the advantages of Lyapunov optimization and deep reinforcement learning (DRL). Specifically, LyDROO first applies Lyapunov optimization to decouple the multi-stage stochastic MINLP into deterministic per-frame MINLP subproblems of much smaller size. Then, it integrates model-based optimization and model-free DRL to solve the per-frame MINLP problems with very low computational complexity. Simulation results show that the proposed LyDROO achieves optimal computation performance while satisfying all the long-term constraints. Besides, it induces very low execution latency that is particularly suitable for real-time implementation in fast fading environments.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Suzhi Bi', 'Liang Huang', 'Hui Wang', 'Ying-Jun Angela Zhang']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.77,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2102.03290v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03290v1",
        "title":"Openslice: An opensource OSS for Delivering Network Slice as a Service",
        "summary":"As the 5G standards mature and awareness of the capabilities of the technology increases, industry verticals are becoming more eager to test new services and develop them to the level of maturity required for market adoption. Network slicing, i.e. multiple virtual networks running on a common infrastructure, is considered a key mechanism to serve the multitude of tenants (e.g. vertical industries) targeted by forthcoming fifth generation (5G) systems, in a flexible and cost-efficient manner. It is predicted that one of the most popular models for customers will be the Network Slice as a Service (NSaaS) model. This model allows a Network Service Customer to order and configure a Network Slice and offered it as a service. This work presents Openlice a Service based, opens-ource OSS for delivering NSaaS following emerging standards from SDOs. We strongly believe that such open source solutions make it easier for organizations to enable complex scenarios especially in the are of Non-Public Networks.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Christos Tranoris']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.92,
        "actuality":4.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2102.03419v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03419v1",
        "title":"Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs",
        "summary":"Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods. These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time. In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this few-shot setting. We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance. Moreover, experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information and only allows for exploiting the coarse-grained positional information of entities. Together, our findings challenge the implicit assumptions and inductive biases of prior work and highlight new directions for research in this area.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Dora Jambor', 'Komal Teru', 'Joelle Pineau', 'William L. Hamilton']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.89,
        "actuality":1.6666666667,
        "controversy":2.0,
        "relevance_magnitude":2.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":1.9721666667
    },
    {
        "arxiv_id":"2102.03444v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03444v1",
        "title":"Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets",
        "summary":"Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. We present a scalable pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. Only a single, dimensionless, a-priori determinable parameter is required. By careful engineering of individual pipeline stages and a novel iterative refinement scheme we are, for the first time, able to analyze the topology of volumes of roughly 1TB on commodity hardware. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen (https:\/\/www.uni-muenster.de\/Voreen\/).",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Dominik Drees', 'Aaron Scherzinger', 'Ren\u00e9 H\u00e4gerling', 'Friedemann Kiefer', 'Xiaoyi Jiang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.82,
        "actuality":3.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.111
    },
    {
        "arxiv_id":"2102.03483v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03483v1",
        "title":"Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles",
        "summary":"Testing and evaluation is a crucial step in the development and deployment of Connected and Automated Vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it is of necessity to test the CAVs in safety-critical scenarios, which rarely happen in naturalistic driving environment. Therefore, how to purposely and systematically generate these corner cases becomes an important problem. Most existing studies focus on generating adversarial examples for perception systems of CAVs, whereas limited efforts have been put on the decision-making systems, which is the highlight of this paper. As the CAVs need to interact with numerous background vehicles (BVs) for a long duration, variables that define the corner cases are usually high dimensional, which makes the generation a challenging problem. In this paper, a unified framework is proposed to generate corner cases for the decision-making systems. To address the challenge brought by high dimensionality, the driving environment is formulated based on Markov Decision Process, and the deep reinforcement learning techniques are applied to learn the behavior policy of BVs. With the learned policy, BVs will behave and interact with the CAVs more aggressively, resulting in more corner cases. To further analyze the generated corner cases, the techniques of feature extraction and clustering are utilized. By selecting representative cases of each cluster and outliers, the valuable corner cases can be identified from all generated corner cases. Simulation results of a highway driving environment show that the proposed methods can effectively generate and identify the valuable corner cases.",
        "published":"2021-02-06T00:00:00.000Z",
        "authors":"['Haowei Sun', 'Shuo Feng', 'Xintao Yan', 'Henry X. Liu']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.88,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2102.03577v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03577v1",
        "title":"Drug Package Recommendation via Interaction-aware Graph Induction",
        "summary":"Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collaborative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance.",
        "published":"2021-02-06T00:00:00.000Z",
        "authors":"['Zhi Zheng', 'Chao Wang', 'Tong Xu', 'Dazhong Shen', 'Penggang Qin', 'Baoxing Huai', 'Tongzhu Liu', 'Enhong Chen']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":4.3333333333,
        "controversy":3.0,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.1943333333
    },
    {
        "arxiv_id":"2102.03640v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03640v1",
        "title":"ORCA: Enabling an Owner-centric and Data-driven Management Paradigm for Future Heterogeneous Edge-IoT Systems",
        "summary":"Integrating Internet of Things (IoT) and edge computing for \"Edge-IoT\" systems, converged with machine intelligence, has the potentials of enabling a wide range of applications in smart homes, factories and cities. Edge-IoT can connect many diverse devices and the IoT asset owners can run heterogeneous IoT systems supported by various vendors or service providers (SPs), using either cloud or local edge computing (or both) for resource assistance. The existing methods typically manage the systems as separate vertical \"silos\", or in a vendor\/SP-centric way, which suffers from significant challenges. In this paper, we present a novel owner-centric management paradigm named \"ORCA\" to address the gaps left by the owner-centric paradigm and empower the IoT assets owners to effectively identify and mitigate potential issues in their own network premises, regardless the vendors\/SPs' situations. ORCA aims to be scalable and extensible in assisting IoT owners to perform intelligent management through a behavior-oriented and data-driven approach. ORCA is an ongoing project and the preliminary results indicate that it can significantly empower the IoT systems owners to better manage their IoT assets.",
        "published":"2021-02-06T00:00:00.000Z",
        "authors":"['Jianli Pan', 'Jianyu Wang', 'Ismail AlQerm', 'Yuanni Liu', 'Zhicheng Yang']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.85,
        "actuality":4.0,
        "controversy":4.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2102.03673v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03673v1",
        "title":"Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection",
        "summary":"Automated systems that detect deception in high-stakes situations can enhance societal well-being across medical, social work, and legal domains. Existing models for detecting high-stakes deception in videos have been supervised, but labeled datasets to train models can rarely be collected for most real-world applications. To address this problem, we propose the first multimodal unsupervised transfer learning approach that detects real-world, high-stakes deception in videos without using high-stakes labels. Our subspace-alignment (SA) approach adapts audio-visual representations of deception in lab-controlled low-stakes scenarios to detect deception in real-world, high-stakes situations. Our best unsupervised SA models outperform models without SA, outperform human ability, and perform comparably to a number of existing supervised models. Our research demonstrates the potential for introducing subspace-based transfer learning to model high-stakes deception and other social behaviors in real-world contexts with a scarcity of labeled behavioral data.",
        "published":"2021-02-06T00:00:00.000Z",
        "authors":"['Leena Mathur', 'Maja J Matari\u0107']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.7221666667
    },
    {
        "arxiv_id":"2102.03747v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03747v1",
        "title":"DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds",
        "summary":"For current object detectors, the scale of the receptive field of feature extraction operators usually increases layer by layer. Those operators are called scale-oriented operators in this paper, such as the convolution layer in CNN, and the set abstraction layer in PointNet++. The scale-oriented operators are appropriate for 2D images with multi-scale objects, but not natural for 3D point clouds with multi-density but scale-invariant objects. In this paper, we put forward a novel density-oriented PointNet (DPointNet) for 3D object detection in point clouds, in which the density of points increases layer by layer. In experiments for object detection, the DPointNet is applied to PointRCNN, and the results show that the model with the new operator can achieve better performance and higher speed than the baseline PointRCNN, which verify the effectiveness of the proposed DPointNet.",
        "published":"2021-02-07T00:00:00.000Z",
        "authors":"['Jie Li', 'Yu Hu']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.85,
        "actuality":2.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6111666667
    },
    {
        "arxiv_id":"2102.03787v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03787v1",
        "title":"Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network",
        "summary":"Recently, real-world recommendation systems need to deal with millions of candidates. It is extremely challenging to conduct sophisticated end-to-end algorithms on the entire corpus due to the tremendous computation costs. Therefore, conventional recommendation systems usually contain two modules. The matching module focuses on the coverage, which aims to efficiently retrieve hundreds of items from large corpora, while the ranking module generates specific ranks for these items. Recommendation diversity is an essential factor that impacts user experience. Most efforts have explored recommendation diversity in ranking, while the matching module should take more responsibility for diversity. In this paper, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity. Specifically, GraphDR builds a huge heterogeneous preference network to record different types of user preferences, and conduct a field-level heterogeneous graph attention network for node aggregation. We also innovatively conduct a neighbor-similarity based loss to balance both recommendation accuracy and diversity for the diversified matching task. In experiments, we conduct extensive online and offline evaluations on a real-world recommendation system with various accuracy and diversity metrics and achieve significant improvements. We also conduct model analyses and case study for a better understanding of our model. Moreover, GraphDR has been deployed on a well-known recommendation system, which affects millions of users. The source code will be released.",
        "published":"2021-02-07T00:00:00.000Z",
        "authors":"['Ruobing Xie', 'Qi Liu', 'Shukai Liu', 'Ziwei Zhang', 'Peng Cui', 'Bo Zhang', 'Leyu Lin']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2102.03889v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03889v1",
        "title":"Machine Learning Methods for Histopathological Image Analysis: A Review",
        "summary":"Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is not only time and resource consuming, but also very challenging even for experienced pathologists, resulting in inter- and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. In this paper, we present a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. In addition, we present a list of publicly available and private datasets that have been used in HI research.",
        "published":"2021-02-07T00:00:00.000Z",
        "authors":"['Jonathan de Matos', 'Steve Tsham Mpinda Ataky', 'Alceu de Souza Britto Jr.', 'Luiz Eduardo Soares de Oliveira', 'Alessandro Lameiras Koerich']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.89,
        "actuality":4.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2102.03923v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03923v1",
        "title":"DroneTrap: Drone Catching in Midair by Soft Robotic Hand with Color-Based Force Detection and Hand Gesture Recognition",
        "summary":"The paper proposes a novel concept of docking drones to make this process as safe and fast as possible. The idea behind the project is that a robot with the gripper grasps the drone in midair. The human operator navigates the robotic arm with the ML-based gesture recognition interface. The 3-finger robot hand with soft fingers and integrated touch-sensors is pneumatically actuated. This allows achieving safety while catching to not destroying the drone's mechanical structure, fragile propellers, and motors. Additionally, the soft hand has a unique technology of providing force information through the color of the fingers to the remote computer vision (CV) system. In this case, not only the control system can understand the force applied but also the human operator. The operator has full control of robot motion and task execution without additional programming by wearing a mocap glove with gesture recognition, which was developed and applied for the high-level control of DroneTrap. The experimental results revealed that the developed color-based force estimation can be applied for rigid object capturing with high precision (95.3\\%). The proposed technology can potentially revolutionize the landing and deployment of drones for parcel delivery on uneven ground, structure inspections, risque operations, etc.",
        "published":"2021-02-07T00:00:00.000Z",
        "authors":"['Aleksey Fedoseev', 'Valerii Serpiva', 'Ekaterina Karmanova', 'Miguel Altamirano Cabrera', 'Vladimir Shirokun', 'Iakov Vasilev', 'Stanislav Savushkin', 'Dzmitry Tsetserukou']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.86,
        "actuality":4.0,
        "controversy":4.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.7778333333
    },
    {
        "arxiv_id":"2102.03994v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.03994v1",
        "title":"Observers Pupillary Responses in Recognising Real and Posed Smiles: A Preliminary Study",
        "summary":"Pupillary responses (PR) change differently for different types of stimuli. This study aims to check whether observers PR can recognise real and posed smiles from a set of smile images and videos. We showed the smile images and smile videos stimuli to observers, and recorded their pupillary responses considering four different situations, namely paired videos, paired images, single videos, and single images. When the same smiler was viewed by observers in both real and posed smile forms, we refer them as paired; otherwise we use the term single. The primary analysis on pupil data revealed that the differences of pupillary response between real and posed smiles are more significant in case of paired videos compared to others. This result is found from timeline analysis, KS-test, and ANOVA test. Overall, our model can recognise real and posed smiles from observers pupillary responses instead of smilers responses. Our research will be applicable in affective computing and computer-human interaction for measuring emotional authenticity.",
        "published":"2021-02-08T00:00:00.000Z",
        "authors":"['Ruiqi Chen', 'Atiqul Islam', 'Tom Gedeon', 'Md Zakir Hossain']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.1389166667
    },
    {
        "arxiv_id":"2102.04201v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04201v2",
        "title":"Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems",
        "summary":"This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM.",
        "published":"2021-01-26T00:00:00.000Z",
        "authors":"['Jennifer Cobbe', 'Michelle Seng Ah Lee', 'Jatinder Singh']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.89,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.47225
    },
    {
        "arxiv_id":"2102.04205v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04205v2",
        "title":"How Pandemic Spread in News: Text Analysis Using Topic Model",
        "summary":"Researches about COVID-19 has increased largely, no matter in the biology field or the others. This research conducted a text analysis using LDA topic model. We firstly scraped totally 1127 articles and 5563 comments on SCMP covering COVID-19 from Jan 20 to May 19, then we trained the LDA model and tuned parameters based on the Cv coherence as the model evaluation method. With the optimal model, dominant topics, representative documents of each topic and the inconsistence between articles and comments are analyzed. 3 possible improvements are discussed at last.",
        "published":"2021-02-05T00:00:00.000Z",
        "authors":"['Minghao Wang', 'Paolo Mengoni']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":4.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1945
    },
    {
        "arxiv_id":"2102.04212v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04212v1",
        "title":"Understanding health and behavioral trends of successful students through machine learning models",
        "summary":"This study analyzes patterns of physical, mental, lifestyle, and personality factors in college students in different periods over the course of a semester and models their relationships with students' academic performance. The data analyzed was collected through smartphones and Fitbit. The use of machine learning models derived from the gathered data was employed to observe the extent of students' behavior associated with their GPA, lifestyle, physical health, mental health, and personality attributes. A mutual agreement method was used in which rather than looking at the accuracy of results, the model parameters and weights of features were used to find common behavioral trends. From the results of the model creation, it was determined that the most significant indicator of academic success defined as a higher GPA, was the places a student spent their time. Lifestyle and personality factors were deemed more significant than mental and physical factors. This study will provide insight into the impact of different factors and the timing of those factors on students' academic performance.",
        "published":"2021-01-23T00:00:00.000Z",
        "authors":"['Abigale Kim', 'Fateme Nikseresht', 'Janine M. Dutcher', 'Michael Tumminia', 'Daniella Villalba', 'Sheldon Cohen', 'Kasey Creswel', 'David Creswell', 'Anind K. Dey', 'Jennifer Mankoff', 'Afsaneh Doryab']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.95,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2102.04216v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04216v1",
        "title":"Social determinants of health in the era of artificial intelligence with electronic health records: A systematic review",
        "summary":"There is growing evidence showing the significant role of social determinant of health (SDOH) on a wide variety of health outcomes. In the era of artificial intelligence (AI), electronic health records (EHRs) have been widely used to conduct observational studies. However, how to make the best of SDOH information from EHRs is yet to be studied. In this paper, we systematically reviewed recently published papers and provided a methodology review of AI methods using the SDOH information in EHR data. A total of 1250 articles were retrieved from the literature between 2010 and 2020, and 74 papers were included in this review after abstract and full-text screening. We summarized these papers in terms of general characteristics (including publication years, venues, countries etc.), SDOH types, disease areas, study outcomes, AI methods to extract SDOH from EHRs and AI methods using SDOH for healthcare outcomes. Finally, we conclude this paper with discussion on the current trends, challenges, and future directions on using SDOH from EHRs.",
        "published":"2021-01-22T00:00:00.000Z",
        "authors":"['Anusha Bompelli', 'Yanshan Wang', 'Ruyuan Wan', 'Esha Singh', 'Yuqi Zhou', 'Lin Xu', 'David Oniani', 'Bhavani Singh Agnikula Kshatriya', 'Joyce', 'E. Balls-Berry', 'Rui Zhang']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.88,
        "actuality":4.0,
        "controversy":4.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6945
    },
    {
        "arxiv_id":"2102.04222v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04222v1",
        "title":"Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media",
        "summary":"This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP methods of tourism. The fuzzy-AHP process will rank tourism trends based on data from social media. Social media is one of the channels with the largest source of data input in determining tourism development. The development uses social media interactions based on the facilities visited, including reviews, stories, likes, forums, blogs, and feedback. This experiment aims to prioritize facilities that are the trend of tourism. The priority ranking uses weight criteria and the ranking process. The highest rank is in the attractions of the Park\/Picnic Area, with the final weight calculation value of 0.6361. Fuzzy-AHP can rank optimally with an MSE value of \\approx 0.0002.",
        "published":"2021-02-08T00:00:00.000Z",
        "authors":"['Shoffan Saifullah']",
        "arxiv_primary_category":"cs.si",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.6943333333
    },
    {
        "arxiv_id":"2102.04250v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04250v1",
        "title":"Riiid! Answer Correctness Prediction Kaggle Challenge: 4th Place Solution Summary",
        "summary":"This paper presents my solution to the challenge \"Riiid! Answer Correctness Prediction\" on Kaggle hosted by Riiid Labs (2020), which scores 0.817 (AUC) and ranks 4th on the final private leaderboard. It is a single transformer-based model heavily inspired from previous works such as SAKT, SAINT and SAINT+. Novel ingredients that I believed to have made a difference are the time-aware attention mechanism, the concatenation of the embeddings of the input sequences and the embedding of continuous features.",
        "published":"2021-02-03T00:00:00.000Z",
        "authors":"['Duc Kinh Le Tran']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.84,
        "actuality":2.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":1.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.2778333333
    },
    {
        "arxiv_id":"2102.04255v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04255v1",
        "title":"AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks",
        "summary":"Despite interest in communicating ethical problems and social contexts within the undergraduate curriculum to advance Public Interest Technology (PIT) goals, interventions at the graduate level remain largely unexplored. This may be due to the conflicting ways through which distinct Artificial Intelligence (AI) research tracks conceive of their interface with social contexts. In this paper we track the historical emergence of sociotechnical inquiry in three distinct subfields of AI research: AI Safety, Fair Machine Learning (Fair ML) and Human-in-the-Loop (HIL) Autonomy. We show that for each subfield, perceptions of PIT stem from the particular dangers faced by past integration of technical systems within a normative social order. We further interrogate how these histories dictate the response of each subfield to conceptual traps, as defined in the Science and Technology Studies literature. Finally, through a comparative analysis of these currently siloed fields, we present a roadmap for a unified approach to sociotechnical graduate pedagogy in AI.",
        "published":"2021-02-04T00:00:00.000Z",
        "authors":"['McKane Andrus', 'Sarah Dean', 'Thomas Krendl Gilbert', 'Nathan Lambert', 'Tom Zick']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.85,
        "actuality":4.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2102.04427v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04427v2",
        "title":"RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization",
        "summary":"With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments. However, most automated systems -- when detecting and moderating toxic language -- do not provide feedback to their users, let alone provide an avenue of recourse for these users to make actionable changes. We present our work, RECAST, an interactive, open-sourced web tool for visualizing these models' toxic predictions, while providing alternative suggestions for flagged toxic language. Our work also provides users with a new path of recourse when using these automated moderation tools. RECAST highlights text responsible for classifying toxicity, and allows users to interactively substitute potentially toxic phrases with neutral alternatives. We examined the effect of RECAST via two large-scale user evaluations, and found that RECAST was highly effective at helping users reduce toxicity as detected through the model. Users also gained a stronger understanding of the underlying toxicity criterion used by black-box models, enabling transparency and recourse. In addition, we found that when users focus on optimizing language for these models instead of their own judgement (which is the implied incentive and goal of deploying automated models), these models cease to be effective classifiers of toxicity compared to human annotations. This opens a discussion for how toxicity detection models work and should work, and their effect on the future of online discourse.",
        "published":"2021-02-08T00:00:00.000Z",
        "authors":"['Austin P Wright', 'Omar Shaikh', 'Haekyu Park', 'Will Epperson', 'Muhammed Ahmed', 'Stephane Pinel', 'Duen Horng Chau', 'Diyi Yang']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.3055833333
    },
    {
        "arxiv_id":"2102.04512v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04512v2",
        "title":"comparing card-based vaccine credential systems with app-based vaccine credential systems",
        "summary":"In this early draft, we compare and contrast the technicalities present in the implementation of a card-based vaccine credential system and an app-based vaccine credentials system. We have chosen the domains of symptom reporting, fraud and impersonation, feasibility, scalability, equity, future data aggregation, importability of health data, and operability to explore each system's features and drawbacks.",
        "published":"2021-02-08T00:00:00.000Z",
        "authors":"['Aryan Mahindra', 'Chandan CV', 'Priyanshi Katiyar', 'Anshuman Sharma', 'Sheshank Shankar', 'Rohan Sukumaran', 'Saurish Srivastava', 'Armaan Bhojwani', 'Rohan Iyer', 'Ishaan Singh', 'Ramesh Raskar']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.83,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.22225
    },
    {
        "arxiv_id":"2102.04621v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04621v1",
        "title":"TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition",
        "summary":"Gait, i.e., the movement pattern of human limbs during locomotion, is a promising biometric for the identification of persons. Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset. Due to the domain shift and class gap, directly applying a model trained on one source dataset to other target datasets usually obtains very poor results. Therefore, this paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. To learn effective prior knowledge for gait representation, we first adopt a backbone network pre-trained on the labeled source data in a supervised manner. Then we design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. During training, the class consistency indicator is adopted to select confident neighborhoods of samples based on their entropy measurements. Moreover, we explore a high-entropy-first neighbor selection strategy, which can effectively transfer prior knowledge to the target domain. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.",
        "published":"2021-02-09T00:00:00.000Z",
        "authors":"['Jinkai Zheng', 'Xinchen Liu', 'Chenggang Yan', 'Jiyong Zhang', 'Wu Liu', 'Xiaoping Zhang', 'Tao Mei']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.85,
        "actuality":3.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4999166667
    },
    {
        "arxiv_id":"2102.04627v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04627v1",
        "title":"SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction",
        "summary":"False information and true information fact checking it, often co-exist in social networks, each competing to influence people in their spread paths. An efficient strategy here to contain false information is to proactively identify if nodes in the spread path are likely to endorse false information (i.e. further spread it) or refutation information (thereby help contain false information spreading). In this paper, we propose SCARLET (truSt and Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict likely action of nodes in the spread path. We aggregate trust and credibility features from a node's neighborhood using historical behavioral data and network structure and explain how features of a spreader's neighborhood vary. Using real world Twitter datasets, we show that the model is able to predict false information spreaders with an accuracy of over 87%.",
        "published":"2021-02-09T00:00:00.000Z",
        "authors":"['Bhavtosh Rath', 'Xavier Morales', 'Jaideep Srivastava']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":5.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.3055833333
    },
    {
        "arxiv_id":"2102.04656v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04656v1",
        "title":"Large-Scale Visual Search with Binary Distributed Graph at Alibaba",
        "summary":"Graph-based approximate nearest neighbor search has attracted more and more attentions due to its online search advantages. Numbers of methods studying the enhancement of speed and recall have been put forward. However, few of them focus on the efficiency and scale of offline graph-construction. For a deployed visual search system with several billions of online images in total, building a billion-scale offline graph in hours is essential, which is almost unachievable by most existing methods. In this paper, we propose a novel algorithm called Binary Distributed Graph to solve this problem. Specifically, we combine binary codes with graph structure to speedup online and offline procedures, and achieve comparable performance with the ones in real-value based scenarios by recalling more binary candidates. Furthermore, the graph-construction is optimized to completely distributed implementation, which significantly accelerates the offline process and gets rid of the limitation of memory and disk within a single machine. Experimental comparisons on Alibaba Commodity Data Set (more than three billion images) show that the proposed method outperforms the state-of-the-art with respect to the online\/offline trade-off.",
        "published":"2021-02-09T00:00:00.000Z",
        "authors":"['Kang Zhao', 'Pan Pan', 'Yun Zheng', 'Yanhao Zhang', 'Changxu Wang', 'Yingya Zhang', 'Yinghui Xu', 'Rong Jin']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":2.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.47225
    },
    {
        "arxiv_id":"2102.04808v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04808v1",
        "title":"Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier",
        "summary":"Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power histogramming (LPH) descriptor, in which appliance power signals are transformed into 2D space and short histograms are extracted to represent each device. Specifically, short local histograms are drawn to represent individual appliance consumption signatures and robustly extract appliance-level data from the aggregated power signal. Furthermore, an improved k-nearest neighbors (IKNN) algorithm is presented to reduce the learning computation time and improve the classification performance. This results in highly improving the discrimination ability between appliances belonging to distinct categories. A deep evaluation of the proposed LPH-IKNN based solution is investigated under different data sets, in which the proposed scheme leads to promising performance. An accuracy of up to 99.65% and 98.51% has been achieved on GREEND and UK-DALE data sets, respectively. While an accuracy of more than 96% has been attained on both WHITED and PLAID data sets. This proves the validity of using 2D descriptors to accurately identify appliances and create new perspectives for the NILM problem.",
        "published":"2021-02-09T00:00:00.000Z",
        "authors":"['Yassine Himeur', 'Abdullah Alsalemi', 'Faycal Bensaali', 'Abbes Amira']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.84,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.22225
    },
    {
        "arxiv_id":"2102.04969v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.04969v1",
        "title":"Semantic Borrowing for Generalized Zero-Shot Learning",
        "summary":"Generalized zero-shot learning (GZSL) is one of the most realistic problems, but also one of the most challenging problems due to the partiality of the classifier to supervised classes. Instance-borrowing methods and synthesizing methods solve this problem to some extent with the help of testing semantics, but therefore neither can be used under the class-inductive instance-inductive (CIII) training setting where testing data are not available, and the latter require the training process of a classifier after generating examples. In contrast, a novel method called Semantic Borrowing for improving GZSL methods with compatibility metric learning under CIII is proposed in this paper. It borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unseen or unknown classes would not be available for training while this approach does NOT need any information of semantics of unseen or unknown classes. The experimental results on representative GZSL benchmark datasets show that it can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification.",
        "published":"2021-01-30T00:00:00.000Z",
        "authors":"['Xiao-wei Chen']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.84,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.2221666667
    },
    {
        "arxiv_id":"2102.05176v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05176v2",
        "title":"Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network",
        "summary":"MetaDL Challenge 2020 focused on image classification tasks in few-shot settings. This paper describes second best submission in the competition. Our meta learning approach modifies the distribution of classes in a latent space produced by a backbone network for each class in order to better follow the Gaussian distribution. After this operation which we call Latent Space Transform algorithm, centers of classes are further aligned in an iterative fashion of the Expectation Maximisation algorithm to utilize information in unlabeled data that are often provided on top of few labelled instances. For this task, we utilize optimal transport mapping using the Sinkhorn algorithm. Our experiments show that this approach outperforms previous works as well as other variants of the algorithm, using K-Nearest Neighbour algorithm, Gaussian Mixture Models, etc.",
        "published":"2021-02-09T00:00:00.000Z",
        "authors":"['Tom\u00e1\u0161 Chobola', 'Daniel Va\u0161ata', 'Pavel Kord\u00edk']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.83,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2102.05212v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05212v1",
        "title":"Polarimetric Monocular Dense Mapping Using Relative Deep Depth Prior",
        "summary":"This paper is concerned with polarimetric dense map reconstruction based on a polarization camera with the help of relative depth information as a prior. In general, polarization imaging is able to reveal information about surface normal such as azimuth and zenith angles, which can support the development of solutions to the problem of dense reconstruction, especially in texture-poor regions. However, polarimetric shape cues are ambiguous due to two types of polarized reflection (specular\/diffuse). Although methods have been proposed to address this issue, they either are offline and therefore not practical in robotics applications, or use incomplete polarimetric cues, leading to sub-optimal performance. In this paper, we propose an online reconstruction method that uses full polarimetric cues available from the polarization camera. With our online method, we can propagate sparse depth values both along and perpendicular to iso-depth contours. Through comprehensive experiments on challenging image sequences, we demonstrate that our method is able to significantly improve the accuracy of the depthmap as well as increase its density, specially in regions of poor texture.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Moein Shakeri', 'Shing Yan Loo', 'Hong Zhang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.85,
        "actuality":1.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.0278333333
    },
    {
        "arxiv_id":"2102.05260v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05260v1",
        "title":"SensPick: Sense Picking for Word Sense Disambiguation",
        "summary":"Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical resources. In this study, we utilize both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task. The experimental evaluation demonstrates that SensPick outperforms traditional and state-of-the-art models on most of the benchmark datasets with a relative improvement of 3.5% in F-1 score. While the improvement is not significant, incorporating semantic relationships brings SensPick in the leading position compared to others.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Sm Zobaed', 'Md Enamul Haque', 'Md Fazle Rabby', 'Mohsen Amini Salehi']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.83,
        "actuality":2.3333333333,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.2221666667
    },
    {
        "arxiv_id":"2102.05284v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05284v1",
        "title":"Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case",
        "summary":"We make significant progress toward the stochastic shortest path problem with adversarial costs and unknown transition. Specifically, we develop algorithms that achieve $\\widetilde{O}(\\sqrt{S^2ADT_\\star K})$ regret for the full-information setting and $\\widetilde{O}(\\sqrt{S^3A^2DT_\\star K})$ regret for the bandit feedback setting, where $D$ is the diameter, $T_\\star$ is the expected hitting time of the optimal policy, $S$ is the number of states, $A$ is the number of actions, and $K$ is the number of episodes. Our work strictly improves (Rosenberg and Mansour, 2020) in the full information setting, extends (Chen et al., 2020) from known transition to unknown transition, and is also the first to consider the most challenging combination: bandit feedback with adversarial costs and unknown transition. To remedy the gap between our upper bounds and the current best lower bounds constructed via a stochastically oblivious adversary, we also propose algorithms with near-optimal regret for this special case.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Liyu Chen', 'Haipeng Luo']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6111666667
    },
    {
        "arxiv_id":"2102.05382v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05382v2",
        "title":"Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments",
        "summary":"This paper presents a data-driven decentralized trajectory optimization approach for multi-robot motion planning in dynamic environments. When navigating in a shared space, each robot needs accurate motion predictions of neighboring robots to achieve predictive collision avoidance. These motion predictions can be obtained among robots by sharing their future planned trajectories with each other via communication. However, such communication may not be available nor reliable in practice. In this paper, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner. The learned model can run efficiently online for each robot and provide interaction-aware trajectory predictions of its neighbors based on observations of their history states. We then incorporate the trajectory prediction model into a decentralized model predictive control (MPC) framework for multi-robot collision avoidance. Simulation results show that our decentralized approach can achieve a comparable level of performance to a centralized planner while being communication-free and scalable to a large number of robots. We also validate our approach with a team of quadrotors in real-world experiments.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Hai Zhu', 'Francisco Martinez Claramunt', 'Bruno Brito', 'Javier Alonso-Mora']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.85,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2102.05444v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05444v3",
        "title":"Information Extraction From Co-Occurring Similar Entities",
        "summary":"Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or DBpedia are, however, far from being complete. In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. In contrast to existing approaches, we do not focus on relationships within a listing (e.g., between two entities in a table row) but on the relationship between a listing's subject entities and the context of the listing. To that end, we propose a descriptive rule mining approach that uses distant supervision to derive rules for these relationships based on a listing's context. Extracted from a suitable data corpus, the rules can be used to extend a knowledge graph with novel entities and assertions. In our experiments we demonstrate that the approach is able to extract up to 3M novel entities and 30M additional assertions from listings in Wikipedia. We find that the extracted information is of high quality and thus suitable to extend Wikipedia-based knowledge graphs like DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an increase of covered entities by roughly 50%.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Nicolas Heist', 'Heiko Paulheim']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.87,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.9445
    },
    {
        "arxiv_id":"2102.05460v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05460v2",
        "title":"The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions",
        "summary":"The explanation dimension of Artificial Intelligence (AI) based system has been a hot topic for the past years. Different communities have raised concerns about the increasing presence of AI in people's everyday tasks and how it can affect people's lives. There is a lot of research addressing the interpretability and transparency concepts of explainable AI (XAI), which are usually related to algorithms and Machine Learning (ML) models. But in decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system. Decision-makers usually need to justify their decision to others in different domains. If that decision is somehow based on or influenced by an AI-system outcome, the explanation about how the AI reached that result is key to building trust between AI and humans in decision-making scenarios. In this position paper, we discuss the role of XAI in decision-making scenarios, our vision of Decision-Making with AI-system in the loop, and explore one case from the literature about how XAI can impact people justifying their decisions, considering the importance of building the human-AI relationship for those scenarios.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Juliana Jansen Ferreira', 'Mateus Monteiro']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.94,
        "actuality":4.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2102.05466v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05466v1",
        "title":"Belief Space Planning for Mobile Robots with Range Sensors using iLQG",
        "summary":"In this work, we use iterative Linear Quadratic Gaussian (iLQG) to plan motions for a mobile robot with range sensors in belief space. We address two limitations that prevent applications of iLQG to the considered robotic system. First, iLQG assumes a differentiable measurement model, which is not true for range sensors. We show that iLQG only requires the differentiability of the belief dynamics. We propose to use a derivative-free filter to approximate the belief dynamics, which does not require explicit differentiability of the measurement model. Second, informative measurements from a range sensor are sparse. Uninformative measurements produce trivial gradient information, which prevent iLQG optimization from converging to a local minimum. We densify the informative measurements by introducing additional parameters in the measurement model. The parameters are iteratively updated in the optimization to ensure convergence to the true measurement model of a range sensor. We show the effectiveness of the proposed modifications through an ablation study. We also apply the proposed method in simulations of large scale real world environments, which show superior performance comparing to the state-of-the-art methods that either assume the separation principle or maximum likelihood measurements.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Ke Sun', 'Vijay Kumar']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.8,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2102.05633v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05633v1",
        "title":"PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments",
        "summary":"In order for a robot to explore an unknown environment autonomously, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires learning values and constructing policies over a belief space, i.e., probability distribution of the robot-world state. Value learning over belief spaces suffer from computational challenges in high-dimensional spaces, such as large spatial environments and long temporal horizons for exploration. At the same time, it should be adaptive and resilient to disturbances at run time in order to ensure the robot's safety, as required in many real-world applications. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. By leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM can address large-scale exploration problems while providing locally near-optimal coverage plans. PLGRIM is a step toward enabling belief space planners on physical robots operating in unknown and complex environments. We validate our proposed framework with a high-fidelity dynamic simulation in diverse environments and with physical hardware, Boston Dynamics' Spot robot, in a lava tube.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Sung-Kyun Kim', 'Amanda Bouman', 'Gautam Salhotra', 'David D. Fan', 'Kyohei Otsu', 'Joel Burdick', 'Ali-akbar Agha-mohammadi']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.5276666667
    },
    {
        "arxiv_id":"2102.05638v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2102.05638v1",
        "title":"Generating Synthetic Text Data to Evaluate Causal Inference Methods",
        "summary":"Drawing causal conclusions from observational data requires making assumptions about the true data-generating process. Causal inference research typically considers low-dimensional data, such as categorical or numerical fields in structured medical records. High-dimensional and unstructured data such as natural language complicates the evaluation of causal inference methods; such evaluations rely on synthetic datasets with known causal effects. Models for natural language generation have been widely studied and perform well empirically. However, existing methods not immediately applicable to producing synthetic datasets for causal evaluations, as they do not allow for quantifying a causal effect on the text itself. In this work, we develop a framework for adapting existing generation models to produce synthetic text datasets with known causal effects. We use this framework to perform an empirical comparison of four recently-proposed methods for estimating causal effects from text data. We release our code and synthetic datasets.",
        "published":"2021-02-10T00:00:00.000Z",
        "authors":"['Zach Wood-Doughty', 'Ilya Shpitser', 'Mark Dredze']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2108.00355v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.00355v1",
        "title":"ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description",
        "summary":"Autonomous systems need to understand the semantics and geometry of their surroundings in order to comprehend and safely execute object-level task specifications. This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations. The model is expressive because it captures the identities, positions, orientations, and shapes of objects in the environment. It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps. Different from other works that rely on a single object representation format, our approach has a bi-level object model that captures both the coarse level scale as well as the fine level shape details. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.",
        "published":"2021-08-01 03:07:31+00:00",
        "authors":"['Mo Shan', 'Qiaojun Feng', 'You-Yi Jau', 'Nikolay Atanasov']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":5.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.7499166667
    },
    {
        "arxiv_id":"2108.00505v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.00505v1",
        "title":"DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways",
        "summary":"Vehicle trajectory prediction is an essential task for enabling many intelligent transportation systems. While there have been some promising advances in the field, there is a need for new agile algorithms with smaller model sizes and lower computational requirements. This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction in highways. In contrast to previous methods, the vehicle dynamics are encoded using Agile Temporal Convolutional Networks (ATCNs) to provide more robust time prediction with less computation. ATCN also uses depthwise convolution, which reduces the complexity of models compared to existing approaches in terms of model size and operations. Overall, our experimental results demonstrate that DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity, making it more suitable for real-world deployment.",
        "published":"2021-08-01 17:33:04+00:00",
        "authors":"['Mohammadreza Baharani', 'Vinit Katariya', 'Nichole Morris', 'Omidreza Shoghli', 'Hamed Tabkhi']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.83,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.97225
    },
    {
        "arxiv_id":"2108.00804v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.00804v1",
        "title":"Relation Aware Semi-autoregressive Semantic Parsing for NL2SQL",
        "summary":"Natural language to SQL (NL2SQL) aims to parse a natural language with a given database into a SQL query, which widely appears in practical Internet applications. Jointly encode database schema and question utterance is a difficult but important task in NL2SQL. One solution is to treat the input as a heterogeneous graph. However, it failed to learn good word representation in question utterance. Learning better word representation is important for constructing a well-designed NL2SQL system. To solve the challenging task, we present a Relation aware Semi-autogressive Semantic Parsing (\\MODN) ~framework, which is more adaptable for NL2SQL. It first learns relation embedding over the schema entities and question words with predefined schema relations with ELECTRA and relation aware transformer layer as backbone. Then we decode the query SQL with a semi-autoregressive parser and predefined SQL syntax. From empirical results and case study, our model shows its effectiveness in learning better word representation in NL2SQL.",
        "published":"2021-08-02 12:21:08+00:00",
        "authors":"['Junyang Huang', 'Yongbo Wang', 'Yongliang Wang', 'Yang Dong', 'Yanghua Xiao']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.73,
        "actuality":2.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.8611666667
    },
    {
        "arxiv_id":"2108.01174v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01174v1",
        "title":"Knowledge-intensive Language Understanding for Explainable AI",
        "summary":"AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will not harm a human. Besides the concerns for fairness, privacy, transparency, and explainability are key to developing trusts in AI systems. As stated in describing trustworthy AI \"Trust comes through understanding. How AI-led decisions are made and what determining factors were included are crucial to understand.\" The subarea of explaining AI systems has come to be known as XAI. Multiple aspects of an AI system can be explained; these include biases that the data might have, lack of data points in a particular region of the example space, fairness of gathering the data, feature importances, etc. However, besides these, it is critical to have human-centered explanations that are directly related to decision-making similar to how a domain expert makes decisions based on \"domain knowledge,\" that also include well-established, peer-validated explicit guidelines. To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.",
        "published":"2021-08-02 21:12:30+00:00",
        "authors":"['Amit Sheth', 'Manas Gaur', 'Kaushik Roy', 'Keyur Faldu']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.92,
        "actuality":5.0,
        "controversy":5.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.6389166667
    },
    {
        "arxiv_id":"2108.01235v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01235v1",
        "title":"Interpretable Trade-offs Between Robot Task Accuracy and Compute Efficiency",
        "summary":"A robot can invoke heterogeneous computation resources such as CPUs, cloud GPU servers, or even human computation for achieving a high-level goal. The problem of invoking an appropriate computation model so that it will successfully complete a task while keeping its compute and energy costs within a budget is called a model selection problem. In this paper, we present an optimal solution to the model selection problem with two compute models, the first being fast but less accurate, and the second being slow but more accurate. The main insight behind our solution is that a robot should invoke the slower compute model only when the benefits from the gain in accuracy outweigh the computational costs. We show that such cost-benefit analysis can be performed by leveraging the statistical correlation between the accuracy of fast and slow compute models. We demonstrate the broad applicability of our approach to diverse problems such as perception using neural networks and safe navigation of a simulated Mars rover.",
        "published":"2021-08-03 01:30:34+00:00",
        "authors":"['Bineet Ghosh', 'Sandeep Chinchali', 'Parasara Sridhar Duggirala']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.87,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2108.01239v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01239v1",
        "title":"fauci-email: a json digest of Anthony Fauci's released emails",
        "summary":"A collection of over 3000 pages of emails sent by Anthony Fauci and his staff were released in an effort to understand the United States government response to the COVID-19 pandemic. We describe how this email data was translated into a resource consisting of json files that make many future studies easy. Findings from our processed data include (i) successful organizational partitions using the simple mincut techniques in Zachary's karate club methodology, (ii) a natural example where the normalized cut and minimum conductance set are extremely different, and (iii) organizational groups identified by optimum modularity clusters that illustrate a working hierarchy. These example uses suggest the data will be useful for future research and pedagogical uses in terms of human and system behavioral interactions. We explain a number of ways to turn email information into a network, a hypergraph, a temporal sequence, and a tensor for subsequent analysis as well as a few examples of such analysis.",
        "published":"2021-08-03 01:37:51+00:00",
        "authors":"['Austin R. Benson', 'Nate Veldt', 'David F. Gleich']",
        "arxiv_primary_category":"cs.si",
        "readability":0.86,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.0555833333
    },
    {
        "arxiv_id":"2108.01388v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01388v1",
        "title":"Visualizing Event Sequence Data for User Behavior Evaluation of In-Vehicle Information Systems",
        "summary":"With modern IVIS becoming more capable and complex than ever, their evaluation becomes increasingly difficult. The analysis of large amounts of user behavior data can help to cope with this complexity and can support UX experts in designing IVIS that serve customer needs and are safe to operate while driving. We, therefore, propose a Multi-level User Behavior Visualization Framework providing effective visualizations of user behavior data that is collected via telematics from production vehicles. Our approach visualizes user behavior data on three different levels: (1) The Task Level View aggregates event sequence data generated through touchscreen interactions to visualize user flows. (2) The Flow Level View allows comparing the individual flows based on a chosen metric. (3) The Sequence Level View provides detailed insights into touch interactions, glance, and driving behavior. Our case study proves that UX experts consider our approach a useful addition to their design process.",
        "published":"2021-08-03 09:52:04+00:00",
        "authors":"['Patrick Ebel', 'Christoph Lingenfelder', 'Andreas Vogelsang']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2108.01407v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01407v2",
        "title":"GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data",
        "summary":"We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, regression and structured output prediction, capable of handling high-throughput heterogeneous data. These methods allow for the construction of robust and accurate predictive models, that are in turn applied to different tasks of spacecraft monitoring and operations planning. More importantly, besides the accurate building of models, GalaxAI implements a visualisation layer, providing mission specialists and operators with a full, detailed and interpretable view of the data analysis process. We show the utility and versatility of GalaxAI on two use-cases concerning two different spacecraft: i) analysis and planning of Mars Express thermal power consumption and ii) predicting of INTEGRAL's crossings through Van Allen belts.",
        "published":"2021-08-03 10:45:20+00:00",
        "authors":"['Ana Kostovska', 'Matej Petkovi\u0107', 'Toma\u017e Stepi\u0161nik', 'Luke Lucas', 'Timothy Finn', 'Jos\u00e9 Mart\u00ednez-Heras', 'Pan\u010de Panov', 'Sa\u0161o D\u017eeroski', 'Alessandro Donati', 'Nikola Simidjievski', 'Dragi Kocev']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.82,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2108.01495v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01495v1",
        "title":"Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study",
        "summary":"Advances in sensing and learning algorithms have led to increasingly mature solutions for human detection by robots, particularly in selected use-cases such as pedestrian detection for self-driving cars or close-range person detection in consumer settings. Despite this progress, the simple question \"which sensor-algorithm combination is best suited for a person detection task at hand?\" remains hard to answer. In this paper, we tackle this issue by conducting a systematic cross-modal analysis of sensor-algorithm combinations typically used in robotics. We compare the performance of state-of-the-art person detectors for 2D range data, 3D lidar, and RGB-D data as well as selected combinations thereof in a challenging industrial use-case. We further address the related problems of data scarcity in the industrial target domain, and that recent research on human detection in 3D point clouds has mostly focused on autonomous driving scenarios. To leverage these methodological advances for robotics applications, we utilize a simple, yet effective multi-sensor transfer learning strategy by extending a strong image-based RGB-D detector to provide cross-modal supervision for lidar detectors in the form of weak 3D bounding box labels. Our results show a large variance among the different approaches in terms of detection performance, generalization, frame rates and computational requirements. As our use-case contains difficulties representative for a wide range of service robot applications, we believe that these results point to relevant open challenges for further research and provide valuable support to practitioners for the design of their robot system.",
        "published":"2021-08-03 13:33:37+00:00",
        "authors":"['Timm Linder', 'Narunas Vaskevicius', 'Robert Schirmer', 'Kai O. Arras']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.2778333333
    },
    {
        "arxiv_id":"2108.01547v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01547v1",
        "title":"EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training",
        "summary":"Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones. In this paper, we propose EVA, a Chinese dialogue system that contains the largest Chinese pre-trained dialogue model with 2.8B parameters. To build this model, we collect the largest Chinese dialogue dataset named WDC-Dialogue from various public social media. This dataset contains 1.4B context-response pairs and is used as the pre-training corpus of EVA. Extensive experiments on automatic and human evaluation show that EVA outperforms other Chinese pre-trained dialogue models especially in the multi-turn interaction of human-bot conversations.",
        "published":"2021-08-03 14:55:24+00:00",
        "authors":"['Hao Zhou', 'Pei Ke', 'Zheng Zhang', 'Yuxian Gu', 'Yinhe Zheng', 'Chujie Zheng', 'Yida Wang', 'Chen Henry Wu', 'Hao Sun', 'Xiaocong Yang', 'Bosi Wen', 'Xiaoyan Zhu', 'Minlie Huang', 'Jie Tang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.92,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2108.01885v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.01885v1",
        "title":"Intelligent Sensing Scheduling for Mobile Target Tracking Wireless Sensor Networks",
        "summary":"Edge computing has emerged as a prospective paradigm to meet ever-increasing computation demands in Mobile Target Tracking Wireless Sensor Networks (MTT-WSN). This paradigm can offload time-sensitive tasks to sink nodes to improve computing efficiency. Nevertheless, it is difficult to execute dynamic and critical tasks in the MTT-WSN network. Besides, the network cannot ensure consecutive tracking due to the limited energy. To address the problems, this paper proposes a new hierarchical target tracking structure based on Edge Intelligence (EI) technology. The structure integrates the computing resource of both mobile nodes and edge servers to provide efficient computation capability for real-time target tracking. Based on the proposed structure, we formulate an energy optimization model with the constrains of system execution latency and trajectory prediction accuracy. Moreover, we propose a long-term dynamic resource allocation algorithm to obtain the optimal resource allocation solution for the ac- curate and consecutive tracking. Simulation results demonstrate that our algorithm outperforms the deep Q-learning over 14.5% in terms of system energy consumption. It can also obtain a significant enhancement in tracking accuracy compared with the non-cooperative scheme.",
        "published":"2021-08-04 07:30:28+00:00",
        "authors":"['Longyu Zhou', 'Supeng Leng', 'Qiang Liu', 'Haoye Chai', 'Jihua Zhou']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.8,
        "actuality":3.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.2221666667
    },
    {
        "arxiv_id":"2108.02243v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02243v1",
        "title":"A Semi-quantitative Covid-19 Individual Risk Model",
        "summary":"This paper introduces a new basic risk model that could also be utilized by Covid-19 warning apps a priori, before an action is performed. Today the common warning apps estimate risk a posteriori and give no advice on particular scenarios. The new model also has the advantage that the individual risks behind the decision-making process would be uniform (in contrast to some current regulations) and it could help to understand the risks better and could also help to reduce risks a priori. It could be easily implemented on a single app screen, needing only some individual preferences to be set and a handful of adjustments to the particular scenario that shall be assessed. The disadvantage as of any simplified semi-quantitative risk models is that calibration is not easy (as some calibration points may even contradict) and that cumulative effects are hard to integrate e. g. the joint effect of combined scenarios. But, in principle calibration is feasible and it may be a good decision to calibrate the model conservatively.",
        "published":"2021-08-04 18:43:28+00:00",
        "authors":"['Jens Braband', 'Hendrik Sch\u00e4be']",
        "arxiv_primary_category":"cs.si",
        "readability":0.91,
        "actuality":4.0,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.2776666667
    },
    {
        "arxiv_id":"2108.02390v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02390v1",
        "title":"Fuzzy Logic based Logical Query Answering on Knowledge Graph",
        "summary":"Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the vector space and conduct query answering via dense similarity search. However, most of the designed logical operators in existing works do not satisfy the axiomatic system of classical logic. Moreover, these logical operators are parameterized so that they require a large number of complex FOL queries as training data, which are often arduous or even inaccessible to collect in most real-world KGs. In this paper, we present FuzzQE, a fuzzy logic based query embedding framework for answering FOL queries over KGs. FuzzQE follows fuzzy logic to define logical operators in a principled and learning free manner. Extensive experiments on two benchmark datasets demonstrate that FuzzQE achieves significantly better performance in answering FOL queries compared to the state-of-the-art methods. In addition, FuzzQE trained with only KG link prediction without any complex queries can achieve comparable performance with the systems trained with all FOL queries.",
        "published":"2021-08-05 05:54:00+00:00",
        "authors":"['Xuelu Chen', 'Ziniu Hu', 'Yizhou Sun']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.79,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2108.02512v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02512v1",
        "title":"Understanding parents' perceptions of children's cybersecurity awareness in Norway",
        "summary":"Children are increasingly using the internet nowadays. While internet use exposes children to various privacy and security risks, few studies have examined how parents perceive and address their children's cybersecurity risks. To address this gap, we conducted a qualitative study with 25 parents living in Norway with children aged between 10 to 15. We conducted semi-structured interviews with the parents and performed a thematic analysis of the interview data. The results of this paper include a list of cybersecurity awareness needs for children from a parental perspective, a list of learning resources for children, and a list of challenges for parents to ensure cybersecurity at home. Our results are useful for developers and educators in developing cybersecurity solutions for children. Future research should focus on defining cybersecurity theories and practices that contribute to children's and parents' awareness about cybersecurity risks, needs, and solutions.",
        "published":"2021-08-05 10:42:49+00:00",
        "authors":"['Farzana Quayyum', 'Jonas Bueie', 'Daniela S. Cruzes', 'Letizia Jaccheri', 'Juan Carlos Torrado Vidal']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.94,
        "actuality":4.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2108.02714v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02714v1",
        "title":"Remote Teaching and Learning in Applied Engineering: A Post-Pandemic Perspective",
        "summary":"The COVID-19 pandemic significantly disrupted the educational sector. Faced with this life-threatening pandemic, educators had to swiftly pivot to an alternate form of course delivery without severely impacting the quality of the educational experience. Following the transition to online learning, educators had to grapple with a host of challenges. With interrupted face-to-face delivery, limited access to state-of-the-art labs, barriers with educational technologies, challenges of academic integrity, and obstacles with remote teamwork and student participation, creative solutions were urgently needed. In this chapter, we provide a rationale for a variety of course delivery models at different stages of the pandemic and highlight the approaches we took to overcome some of the pressing challenges of remote education. We also discuss how we ensured that hands-on learning remains an integral part of engineering curricula, and we argue that some of the applied changes during the pandemic will likely serve as a catalyst for modernizing education.",
        "published":"2021-08-05 16:28:05+00:00",
        "authors":"['Mouhamed Abdulla', 'Weijing Ma']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.92,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2108.02817v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02817v1",
        "title":"THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy",
        "summary":"Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.",
        "published":"2021-08-05 19:05:15+00:00",
        "authors":"['Carla Floricel', 'Nafiul Nipu', 'Mikayla Biggs', 'Andrew Wentzel', 'Guadalupe Canahuate', 'Lisanne Van Dijk', 'Abdallah Mohamed', 'C. David Fuller', 'G. Elisabeta Marai']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.4443333333
    },
    {
        "arxiv_id":"2108.02927v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02927v2",
        "title":"DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features",
        "summary":"Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their local features. Previous learning-based studies mainly focus on either global or local image representation learning to tackle the retrieval task. In this paper, we abandon the two-stage paradigm and seek to design an effective single-stage solution by integrating local and global information inside images into compact image representations. Specifically, we propose a Deep Orthogonal Local and Global (DOLG) information fusion framework for end-to-end image retrieval. It attentively extracts representative local information with multi-atrous convolutions and self-attention at first. Components orthogonal to the global image representation are then extracted from the local information. At last, the orthogonal components are concatenated with the global representation as a complementary, and then aggregation is performed to generate the final representation. The whole framework is end-to-end differentiable and can be trained with image-level labels. Extensive experimental results validate the effectiveness of our solution and show that our model achieves state-of-the-art image retrieval performances on Revisited Oxford and Paris datasets.",
        "published":"2021-08-06 03:14:09+00:00",
        "authors":"['Min Yang', 'Dongliang He', 'Miao Fan', 'Baorong Shi', 'Xuetong Xue', 'Fu Li', 'Errui Ding', 'Jizhou Huang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.5555
    },
    {
        "arxiv_id":"2108.02943v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.02943v1",
        "title":"Unsupervised Learning of Debiased Representations with Pseudo-Attributes",
        "summary":"Dataset bias is a critical challenge in machine learning, and its negative impact is aggravated when models capture unintended decision rules with spurious correlations. Although existing works often handle this issue using human supervision, the availability of the proper annotations is impractical and even unrealistic. To better tackle this challenge, we propose a simple but effective debiasing technique in an unsupervised manner. Specifically, we perform clustering on the feature embedding space and identify pseudoattributes by taking advantage of the clustering results even without an explicit attribute supervision. Then, we employ a novel cluster-based reweighting scheme for learning debiased representation; this prevents minority groups from being discounted for minimizing the overall loss, which is desirable for worst-case generalization. The extensive experiments demonstrate the outstanding performance of our approach on multiple standard benchmarks, which is even as competitive as the supervised counterpart.",
        "published":"2021-08-06 05:20:46+00:00",
        "authors":"['Seonguk Seo', 'Joon-Young Lee', 'Bohyung Han']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.85,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4999166667
    },
    {
        "arxiv_id":"2108.03122v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03122v2",
        "title":"Computation and Communication Co-Design for Real-Time Monitoring and Control in Multi-Agent Systems",
        "summary":"We investigate the problem of co-designing computation and communication in a multi-agent system (e.g. a sensor network or a multi-robot team). We consider the realistic setting where each agent acquires sensor data and is capable of local processing before sending updates to a base station, which is in charge of making decisions or monitoring phenomena of interest in real time. Longer processing at an agent leads to more informative updates but also larger delays, giving rise to a delay-accuracy-tradeoff in choosing the right amount of local processing at each agent. We assume that the available communication resources are limited due to interference, bandwidth, and power constraints. Thus, a scheduling policy needs to be designed to suitably share the communication channel among the agents. To that end, we develop a general formulation to jointly optimize the local processing at the agents and the scheduling of transmissions. Our novel formulation leverages the notion of Age of Information to quantify the freshness of data and capture the delays caused by computation and communication. We develop efficient resource allocation algorithms using the Whittle index approach and demonstrate our proposed algorithms in two practical applications: multi-agent occupancy grid mapping in time-varying environments, and ride sharing in autonomous vehicle networks. Our experiments show that the proposed co-design approach leads to a substantial performance improvement (18-82% in our tests).",
        "published":"2021-08-06 13:45:30+00:00",
        "authors":"['Vishrant Tripathi', 'Luca Ballotta', 'Luca Carlone', 'Eytan Modiano']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.91,
        "actuality":2.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.1389166667
    },
    {
        "arxiv_id":"2108.03294v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03294v3",
        "title":"A Smart and Defensive Human-Machine Approach to Code Analysis",
        "summary":"Static analysis remains one of the most popular approaches for detecting and correcting poor or vulnerable program code. It involves the examination of code listings, test results, or other documentation to identify errors, violations of development standards, or other problems, with the ultimate goal of fixing these errors so that systems and software are as secure as possible. There exists a plethora of static analysis tools, which makes it challenging for businesses and programmers to select a tool to analyze their program code. It is imperative to find ways to improve code analysis so that it can be employed by cyber defenders to mitigate security risks. In this research, we propose a method that employs the use of virtual assistants to work with programmers to ensure that software are as safe as possible in order to protect safety-critical systems from data breaches and other attacks. The proposed method employs a recommender system that uses various metrics to help programmers select the most appropriate code analysis tool for their project and guides them through the analysis process. The system further tracks the user's behavior regarding the adoption of the recommended practices.",
        "published":"2021-08-06 20:42:07+00:00",
        "authors":"['Fitzroy D. Nembhard', 'Marco M. Carvalho']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.93,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.0276666667
    },
    {
        "arxiv_id":"2108.03357v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03357v1",
        "title":"A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions",
        "summary":"Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.",
        "published":"2021-08-07 03:26:16+00:00",
        "authors":"['Tianzi Zang', 'Yanmin Zhu', 'Haobing Liu', 'Ruohan Zhang', 'Jiadi Yu']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6945
    },
    {
        "arxiv_id":"2108.03434v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03434v1",
        "title":"NASOA: Towards Faster Task-oriented Online Fine-tuning with a Zoo of Models",
        "summary":"Fine-tuning from pre-trained ImageNet models has been a simple, effective, and popular approach for various computer vision tasks. The common practice of fine-tuning is to adopt a default hyperparameter setting with a fixed pre-trained model, while both of them are not optimized for specific tasks and time constraints. Moreover, in cloud computing or GPU clusters where the tasks arrive sequentially in a stream, faster online fine-tuning is a more desired and realistic strategy for saving money, energy consumption, and CO2 emission. In this paper, we propose a joint Neural Architecture Search and Online Adaption framework named NASOA towards a faster task-oriented fine-tuning upon the request of users. Specifically, NASOA first adopts an offline NAS to identify a group of training-efficient networks to form a pretrained model zoo. We propose a novel joint block and macro-level search space to enable a flexible and efficient search. Then, by estimating fine-tuning performance via an adaptive model by accumulating experience from the past tasks, an online schedule generator is proposed to pick up the most suitable model and generate a personalized training regime with respect to each desired task in a one-shot fashion. The resulting model zoo is more training efficient than SOTA models, e.g. 6x faster than RegNetY-16GF, and 1.7x faster than EfficientNetB3. Experiments on multiple datasets also show that NASOA achieves much better fine-tuning results, i.e. improving around 2.1% accuracy than the best performance in RegNet series under various constraints and tasks; 40x faster compared to the BOHB.",
        "published":"2021-08-07 12:03:14+00:00",
        "authors":"['Hang Xu', 'Ning Kang', 'Gengwei Zhang', 'Chuanlong Xie', 'Xiaodan Liang', 'Zhenguo Li']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":4.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2108.03440v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03440v1",
        "title":"Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation",
        "summary":"Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when bias is amplified over time as a few popular items are repeatedly over-represented in recommendation lists. This phenomenon can be viewed as a recommendation feedback loop: the system repeatedly recommends certain items at different time points and interactions of users with those items will amplify bias towards those items over time. This issue has been extensively studied in the literature on model-based or neighborhood-based recommendation algorithms, but less work has been done on online recommendation models such as those based on multi-armed Bandit algorithms. In this paper, we study exposure bias in a class of well-known bandit algorithms known as Linear Cascade Bandits. We analyze these algorithms on their ability to handle exposure bias and provide a fair representation for items and suppliers in the recommendation results. Our analysis reveals that these algorithms fail to treat items and suppliers fairly and do not sufficiently explore the item space for each user. To mitigate this bias, we propose a discounting factor and incorporate it into these algorithms that controls the exposure of items at each time step. To show the effectiveness of the proposed discounting factor on mitigating exposure bias, we perform experiments on two datasets using three cascading bandit algorithms and our experimental results show that the proposed method improves the exposure fairness for items and suppliers.",
        "published":"2021-08-07 12:40:52+00:00",
        "authors":"['Masoud Mansoury', 'Himan Abdollahpouri', 'Bamshad Mobasher', 'Mykola Pechenizkiy', 'Robin Burke', 'Milad Sabouri']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.83325
    },
    {
        "arxiv_id":"2108.03507v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03507v1",
        "title":"ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation",
        "summary":"Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects. To empower conventional CNNs in learning geometric clues of lanes, we propose a deep network named ContinuityLearner to better learn geometric prior within lane. Specifically, our proposed CNN-based paradigm involves a novel Context-encoding image feature learning network to generate class-dependent image feature maps and a new encoding layer to exploit the geometric continuity feature representation by fusing both spatial and visual information of lane together. The ContinuityLearner, performing on the geometric continuity feature of lanes, is trained to directly predict the lane in traffic scenarios with integrated and continuous instance semantic. The experimental results on the CULane dataset and the Tusimple benchmark demonstrate that our ContinuityLearner has superior performance over other state-of-the-art techniques in lane segmentation.",
        "published":"2021-08-07 19:23:29+00:00",
        "authors":"['Haoyu Fang', 'Jing Zhu', 'Yi Fang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.79,
        "actuality":4.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8889166667
    },
    {
        "arxiv_id":"2108.03670v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03670v1",
        "title":"#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs",
        "summary":"COVID-19 has caused lasting damage to almost every domain in public health, society, and economy. To monitor the pandemic trend, existing studies rely on the aggregation of traditional statistical models and epidemic spread theory. In other words, historical statistics of COVID-19, as well as the population mobility data, become the essential knowledge for monitoring the pandemic trend. However, these solutions can barely provide precise prediction and satisfactory explanations on the long-term disease surveillance while the ubiquitous social media resources can be the key enabler for solving this problem. For example, serious discussions may occur on social media before and after some breaking events take place. These events, such as marathon and parade, may impact the spread of the virus. To take advantage of the social media data, we propose a novel framework, Social Media enhAnced pandemic suRveillance Technique (SMART), which is composed of two modules: (i) information extraction module to construct heterogeneous knowledge graphs based on the extracted events and relationships among them; (ii) time series prediction module to provide both short-term and long-term forecasts of the confirmed cases and fatality at the state-level in the United States and to discover risk factors for COVID-19 interventions. Extensive experiments show that our method largely outperforms the state-of-the-art baselines by 7.3% and 7.4% in confirmed case\/fatality prediction, respectively.",
        "published":"2021-08-08 15:46:05+00:00",
        "authors":"['Yichao Zhou', 'Jyun-yu Jiang', 'Xiusi Chen', 'Wei Wang']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":4.6666666667,
        "controversy":4.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":4.1945
    },
    {
        "arxiv_id":"2108.03738v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03738v1",
        "title":"Human-in-the-loop Extraction of Interpretable Concepts in Deep Learning Models",
        "summary":"The interpretation of deep neural networks (DNNs) has become a key topic as more and more people apply them to solve various problems and making critical decisions. Concept-based explanations have recently become a popular approach for post-hoc interpretation of DNNs. However, identifying human-understandable visual concepts that affect model decisions is a challenging task that is not easily addressed with automatic approaches. We present a novel human-in-the-loop approach to generate user-defined concepts for model interpretation and diagnostics. Central to our proposal is the use of active learning, where human knowledge and feedback are combined to train a concept extractor with very little human labeling effort. We integrate this process into an interactive system, ConceptExtract. Through two case studies, we show how our approach helps analyze model behavior and extract human-friendly concepts for different machine learning tasks and datasets and how to use these concepts to understand the predictions, compare model performance and make suggestions for model refinement. Quantitative experiments show that our active learning approach can accurately extract meaningful visual concepts. More importantly, by identifying visual concepts that negatively affect model performance, we develop the corresponding data augmentation strategy that consistently improves model performance.",
        "published":"2021-08-08 21:33:56+00:00",
        "authors":"['Zhenge Zhao', 'Panpan Xu', 'Carlos Scheidegger', 'Liu Ren']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2108.03862v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03862v1",
        "title":"Safe Vessel Navigation Visually Aided by Autonomous Unmanned Aerial Vehicles in Congested Harbors and Waterways",
        "summary":"In the maritime sector, safe vessel navigation is of great importance, particularly in congested harbors and waterways. The focus of this work is to estimate the distance between an object of interest and potential obstacles using a companion UAV. The proposed approach fuses GPS data with long-range aerial images. First, we employ semantic segmentation DNN for discriminating the vessel of interest, water, and potential solid objects using raw image data. The network is trained with both real and images generated and automatically labeled from a realistic AirSim simulation environment. Then, the distances between the extracted vessel and non-water obstacle blobs are computed using a novel GSD estimation algorithm. To the best of our knowledge, this work is the first attempt to detect and estimate distances to unknown objects from long-range visual data captured with conventional RGB cameras and auxiliary absolute positioning systems (e.g. GPS). The simulation results illustrate the accuracy and efficacy of the proposed method for visually aided navigation of vessels assisted by UAV.",
        "published":"2021-08-09 08:15:17+00:00",
        "authors":"['Jonas le Fevre Sejersen', 'Rui Pimentel de Figueiredo', 'Erdal Kayacan']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.86,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.47225
    },
    {
        "arxiv_id":"2108.03863v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03863v1",
        "title":"Utilizing the RRT*-Algorithm for Collision Avoidance in UAV Photogrammetry Missions",
        "summary":"This paper presents the application of the Rapidly-exploring Random Tree Star (RRT*) algorithm for multicopter collision avoidance in photogrammetry missions. For better applicability, the presented algorithm redirects the drone onto a predefined mission's path. The experiments are conducted in the simulation software gazebo utilizing a ROS interface to the widely known autopilot software PX4. For obstacle detection, a simulated Intel D435 stereo camera is used. The experiments include two different scenarios, each conducted with two different maximum velocities. The results show that the probabilistic RRT*-algorithm can avoid obstacles successfully and intelligibly even at speeds up to 6 m\/s. The main problems persist in the dynamic behavior, the inertia of the multicopter, and the limitations of the sensor technology.",
        "published":"2021-08-09 08:17:00+00:00",
        "authors":"['Lars Killian', 'Jan Backhaus']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.78,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.1945
    },
    {
        "arxiv_id":"2108.03897v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03897v1",
        "title":"Deep Convolutional Neural Network for Low Projection SPECT Imaging Reconstruction",
        "summary":"In this paper, we present a novel method for tomographic image reconstruction in SPECT imaging with a low number of projections. Deep convolutional neural networks (CNN) are employed in the new reconstruction method. Projection data from software phantoms were used to train the CNN network. For evaluation of the efficacy of the proposed method, software phantoms and hardware phantoms based on the FOV SPECT system were used. The resulting tomographic images are compared to those produced by the \"Maximum Likelihood Expectation Maximisation\" (MLEM).",
        "published":"2021-08-09 09:30:45+00:00",
        "authors":"['Charalambos Chrysostomou', 'Loizos Koutsantonis', 'Christos Lemesios', 'Costas N. Papanicolas']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.81,
        "actuality":4.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1945
    },
    {
        "arxiv_id":"2108.03989v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.03989v1",
        "title":"Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning",
        "summary":"Nowadays, artificial neural networks are widely used for users' online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users' intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users' intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users' actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy.",
        "published":"2021-08-09 12:41:57+00:00",
        "authors":"['Yu Li', 'Fei Xiong', 'Ziyi Wang', 'Zulong Chen', 'Chuanfei Xu', 'Yuyu Yin', 'Li Zhou']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.91,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2108.04282v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04282v1",
        "title":"Why do people participate in small online communities?",
        "summary":"Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a ``successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes.",
        "published":"2021-08-09 18:18:05+00:00",
        "authors":"['Sohyeon Hwang', 'Jeremy Foote']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":2.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.5278333333
    },
    {
        "arxiv_id":"2108.04445v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04445v1",
        "title":"Lifelong Intent Detection via Multi-Strategy Rebalancing",
        "summary":"Conventional Intent Detection (ID) models are usually trained offline, which relies on a fixed dataset and a predefined set of intent classes. However, in real-world applications, online systems usually involve continually emerging new user intents, which pose a great challenge to the offline training paradigm. Recently, lifelong learning has received increasing attention and is considered to be the most promising solution to this challenge. In this paper, we propose Lifelong Intent Detection (LID), which continually trains an ID model on new data to learn newly emerging intents while avoiding catastrophically forgetting old data. Nevertheless, we find that existing lifelong learning methods usually suffer from a serious imbalance between old and new data in the LID task. Therefore, we propose a novel lifelong learning method, Multi-Strategy Rebalancing (MSR), which consists of cosine normalization, hierarchical knowledge distillation, and inter-class margin loss to alleviate the multiple negative effects of the imbalance problem. Experimental results demonstrate the effectiveness of our method, which significantly outperforms previous state-of-the-art lifelong learning methods on the ATIS, SNIPS, HWU64, and CLINC150 benchmarks.",
        "published":"2021-08-10 04:35:13+00:00",
        "authors":"['Qingbin Liu', 'Xiaoyan Yu', 'Shizhu He', 'Kang Liu', 'Jun Zhao']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.85,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.1389166667
    },
    {
        "arxiv_id":"2108.04468v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04468v1",
        "title":"End-to-End User Behavior Retrieval in Click-Through RatePrediction Model",
        "summary":"Click-Through Rate (CTR) prediction is one of the core tasks in recommender systems (RS). It predicts a personalized click probability for each user-item pair. Recently, researchers have found that the performance of CTR model can be improved greatly by taking user behavior sequence into consideration, especially long-term user behavior sequence. The report on an e-commerce website shows that 23\\% of users have more than 1000 clicks during the past 5 months. Though there are numerous works focus on modeling sequential user behaviors, few works can handle long-term user behavior sequence due to the strict inference time constraint in real world system. Two-stage methods are proposed to push the limit for better performance. At the first stage, an auxiliary task is designed to retrieve the top-$k$ similar items from long-term user behavior sequence. At the second stage, the classical attention mechanism is conducted between the candidate item and $k$ items selected in the first stage. However, information gap happens between retrieval stage and the main CTR task. This goal divergence can greatly diminishing the performance gain of long-term user sequence. In this paper, inspired by Reformer, we propose a locality-sensitive hashing (LSH) method called ETA (End-to-end Target Attention) which can greatly reduce the training and inference cost and make the end-to-end training with long-term user behavior sequence possible. Both offline and online experiments confirm the effectiveness of our model. We deploy ETA into a large-scale real world E-commerce system and achieve extra 3.1\\% improvements on GMV (Gross Merchandise Value) compared to a two-stage long user sequence CTR model.",
        "published":"2021-08-10 06:28:29+00:00",
        "authors":"['Qiwei Chen', 'Changhua Pei', 'Shanshan Lv', 'Chao Li', 'Junfeng Ge', 'Wenwu Ou']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.9,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.4721666667
    },
    {
        "arxiv_id":"2108.04494v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04494v1",
        "title":"Finding NeMo: Fishing in banking networks using network motifs",
        "summary":"Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discovering new fraud schemes. This work explores graph patterns in a real-world transaction dataset by extracting and analyzing its network motifs. Since banking graphs are heterogeneous, we focus on heterogeneous network motifs. Additionally, we propose a novel network randomization process that generates valid banking graphs. From our exploratory analysis, we conclude that network motifs extract insightful and interpretable patterns.",
        "published":"2021-08-10 08:00:28+00:00",
        "authors":"['Xavier Fontes', 'David Apar\u00edcio', 'Maria In\u00eas Silva', 'Beatriz Malveiro', 'Jo\u00e3o Tiago Ascens\u00e3o', 'Pedro Bizarro']",
        "arxiv_primary_category":"cs.si",
        "readability":0.76,
        "actuality":4.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.6389166667
    },
    {
        "arxiv_id":"2108.04669v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04669v1",
        "title":"Exploring the Effect of Visual Cues on Eye Gaze During AR-Guided Picking and Assembly Tasks",
        "summary":"In this paper, we present an analysis of eye gaze patterns pertaining to visual cues in augmented reality (AR) for head-mounted displays (HMDs). We conducted an experimental study involving a picking and assembly task, which was guided by different visual cues. We compare these visual cues along multiple dimensions (in-view vs. out-of-view, static vs. dynamic, sequential vs. simultaneous) and analyze quantitative metrics such as gaze distribution, gaze duration, and gaze path distance. Our results indicate that visual cues in AR significantly affect eye gaze patterns. Specifically, we show that the effect varies depending on the type of visual cue. We discuss these empirical results with respect to visual attention theory.",
        "published":"2021-08-10 13:19:39+00:00",
        "authors":"['Arne Seeliger', 'Gerrit Merz', 'Christian Holz', 'Stefan Feuerriegel']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.81,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3889166667
    },
    {
        "arxiv_id":"2108.04769v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04769v1",
        "title":"On the Foundations of Grounding in Answer Set Programming",
        "summary":"We provide a comprehensive elaboration of the theoretical foundations of variable instantiation, or grounding, in Answer Set Programming (ASP). Building on the semantics of ASP's modeling language, we introduce a formal characterization of grounding algorithms in terms of (fixed point) operators. A major role is played by dedicated well-founded operators whose associated models provide semantic guidance for delineating the result of grounding along with on-the-fly simplifications. We address an expressive class of logic programs that incorporates recursive aggregates and thus amounts to the scope of existing ASP modeling languages. This is accompanied with a plain algorithmic framework detailing the grounding of recursive aggregates. The given algorithms correspond essentially to the ones used in the ASP grounder gringo.",
        "published":"2021-08-10 16:23:49+00:00",
        "authors":"['Roland Kaminski', 'Torsten Schaub']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.78,
        "actuality":2.0,
        "controversy":1.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.3611666667
    },
    {
        "arxiv_id":"2108.04949v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.04949v1",
        "title":"A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models",
        "summary":"Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, failure to adjust for SBDoH factors will potentially cause confounding issues and misclassification errors in either statistical analyses and machine learning-based models. However, there are limited studies to examine SBDoH factors in clinical outcomes due to the lack of structured SBDoH information in current electronic health record (EHR) systems, while much of the SBDoH information is documented in clinical narratives. Natural language processing (NLP) is thus the key technology to extract such information from unstructured clinical text. However, there is not a mature clinical NLP system focusing on SBDoH. In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes). The experimental results show that the BERT-based NLP model achieved the best strict\/lenient F1-score of 0.8791 and 0.8999, respectively. The comparison between NLP extracted SBDoH information and structured EHRs in the lung cancer patient cohort of 864 patients with 161,933 various types of clinical notes showed that much more detailed information about smoking, education, and employment were only captured in clinical narratives and that it is necessary to use both clinical narratives and structured EHRs to construct a more complete picture of patients' SBDoH factors.",
        "published":"2021-08-10 22:11:31+00:00",
        "authors":"['Zehao Yu', 'Xi Yang', 'Chong Dang', 'Songzi Wu', 'Prakash Adekkanattu', 'Jyotishman Pathak', 'Thomas J. George', 'William R. Hogan', 'Yi Guo', 'Jiang Bian', 'Yonghui Wu']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.83,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.0276666667
    },
    {
        "arxiv_id":"2108.05009v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05009v1",
        "title":"Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion",
        "summary":"We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, unlike existing multimodal methods that necessitate individual encoders for different modalities, we verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific batch normalization layers in the encoder, which also enables implicit fusion via joint feature representation learning. Secondly, we propose a bidirectional multi-layer fusion scheme, where multimodal features can be exploited progressively. To take advantage of such scheme, we introduce two asymmetric fusion operations including channel shuffle and pixel shift, which learn different fused features with respect to different fusion directions. These two operations are parameter-free and strengthen the multimodal feature interactions across channels as well as enhance the spatial feature discrimination within channels. We conduct extensive experiments on semantic segmentation and image translation tasks, based on three publicly available datasets covering diverse modalities. Results indicate that our proposed framework is general, compact and is superior to state-of-the-art fusion frameworks.",
        "published":"2021-08-11 03:42:13+00:00",
        "authors":"['Yikai Wang', 'Fuchun Sun', 'Ming Lu', 'Anbang Yao']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.8,
        "actuality":1.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.2499166667
    },
    {
        "arxiv_id":"2108.05057v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05057v2",
        "title":"A Channel-Aware Routing Protocol With Nearest Neighbor Regression For Underwater Sensor Networks",
        "summary":"The underwater acoustic channel is one of the most challenging communication channels. Due to periodical tidal and daily climatic variation, underwater noise is periodically fluctuating, which result in the periodical changing of acoustic channel quality in long-term. Also, time-variant channel quality leads to routing failure. Routing protocols with acoustic channel estimation, namely underwater channel-aware routing protocols are recently proposed to maintain the routing performance. However, channel estimation algorithms for these routing protocols are mostly linear and rarely consider periodicity of acoustic channels. In this paper, we introduce acoustic channel estimation based on nearest neighbor regression for underwater acoustic networks. We extend nearest neighbor regression for SNR (Signal-to-Noise Ratio) time series prediction, providing an outstanding prediction accuracy for intricately periodical and fluctuating received SNR time series. Moreover, we propose a quick search algorithm and use statistical storage compression to optimize the time and space complexity of the algorithm. In contrast with linear methods, this algorithm significantly improves channel prediction accuracy (over three times at most) on both simulation and sea trial data sets. With this channel estimation method, we then propose a Depth-Based Channel-Aware Routing protocol (DBCAR). Taking advantage of depth-greedy forwarding and channel-aware reliable communication, DBCAR has an outstanding network performance on packet delivery ratio, average energy consumption and average transmission delay which is validated through extensive simulations.",
        "published":"2021-08-11 06:50:59+00:00",
        "authors":"['Boyu Diao', 'Chao Li', 'Qi Wang', 'Zhulin An', 'Yongjun Xu']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.82,
        "actuality":2.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.58325
    },
    {
        "arxiv_id":"2108.05065v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05065v1",
        "title":"Technical Report for A Service-Fair UAV-NOMA System for Large-scale IoT scenarios",
        "summary":"Integrating unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) technology can perform better in $5$G communication systems. To guarantee the fairness of communication services for the sensor nodes deployed in large-scale scenarios, we adopt the graph theory and the smallest enclosing algorithm to design a user scheduling strategy to select the communication sensor nodes before communication processes.",
        "published":"2021-08-11 07:07:42+00:00",
        "authors":"['Xindi Wang']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2108.05152v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05152v1",
        "title":"Estimation of Fair Ranking Metrics with Incomplete Judgments",
        "summary":"There is increasing attention to evaluating the fairness of search system ranking decisions. These metrics often consider the membership of items to particular groups, often identified using protected attributes such as gender or ethnicity. To date, these metrics typically assume the availability and completeness of protected attribute labels of items. However, the protected attributes of individuals are rarely present, limiting the application of fair ranking metrics in large scale systems. In order to address this problem, we propose a sampling strategy and estimation technique for four fair ranking metrics. We formulate a robust and unbiased estimator which can operate even with very limited number of labeled items. We evaluate our approach using both simulated and real world data. Our experimental results demonstrate that our method can estimate this family of fair ranking metrics and provides a robust, reliable alternative to exhaustive or random data annotation.",
        "published":"2021-08-11 10:57:00+00:00",
        "authors":"['\u00d6mer K\u0131rnap', 'Fernando Diaz', 'Asia Biega', 'Michael Ekstrand', 'Ben Carterette', 'Emine Y\u0131lmaz']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.08325
    },
    {
        "arxiv_id":"2108.05428v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05428v1",
        "title":"Determining ActionReversibility in STRIPS Using Answer Set and Epistemic Logic Programming",
        "summary":"In the context of planning and reasoning about actions and change, we call an action reversible when its effects can be reverted by applying other actions, returning to the original state. Renewed interest in this area has led to several results in the context of the PDDL language, widely used for describing planning tasks. In this paper, we propose several solutions to the computational problem of deciding the reversibility of an action. In particular, we leverage an existing translation from PDDL to Answer Set Programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic Logic Programming (ELP), an extension of ASP with epistemic operators, and compare and contrast their strengths and weaknesses. Under consideration for acceptance in TPLP.",
        "published":"2021-08-11 20:00:34+00:00",
        "authors":"['Wolfgang Faber', 'Michael Morak', 'Luk\u00e1\u0161 Chrpa']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.85,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6945
    },
    {
        "arxiv_id":"2108.05538v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05538v1",
        "title":"Exploring Head-based Mode-Switching in Virtual Reality",
        "summary":"Mode-switching supports multilevel operations using a limited number of input methods. In Virtual Reality (VR) head-mounted displays (HMD), common approaches for mode-switching use buttons, controllers, and users' hands. However, they are inefficient and challenging to do with tasks that require both hands (e.g., when users need to use two hands during drawing operations). Using head gestures for mode-switching can be an efficient and cost-effective way, allowing for a more continuous and smooth transition between modes. In this paper, we explore the use of head gestures for mode-switching especially in scenarios when both users' hands are performing tasks. We present a first user study that evaluated eight head gestures that could be suitable for VR HMD with a dual-hand line-drawing task. Results show that move forward, move backward, roll left, and roll right led to better performance and are preferred by participants. A second study integrating these four gestures in Tilt Brush, an open-source painting VR application, is conducted to further explore the applicability of these gestures and derive insights. Results show that Tilt Brush with head gestures allowed users to change modes with ease and led to improved interaction and user experience. The paper ends with a discussion on some design recommendations for using head-based mode-switching in VR HMD.",
        "published":"2021-08-12 05:09:20+00:00",
        "authors":"['Rongkai Shi', 'Nan Zhu', 'Hai-Ning Liang', 'Shengdong Zhao']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.9,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2108.05649v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05649v1",
        "title":"Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought",
        "summary":"Deep learning is gaining instant popularity in computer aided diagnosis of COVID-19. Due to the high sensitivity of Computed Tomography (CT) to this disease, CT-based COVID-19 detection with visual models is currently at the forefront of medical imaging research. Outcomes published in this direction are frequently claiming highly accurate detection under deep transfer learning. This is leading medical technologists to believe that deep transfer learning is the mainstream solution for the problem. However, our critical analysis of the literature reveals an alarming performance disparity between different published results. Hence, we conduct a systematic thorough investigation to analyze the effectiveness of deep transfer learning for COVID-19 detection with CT images. Exploring 14 state-of-the-art visual models with over 200 model training sessions, we conclusively establish that the published literature is frequently overestimating transfer learning performance for the problem, even in the prestigious scientific sources. The roots of overestimation trace back to inappropriate data curation. We also provide case studies that consider more realistic scenarios, and establish transparent baselines for the problem. We hope that our reproducible investigation will help in curbing hype-driven claims for the critical problem of COVID-19 diagnosis, and pave the way for a more transparent performance evaluation of techniques for CT-based COVID-19 detection.",
        "published":"2021-08-12 10:34:22+00:00",
        "authors":"['Fouzia Altaf', 'Syed M. S. Islam', 'Naveed Akhtar']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.8055
    },
    {
        "arxiv_id":"2108.05698v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05698v1",
        "title":"Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data",
        "summary":"With the increasing popularity of deep learning on edge devices, compressing large neural networks to meet the hardware requirements of resource-constrained devices became a significant research direction. Numerous compression methodologies are currently being used to reduce the memory sizes and energy consumption of neural networks. Knowledge distillation (KD) is among such methodologies and it functions by using data samples to transfer the knowledge captured by a large model (teacher) to a smaller one(student). However, due to various reasons, the original training data might not be accessible at the compression stage. Therefore, data-free model compression is an ongoing research problem that has been addressed by various works. In this paper, we point out that catastrophic forgetting is a problem that can potentially be observed in existing data-free distillation methods. Moreover, the sample generation strategies in some of these methods could result in a mismatch between the synthetic and real data distributions. To prevent such problems, we propose a data-free KD framework that maintains a dynamic collection of generated samples over time. Additionally, we add the constraint of matching the real data distribution in sample generation strategies that target maximum information gain. Our experiments demonstrate that we can improve the accuracy of the student models obtained via KD when compared with state-of-the-art approaches on the SVHN, Fashion MNIST and CIFAR100 datasets.",
        "published":"2021-08-11 08:11:08+00:00",
        "authors":"['Kuluhan Binici', 'Nam Trung Pham', 'Tulika Mitra', 'Karianto Leman']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.9445
    },
    {
        "arxiv_id":"2108.05774v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05774v1",
        "title":"HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations",
        "summary":"Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in dense vector space and employed in downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., mapping the connectivity patterns of the relations (i.e., symmetric\/asymmetric, inverse, and composition) to a geometric interpretation such as rotations. Other approaches model the representations in higher dimensional space such as four-dimensional space (4D) to enhance the ability to infer the connectivity patterns (i.e., expressiveness). However, modeling relation and entity in a 4D space often comes at the cost of interpretability. This paper proposes HopfE, a novel KGE approach aiming to achieve the interpretability of inferred relations in the four-dimensional space. We first model the structural embeddings in 3D Euclidean space and view the relation operator as an SO(3) rotation. Next, we map the entity embedding vector from a 3D space to a 4D hypersphere using the inverse Hopf Fibration, in which we embed the semantic information from the KG ontology. Thus, HopfE considers the structural and semantic properties of the entities without losing expressivity and interpretability. Our empirical results on four well-known benchmarks achieve state-of-the-art performance for the KG completion task.",
        "published":"2021-08-12 14:34:02+00:00",
        "authors":"['Anson Bastos', 'Kuldeep Singh', 'Abhishek Nadgeri', 'Saeedeh Shekarpour', 'Isaiah Onando Mulang', 'Johannes Hoffart']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.77,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6945
    },
    {
        "arxiv_id":"2108.05872v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05872v1",
        "title":"HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning",
        "summary":"Sparse rewards and long time horizons remain challenging for reinforcement learning algorithms. Exploration bonuses can help in sparse reward settings by encouraging agents to explore the state space, while hierarchical approaches can assist with long-horizon tasks by decomposing lengthy tasks into shorter subtasks. We propose HAC Explore (HACx), a new method that combines these approaches by integrating the exploration bonus method Random Network Distillation (RND) into the hierarchical approach Hierarchical Actor-Critic (HAC). HACx outperforms either component method on its own, as well as an existing approach to combining hierarchy and exploration, in a set of difficult simulated robotics tasks. HACx is the first RL method to solve a sparse reward, continuous-control task that requires over 1,000 actions.",
        "published":"2021-08-12 17:42:12+00:00",
        "authors":"['Willie McClinton', 'Andrew Levy', 'George Konidaris']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.78,
        "actuality":2.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2108.05948v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05948v2",
        "title":"Deep adversarial attack on target detection systems",
        "summary":"Target detection systems identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. Deep Convolutional Neural Network (DCNN) classifiers have proven to be successful tools for computer vision applications. However,prior research confirms that even state of the art classifier models are susceptible to adversarial attacks. In this paper, we show how to generate adversarial infrared images by adding small perturbations to the targets region to deceive a DCNN-based target detector at remarkable levels. We demonstrate significant progress in developing visually imperceptible adversarial infrared images where the targets are visually recognizable by an expert but a DCNN-based target detector cannot detect the targets in the image.",
        "published":"2021-08-12 20:00:55+00:00",
        "authors":"['Uche M. Osahor', 'Nasser M. Nasrabadi']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.85,
        "actuality":2.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2108.05962v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.05962v1",
        "title":"DRQN-based 3D Obstacle Avoidance with a Limited Field of View",
        "summary":"In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonomous navigation for an indoor mobile robot using a depth camera with a narrow field of view. We first train a neural network with LSTM units in a 3D simulator of mobile robots to approximate the Q-value function in double DRQN. We also use a curriculum learning strategy to accelerate and stabilize the training process. Then we deploy the trained model to a real robot to perform 3D obstacle avoidance in its navigation. We evaluate the proposed approach both in the simulated environment and on a robot in the real world. The experimental results show that the approach is efficient and easy to be deployed, and it performs well for 3D obstacle avoidance with a narrow observation angle, which outperforms other existing DRL-based models by 15.5% on success rate.",
        "published":"2021-08-12 21:03:44+00:00",
        "authors":"[\"Yu'an Chen\", 'Guangda Chen', 'Lifan Pan', 'Jun Ma', 'Yu Zhang', 'Yanyong Zhang', 'Jianmin Ji']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2108.06606v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.06606v1",
        "title":"Prediction Analysis of Optical Tracker Parameters using Machine Learning Approaches for efficient Head Tracking",
        "summary":"A head tracker is a crucial part of the head mounted display systems, as it tracks the head of the pilot in the plane\/cockpit simulator. The operational flaws of head trackers are also dependent on different environmental conditions like different lighting conditions and stray light interference. In this letter, an optical tracker has been employed to gather the 6-DoF data of head movements under different environmental conditions. Also, the effect of different environmental conditions and variation in distance between the receiver and optical transmitter on the 6-DoF data was analyzed.",
        "published":"2021-08-14 19:27:38+00:00",
        "authors":"['Aman Kataria', 'Smarajit Ghosh', 'Vinod Karar']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.9,
        "actuality":3.0,
        "controversy":1.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2108.06625v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.06625v2",
        "title":"Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer",
        "summary":"In order to model the evolution of user preference, we should learn user\/item embeddings based on time-ordered item purchasing sequences, which is defined as Sequential Recommendation (SR) problem. Existing methods leverage sequential patterns to model item transitions. However, most of them ignore crucial temporal collaborative signals, which are latent in evolving user-item interactions and coexist with sequential patterns. Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging. Firstly, it is hard to simultaneously encode sequential patterns and collaborative signals. Secondly, it is non-trivial to express the temporal effects of collaborative signals. Hence, we design a new framework Temporal Graph Sequential Recommender (TGSRec) upon our defined continuous-time bi-partite graph. We propose a novel Temporal Collaborative Trans-former (TCT) layer in TGSRec, which advances the self-attention mechanism by adopting a novel collaborative attention. TCT layer can simultaneously capture collaborative signals from both users and items, as well as considering temporal dynamics inside sequential patterns. We propagate the information learned fromTCTlayerover the temporal graph to unify sequential patterns and temporal collaborative signals. Empirical results on five datasets show that TGSRec significantly outperforms other baselines, in average up to 22.5% and 22.1%absolute improvements in Recall@10and MRR, respectively.",
        "published":"2021-08-14 22:50:53+00:00",
        "authors":"['Ziwei Fan', 'Zhiwei Liu', 'Jiawei Zhang', 'Yun Xiong', 'Lei Zheng', 'Philip S. Yu']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.75,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.0555
    },
    {
        "arxiv_id":"2108.06817v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.06817v1",
        "title":"Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks",
        "summary":"With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various mixed integer linear programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, convolutional neural networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the internal effects among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in real-time.",
        "published":"2021-08-15 21:32:47+00:00",
        "authors":"['Yantong Wang', 'Ye Hu', 'Zhaohui Yang', 'Walid Saad', 'Kai-Kit Wong', 'Vasilis Friderikos']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.84,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2108.06837v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.06837v1",
        "title":"ALTo: Ad Hoc High-Accuracy Touch Interaction Using Acoustic Localization",
        "summary":"Millions of people around the world face motor impairments due to Parkinson's, cerebral palsy, muscular dystrophy and other physical disabilities. The goal of this project is to increase the usable surface-area of devices for users with these disabilities by creating a simple, inexpensive, and portable way to enable high accuracy touch interaction with large surfaces such as a table or even a wall. This project uses a novel approach that analyzes the acoustic signals at four piezoelectric microphones placed on the interactive surface to identify sounds related to the same event (e.g., a finger tap) at each of the microphones. ALTo (Acoustic Localized Touch) uses the results of this signal processing to compute the time difference of arrival (TDOA) across the microphones. The collected TDOA data is used to compute an approximate location of a sound source (e.g., a finger tap) using a collection of hyperbolic equations. An experimental evaluation of a system prototype was used to identify a number of software and signal processing optimizations needed to significantly improve accuracy and create a usable system. The results of the research indicate that it is possible to detect the location of a touch with high accuracy. The ALTo prototype achieves an accuracy of 1.45cm in the x-direction and 2.72cm the y-direction which is within the range for the target usage (i.e., those with motor impairments).",
        "published":"2021-08-15 23:06:52+00:00",
        "authors":"['Arvind Seshan']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":4.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":5.0,
        "newsworthiness_crowd_sum":4.0
    },
    {
        "arxiv_id":"2108.06933v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.06933v1",
        "title":"Decision Making in Project Groups Formation: Students' Perception and Reconciliation",
        "summary":"Academics is a huge repository of research avenue. Students tend to behave and adapt to the classroom based on their peer influences. Peers help in the increase of communication skills. Research shows group study is more effective than individual study. Group formation is influenced by several factors, like friends, demographic and linguistically similar. Toppers are more considerate but again based on comfort level friends are chosen. In this paper we analyze the change in preferences of group mates, and inferences are drawn over their reconciliations.",
        "published":"2021-08-16 07:11:18+00:00",
        "authors":"['Rajeshwari K', 'Apurva Rohit Hegde', 'Drishika Patil']",
        "arxiv_primary_category":"cs.si",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2108.06973v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.06973v1",
        "title":"Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?",
        "summary":"Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median popularity, to quantify popularity bias. Moreover, it does so irrespective of user characteristics other than the inclination to popular content. In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's tau rank-order correlation). This results in a more detailed picture of the characteristics of popularity bias. Furthermore, we investigate whether such algorithmic popularity bias affects users of different genders in the same way. We focus on music recommendation and conduct experiments on the recently released standardized LFM-2b dataset, containing listening profiles of Last.fm users. We investigate the algorithmic popularity bias of seven common recommendation algorithms (five collaborative filtering and two baselines). Our experiments show that (1) the studied metrics provide novel insights into popularity bias in comparison with only using average differences, (2) algorithms less inclined towards popularity bias amplification do not necessarily perform worse in terms of utility (NDCG), (3) the majority of the investigated recommenders intensify the popularity bias of the female users.",
        "published":"2021-08-16 08:58:46+00:00",
        "authors":"['Oleg Lesota', 'Alessandro B. Melchiorre', 'Navid Rekabsaz', 'Stefan Brandl', 'Dominik Kowald', 'Elisabeth Lex', 'Markus Schedl']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.5278333333
    },
    {
        "arxiv_id":"2108.07041v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07041v1",
        "title":"Implicitly Regularized RL with Implicit Q-Values",
        "summary":"The $Q$-function is a central quantity in many Reinforcement Learning (RL) algorithms for which RL agents behave following a (soft)-greedy policy w.r.t. to $Q$. It is a powerful tool that allows action selection without a model of the environment and even without explicitly modeling the policy. Yet, this scheme can only be used in discrete action tasks, with small numbers of actions, as the softmax cannot be computed exactly otherwise. Especially the usage of function approximation, to deal with continuous action spaces in modern actor-critic architectures, intrinsically prevents the exact computation of a softmax. We propose to alleviate this issue by parametrizing the $Q$-function implicitly, as the sum of a log-policy and of a value function. We use the resulting parametrization to derive a practical off-policy deep RL algorithm, suitable for large action spaces, and that enforces the softmax relation between the policy and the $Q$-value. We provide a theoretical analysis of our algorithm: from an Approximate Dynamic Programming perspective, we show its equivalence to a regularized version of value iteration, accounting for both entropy and Kullback-Leibler regularization, and that enjoys beneficial error propagation results. We then evaluate our algorithm on classic control tasks, where its results compete with state-of-the-art methods.",
        "published":"2021-08-16 12:20:47+00:00",
        "authors":"['Nino Vieillard', 'Marcin Andrychowicz', 'Anton Raichuk', 'Olivier Pietquin', 'Matthieu Geist']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.8611666667
    },
    {
        "arxiv_id":"2108.07119v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07119v2",
        "title":"Creating and Querying Personalized Versions of Wikidata on a Laptop",
        "summary":"Application developers today have three choices for exploiting the knowledge present in Wikidata: they can download the Wikidata dumps in JSON or RDF format, they can use the Wikidata API to get data about individual entities, or they can use the Wikidata SPARQL endpoint. None of these methods can support complex, yet common, query use cases, such as retrieval of large amounts of data or aggregations over large fractions of Wikidata. This paper introduces KGTK Kypher, a query language and processor that allows users to create personalized variants of Wikidata on a laptop. We present several use cases that illustrate the types of analyses that Kypher enables users to run on the full Wikidata KG on a laptop, combining data from external resources such as DBpedia. The Kypher queries for these use cases run much faster on a laptop than the equivalent SPARQL queries on a Wikidata clone running on a powerful server with 24h time-out limits.",
        "published":"2021-08-06 00:00:33+00:00",
        "authors":"['Hans Chalupsky', 'Pedro Szekely', 'Filip Ilievski', 'Daniel Garijo', 'Kartik Shenoy']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.79,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2108.07383v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07383v1",
        "title":"Learning to Cluster via Same-Cluster Queries",
        "summary":"We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries. Different from previous approaches, we do not assume that the total number of clusters is known at the beginning and do not require that the true clusters are consistent with a predefined objective function such as the K-means. These relaxations are critical from the practical perspective and, meanwhile, make the problem more challenging. We propose two algorithms with provable theoretical guarantees and verify their effectiveness via an extensive set of experiments on both synthetic and real-world data.",
        "published":"2021-08-17 00:37:11+00:00",
        "authors":"['Yi Li', 'Yan Song', 'Qin Zhang']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":1.6666666667,
        "controversy":2.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.1388333333
    },
    {
        "arxiv_id":"2108.07711v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07711v1",
        "title":"Explainability Auditing for Intelligent Systems: A Rationale for Multi-Disciplinary Perspectives",
        "summary":"National and international guidelines for trustworthy artificial intelligence (AI) consider explainability to be a central facet of trustworthy systems. This paper outlines a multi-disciplinary rationale for explainability auditing. Specifically, we propose that explainability auditing can ensure the quality of explainability of systems in applied contexts and can be the basis for certification as a means to communicate whether systems meet certain explainability standards and requirements. Moreover, we emphasize that explainability auditing needs to take a multi-disciplinary perspective, and we provide an overview of four perspectives (technical, psychological, ethical, legal) and their respective benefits with respect to explainability auditing.",
        "published":"2021-08-05 09:12:00+00:00",
        "authors":"['Markus Langer', 'Kevin Baum', 'Kathrin Hartmann', 'Stefan Hessel', 'Timo Speith', 'Jonas Wahl']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.83,
        "actuality":4.0,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2108.07714v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07714v1",
        "title":"Harnessing value from data science in business: ensuring explainability and fairness of solutions",
        "summary":"The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems. For fairness, the authors discuss the bias-inducing specifics, as well as relevant mitigation methods, concluding with a set of recipes for introducing fairness in data-driven organizations. Additionally, for XAI, the authors audit specific algorithms paired with demonstrational business use-cases, discuss a plethora of techniques of explanations quality quantification and provide an overview of future research avenues.",
        "published":"2021-08-10 11:59:38+00:00",
        "authors":"['Krzysztof Chomiak', 'Micha\u0142 Miktus']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.81,
        "actuality":4.6666666667,
        "controversy":4.0,
        "relevance_magnitude":5.0,
        "relevance_valence":5.0,
        "newsworthiness_crowd_sum":4.6666666667
    },
    {
        "arxiv_id":"2108.07776v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07776v1",
        "title":"SPAN: Subgraph Prediction Attention Network for Dynamic Graphs",
        "summary":"This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction. This proposed end-to-end model learns a mapping from the subgraph structures in the current snapshot to the subgraph structures in the next snapshot directly, i.e., edge existence among multiple nodes in the subgraph. A new mechanism named cross-attention with a twin-tower module is designed to integrate node attribute information and topology information collaboratively for learning subgraph evolution. We compare our model with several state-of-the-art methods for subgraph prediction and subgraph pattern prediction in multiple real-world homogeneous and heterogeneous dynamic graphs, respectively. Experimental results demonstrate that our model outperforms other models in these two tasks, with a gain increase from 5.02% to 10.88%.",
        "published":"2021-08-17 17:29:52+00:00",
        "authors":"['Yuan Li', 'Chuanchang Chen', 'Yubo Tao', 'Hai Lin']",
        "arxiv_primary_category":"cs.si",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2108.07855v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.07855v1",
        "title":"Understanding Data Visualization Design Practice",
        "summary":"Professional roles for data visualization designers are growing in popularity, and interest in relationships between the academic research and professional practice communities is gaining traction. However, despite the potential for knowledge sharing between these communities, we have little understanding of the ways in which practitioners design in real-world, professional settings. Inquiry in numerous design disciplines indicates that practitioners approach complex situations in ways that are fundamentally different from those of researchers. In this work, I take a practice-led approach to understanding visualization design practice on its own terms. Twenty data visualization practitioners were interviewed and asked about their design process, including the steps they take, how they make decisions, and the methods they use. Findings suggest that practitioners do not follow highly systematic processes, but instead rely on situated forms of knowing and acting in which they draw from precedent and use methods and principles that are determined appropriate in the moment. These findings have implications for how visualization researchers understand and engage with practitioners, and how educators approach the training of future data visualization designers.",
        "published":"2021-08-17 19:56:52+00:00",
        "authors":"['Paul Parsons']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.93,
        "actuality":2.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2108.08022v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.08022v1",
        "title":"SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation",
        "summary":"Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, despite being extensively studied, these methods still suffer from some limitations. First, previous studies either encode the document or extract latent sentiment via neural networks, which are difficult to interpret the sentiment of reviewers intuitively. Second, they neglect the personalized interaction of reviews with user\/item, i.e., each review has different contributions when modeling the sentiment preference of user\/item. To remedy these issues, we propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. Specifically, we first encode user\/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels. Finally, we design a rating prediction task that contains a rating learner with an interactive and fusion module to fuse the identity (i.e., user and item ID) and each review representation so that various interactive features can synergistically influence the final rating score. Experimental results on five real-world datasets demonstrate that the proposed model is superior to state-of-the-art models.",
        "published":"2021-08-18 08:04:38+00:00",
        "authors":"['Kai Zhang', 'Hao Qian', 'Qi Liu', 'Zhiqiang Zhang', 'Jun Zhou', 'Jianhui Ma', 'Enhong Chen']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.87,
        "actuality":1.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.5278333333
    },
    {
        "arxiv_id":"2108.08593v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.08593v1",
        "title":"3D Shapes Local Geometry Codes Learning with SDF",
        "summary":"A signed distance function (SDF) as the 3D shape description is one of the most effective approaches to represent 3D geometry for rendering and reconstruction. Our work is inspired by the state-of-the-art method DeepSDF that learns and analyzes the 3D shape as the iso-surface of its shell and this method has shown promising results especially in the 3D shape reconstruction and compression domain. In this paper, we consider the degeneration problem of reconstruction coming from the capacity decrease of the DeepSDF model, which approximates the SDF with a neural network and a single latent code. We propose Local Geometry Code Learning (LGCL), a model that improves the original DeepSDF results by learning from a local shape geometry of the full 3D shape. We add an extra graph neural network to split the single transmittable latent code into a set of local latent codes distributed on the 3D shape. Mentioned latent codes are used to approximate the SDF in their local regions, which will alleviate the complexity of the approximation compared to the original DeepSDF. Furthermore, we introduce a new geometric loss function to facilitate the training of these local latent codes. Note that other local shape adjusting methods use the 3D voxel representation, which in turn is a problem highly difficult to solve or even is insolvable. In contrast, our architecture is based on graph processing implicitly and performs the learning regression process directly in the latent code space, thus make the proposed architecture more flexible and also simple for realization. Our experiments on 3D shape reconstruction demonstrate that our LGCL method can keep more details with a significantly smaller size of the SDF decoder and outperforms considerably the original DeepSDF method under the most important quantitative metrics.",
        "published":"2021-08-19 09:56:03+00:00",
        "authors":"['Shun Yao', 'Fei Yang', 'Yongmei Cheng', 'Mikhail G. Mozerov']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.84,
        "actuality":3.0,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0000833333
    },
    {
        "arxiv_id":"2108.08990v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.08990v1",
        "title":"Few Shot Activity Recognition Using Variational Inference",
        "summary":"There has been a remarkable progress in learning a model which could recognise novel classes with only a few labeled examples in the last few years. Few-shot learning (FSL) for action recognition is a challenging task of recognising novel action categories which are represented by few instances in the training data. We propose a novel variational inference based architectural framework (HF-AR) for few shot activity recognition. Our framework leverages volume-preserving Householder Flow to learn a flexible posterior distribution of the novel classes. This results in better performance as compared to state-of-the-art few shot approaches for human activity recognition. approach consists of base model and an adapter model. Our architecture consists of a base model and an adapter model. The base model is trained on seen classes and it computes an embedding that represent the spatial and temporal insights extracted from the input video, e.g. combination of Resnet-152 and LSTM based encoder-decoder model. The adapter model applies a series of Householder transformations to compute a flexible posterior distribution that lends higher accuracy in the few shot approach. Extensive experiments on three well-known datasets: UCF101, HMDB51 and Something-Something-V2, demonstrate similar or better performance on 1-shot and 5-shot classification as compared to state-of-the-art few shot approaches that use only RGB frame sequence as input. To the best of our knowledge, we are the first to explore variational inference along with householder transformations to capture the full rank covariance matrix of posterior distribution, for few shot learning in activity recognition.",
        "published":"2021-08-20 03:57:58+00:00",
        "authors":"['Neeraj Kumar', 'Siddhansh Narang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.84,
        "actuality":2.3333333333,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6111666667
    },
    {
        "arxiv_id":"2108.09044v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09044v1",
        "title":"Towards A Simple and Efficient VDTN Routing Protocol for Data Collection in Smart Cities",
        "summary":"Smart cities today can utilize Vehicular Delay Tolerant Networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available Intelligent Transportation Systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their Vehicle-to-Everything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: Direct Delivery, First Contact, Epidemic, and Spray & Wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities.",
        "published":"2021-08-20 08:17:39+00:00",
        "authors":"['Ngurah Indra Er', 'Kamal Deep Singh', 'Christophe Couturier', 'Jean-Marie Bonnin']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.89,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.1388333333
    },
    {
        "arxiv_id":"2108.09079v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09079v1",
        "title":"Structure-Preserving Deraining with Residue Channel Prior Guidance",
        "summary":"Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures. To solve this problem, we propose a Structure-Preserving Deraining Network (SPDNet) with RCP guidance. SPDNet directly generates high-quality rain-free images with clear and accurate structures under the guidance of RCP but does not rely on any rain-generating assumptions. Specifically, we found that the RCP of images contains more accurate structural information than rainy images. Therefore, we introduced it to our deraining network to protect structure information of the rain-free image. Meanwhile, a Wavelet-based Multi-Level Module (WMLM) is proposed as the backbone for learning the background information of rainy images and an Interactive Fusion Module (IFM) is designed to make full use of RCP information. In addition, an iterative guidance strategy is proposed to gradually improve the accuracy of RCP, refining the result in a progressive path. Extensive experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves new state-of-the-art results. Code: https:\/\/github.com\/Joyies\/SPDNet",
        "published":"2021-08-20 09:09:56+00:00",
        "authors":"['Qiaosi Yi', 'Juncheng Li', 'Qinyan Dai', 'Faming Fang', 'Guixu Zhang', 'Tieyong Zeng']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":2.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.72225
    },
    {
        "arxiv_id":"2108.09176v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09176v1",
        "title":"Controller Placement in SDN-enabled 5G Satellite-Terrestrial Networks",
        "summary":"SDN-enabled Integrated satellite-terrestrial networks (ISTNs), can provide several advantages including global seamless coverage, high reliability, low latency, etc. and can be a key enabler towards next generation networks. To deal with the complexity of the control and management of the integrated network, leveraging the concept of software-defined networking (SDN) will be helpful. In this regard, the SDN controller placement problem in SDN-enabled ISTNs becomes of paramount importance. In this paper, we formulate an optimization problem for the SDN controller placement with the objective of minimizing the average failure probability of SDN control paths to ensure the SDN switches receive the instructions in the most reliable fashion. Simultaneously, we aim at deploying the SDN controllers close to the satellite gateways to ensure the connection between the two layers occurs with the lowest latency. We first model the problem as a mixed integer linear program (MILP). To reduce the time complexity of the MILP model, we use submodular optimization techniques to generate near-optimal solutions in a time-efficient manner. Finally, we verify the effectiveness of our approach by means of simulation, showing that the approximation method results in a reasonable optimality gap with respect to the exact MILP solution.",
        "published":"2021-08-20 13:47:23+00:00",
        "authors":"['Nariman Torkzaban', 'John S. Baras']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.8,
        "actuality":4.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.6389166667
    },
    {
        "arxiv_id":"2108.09404v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09404v2",
        "title":"Safe Transformative AI via a Windfall Clause",
        "summary":"Society could soon see transformative artificial intelligence (TAI). Models of competition for TAI show firms face strong competitive pressure to deploy TAI systems before they are safe. This paper explores a proposed solution to this problem, a Windfall Clause, where developers commit to donating a significant portion of any eventual extremely large profits to good causes. However, a key challenge for a Windfall Clause is that firms must have reason to join one. Firms must also believe these commitments are credible. We extend a model of TAI competition with a Windfall Clause to show how firms and policymakers can design a Windfall Clause which overcomes these challenges. Encouragingly, firms benefit from joining a Windfall Clause under a wide range of scenarios. We also find that firms join the Windfall Clause more often when the competition is more dangerous. Even when firms learn each other's capabilities, firms rarely wish to withdraw their support for the Windfall Clause. These three findings strengthen the case for using a Windfall Clause to promote the safe development of TAI.",
        "published":"2021-08-20 23:59:27+00:00",
        "authors":"['Paolo Bova', 'Jonas Emanuel M\u00fcller', 'Benjamin Harack']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.91,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.6943333333
    },
    {
        "arxiv_id":"2108.09416v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09416v1",
        "title":"2020 U.S. Presidential Election: Analysis of Female and Male Users on Twitter",
        "summary":"Social media is commonly used by the public during election campaigns to express their opinions regarding different issues. Among various social media channels, Twitter provides an efficient platform for researchers and politicians to explore public opinion regarding a wide range of topics such as economy and foreign policy. Current literature mainly focuses on analyzing the content of tweets without considering the gender of users. This research collects and analyzes a large number of tweets and uses computational, human coding, and statistical analyses to identify topics in more than 300,000 tweets posted during the 2020 U.S. presidential election and to compare female and male users regarding the average weight of the topics. Our findings are based upon a wide range of topics, such as tax, climate change, and the COVID-19 pandemic. Out of the topics, there exists a significant difference between female and male users for more than 70% of topics. Our research approach can inform studies in the areas of informatics, politics, and communication, and it can be used by political campaigns to obtain a gender-based understanding of public opinion.",
        "published":"2021-08-21 01:31:03+00:00",
        "authors":"['Amir Karami', 'Spring B. Clark', 'Anderson Mackenzie', 'Dorathea Lee', 'Michael Zhu', 'Hannah R. Boyajieff', 'Bailey Goldschmidt']",
        "arxiv_primary_category":"cs.si",
        "readability":0.94,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.8611666667
    },
    {
        "arxiv_id":"2108.09516v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09516v1",
        "title":"Networks of Twin Peaks: the Dale Cooper Effect",
        "summary":"In this work, we analyse character networks in the cult TV show Twin Peaks. In the small-scale community network of Twin Peaks we discovered a new storytelling network phenomenon we called the Dale Cooper Effect, a phase transition in network structure. It is a sharp demarcation between the two statistically and topologically distinct networks of characters, where the point of demarcation is the protagonist himself (Special Agent Dale Cooper) introduced as a median character.",
        "published":"2021-08-21 14:15:57+00:00",
        "authors":"['Harun \u0160iljak']",
        "arxiv_primary_category":"cs.si",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2108.09538v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09538v1",
        "title":"Using Trajectory Compression Rate to Predict Changes in Cybersickness in Virtual Reality Games",
        "summary":"Identifying cybersickness in virtual reality (VR) applications such as games in a fast, precise, non-intrusive, and non-disruptive way remains challenging. Several factors can cause cybersickness, and their identification will help find its origins and prevent or minimize it. One such factor is virtual movement. Movement, whether physical or virtual, can be represented in different forms. One way to represent and store it is with a temporally annotated point sequence. Because a sequence is memory-consuming, it is often preferable to save it in a compressed form. Compression allows redundant data to be eliminated while still preserving changes in speed and direction. Since changes in direction and velocity in VR can be associated with cybersickness, changes in compression rate can likely indicate changes in cybersickness levels. In this research, we explore whether quantifying changes in virtual movement can be used to estimate variation in cybersickness levels of VR users. We investigate the correlation between changes in the compression rate of movement data in two VR games with changes in players' cybersickness levels captured during gameplay. Our results show (1) a clear correlation between changes in compression rate and cybersickness, and(2) that a machine learning approach can be used to identify these changes. Finally, results from a second experiment show that our approach is feasible for cybersickness inference in games and other VR applications that involve movement.",
        "published":"2021-08-21 16:26:04+00:00",
        "authors":"['Diego Monteiro', 'Hai-Ning Liang', 'Xiaohang Tang', 'Pourang Irani']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":2.6666666667,
        "controversy":1.0,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.4165833333
    },
    {
        "arxiv_id":"2108.09748v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09748v1",
        "title":"Towards a Resilient Information System for Agriculture Extension Information Service: An Exploratory Study",
        "summary":"Although digital technologies are contributing to human development, several information systems (IS) interventions for development especially in developing countries do not perform as expected nor deliver anticipated outcomes at scale. This raises questions about how to develop and enhance resilient IS for development, an area that requires more research attention. A sound and systematic understanding of the mechanisms local communities apply to maintain resilience and the key transformation areas for a resilient IS development will help to improve the situation. This study addresses how stakeholders can ensure resilient information provision within the Agricultural Extension Information Service (AEIS) and identifies the challenges in designing resilient IS. Conceptually, the study draws from the IS resilience framework. Empirically, it draws from interview data collected from the AEIS provision practice in Ethiopia. The findings show the robustness, self-organization, learning, redundancy, rapidity, scale, diversity and equality mechanisms, the challenges and the key transformations required to advance the resilience of IS for AEIS. The study contributes to the conversation on the application of the IS resilience framework in analyzing local information provision practices as well as to practice highlighting the key transformation areas to improve the effectiveness and impact of AEIS.",
        "published":"2021-08-22 15:10:01+00:00",
        "authors":"['Muluneh Atinaf', 'Alemayehu Molla', 'Salehu Anteneh']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.9,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.1943333333
    },
    {
        "arxiv_id":"2108.09751v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09751v1",
        "title":"Connecting the Dots in Nutritional Rehabilitation: A Qualitative Study on ICT and Community Based Care",
        "summary":"'Fragmentation in care' continuum is often considered as a shortcoming of Health system whereas, 'Integration of care' is widely acclaimed as a viable solution to fragmentation. In last two decades, Information and communication technologies (ICTs), by virtue of their ability to integrate information for action, has been extensively used in addressing many public health problems like malnutrition. Tackling the public health challenge of malnutrition demands attention to interconnectedness and interactions between multiple systems. In this paper, using a case study of an ICT application used by community workers for malnutrition management in Karnataka, we argue that lack of recognition of interconnectedness and interactions among stakeholders and context can pose a challenge to integration of care. ICTs can be key enablers to overcome fragmentation, provided it recognizes the inherent complexities of malnutrition and its management. We argue that for an effective ICT enabled integration of Severe Acute Malnutrition (SAM) management, a thorough understanding of perspectives of multiple stakeholders together with rich picture of the contextual dynamics should not be ignored at design and implementation phase.",
        "published":"2021-08-22 15:19:32+00:00",
        "authors":"['Deepa Austin', 'Amit Prakash']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.2778333333
    },
    {
        "arxiv_id":"2108.09759v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09759v1",
        "title":"E-commerce for Rural Micro-Entrepreneurs: Mapping Restrictions, Ecologies of Use and Trends for Development",
        "summary":"This paper addresses the struggle of rural micro-entrepreneurs in the Global South in utilizing e-commerce to reach wider markets. This research paper looks at the adoption of e-commerce as a sustainable marketplace by the micro-entrepreneur sellers from the lower socio-economic rural communities in India, a booming digital economy in the Global South. 'Sustainability' here refers to a model for sustainable economic development sustaining the e-commerce as business model for the rural micro-entrepreneurs to flourish. This paper explores rural development by dismantling the factors that shape the ways technology and trade impact micro-entrepreneurs. The aim is to offer recommendations and solutions to contribute building the e-commerce as a sustainable marketplace for rural micro-entrepreneurs. Recent information and economic policy changes in India, along with the expansion of mobile infrastructure and a growing user base in rural regions makes this research timely and important. By scrutinizing the infrastructure and auditing the information needs and challenges of users, this research will illuminate the gaps that are leading to a lack of sustainable economic development, and information asymmetries discouraging the rural micro-entrepreneurs from selling online. The purpose of the paper is to find hurdles in the sustainable development of e-commerce as a business solution.",
        "published":"2021-08-22 15:41:35+00:00",
        "authors":"['Aditi Bhatia-Kalluri']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.92,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2108.09798v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09798v1",
        "title":"Power Participation in Digital Citizen Engagement in South African Local Government: The Case of MOBISAM",
        "summary":"A lack of service delivery and accountability are two characteristic challenges of numerous municipalities (local government) in South Africa. MobiSAM was introduced as a collaborative effort between a local university, civil society, residents, and local government to grow digital citizen engagement, facilitate two-way communication between local government and its citizenry, and contribute to the improved provision of basic services. Through the course of the project, it became clear that power, with respect to knowledge, was at play in the interactions or lack thereof between local government and citizens. This work-in-progress paper begins the journey of exploration of the power\/knowledge dynamics at play in the MobiSAM project (using the SECI model and the Power Cube) by unpacking and understanding the nature of knowledge processed in the project and the associated knowledge creation processes that ensued between the different project stakeholders over time. The influence of power in determining the effective transfer of knowledge between key stakeholders of the project, for capacity building and organisational learning has emerged as an important issue in need of thorough investigation and critical analysis. This work in progress paper presents the preliminary framing of the research findings on the phases associated with the interplay of knowledge and power in the MobiSAM project, which are: 1) Realisation, 2) Navigating Responsiveness, and 3) Emergence.",
        "published":"2021-08-22 17:48:36+00:00",
        "authors":"['Caroline Khene', 'Ingrid Sieb\u00f6rger', 'Mamello Thinyane', 'Clement Simuja']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.91,
        "actuality":3.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3889166667
    },
    {
        "arxiv_id":"2108.09803v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09803v1",
        "title":"Factors Enhancing E-Government Service Gaps in a Developing Country Context",
        "summary":"Globally, the discourse of e-government has gathered momentum in public service delivery. No country has been left untouched in the implementation of e-government. Several government departments and agencies are now using information and communication technology (ICTs) to deliver government services and information to citizens, other government departments, and businesses. However, most of the government departments have not provided all of their services electronically or at least the most important ones. Thus, this creates a phenomenon of e-government service gaps. The objective of this study was to investigate the contextual factors enhancing e-government service gaps in a developing country. To achieve this aim, the TOE framework was employed together with a qualitative case study to guide data collection and analysis. The data was collected through semi-structured interviews from government employees who are involved in the implementation of e-government services in Zimbabwe as well as from citizens and businesses. Eleven (11) factors were identified and grouped under the TOE framework. This research contributes significantly to the implementation and utilisation of e-government services in Zimbabwe. The study also contributes to providing a strong theoretical understanding of the factors that enhance e-government service gaps explored in the research model.",
        "published":"2021-08-22 17:58:31+00:00",
        "authors":"['Gilbert Mahlangu', 'Ephias Ruhode']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.95,
        "actuality":4.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2108.09813v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09813v1",
        "title":"Towards an Integrated Knowledge Management and Information and Communication Technology Framework for Improving Disaster Response in a Developing Country Context",
        "summary":"This paper is part of an ongoing project that seeks to address a gap in disaster information coordination and collaboration in Zimbabwe. There is lack of coordinated information and knowledge in natural disaster and emergency situations in Zimbabwe. This results in weak collaboration links among the various organizations that respond to emergencies, leading to slow decision making processes and long response times. This negatively affects the affected communities, exacerbating poverty in Zimbabwe. This has been evidenced in the recent catastrophic cyclone Idai where many people were left dead, infrastructure destroyed and some people marooned. To address this, the research seeks to develop an integrated Knowledge Management and ICT framework that aid in coordination and collaboration among the various crisis responders. This will be achieved through a case study approach using Zimbabwe's Civil Protection Unit. PAR within DSRM will be used to gather data from CPU as well as with NGO respondents, traditional leaders and disaster response experts. Findings will be compared and contrasted with secondary data gathered in literature, this, with collected data will be used in developing a home grown coordination and collaboration solution. Qualitative approach to data collection will be adopted using interviews, visioning workshops and document analysis.",
        "published":"2021-08-22 18:26:04+00:00",
        "authors":"['Teurai Matekenya', 'Ephias Ruhode']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.93,
        "actuality":4.0,
        "controversy":1.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.6943333333
    },
    {
        "arxiv_id":"2108.09858v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09858v1",
        "title":"Data Augmentation Using Many-To-Many RNNs for Session-Aware Recommender Systems",
        "summary":"The ACM WSDM WebTour 2021 Challenge organized by Booking.com focuses on applying Session-Aware recommender systems in the travel domain. Given a sequence of travel bookings in a user trip, we look to recommend the user's next destination. To handle the large dimensionality of the output's space, we propose a many-to-many RNN model, predicting the next destination chosen by the user at every sequence step as opposed to only the final one. We show how this is a computationally efficient alternative to doing data augmentation in a many-to-one RNN, where we consider every subsequence of a session starting from the first element. Our solution achieved 4th place in the final leaderboard, with an accuracy@4 of 0.5566.",
        "published":"2021-08-22 22:12:25+00:00",
        "authors":"['Mart\u00edn Baigorria Alonso']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2108.09864v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09864v1",
        "title":"A Round-Robin Packet Scheduler for Hierarchical Max-Min Fairness",
        "summary":"Hierarchical link sharing addresses the demand for fine-grain traffic control at multiple levels of aggregation. At present, packet schedulers that can support hierarchical link sharing are not suitable for an implementation at line rates, and deployed schedulers perform poorly when distributing excess capacity to classes that need additional bandwidth. We present HLS, a packet scheduler that ensures a hierarchical max-min fair allocation of the link bandwidth. HLS supports minimum rate guarantees and isolation between classes. Since it is realized as a non-hierarchical round robin scheduler, it is suitable to operate at high rates. We implement HLS in the Linux kernel and evaluate it with respect to achieved rate allocations and overhead. We compare the results with those obtained for CBQ and HTB, the existing scheduling algorithms in Linux for hierarchical link sharing. We show that the overhead of HLS is comparable to that of other classful packet schedulers.",
        "published":"2021-08-22 22:54:22+00:00",
        "authors":"['Natchanon Luangsomboon', 'J\u00f6rg Liebeherr']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.82,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2108.09947v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.09947v1",
        "title":"For Better or for Worse? A Framework for Critical Analysis of ICT4D for Women",
        "summary":"Diffusion of ICTs provide possibilities for women empowerment by greater participation and enhanced gender-based digital equality. However, a critical analysis reveals that as ICT diffusion widens, there is a persistent threat of widening the gender-based digital divide and exposes women to online sexual abuses, predominantly in developing countries characterized by the gendered nature of the social structure. Instead of accepting ICT as the facilitator to women empowerment, in this paper, we develop a critical research framework for a gender-focused examination of ICT4D studies. Critical research methodology provides the appropriate ontology unveiling social realities through challenging the status quo and exposing the deeper societal inequalities. Using the critical research framework developed, we investigate past ICT4D initiatives and artifacts from literature and draw critical conclusions of its benefits and issues. This study would aid future ICT4D research to investigate areas of gender discrimination and understand the role of ICTs in a critical light.",
        "published":"2021-08-23 05:42:24+00:00",
        "authors":"['Abhipsa Pal', \"Rahul De'\"]",
        "arxiv_primary_category":"cs.cy",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2108.10092v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.10092v1",
        "title":"Medical Graphs in Patient Information Systems in Primary Care",
        "summary":"Graphs are very effective tools in visualizing information and are used in many fields including the medical field. In most developing countries primary care, graphs are used to monitor child growth. These measures are therefore often displayed using line graphs, basing it on three indicators (stunting, underweight and wasting) based on the WHO 2006 Child Growth Standard. Most literature on information visualization of electronic health record data focuses on aggregate data visualization tools. This research therefore, was set out to provide such an overview of requirements for computerized graphs for individual patient data, implemented in a way that all kinds of medical graphs showing the development of medical measures over time can be displayed. This research was interpretive, using a user-centric approach for data collection where interviews and web search was used to ensure that the graphs developed are fit the user requirements. This followed prototype development using one of the three free, open source software libraries for Android that were evaluated. The prototype was then used to refine the user requirements. The health workers interpreted the graphs developed flawlessly.",
        "published":"2021-08-23 11:44:35+00:00",
        "authors":"['Thea Hvalen Thodesen', 'Uy Tran', 'Jens Kaasboll', 'Chipo Kanjo', 'Tiwonge Manda']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.88,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8889166667
    },
    {
        "arxiv_id":"2108.10273v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.10273v1",
        "title":"Collect & Infer -- a fresh look at data-efficient Reinforcement Learning",
        "summary":"This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency. Data-efficient RL has gone through three major stages: pure on-line RL where every data-point is considered only once, RL with a replay buffer where additional learning is done on a portion of the experience, and finally transition memory based RL, where, conceptually, all transitions are stored and re-used in every update step. While inferring knowledge from all explicitly stored experience has lead to a tremendous gain in data-efficiency, the question of how this data is collected has been vastly understudied. We argue that data-efficiency can only be achieved through careful consideration of both aspects. We propose to make this insight explicit via a paradigm that we call 'Collect and Infer', which explicitly models RL as two separate but interconnected processes, concerned with data collection and knowledge inference respectively. We discuss implications of the paradigm, how its ideas are reflected in the literature, and how it can guide future research into data efficient RL.",
        "published":"2021-08-23 16:18:47+00:00",
        "authors":"['Martin Riedmiller', 'Jost Tobias Springenberg', 'Roland Hafner', 'Nicolas Heess']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.87,
        "actuality":3.0,
        "controversy":3.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2108.10379v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.10379v1",
        "title":"Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Models",
        "summary":"As Machine Translation (MT) has become increasingly more powerful, accessible, and widespread, the potential for the perpetuation of bias has grown alongside its advances. While overt indicators of bias have been studied in machine translation, we argue that covert biases expose a problem that is further entrenched. Through the use of the gender-neutral language Turkish and the gendered language English, we examine cases of both overt and covert gender bias in MT models. Specifically, we introduce a method to investigate asymmetrical gender markings. We also assess bias in the attribution of personhood and examine occupational and personality stereotypes through overt bias indicators in MT models. Our work explores a deeper layer of bias in MT models and demonstrates the continued need for language-specific, interdisciplinary methodology in MT model development.",
        "published":"2021-08-23 19:25:56+00:00",
        "authors":"['Chloe Ciora', 'Nur Iren', 'Malihe Alikhani']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.84,
        "actuality":4.6666666667,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.8888333333
    },
    {
        "arxiv_id":"2108.10665v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.10665v1",
        "title":"Sharing Practices for Datasets Related to Accessibility and Aging",
        "summary":"Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.",
        "published":"2021-08-24 12:04:29+00:00",
        "authors":"['Rie Kamikubo', 'Utkarsh Dwivedi', 'Hernisa Kacorri']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.85,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2108.10829v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.10829v1",
        "title":"HapticBots: Distributed Encountered-type Haptics for VR with Multiple Shape-changing Mobile Robots",
        "summary":"HapticBots introduces a novel encountered-type haptic approach for Virtual Reality (VR) based on multiple tabletop-size shape-changing robots. These robots move on a tabletop and change their height and orientation to haptically render various surfaces and objects on-demand. Compared to previous encountered-type haptic approaches like shape displays or robotic arms, our proposed approach has an advantage in deployability, scalability, and generalizability -- these robots can be easily deployed due to their compact form factor. They can support multiple concurrent touch points in a large area thanks to the distributed nature of the robots. We propose and evaluate a novel set of interactions enabled by these robots which include: 1) rendering haptics for VR objects by providing just-in-time touch-points on the user's hand, 2) simulating continuous surfaces with the concurrent height and position change, and 3) enabling the user to pick up and move VR objects through graspable proxy objects. Finally, we demonstrate HapticBots with various applications, including remote collaboration, education and training, design and 3D modeling, and gaming and entertainment.",
        "published":"2021-08-24 16:26:22+00:00",
        "authors":"['Ryo Suzuki', 'Eyal Ofek', 'Mike Sinclair', 'Daneil Leithinger', 'Mar Gonzalez-Franco']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":2.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2108.11303v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.11303v1",
        "title":"CancerBERT: a BERT model for Extracting Breast Cancer Phenotypes from Electronic Health Records",
        "summary":"Accurate extraction of breast cancer patients' phenotypes is important for clinical decision support and clinical research. Current models do not take full advantage of cancer domain-specific corpus, whether pre-training Bidirectional Encoder Representations from Transformer model on cancer-specific corpus could improve the performances of extracting breast cancer phenotypes from texts data remains to be explored. The objective of this study is to develop and evaluate the CancerBERT model for extracting breast cancer phenotypes from clinical texts in electronic health records. This data used in the study included 21,291 breast cancer patients diagnosed from 2010 to 2020, patients' clinical notes and pathology reports were collected from the University of Minnesota Clinical Data Repository (UMN). Results: About 3 million clinical notes and pathology reports in electronic health records for 21,291 breast cancer patients were collected to train the CancerBERT model. 200 pathology reports and 50 clinical notes of breast cancer patients that contain 9,685 sentences and 221,356 tokens were manually annotated by two annotators. 20% of the annotated data was used as a test set. Our CancerBERT model achieved the best performance with macro F1 scores equal to 0.876 (95% CI, 0.896-0.902) for exact match and 0.904 (95% CI, 0.896-0.902) for the lenient match. The NER models we developed would facilitate the automated information extraction from clinical texts to further help clinical decision support. Conclusions and Relevance: In this study, we focused on the breast cancer-related concepts extraction from EHR data and obtained a comprehensive annotated dataset that contains 7 types of breast cancer-related concepts. The CancerBERT model with customized vocabulary could significantly improve the performance for extracting breast cancer phenotypes from clinical texts.",
        "published":"2021-08-25 16:00:56+00:00",
        "authors":"['Sicheng Zhou', 'Liwei Wang', 'Nan Wang', 'Hongfang Liu', 'Rui Zhang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.6389166667
    },
    {
        "arxiv_id":"2108.11455v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.11455v1",
        "title":"Evaluating Students Perspectives on ICT Readiness in Somali Higher Education towards Teaching -- Learning Acceptance",
        "summary":"Along the rapid development of Information and communication technology (ICT) tools and growth of Internet access offer opportunities that facilitate teaching and learning activities in the context of higher education. However, the study of ICTs readiness and acceptance in Somalia higher education is meagre. This research aims to examine the current state of ICT readiness among university students and explores the factors that affect their readiness acceptance. It proposes an extended model, based on the Technology Acceptance Model (TAM), which explains how University students beliefs influence their readiness to accept ICT applications in their learning. Survey responses of 304 students from undergraduate and Graduate in Somalia higher education were collected and analyzed using structural equation modelling. The results of the data analysis demonstrated that the TAM explained university students readiness acceptance of ICT applications reasonably well. More specifically, perceived usefulness, Ease of Use, ICT Selfefficacy, Teaching-Learning autonomy, Students Optimism and Availability of ICT infrastructure are robust predictors of Students ICT readiness acceptance. Results also showed that internet affordability, network speed and quality, innovativeness, discomfort and insecurity do not have a meaningful effect on perceived usefulness and Ease of Use towards ICT readiness acceptance. Through the empirical results, this study helped us understand why students choose to engage in ICT applications for their learning context. Keywords: ICT readiness acceptance, Higher education,Teaching- Learning, Technology Acceptance Model",
        "published":"2021-08-25 20:01:32+00:00",
        "authors":"['Yunis Ali Ahmed', 'Mohamed M. Mohamed', 'Abdifatah Farah Ali', 'Mohamud M. Alasso', 'Ahmed Dahir Siyad Mohammad Nazir Ahmad']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2108.11460v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.11460v1",
        "title":"Podcast Metadata and Content: Episode Relevance andAttractiveness in Ad Hoc Search",
        "summary":"Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.",
        "published":"2021-08-25 20:20:57+00:00",
        "authors":"['Ben Carterette', 'Rosie Jones', 'Gareth F. Jones', 'Maria Eskevich', 'Sravana Reddy', 'Ann Clifton', 'Yongze Yu', 'Jussi Karlgren', 'Ian Soboroff']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":4.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2108.11480v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.11480v1",
        "title":"On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval",
        "summary":"Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) techniques, has been increasing in popularity. Two families of approaches have emerged, depending on whether documents and queries are represented by single or multiple embeddings. ColBERT, the exemplar of the latter, uses an ANN index and approximate scores to identify a set of candidate documents for each query embedding, which are then re-ranked using accurate document representations. In this manner, a large number of documents can be retrieved for each query, hindering the efficiency of the approach. In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored. Experiments conducted on the MSMARCO passage ranking corpus demonstrate that, by cutting of the candidate set by using the approximate scores to only 200 documents, we can still obtain an effective ranking without statistically significant differences in effectiveness, and resulting in a 2x speedup in efficiency.",
        "published":"2021-08-25 21:23:30+00:00",
        "authors":"['Craig Macdonald', 'Nicola Tonellotto']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":2.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2108.11807v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.11807v1",
        "title":"Human readable network troubleshooting based on anomaly detection and feature scoring",
        "summary":"Network troubleshooting is still a heavily human-intensive process. To reduce the time spent by human operators in the diagnosis process, we present a system based on (i) unsupervised learning methods for detecting anomalies in the time domain, (ii) an attention mechanism to rank features in the feature space and finally (iii) an expert knowledge module able to seamlessly incorporate previously collected domain-knowledge. In this paper, we thoroughly evaluate the performance of the full system and of its individual building blocks: particularly, we consider (i) 10 anomaly detection algorithms as well as (ii) 10 attention mechanisms, that comprehensively represent the current state of the art in the respective fields. Leveraging a unique collection of expert-labeled datasets worth several months of real router telemetry data, we perform a thorough performance evaluation contrasting practical results in constrained stream-mode settings, with the results achievable by an ideal oracle in academic settings. Our experimental evaluation shows that (i) the proposed system is effective in achieving high levels of agreement with the expert, and (ii) that even a simple statistical approach is able to extract useful information from expert knowledge gained in past cases, significantly improving troubleshooting performance.",
        "published":"2021-08-26 14:20:36+00:00",
        "authors":"['Jose M. Navarro', 'Alexis Huet', 'Dario Rossi']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.89,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2108.11914v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.11914v2",
        "title":"User-Centric Semi-Automated Infographics Authoring and Recommendation",
        "summary":"Designing infographics can be a tedious process for non-experts and time-consuming even for professional designers. Based on the literature and a formative study, we propose a flexible framework for automated and semi-automated infographics design. This framework captures the main design components in infographics and streamlines the generation workflow into three steps, allowing users to control and optimize each aspect independently. Based on the framework, we also propose an interactive tool, \\name{}, for assisting novice designers with creating high-quality infographics from an input in a markdown format by offering recommendations of different design components of infographics. Simultaneously, more experienced designers can provide custom designs and layout ideas to the tool using a canvas to control the automated generation process partially. As part of our work, we also contribute an individual visual group (VG) and connection designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs. This dataset plays a crucial role in diversifying the infographic designs created by our framework. We evaluate our approach with a comparison against similar tools, a user study with novice and expert designers, and a case study. Results confirm that our framework and \\name{} excel in creating customized infographics and exploring a large variety of designs.",
        "published":"2021-08-26 17:09:59+00:00",
        "authors":"['Anjul Tyagi', 'Jian Zhao', 'Pushkar Patel', 'Swasti Khurana', 'Klaus Mueller']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.83,
        "actuality":2.3333333333,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.3611666667
    },
    {
        "arxiv_id":"2108.12001v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12001v1",
        "title":"Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks",
        "summary":"Adversarial defenses train deep neural networks to be invariant to the input perturbations from adversarial attacks. Almost all defense strategies achieve this invariance through adversarial training i.e. training on inputs with adversarial perturbations. Although adversarial training is successful at mitigating adversarial attacks, the behavioral differences between adversarially-trained (AT) models and standard models are still poorly understood. Motivated by a recent study on learning robustness without input perturbations by distilling an AT model, we explore what is learned during adversarial training by analyzing the distribution of logits in AT models. We identify three logit characteristics essential to learning adversarial robustness. First, we provide a theoretical justification for the finding that adversarial training shrinks two important characteristics of the logit distribution: the max logit values and the \"logit gaps\" (difference between the logit max and next largest values) are on average lower for AT models. Second, we show that AT and standard models differ significantly on which samples are high or low confidence, then illustrate clear qualitative differences by visualizing samples with the largest confidence difference. Finally, we find learning information about incorrect classes to be essential to learning robustness by manipulating the non-max logit information during distillation and measuring the impact on the student's robustness. Our results indicate that learning some adversarial robustness without input perturbations requires a model to learn specific sample-wise confidences and incorrect class orderings that follow complex distributions.",
        "published":"2021-08-26 19:09:15+00:00",
        "authors":"['Landan Seguin', 'Anthony Ndirango', 'Neeli Mishra', 'SueYeon Chung', 'Tyler Lee']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.83,
        "actuality":3.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.0555
    },
    {
        "arxiv_id":"2108.12061v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12061v1",
        "title":"Using GAN-based models to sentimental analysis on imbalanced datasets in education domain",
        "summary":"While the whole world is still struggling with the COVID-19 pandemic, online learning and home office become more common. Many schools transfer their courses teaching to the online classroom. Therefore, it is significant to mine the students' feedback and opinions from their reviews towards studies so that both schools and teachers can know where they need to improve. This paper trains machine learning and deep learning models using both balanced and imbalanced datasets for sentiment classification. Two SOTA category-aware text generation GAN models: CatGAN and SentiGAN, are utilized to synthesize text used to balance the highly imbalanced dataset. Results on three datasets with different imbalance degree from distinct domains show that when using generated text to balance the dataset, the F1-score of machine learning and deep learning model on sentiment classification increases 2.79% ~ 9.28%. Also, the results indicate that the average growth degree for CR100k is higher than CR23k, the average growth degree for deep learning is more increased than machine learning algorithms, and the average growth degree for more complex deep learning models is more increased than simpler deep learning models in experiments.",
        "published":"2021-08-26 23:00:27+00:00",
        "authors":"['Ru Yang', 'Maryam Edalati']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.9,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.7778333333
    },
    {
        "arxiv_id":"2108.12068v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12068v1",
        "title":"An Automatic Image Content Retrieval Method for better Mobile Device Display User Experiences",
        "summary":"A growing number of commercially available mobile phones come with integrated high-resolution digital cameras. That enables a new class of dedicated applications to image analysis such as mobile visual search, image cropping, object detection, content-based image retrieval, image classification. In this paper, a new mobile application for image content retrieval and classification for mobile device display is proposed to enrich the visual experience of users. The mobile application can extract a certain number of images based on the content of an image with visual saliency methods aiming at detecting the most critical regions in a given image from a perceptual viewpoint. First, the most critical areas from a perceptual perspective are extracted using the local maxima of a 2D saliency function. Next, a salient region is cropped using the bounding box centred on the local maxima of the thresholded Saliency Map of the image. Then, each image crop feds into an Image Classification system based on SVM and SIFT descriptors to detect the class of object present in the image. ImageNet repository was used as the reference for semantic category classification. Android platform was used to implement the mobile application on a client-server architecture. A mobile client sends the photo taken by the camera to the server, which processes the image and returns the results (image contents such as image crops and related target classes) to the mobile client. The application was run on thousands of pictures and showed encouraging results towards a better user visual experience with mobile displays.",
        "published":"2021-08-26 23:44:34+00:00",
        "authors":"['Alessandro Bruno']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.87,
        "actuality":2.3333333333,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.7221666667
    },
    {
        "arxiv_id":"2108.12149v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12149v2",
        "title":"Cleaning Inconsistent Data in Temporal DL-Lite Under Best Repair Semantics",
        "summary":"In this paper, we address the problem of handling inconsistent data in Temporal Description Logic (TDL) knowledge bases. Considering the data part of the Knowledge Base as the source of inconsistency over time, we propose an ABox repair approach. This is the first work handling the repair in TDL Knowledge bases. To do so, our goal is twofold: 1) detect temporal inconsistencies and 2) propose a data temporal reparation. For the inconsistency detection, we propose a reduction approach from TDL to DL which allows to provide a tight NP-complete upper bound for TDL concept satisfiability and to use highly optimised DL reasoners that can bring precise explanation (the set of inconsistent data assertions). Thereafter, from the obtained explanation, we propose a method for automatically computing the best repair in the temporal setting based on the allowed rigid predicates and the time order of assertions.",
        "published":"2021-08-27 07:45:01+00:00",
        "authors":"['Mourad Ouziri', 'Sabiha Tahrat', 'Salima Benbernou', 'Mourad Ouzirri']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.81,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.3611666667
    },
    {
        "arxiv_id":"2108.12255v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12255v1",
        "title":"Assessing Gender Bias in the Information Systems Field: An Analysis of the Impact on Citations",
        "summary":"Gender bias, a systemic and unfair difference in how men and women are treated in a given domain, is widely studied across different academic fields. Yet, there are barely any studies of the phenomenon in the field of academic information systems (IS), which is surprising especially in the light of the proliferation of such studies in the Science, Technology, Mathematics and Technology (STEM) disciplines. To assess potential gender bias in the IS field, this paper outlines a study to estimate the impact of scholarly citations that female IS academics accumulate vis-\\`a-vis their male colleagues. Drawing on a scientometric study of the 7,260 papers published in the most prestigious IS journals (known as the AIS Basket of Eight), our analysis aims to unveil potential bias in the accumulation of citations between genders in the field. We use panel regression to estimate the gendered citations accumulation in the field. By doing so we propose to contribute knowledge on a core dimension of gender bias in academia, which is, so far, almost completely unexplored in the IS field.",
        "published":"2021-08-22 18:18:52+00:00",
        "authors":"['Silvia Masiero', 'Aleksi Aaltonen']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.88,
        "actuality":4.3333333333,
        "controversy":4.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.08325
    },
    {
        "arxiv_id":"2108.12286v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12286v1",
        "title":"Key Lessons Learned from Working During Covid-19 on a Project in the World's Biggest Refugee Camp",
        "summary":"Using a case study structure, this research-in-progress paper elaborates the struggles of working on a humanitarian project during the Covid-19 period. The authors identify six specific challenges and propose innovations to address each of these challenges. The challenges are the following: supply chain, design of solutions, human resource development, connectivity, and user data collection. This unprecedented situation has been a testing ground for new innovative solutions for work in conflict zones.",
        "published":"2021-08-22 17:02:28+00:00",
        "authors":"['Faheem Hussain', 'Suzana Brown']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.94,
        "actuality":5.0,
        "controversy":2.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":5.0,
        "newsworthiness_crowd_sum":4.0833333333
    },
    {
        "arxiv_id":"2108.12330v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12330v1",
        "title":"SMT-Based Safety Verification of Data-Aware Processes under Ontologies (Extended Version)",
        "summary":"In the context of verification of data-aware processes (DAPs), a formal approach based on satisfiability modulo theories (SMT) has been considered to verify parameterised safety properties of so-called artifact-centric systems. This approach requires a combination of model-theoretic notions and algorithmic techniques based on backward reachability. We introduce here a variant of one of the most investigated models in this spectrum, namely simple artifact systems (SASs), where, instead of managing a database, we operate over a description logic (DL) ontology expressed in (a slight extension of) RDFS. This DL, enjoying suitable model-theoretic properties, allows us to define DL-based SASs to which backward reachability can still be applied, leading to decidability in PSPACE of the corresponding safety problems.",
        "published":"2021-08-27 15:04:11+00:00",
        "authors":"['Diego Calvanese', 'Alessandro Gianola', 'Andrea Mazzullo', 'Marco Montali']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.77,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.9445
    },
    {
        "arxiv_id":"2108.12390v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12390v1",
        "title":"Two-In-One: A Design Space for Mapping Unimanual Input into Bimanual Interactions in VR for Users with Limited Movement",
        "summary":"Virtual Reality (VR) applications often require users to perform actions with two hands when performing tasks and interacting with objects in virtual environments. Although bimanual interactions in VR can resemble real-world interactions -- thus increasing realism and improving immersion -- they can also pose significant accessibility challenges to people with limited mobility, such as for people who have full use of only one hand. An opportunity exists to create accessible techniques that take advantage of users' abilities, but designers currently lack structured tools to consider alternative approaches. To begin filling this gap, we propose Two-in-One, a design space that facilitates the creation of accessible methods for bimanual interactions in VR from unimanual input. Our design space comprises two dimensions, bimanual interactions and computer assistance, and we provide a detailed examination of issues to consider when creating new unimanual input techniques that map to bimanual interactions in VR. We used our design space to create three interaction techniques that we subsequently implemented for a subset of bimanual interactions and received user feedback through a video elicitation study with 17 people with limited mobility. Our findings explore complex tradeoffs associated with autonomy and agency and highlight the need for additional settings and methods to make VR accessible to people with limited mobility.",
        "published":"2021-08-27 16:52:50+00:00",
        "authors":"['Momona Yamagami', 'Sasa Junuzovic', 'Mar Gonzalez-Franco', 'Eyal Ofek', 'Edward Cutrell', 'John Porter', 'Andrew Wilson', 'Martez Mott']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":4.0,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2108.12540v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12540v1",
        "title":"A Visual Analytics System for Water Distribution System Optimization",
        "summary":"The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system's effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes.",
        "published":"2021-08-28 01:08:25+00:00",
        "authors":"['Yiran Li', 'Erin Musabandesu', 'Takanori Fujiwara', 'Frank J. Loge', 'Kwan-Liu Ma']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.85,
        "actuality":4.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.111
    },
    {
        "arxiv_id":"2108.12573v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12573v1",
        "title":"A Pluralist Approach to Democratizing Online Discourse",
        "summary":"Online discourse takes place in corporate-controlled spaces thought by users to be public realms. These platforms in name enable free speech but in practice implement varying degrees of censorship either by government edict or by uneven and unseen corporate policy. This kind of censorship has no countervailing accountability mechanism, and as such platform owners, moderators, and algorithms shape public discourse without recourse or transparency. Systems research has explored approaches to decentralizing or democratizing Internet infrastructure for decades. In parallel, the Internet censorship literature is replete with efforts to measure and overcome online censorship. However, in the course of designing specialized open-source platforms and tools, projects generally neglect the needs of supportive but uninvolved `average' users. In this paper, we propose a pluralistic approach to democratizing online discourse that considers both the systems-related and user-facing issues as first-order design goals.",
        "published":"2021-08-28 05:01:56+00:00",
        "authors":"['Jay Chen', 'Barath Raghavan', 'Paul Schmitt', 'Tai Liu']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.86,
        "actuality":4.6666666667,
        "controversy":4.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.3889166667
    },
    {
        "arxiv_id":"2108.12589v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12589v1",
        "title":"Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems",
        "summary":"As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models, have shown promising results for few-shot learning in ToD. In this paper, we devise a self-training approach to utilize the abundant unlabeled dialog data to further improve state-of-the-art pre-trained models in few-shot learning scenarios for ToD systems. Specifically, we propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. Moreover, a new text augmentation technique (GradAug) is proposed to better train the Student by replacing non-crucial tokens using a masked language model. We conduct extensive experiments and present analyses on four downstream tasks in ToD, including intent classification, dialog state tracking, dialog act prediction, and response selection. Empirical results demonstrate that the proposed self-training approach consistently improves state-of-the-art pre-trained models (BERT, ToD-BERT) when only a small number of labeled data are available.",
        "published":"2021-08-28 07:22:06+00:00",
        "authors":"['Fei Mi', 'Wanhao Zhou', 'Fengyu Cai', 'Lingjing Kong', 'Minlie Huang', 'Boi Faltings']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.88,
        "actuality":2.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2108.12711v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.12711v1",
        "title":"Learning to Track Objects from Unlabeled Videos",
        "summary":"In this paper, we propose to learn an Unsupervised Single Object Tracker (USOT) from scratch. We identify that three major challenges, i.e., moving object discovery, rich temporal variation exploitation, and online update, are the central causes of the performance bottleneck of existing unsupervised trackers. To narrow the gap between unsupervised trackers and supervised counterparts, we propose an effective unsupervised learning approach composed of three stages. First, we sample sequentially moving objects with unsupervised optical flow and dynamic programming, instead of random cropping. Second, we train a naive Siamese tracker from scratch using single-frame pairs. Third, we continue training the tracker with a novel cycle memory learning scheme, which is conducted in longer temporal spans and also enables our tracker to update online. Extensive experiments show that the proposed USOT learned from unlabeled videos performs well over the state-of-the-art unsupervised trackers by large margins, and on par with recent supervised deep trackers. Code is available at https:\/\/github.com\/VISION-SJTU\/USOT.",
        "published":"2021-08-28 22:10:06+00:00",
        "authors":"['Jilai Zheng', 'Chao Ma', 'Houwen Peng', 'Xiaokang Yang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.82,
        "actuality":2.6666666667,
        "controversy":4.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.2221666667
    },
    {
        "arxiv_id":"2108.13063v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.13063v1",
        "title":"Satisfiability and Containment of Recursive SHACL",
        "summary":"The Shapes Constraint Language (SHACL) is the recent W3C recommendation language for validating RDF data, by verifying certain shapes on graphs. Previous work has largely focused on the validation problem and the standard decision problems of satisfiability and containment, crucial for design and optimisation purposes, have only been investigated for simplified versions of SHACL. Moreover, the SHACL specification does not define the semantics of recursively-defined constraints, which led to several alternative recursive semantics being proposed in the literature. The interaction between these different semantics and important decision problems has not been investigated yet. In this article we provide a comprehensive study of the different features of SHACL, by providing a translation to a new first-order language, called SCL, that precisely captures the semantics of SHACL. We also present MSCL, a second-order extension of SCL, which allows us to define, in a single formal logic framework, the main recursive semantics of SHACL. Within this language we also provide an effective treatment of filter constraints which are often neglected in the related literature. Using this logic we provide a detailed map of (un)decidability and complexity results for the satisfiability and containment decision problems for different SHACL fragments. Notably, we prove that both problems are undecidable for the full language, but we present decidable combinations of interesting features, even in the face of recursion.",
        "published":"2021-08-30 08:51:03+00:00",
        "authors":"['Paolo Pareti', 'George Konstantinidis', 'Fabio Mogavero']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.82,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.4165833333
    },
    {
        "arxiv_id":"2108.13299v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2108.13299v1",
        "title":"Incremental Learning for Personalized Recommender Systems",
        "summary":"Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment. The former requires a complex machine learning model that is trained on a large amount of data; the latter requires frequent update to the model. We present an incremental learning solution to provide both the training efficiency and the model quality. Our solution is based on sequential Bayesian update and quadratic approximation. Our focus is on large-scale personalized logistic regression models, with extensions to deep learning models. This paper fills in the gap between the theory and the practice by addressing a few implementation challenges that arise when applying incremental learning to large personalized recommender systems. Detailed offline and online experiments demonstrated our approach can significantly shorten the training time while maintaining the model accuracy. The solution is deployed in LinkedIn and directly applicable to industrial scale recommender systems.",
        "published":"2021-08-13 04:21:21+00:00",
        "authors":"['Yunbo Ouyang', 'Jun Shi', 'Haichao Wei', 'Huiji Gao']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.6388333333
    },
    {
        "arxiv_id":"2109.00137v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00137v1",
        "title":"Implicit Behavioral Cloning",
        "summary":"We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models. We present extensive experiments on this finding, and we provide both intuitive insight and theoretical arguments distinguishing the properties of implicit models compared to their explicit counterparts, particularly with respect to approximating complex, potentially discontinuous and multi-valued (set-valued) functions. On robotic policy learning tasks we show that implicit behavioral cloning policies with energy-based models (EBM) often outperform common explicit (Mean Square Error, or Mixture Density) behavioral cloning policies, including on tasks with high-dimensional action spaces and visual image inputs. We find these policies provide competitive results or outperform state-of-the-art offline reinforcement learning methods on the challenging human-expert tasks from the D4RL benchmark suite, despite using no reward information. In the real world, robots with implicit policies can learn complex and remarkably subtle behaviors on contact-rich tasks from human demonstrations, including tasks with high combinatorial complexity and tasks requiring 1mm precision.",
        "published":"2021-09-01 01:20:25+00:00",
        "authors":"['Pete Florence', 'Corey Lynch', 'Andy Zeng', 'Oscar Ramirez', 'Ayzaan Wahid', 'Laura Downs', 'Adrian Wong', 'Johnny Lee', 'Igor Mordatch', 'Jonathan Tompson']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.85,
        "actuality":4.3333333333,
        "controversy":4.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.66675
    },
    {
        "arxiv_id":"2109.00157v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00157v2",
        "title":"A Survey of Exploration Methods in Reinforcement Learning",
        "summary":"Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments. Reinforcement learning agents depend crucially on exploration to obtain informative data for the learning process as the lack of enough information could hinder effective learning. In this article, we provide a survey of modern exploration methods in (Sequential) reinforcement learning, as well as a taxonomy of exploration methods.",
        "published":"2021-09-01 02:36:14+00:00",
        "authors":"['Susan Amin', 'Maziar Gomrokchi', 'Harsh Satija', 'Herke van Hoof', 'Doina Precup']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.84,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8889166667
    },
    {
        "arxiv_id":"2109.00217v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00217v1",
        "title":"Multi-Sample based Contrastive Loss for Top-k Recommendation",
        "summary":"The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the importance of the positive and negative samples as the same. On the one hand, CL faces the imbalance problem of one positive sample and many negative samples. On the other hand, positive items are so few in sparser datasets that their importance should be emphasized. Moreover, the other important issue is that the sparse positive items are still not sufficiently utilized in recommendations. So we propose a new data augmentation method by using multiple positive items (or samples) simultaneously with the CL loss function. Therefore, we propose a Multi-Sample based Contrastive Loss (MSCL) function which solves the two problems by balancing the importance of positive and negative samples and data augmentation. And based on the graph convolution network (GCN) method, experimental results demonstrate the state-of-the-art performance of MSCL. The proposed MSCL is simple and can be applied in many methods. We will release our code on GitHub upon the acceptance.",
        "published":"2021-09-01 07:32:13+00:00",
        "authors":"['Hao Tang', 'Guoshuai Zhao', 'Yuxia Wu', 'Xueming Qian']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.3611666667
    },
    {
        "arxiv_id":"2109.00381v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00381v1",
        "title":"Building a Legal Dialogue System: Development Process, Challenges and Opportunities",
        "summary":"This paper presents key principles and solutions to the challenges faced in designing a domain-specific conversational agent for the legal domain. It includes issues of scope, platform, architecture and preparation of input data. It provides functionality in answering user queries and recording user information including contact details and case-related information. It utilises deep learning technology built upon Amazon Web Services (AWS) LEX in combination with AWS Lambda. Due to lack of publicly available data, we identified two methods including crowdsourcing experiments and archived enquiries to develop a number of linguistic resources. This includes a training dataset, set of predetermined responses for the conversational agent, a set of regression test cases and a further conversation test set. We propose a hierarchical bot structure that facilitates multi-level delegation and report model accuracy on the regression test set. Additionally, we highlight features that are added to the bot to improve the conversation flow and overall user experience.",
        "published":"2021-09-01 13:35:42+00:00",
        "authors":"['Mudita Sharma', 'Tony Russell-Rose', 'Lina Barakat', 'Akitaka Matsuo']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.3055833333
    },
    {
        "arxiv_id":"2109.00520v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00520v1",
        "title":"The Role of Explainability in Assuring Safety of Machine Learning in Healthcare",
        "summary":"Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing machine learning (ML). In many cases, ML is used on ill-defined problems, e.g. optimising sepsis treatment, where there is no clear, pre-defined specification against which to assess validity. This problem is exacerbated by the \"opaque\" nature of ML where the learnt model is not amenable to human scrutiny. Explainable AI methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system. However, there is not much work explicitly investigating the role of explainability for safety assurance in the context of ML development. This paper identifies ways in which explainable AI methods can contribute to safety assurance of ML-based systems. It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how explainable AI methods can be employed to produce evidence to support safety assurance. The results are also represented in a safety argument to show where, and in what way, explainable AI methods can contribute to a safety case. Overall, we conclude that explainable AI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety.",
        "published":"2021-09-01 09:32:14+00:00",
        "authors":"['Yan Jia', 'John McDermid', 'Tom Lawton', 'Ibrahim Habli']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.89,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6111666667
    },
    {
        "arxiv_id":"2109.00666v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00666v1",
        "title":"TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks",
        "summary":"With the increasing reliance on automated decision making, the issue of algorithmic fairness has gained increasing importance. In this paper, we propose a Generative Adversarial Network for tabular data generation. The model includes two phases of training. In the first phase, the model is trained to accurately generate synthetic data similar to the reference dataset. In the second phase we modify the value function to add fairness constraint, and continue training the network to generate data that is both accurate and fair. We test our results in both cases of unconstrained, and constrained fair data generation. In the unconstrained case, i.e. when the model is only trained in the first phase and is only meant to generate accurate data following the same joint probability distribution of the real data, the results show that the model beats state-of-the-art GANs proposed in the literature to produce synthetic tabular data. Also, in the constrained case in which the first phase of training is followed by the second phase, we train the network and test it on four datasets studied in the fairness literature and compare our results with another state-of-the-art pre-processing method, and present the promising results that it achieves. Comparing to other studies utilizing GANs for fair data generation, our model is comparably more stable by using only one critic, and also by avoiding major problems of original GAN model, such as mode-dropping and non-convergence, by implementing a Wasserstein GAN.",
        "published":"2021-09-02 01:48:01+00:00",
        "authors":"['Amirarsalan Rajabi', 'Ozlem Ozmen Garibay']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":4.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.0555833333
    },
    {
        "arxiv_id":"2109.00747v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00747v2",
        "title":"MIR-VIO: Mutual Information Residual-based Visual Inertial Odometry with UWB Fusion for Robust Localization",
        "summary":"For many years, there has been an impressive progress on visual odometry applied to mobile robots and drones. However, the visual perception is still in the spotlight as a challenging field because the vision sensor has some problems in obtaining correct scale information with a monocular camera and also is vulnerable to a situation in which illumination is changed. In this paper, UWB sensor fusion is proposed in the visual inertial odometry algorithm as a solution to mitigate this problem. We designed a cost function based on mutual information considering the UWB. Considering the characteristic of the UWB signal model, where the uncertainty increases as the distance between the UWB anchor and the tag increases, we introduced a new residual term to the cost function. When the experiment was conducted in an indoor environment with the above methodology, the initialization problem in an environment with few feature points was solved through the UWB sensor fusion, and localization became robust. And when the residual term using the concept of mutual information was used, the most robust odometry could be obtained.",
        "published":"2021-09-02 07:04:30+00:00",
        "authors":"['Sungjae Shin', 'Eungchang Lee', 'Junho Choi', 'Hyun Myung']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.87,
        "actuality":1.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.3334166667
    },
    {
        "arxiv_id":"2109.00833v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00833v1",
        "title":"Towards a Reference Architecture for Future Industrial Internet of Things Networks",
        "summary":"With the continuing decrease of sensor technology prices as well as the increase of communication and analytical capabilities of modern internet of things devices, the continuously generated amount of data is constantly growing. Various use cases show the untapped potential of this data for new business models. However, conventional industrial IT networks of traditional manufacturing companies can hardly meet the modern requirements emerging with today's and future industrial internet of things applications. Outdated and rigid network infrastructures are one of the main reasons for hesitant innovation efforts and cross-organizational collaborations as well as the slow adoption of modern business models by traditional manufacturing companies. Following the design science research paradigm, our work contributes by elaborating on a comprehensive list of requirements for future industrial internet of things networks from a theoretical and practical perspective as well as a proposed reference architecture acting as a blueprint for future implementations.",
        "published":"2021-09-02 10:33:53+00:00",
        "authors":"['Dominik Martin', 'Niklas K\u00fchl', 'Marcel Schwenk']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.93,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2109.00968v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.00968v2",
        "title":"Self-supervised Representation Learning for Trip Recommendation",
        "summary":"Trip recommendation is a significant and engaging location-based service that can help new tourists make more customized travel plans. It often attempts to suggest a sequence of point of interests (POIs) for a user who requests a personalized travel demand. Conventional methods either leverage the heuristic algorithms (e.g., dynamic programming) or statistical analysis (e.g., Markov models) to search or rank a POI sequence. These procedures may fail to capture the diversity of human needs and transitional regularities. They even provide recommendations that deviate from tourists' real travel intention when the trip data is sparse. Although recent deep recursive models (e.g., RNN) are capable of alleviating these concerns, existing solutions hardly recognize the practical reality, such as the diversity of tourist demands, uncertainties in the trip generation, and the complex visiting preference. Inspired by the advance in deep learning, we introduce a novel self-supervised representation learning framework for trip recommendation -- SelfTrip, aiming at tackling the aforementioned challenges. Specifically, we propose a two-step contrastive learning mechanism concerning the POI representation, as well as trip representation. Furthermore, we present four trip augmentation methods to capture the visiting uncertainties in trip planning. We evaluate our SelfTrip on four real-world datasets, and extensive results demonstrate the promising gain compared with several cutting-edge benchmarks, e.g., up to 4% and 12% on F1 and pair-F1, respectively.",
        "published":"2021-09-02 14:11:09+00:00",
        "authors":"['Qiang Gao', 'Wei Wang', 'Kunpeng Zhang', 'Xin Yang', 'Congcong Miao']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":3.0,
        "controversy":1.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.5555
    },
    {
        "arxiv_id":"2109.01013v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01013v1",
        "title":"On Dedicated CDCL Strategies for PB Solvers",
        "summary":"Current implementations of pseudo-Boolean (PB) solvers working on native PB constraints are based on the CDCL architecture which empowers highly efficient modern SAT solvers. In particular, such PB solvers not only implement a (cutting-planes-based) conflict analysis procedure, but also complementary strategies for components that are crucial for the efficiency of CDCL, namely branching heuristics, learned constraint deletion and restarts. However, these strategies are mostly reused by PB solvers without considering the particular form of the PB constraints they deal with. In this paper, we present and evaluate different ways of adapting CDCL strategies to take the specificities of PB constraints into account while preserving the behavior they have in the clausal setting. We implemented these strategies in two different solvers, namely Sat4j (for which we consider three configurations) and RoundingSat. Our experiments show that these dedicated strategies allow to improve, sometimes significantly, the performance of these solvers, both on decision and optimization problems.",
        "published":"2021-09-02 15:22:27+00:00",
        "authors":"['Daniel Le Berre', 'Romain Wallon']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.76,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.6388333333
    },
    {
        "arxiv_id":"2109.01127v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01127v1",
        "title":"Cross-platform analysis of user comments in YouTube videos linked on Reddit conspiracy theory forum",
        "summary":"We perform a cross-platform analysis in which we study how does linking YouTube content on Reddit conspiracy forum impact language used in user comments on YouTube. Our findings show a slight change in user language in that it becomes more similar to language used on Reddit.",
        "published":"2021-09-02 17:53:44+00:00",
        "authors":"['Tomislav Duricic', 'Volker Seiser', 'Elisabeth Lex']",
        "arxiv_primary_category":"cs.si",
        "readability":0.96,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.7778333333
    },
    {
        "arxiv_id":"2109.01220v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01220v1",
        "title":"An Oracle and Observations for the OpenAI Gym \/ ALE Freeway Environment",
        "summary":"The OpenAI Gym project contains hundreds of control problems whose goal is to provide a testbed for reinforcement learning algorithms. One such problem is Freeway-ram-v0, where the observations presented to the agent are 128 bytes of RAM. While the goals of the project are for non-expert AI agents to solve the control problems with general training, in this work, we seek to learn more about the problem, so that we can better evaluate solutions. In particular, we develop on oracle to play the game, so that we may have baselines for success. We present details of the oracle, plus optimal game-playing situations that can be used for training and testing AI agents.",
        "published":"2021-09-02 21:38:06+00:00",
        "authors":"['James S. Plank', 'Catherine D. Schuman', 'Robert M. Patton']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.91,
        "actuality":3.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.2221666667
    },
    {
        "arxiv_id":"2109.01376v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01376v1",
        "title":"Deep Learning for Fitness",
        "summary":"We present Fitness tutor, an application for maintaining correct posture during workout exercises or doing yoga. Current work on fitness focuses on suggesting food supplements, accessing workouts, workout wearables does a great job in improving the fitness. Meanwhile, the current situation is making difficult to monitor workouts by trainee. Inspired by healthcare innovations like robotic surgery, we design a novel application Fitness tutor which can guide the workouts using pose estimation. Pose estimation can be deployed on the reference image for gathering data and guide the user with the data. This allow Fitness tutor to guide the workouts (both exercise and yoga) in remote conditions with a single reference posture as image. We use posenet model in tensorflow with p5js for developing skeleton. Fitness tutor is an application of pose estimation model in bringing a realtime teaching experience in fitness. Our experiments shows that it can leverage potential of pose estimation models by providing guidance in realtime.",
        "published":"2021-09-03 08:42:07+00:00",
        "authors":"['Mahendran N']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":3.6666666667,
        "controversy":1.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.0555833333
    },
    {
        "arxiv_id":"2109.01445v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01445v1",
        "title":"Is Machine Learning Ready for Traffic Engineering Optimization?",
        "summary":"Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze whether modern Machine Learning (ML) methods are ready to be used for TE optimization. We address this open question through a comparative analysis between the state of the art in ML and the state of the art in TE. To this end, we first present a novel distributed system for TE that leverages the latest advancements in ML. Our system implements a novel architecture that combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks (GNN) to minimize network congestion. In our evaluation, we compare our MARL+GNN system with DEFO, a network optimizer based on Constraint Programming that represents the state of the art in TE. Our experimental results show that the proposed MARL+GNN solution achieves equivalent performance to DEFO in a wide variety of network scenarios including three real-world network topologies. At the same time, we show that MARL+GNN can achieve significant reductions in execution time (from the scale of minutes with DEFO to a few seconds with our solution).",
        "published":"2021-09-03 11:10:14+00:00",
        "authors":"['Guillermo Bern\u00e1rdez', 'Jos\u00e9 Su\u00e1rez-Varela', 'Albert L\u00f3pez', 'Bo Wu', 'Shihan Xiao', 'Xiangle Cheng', 'Pere Barlet-Ros', 'Albert Cabellos-Aparicio']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.82,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3889166667
    },
    {
        "arxiv_id":"2109.01762v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01762v2",
        "title":"(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys",
        "summary":"Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture \"stance\" as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both \"Pro\" and \"Anti\" stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.",
        "published":"2021-09-04 01:37:15+00:00",
        "authors":"['Kenneth Joseph', 'Sarah Shugars', 'Ryan Gallagher', 'Jon Green', 'Alexi Quintana Math\u00e9', 'Zijian An', 'David Lazer']",
        "arxiv_primary_category":"cs.si",
        "readability":0.89,
        "actuality":3.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.91675
    },
    {
        "arxiv_id":"2109.01765v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01765v1",
        "title":"Effective user intent mining with unsupervised word representation models and topic modelling",
        "summary":"Understanding the intent behind chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increase in text conversation between customers and agents. In this paper, we propose an approach to data mining the conversation intents behind the textual data. Using the customer service data set, we train unsupervised text representation models, and then develop an intent mapping model which would rank the predefined intents base on cosine similarity between sentences and intents. Topic-modeling techniques are used to define intents and domain experts are also involved to interpret topic modelling results. With this approach, we can get a good understanding of the user intentions behind the unlabelled customer service textual data.",
        "published":"2021-09-04 01:52:12+00:00",
        "authors":"['Bencheng Wei']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.89,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2109.01936v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.01936v1",
        "title":"Influence of NaMo App on Twitter",
        "summary":"Social media plays a crucial role in today's society. It results in paradigm changes in how people relate and communicate, convey and exchange ideas. Moreover, social media has evolved into critical knowledge networks for consumers and also affects decision-making. In elections, social media became an integral part of political campaigning to reach a greater audience and gather more support. The 2019 Lok Sabha election saw a massive spike in the usage of online social media platforms such as Twitter, Facebook, and WhatsApp; with every major political party launching its own organized social media campaigns. In 2014, Bhartiya Janta Party (BJP) took one step ahead in organizing the campaign by launching its app - NaMo App. We focus our research on Twitter and NaMo App during the 2019 Lok Sabha elections and CAA protests. Twitter is a platform where every individual can express their views and is not biased. In contrast, NaMo App is one of the first apps centered around a specific political party. It acted as a digital medium for BJP for organizing the political campaign to make people's opinion in their favour. This research aims to characterize the role of the NaMo App in a more traditional network as Twitter in shaping political discourse and studies the existence of an online echo chamber. We began by analyzing the amount and type of content shared using the NaMo App on Twitter. We performed content and network analysis for the existence of the echo chamber. We also applied Hawkes process to see the influence that NaMo App has on Twitter. Through this research, we can conclude that the users who share content using NaMo App, may be part of an online echo chamber and are likely to be BJP workers. We show the reach and influence that the NaMo App has on Twitter is significantly less, indicating its inability to break through the diverse audience and change the narrative on Twitter.",
        "published":"2021-09-04 21:35:18+00:00",
        "authors":"['Shreya Sharma', 'Samiya Caur', 'Hitkul', 'Ponnurangam Kumaraguru']",
        "arxiv_primary_category":"cs.si",
        "readability":0.91,
        "actuality":4.6666666667,
        "controversy":4.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.5834166667
    },
    {
        "arxiv_id":"2109.02206v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02206v1",
        "title":"Achieving Deterministic Service in Mobile Edge Computing (MEC) Networks",
        "summary":"Mobile edge computing (MEC) is proposed to boost high-efficient and time-sensitive 5G applications. However, the \"microburst\" may occur even in lightly-loaded scenarios, which leads to the indeterministic service latency (i.e., unpredictable delay or delay variation), hence hindering the deployment of MEC. Deterministic IP networking (DIP) has been proposed that can provide bounds on latency, and high reliability in the large-scale networks. Nevertheless, the direct migration of DIP into the MEC network is non-trivial owing to its original design for the Ethernet with homogeneous devices. Meanwhile, DIP also faces the challenges on the network throughput and scheduling flexibility. In this paper, we delve into the adoption of DIP for the MEC networks and some of the relevant aspects. A deterministic MEC (D-MEC) network is proposed to deliver the deterministic service (i.e., providing the MEC service with bounded service latency). In the D-MEC network, two mechanisms, including the cycle mapping and cycle shifting, are designed to enable: (i) seamless and deterministic transmission with heterogeneous underlaid resources; and (ii) traffic shaping on the edges to improve the resource utilization. We also formulate a joint configuration to maximize the network throughput with deterministic QoS guarantees. Extensive simulations verify that the proposed D-MEC network can achieve a deterministic MEC service, even in the highly-loaded scenarios.",
        "published":"2021-09-06 01:43:38+00:00",
        "authors":"['Binwei Wu', 'Jiasen Wang', 'Yanyan Wang', 'Weiqian Tan', 'Yudong Huang']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.78,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.83325
    },
    {
        "arxiv_id":"2109.02216v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02216v2",
        "title":"Learning Fine-Grained Motion Embedding for Landscape Animation",
        "summary":"In this paper we focus on landscape animation, which aims to generate time-lapse videos from a single landscape image. Motion is crucial for landscape animation as it determines how objects move in videos. Existing methods are able to generate appealing videos by learning motion from real time-lapse videos. However, current methods suffer from inaccurate motion generation, which leads to unrealistic video results. To tackle this problem, we propose a model named FGLA to generate high-quality and realistic videos by learning Fine-Grained motion embedding for Landscape Animation. Our model consists of two parts: (1) a motion encoder which embeds time-lapse motion in a fine-grained way. (2) a motion generator which generates realistic motion to animate input images. To train and evaluate on diverse time-lapse videos, we build the largest high-resolution Time-lapse video dataset with Diverse scenes, namely Time-lapse-D, which includes 16,874 video clips with over 10 million frames. Quantitative and qualitative experimental results demonstrate the superiority of our method. In particular, our method achieves relative improvements by 19% on LIPIS and 5.6% on FVD compared with state-of-the-art methods on our dataset. A user study carried out with 700 human subjects shows that our approach visually outperforms existing methods by a large margin.",
        "published":"2021-09-06 02:47:11+00:00",
        "authors":"['Hongwei Xue', 'Bei Liu', 'Huan Yang', 'Jianlong Fu', 'Houqiang Li', 'Jiebo Luo']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.7778333333
    },
    {
        "arxiv_id":"2109.02323v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02323v1",
        "title":"Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery",
        "summary":"Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This task automation could lead to reduced surgeon's cognitive workload, increased precision in critical aspects of the surgery, and fewer patient-related complications. However, current DRL methods do not guarantee any safety criteria as they maximise cumulative rewards without considering the risks associated with the actions performed. Due to this limitation, the application of DRL in the safety-critical paradigm of robot-assisted Minimally Invasive Surgery (MIS) has been constrained. In this work, we introduce a Safe-DRL framework that incorporates safety constraints for the automation of surgical subtasks via DRL training. We validate our approach in a virtual scene that replicates a tissue retraction task commonly occurring in multiple phases of an MIS. Furthermore, to evaluate the safe behaviour of the robotic arms, we formulate a formal verification tool for DRL methods that provides the probability of unsafe configurations. Our results indicate that a formal analysis guarantees safety with high confidence such that the robotic instruments operate within the safe workspace and avoid hazardous interaction with other anatomical structures.",
        "published":"2021-09-06 09:49:00+00:00",
        "authors":"['Ameya Pore', 'Davide Corsi', 'Enrico Marchesini', \"Diego Dall'Alba\", 'Alicia Casals', 'Alessandro Farinelli', 'Paolo Fiorini']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.82,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.22225
    },
    {
        "arxiv_id":"2109.02382v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02382v1",
        "title":"SENSATION: An Authoring Tool to Support Event-State Paradigm in End-User Development",
        "summary":"In this paper, we present the design and the evaluation of an authoring tool for End-User Development, which supports the definition of Trigger-Actions rules that combines events and states in the triggers. The possibility of using either states or events in triggers has already been discussed in the literature. However, it is recognized that the state\/event distinction is difficult to manage for users. In this paper, we propose an authoring tool that provides explicit support for managing this distinction. We compare it with a state-of-the-art authoring tool that implements the classical event-event paradigm.",
        "published":"2021-09-06 11:58:40+00:00",
        "authors":"['Giuseppe Desolda', 'Francesco Greco', 'Francisco Guarnieri', 'Nicole Mariz', 'Massimo Zancanaro']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.9,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3889166667
    },
    {
        "arxiv_id":"2109.02426v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02426v1",
        "title":"Complementary Calibration: Boosting General Continual Learning with Collaborative Distillation and Self-Supervision",
        "summary":"General Continual Learning (GCL) aims at learning from non independent and identically distributed stream data without catastrophic forgetting of the old tasks that don't rely on task boundaries during both training and testing stages. We reveal that the relation and feature deviations are crucial problems for catastrophic forgetting, in which relation deviation refers to the deficiency of the relationship among all classes in knowledge distillation, and feature deviation refers to indiscriminative feature representations. To this end, we propose a Complementary Calibration (CoCa) framework by mining the complementary model's outputs and features to alleviate the two deviations in the process of GCL. Specifically, we propose a new collaborative distillation approach for addressing the relation deviation. It distills model's outputs by utilizing ensemble dark knowledge of new model's outputs and reserved outputs, which maintains the performance of old tasks as well as balancing the relationship among all classes. Furthermore, we explore a collaborative self-supervision idea to leverage pretext tasks and supervised contrastive learning for addressing the feature deviation problem by learning complete and discriminative features for all classes. Extensive experiments on four popular datasets show that our CoCa framework achieves superior performance against state-of-the-art methods.",
        "published":"2021-09-03 06:35:27+00:00",
        "authors":"['Zhong Ji', 'Jin Li', 'Qiang Wang', 'Zhongfei Zhang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.83,
        "actuality":2.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.2776666667
    },
    {
        "arxiv_id":"2109.02526v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02526v1",
        "title":"Teachers Without Borders",
        "summary":"An educator who is also known as a lecturer in the university system has three main areas of focus, which include learning that is helping students to acquire knowledge, competence and virtue, research, implying developing new knowledge, breaking new grounds and community service, by focusing on applying the knowledge to real life situations to improve life and living conditions of the society. As the worlds geographical boundaries keep getting redefined in the context of a global village, the constituency of teachers keeps getting redefined as well. This essay aims to address issues about modern constituent and platform of teachers in Nigeria for service delivery in the context of a globalised world. It also focuses on how to reach out to these new set of communities brought about by globalisation to remain relevant, effective and efficient alongside their perceived challenges and possible solutions in Nigerian context.",
        "published":"2021-09-06 15:05:31+00:00",
        "authors":"['Elochukwu A. Ukwandu', 'Robert Onyeneke']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.94,
        "actuality":4.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.5276666667
    },
    {
        "arxiv_id":"2109.02575v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02575v1",
        "title":"Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization",
        "summary":"Modern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize at test time to new compositions\/structures that have not been observed during training. Recent research has shown that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. In this work, we investigate automatic generation of synthetic utterance-program pairs for improving compositional generalization in semantic parsing. Given a small training set of annotated examples and an \"infinite\" pool of synthetic examples, we select a subset of synthetic examples that are structurally-diverse and use them to improve compositional generalization. We evaluate our approach on a new split of the schema2QA dataset, and show that it leads to dramatic improvements in compositional generalization as well as moderate improvements in the traditional i.i.d setup. Moreover, structurally-diverse sampling achieves these improvements with as few as 5K examples, compared to 1M examples when sampling uniformly at random -- a 200x improvement in data efficiency.",
        "published":"2021-09-06 16:20:47+00:00",
        "authors":"['Inbar Oren', 'Jonathan Herzig', 'Jonathan Berant']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.82,
        "actuality":2.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":2.97225
    },
    {
        "arxiv_id":"2109.02610v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02610v1",
        "title":"EsmamDS: A more diverse exceptional survival model mining approach",
        "summary":"A variety of works in the literature strive to uncover the factors associated with survival behaviour. However, the computational tools to provide such information are global models designed to predict if or when a (survival) event will occur. When approaching the problem of explaining differences in survival behaviour, those approaches rely on (assumptions of) predictive features followed by risk stratification. In other words, they lack the ability to discover new information on factors related to survival. In contrast, we approach such a problem from the perspective of descriptive supervised pattern mining to discover local patterns associated with different survival behaviours. Hence, we introduce the EsmamDS algorithm: an Exceptional Model Mining framework to provide straightforward characterisations of subgroups presenting unusual survival models -- given by the Kaplan-Meier estimates. This work builds on the Esmam algorithm to address the problem of pattern redundancy and provide a more informative and diverse characterisation of survival behaviour.",
        "published":"2021-09-06 17:16:59+00:00",
        "authors":"['Juliana Barcellos Mattos', 'Paulo S. G. de Mattos Neto', 'Renato Vimieiro']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":2.0,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.4165833333
    },
    {
        "arxiv_id":"2109.02688v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02688v1",
        "title":"Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification",
        "summary":"Annotated images are required for both supervised model training and evaluation in image classification. Manually annotating images is arduous and expensive, especially for multi-labeled images. A recent trend for conducting such laboursome annotation tasks is through crowdsourcing, where images are annotated by volunteers or paid workers online (e.g., workers of Amazon Mechanical Turk) from scratch. However, the quality of crowdsourcing image annotations cannot be guaranteed, and incompleteness and incorrectness are two major concerns for crowdsourcing annotations. To address such concerns, we have a rethinking of crowdsourcing annotations: Our simple hypothesis is that if the annotators only partially annotate multi-label images with salient labels they are confident in, there will be fewer annotation errors and annotators will spend less time on uncertain labels. As a pleasant surprise, with the same annotation budget, we show a multi-label image classifier supervised by images with salient annotations can outperform models supervised by fully annotated images. Our method contributions are 2-fold: An active learning way is proposed to acquire salient labels for multi-label images; and a novel Adaptive Temperature Associated Model (ATAM) specifically using partial annotations is proposed for multi-label image classification. We conduct experiments on practical crowdsourcing data, the Open Street Map (OSM) dataset and benchmark dataset COCO 2014. When compared with state-of-the-art classification methods trained on fully annotated images, the proposed ATAM can achieve higher accuracy. The proposed idea is promising for crowdsourcing data annotation. Our code will be publicly available.",
        "published":"2021-09-06 18:28:53+00:00",
        "authors":"['Jianzhe Lin', 'Tianze Yu', 'Z. Jane Wang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.8,
        "actuality":4.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1111666667
    },
    {
        "arxiv_id":"2109.02797v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02797v1",
        "title":"Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach",
        "summary":"The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay. The transformer architecture proves amenable to training on solved text archives describing mazes, Rubik's Cube, and Sudoku solvers. The method benefits from fine-tuning the transformer architecture to visualize plausible strategies derived outside any guidance from human heuristics or domain expertise. The large search space ($>10^{19}$) for the games provides a puzzle environment in which the solution has few intermediate rewards and a final move that solves the challenge.",
        "published":"2021-09-07 01:20:28+00:00",
        "authors":"['David Noever', 'Ryerson Burdick']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.76,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2109.02866v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02866v1",
        "title":"Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education",
        "summary":"Medical students will almost inevitably encounter powerful medical AI systems early in their careers. Yet, contemporary medical education does not adequately equip students with the basic clinical proficiency in medical AI needed to use these tools safely and effectively. Education reform is urgently needed, but not easily implemented, largely due to an already jam-packed medical curricula. In this article, we propose an education reform framework as an effective and efficient solution, which we call the Embedded AI Ethics Education Framework. Unlike other calls for education reform to accommodate AI teaching that are more radical in scope, our framework is modest and incremental. It leverages existing bioethics or medical ethics curricula to develop and deliver content on the ethical issues associated with medical AI, especially the harms of technology misuse, disuse, and abuse that affect the risk-benefit analyses at the heart of healthcare. In doing so, the framework provides a simple tool for going beyond the \"What?\" and the \"Why?\" of medical AI ethics education, to answer the \"How?\", giving universities, course directors, and\/or professors a broad road-map for equipping their students with the necessary clinical proficiency in medical AI.",
        "published":"2021-09-07 04:57:29+00:00",
        "authors":"['Thomas P Quinn', 'Simon Coghlan']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.92,
        "actuality":4.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2109.02927v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02927v1",
        "title":"Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformers",
        "summary":"Twitter bot detection has become an important and challenging task to combat misinformation and protect the integrity of the online discourse. State-of-the-art approaches generally leverage the topological structure of the Twittersphere, while they neglect the heterogeneity of relations and influence among users. In this paper, we propose a novel bot detection framework to alleviate this problem, which leverages the topological structure of user-formed heterogeneous graphs and models varying influence intensity between users. Specifically, we construct a heterogeneous information network with users as nodes and diversified relations as edges. We then propose relational graph transformers to model heterogeneous influence between users and learn node representations. Finally, we use semantic attention networks to aggregate messages across users and relations and conduct heterogeneity-aware Twitter bot detection. Extensive experiments demonstrate that our proposal outperforms state-of-the-art methods on a comprehensive Twitter bot detection benchmark. Additional studies also bear out the effectiveness of our proposed relational graph transformers, semantic attention networks and the graph-based approach in general.",
        "published":"2021-09-07 08:28:57+00:00",
        "authors":"['Shangbin Feng', 'Zhaoxuan Tan', 'Rui Li', 'Minnan Luo']",
        "arxiv_primary_category":"cs.si",
        "readability":0.81,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1945
    },
    {
        "arxiv_id":"2109.02956v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.02956v2",
        "title":"Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety",
        "summary":"Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport's response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer's in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.",
        "published":"2021-09-07 09:22:15+00:00",
        "authors":"['Scott McLachlan', 'Martin Neil', 'Kudakwashe Dube', 'Ronny Bogani', 'Norman Fenton', 'Burkhard Schaffer']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.91,
        "actuality":5.0,
        "controversy":3.0,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.361
    },
    {
        "arxiv_id":"2109.03042v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03042v1",
        "title":"Temporal complex networks modeling applied to vehicular ad-hoc networks",
        "summary":"VANETs solutions use aggregated graph representation to model the interaction among the vehicles and different aggregated complex network measures to quantify some topological characteristics. This modeling ignores the temporal interactions between the cars, causing loss of information or unrealistic behavior. This work proposes the use of both temporal graphs and temporal measures to model VANETs applications. To verify the viability of this model, we initially perform a comparative analysis between the temporal and aggregated modeling considering five different real datasets. This analysis shows that the aggregated model is inefficient in modeling the temporal aspects of networks. After that, we perform a network evaluation through a simulation by considering the impact of temporal modeling applied to the deployment of RSUs. First, we compare a solution based on our temporal modeling with a greedy algorithm based on an aggregated model to choose the positions of RSUs. In a scenario with 70 RSUs, we have 77% and 65% of coverage in the temporal and aggregated model (greedy algorithm), respectively. Second, we evaluate the use of aggregated and temporal measures applied as features in a genetic algorithm. The approach with temporal betweenness had the better result with 90% of the coverage area against 61% of aggregated one applied to the same scenario.",
        "published":"2021-09-07 12:34:30+00:00",
        "authors":"['Fillipe Santos', 'Andre L. L. Aquino', 'Edmundo R. M. Madeira', 'Raquel S. Cabral']",
        "arxiv_primary_category":"cs.si",
        "readability":0.8,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.1388333333
    },
    {
        "arxiv_id":"2109.03312v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03312v1",
        "title":"Self-adaptive Architectures in IoT Systems: A Systematic Literature Review",
        "summary":"Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in the bandith consumption or new devices joining\/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with dynamic situations involving simultaneous types of events. In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on the architecture QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.",
        "published":"2021-09-07 20:11:25+00:00",
        "authors":"['Iv\u00e1n Alfonso', 'Kelly Garc\u00e9s', 'Harold Castro', 'Jordi Cabot']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.91,
        "actuality":4.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2109.03431v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03431v1",
        "title":"Fixed Support Tree-Sliced Wasserstein Barycenter",
        "summary":"The Wasserstein barycenter has been widely studied in various fields, including natural language processing, and computer vision. However, it requires a high computational cost to solve the Wasserstein barycenter problem because the computation of the Wasserstein distance requires a quadratic time with respect to the number of supports. By contrast, the Wasserstein distance on a tree, called the tree-Wasserstein distance, can be computed in linear time and allows for the fast comparison of a large number of distributions. In this study, we propose a barycenter under the tree-Wasserstein distance, called the fixed support tree-Wasserstein barycenter (FS-TWB) and its extension, called the fixed support tree-sliced Wasserstein barycenter (FS-TSWB). More specifically, we first show that the FS-TWB and FS-TSWB problems are convex optimization problems and can be solved by using the projected subgradient descent. Moreover, we propose a more efficient algorithm to compute the subgradient and objective function value by using the properties of tree-Wasserstein barycenter problems. Through real-world experiments, we show that, by using the proposed algorithm, the FS-TWB and FS-TSWB can be solved two orders of magnitude faster than the original Wasserstein barycenter.",
        "published":"2021-09-08 04:50:33+00:00",
        "authors":"['Yuki Takezawa', 'Ryoma Sato', 'Zornitsa Kozareva', 'Sujith Ravi', 'Makoto Yamada']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.77,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":2.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6111666667
    },
    {
        "arxiv_id":"2109.03487v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03487v1",
        "title":"Social Analysis of Young Basque Speaking Communities in Twitter",
        "summary":"In this paper we take into account both social and linguistic aspects to perform demographic analysis by processing a large amount of tweets in Basque language. The study of demographic characteristics and social relationships are approached by applying machine learning and modern deep-learning Natural Language Processing (NLP) techniques, combining social sciences with automatic text processing. More specifically, our main objective is to combine demographic inference and social analysis in order to detect young Basque Twitter users and to identify the communities that arise from their relationships or shared content. This social and demographic analysis will be entirely based on the~automatically collected tweets using NLP to convert unstructured textual information into interpretable knowledge.",
        "published":"2021-09-08 08:19:08+00:00",
        "authors":"['J. Fernandez de Landa', 'R. Agerri']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.89,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.1945
    },
    {
        "arxiv_id":"2109.03538v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03538v1",
        "title":"Tracing Affordance and Item Adoption on Music Streaming Platforms",
        "summary":"Popular music streaming platforms offer users a diverse network of content exploration through a triad of affordances: organic, algorithmic and editorial access modes. Whilst offering great potential for discovery, such platform developments also pose the modern user with daily adoption decisions on two fronts: platform affordance adoption and the adoption of recommendations therein. Following a carefully constrained set of Deezer users over a 2-year observation period, our work explores factors driving user behaviour in the broad sense, by differentiating users on the basis of their temporal daily usage, adoption of the main platform affordances, and the ways in which they react to them, especially in terms of recommendation adoption. Diverging from a perspective common in studies on the effects of recommendation, we assume and confirm that users exhibit very diverse behaviours in using and adopting the platform affordances. The resulting complex and quite heterogeneous picture demonstrates that there is no blanket answer for adoption practices of both recommendation features and recommendations.",
        "published":"2021-09-08 10:41:23+00:00",
        "authors":"['Dougal Shakespeare', 'Camille Roth']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.86,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.2500833333
    },
    {
        "arxiv_id":"2109.03554v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03554v2",
        "title":"Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Task Generalization",
        "summary":"While artificial neural networks (ANNs) have been widely adopted in machine learning, researchers are increasingly obsessed by the gaps between ANNs and biological neural networks (BNNs). In this paper, we propose a framework named as Evolutionary Plastic Recurrent Neural Networks} (EPRNN). Inspired by BNN, EPRNN composes Evolution Strategies, Plasticity Rules, and Recursion-based Learning all in one meta learning framework for generalization to different tasks. More specifically, EPRNN incorporates with nested loops for meta learning -- an outer loop searches for optimal initial parameters of the neural network and learning rules; an inner loop adapts to specific tasks. In the inner loop of EPRNN, we effectively attain both long term memory and short term memory by forging plasticity with recursion-based learning mechanisms, both of which are believed to be responsible for memristance in BNNs. The inner-loop setting closely simulate that of BNNs, which neither query from any gradient oracle for optimization nor require the exact forms of learning objectives. To evaluate the performance of EPRNN, we carry out extensive experiments in two groups of tasks: Sequence Predicting, and Wheeled Robot Navigating. The experiment results demonstrate the unique advantage of EPRNN compared to state-of-the-arts based on plasticity and recursion while yielding comparably good performance against deep learning based approaches in the tasks. The experiment results suggest the potential of EPRNN to generalize to variety of tasks and encourage more efforts in plasticity and recursion based learning mechanisms.",
        "published":"2021-09-08 11:34:14+00:00",
        "authors":"['Fan Wang', 'Hao Tian', 'Haoyi Xiong', 'Hua Wu', 'Yang Cao', 'Yu Kang', 'Haifeng Wang']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.79,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2109.03856v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03856v1",
        "title":"Local Augmentation for Graph Neural Networks",
        "summary":"Data augmentation has been widely used in image data and linguistic data but remains under-explored on graph-structured data. Existing methods focus on augmenting the graph data from a global perspective and largely fall into two genres: structural manipulation and adversarial training with feature noise injection. However, the structural manipulation approach suffers information loss issues while the adversarial training approach may downgrade the feature quality by injecting noise. In this work, we introduce the local augmentation, which enhances node features by its local subgraph structures. Specifically, we model the data argumentation as a feature generation process. Given the central node's feature, our local augmentation approach learns the conditional distribution of its neighbors' features and generates the neighbors' optimal feature to boost the performance of downstream tasks. Based on the local augmentation, we further design a novel framework: LA-GNN, which can apply to any GNN models in a plug-and-play manner. Extensive experiments and analyses show that local augmentation consistently yields performance improvement for various GNN architectures across a diverse set of benchmarks. Code is available at https:\/\/github.com\/Soughing0823\/LAGNN.",
        "published":"2021-09-08 18:10:08+00:00",
        "authors":"['Songtao Liu', 'Hanze Dong', 'Lanqing Li', 'Tingyang Xu', 'Yu Rong', 'Peilin Zhao', 'Junzhou Huang', 'Dinghao Wu']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.84,
        "actuality":3.0,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2109.03915v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.03915v1",
        "title":"Follow the guides: disentangling human and algorithmic curation in online music consumption",
        "summary":"The role of recommendation systems in the diversity of content consumption on platforms is a much-debated issue. The quantitative state of the art often overlooks the existence of individual attitudes toward guidance, and eventually of different categories of users in this regard. Focusing on the case of music streaming, we analyze the complete listening history of about 9k users over one year and demonstrate that there is no blanket answer to the intertwinement of recommendation use and consumption diversity: it depends on users. First we compute for each user the relative importance of different access modes within their listening history, introducing a trichotomy distinguishing so-called `organic' use from algorithmic and editorial guidance. We thereby identify four categories of users. We then focus on two scales related to content diversity, both in terms of dispersion -- how much users consume the same content repeatedly -- and popularity -- how popular is the content they consume. We show that the two types of recommendation offered by music platforms -- algorithmic and editorial -- may drive the consumption of more or less diverse content in opposite directions, depending also strongly on the type of users. Finally, we compare users' streaming histories with the music programming of a selection of popular French radio stations during the same period. While radio programs are usually more tilted toward repetition than users' listening histories, they often program more songs from less popular artists. On the whole, our results highlight the nontrivial effects of platform-mediated recommendation on consumption, and lead us to speak of `filter niches' rather than `filter bubbles'. They hint at further ramifications for the study and design of recommendation systems.",
        "published":"2021-09-08 20:14:48+00:00",
        "authors":"['Quentin Villermet', 'J\u00e9r\u00e9mie Poiroux', 'Manuel Moussallam', 'Thomas Louail', 'Camille Roth']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.92,
        "actuality":3.0,
        "controversy":1.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.3888333333
    },
    {
        "arxiv_id":"2109.04014v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04014v1",
        "title":"Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering",
        "summary":"Knowledge-based visual question answering (VQA) requires answering questions with external knowledge in addition to the content of images. One dataset that is mostly used in evaluating knowledge-based VQA is OK-VQA, but it lacks a gold standard knowledge corpus for retrieval. Existing work leverage different knowledge bases (e.g., ConceptNet and Wikipedia) to obtain external knowledge. Because of varying knowledge bases, it is hard to fairly compare models' performance. To address this issue, we collect a natural language knowledge base that can be used for any VQA system. Moreover, we propose a Visual Retriever-Reader pipeline to approach knowledge-based VQA. The visual retriever aims to retrieve relevant knowledge, and the visual reader seeks to predict answers based on given knowledge. We introduce various ways to retrieve knowledge using text and images and two reader styles: classification and extraction. Both the retriever and reader are trained with weak supervision. Our experimental results show that a good retriever can significantly improve the reader's performance on the OK-VQA challenge. The code and corpus are provided in https:\/\/github.com\/luomancs\/retriever\\_reader\\_for\\_okvqa.git",
        "published":"2021-09-09 03:21:32+00:00",
        "authors":"['Man Luo', 'Yankai Zeng', 'Pratyay Banerjee', 'Chitta Baral']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.82,
        "actuality":4.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.4443333333
    },
    {
        "arxiv_id":"2109.04165v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04165v1",
        "title":"Modelling GDPR-Compliant Explanations for Trustworthy AI",
        "summary":"Through the General Data Protection Regulation (GDPR), the European Union has set out its vision for Automated Decision- Making (ADM) and AI, which must be reliable and human-centred. In particular we are interested on the Right to Explanation, that requires industry to produce explanations of ADM. The High-Level Expert Group on Artificial Intelligence (AI-HLEG), set up to support the implementation of this vision, has produced guidelines discussing the types of explanations that are appropriate for user-centred (interactive) Explanatory Tools. In this paper we propose our version of Explanatory Narratives (EN), based on user-centred concepts drawn from ISO 9241, as a model for user-centred explanations aligned with the GDPR and the AI-HLEG guidelines. Through the use of ENs we convert the problem of generating explanations for ADM into the identification of an appropriate path over an Explanatory Space, allowing explainees to interactively explore it and produce the explanation best suited to their needs. To this end we list suitable exploration heuristics, we study the properties and structure of explanations, and discuss the proposed model identifying its weaknesses and strengths.",
        "published":"2021-09-09 10:51:15+00:00",
        "authors":"['Francesco Sovrano', 'Fabio Vitali', 'Monica Palmirani']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":4.6666666667,
        "controversy":4.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.08325
    },
    {
        "arxiv_id":"2109.04206v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04206v2",
        "title":"QUINT: Node embedding using network hashing",
        "summary":"Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods (such as deepwalk, node2vec, LINE) are based on a neural architecture, thus unable to scale on large networks both in terms of time and space usage. Recently, we proposed BinSketch, a sketching technique for compressing binary vectors to binary vectors. In this paper, we show how to extend BinSketch and use it for network hashing. Our proposal named QUINT is built upon BinSketch, and it embeds nodes of a sparse network onto a low-dimensional space using simple bi-wise operations. QUINT is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks. Extensive experiments are conducted to compare QUINT with seven state-of-the-art network embedding methods for two end tasks - link prediction and node classification. We observe huge performance gain for QUINT in terms of speedup (up to 7000x) and space saving (up to 80x) due to its bit-wise nature to obtain node embedding. Moreover, QUINT is a consistent top-performer for both the tasks among the baselines across all the datasets. Our empirical observations are backed by rigorous theoretical analysis to justify the effectiveness of QUINT. In particular, we prove that QUINT retains enough structural information which can be used further to approximate many topological properties of networks with high confidence.",
        "published":"2021-09-09 12:26:44+00:00",
        "authors":"['Debajyoti Bera', 'Rameshwar Pratap', 'Bhisham Dev Verma', 'Biswadeep Sen', 'Tanmoy Chakraborty']",
        "arxiv_primary_category":"cs.si",
        "readability":0.81,
        "actuality":3.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2109.04374v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04374v2",
        "title":"IFBiD: Inference-Free Bias Detection",
        "summary":"This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply looking at their weights. Furthermore, it is also a step towards understanding neural networks and how they work. We show that it is indeed possible to know if a model is biased or not simply by looking at its weights, without the model inference for an specific input. We analyze how bias is encoded in the weights of deep networks through a toy example using the Colored MNIST database and we also provide a realistic case study in gender detection from face images using state-of-the-art methods and experimental resources. To do so, we generated two databases with 36K and 48K biased models each. In the MNIST models we were able to detect whether they presented a strong or low bias with more than 99% accuracy, and we were also able to classify between four levels of bias with more than 70% accuracy. For the face models, we achieved 90% accuracy in distinguishing between models biased towards Asian, Black, or Caucasian ethnicity.",
        "published":"2021-09-09 16:01:31+00:00",
        "authors":"['Ignacio Serna', 'Aythami Morales', 'Julian Fierrez', 'Javier Ortega-Garcia']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.9,
        "actuality":3.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.1388333333
    },
    {
        "arxiv_id":"2109.04385v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04385v1",
        "title":"Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification",
        "summary":"Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TextFooler, Genetic, BAE and SememePSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient.",
        "published":"2021-09-09 16:16:04+00:00",
        "authors":"['Maximilian Mozes', 'Max Bartolo', 'Pontus Stenetorp', 'Bennett Kleinberg', 'Lewis D. Griffin']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.7778333333
    },
    {
        "arxiv_id":"2109.04611v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04611v1",
        "title":"Query-driven Segment Selection for Ranking Long Documents",
        "summary":"Transformer-based rankers have shown state-of-the-art performance. However, their self-attention operation is mostly unable to process long sequences. One of the common approaches to train these rankers is to heuristically select some segments of each document, such as the first segment, as training data. However, these segments may not contain the query-related parts of documents. To address this problem, we propose query-driven segment selection from long documents to build training data. The segment selector provides relevant samples with more accurate labels and non-relevant samples which are harder to be predicted. The experimental results show that the basic BERT-based ranker trained with the proposed segment selector significantly outperforms that trained by the heuristically selected segments, and performs equally to the state-of-the-art model with localized self-attention that can process longer input sequences. Our findings open up new direction to design efficient transformer-based rankers.",
        "published":"2021-09-10 01:50:12+00:00",
        "authors":"['Youngwoo Kim', 'Razieh Rahimi', 'Hamed Bonab', 'James Allan']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.87,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.7499166667
    },
    {
        "arxiv_id":"2109.04685v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04685v1",
        "title":"Residual 3D Scene Flow Learning with Context-Aware Feature Extraction",
        "summary":"Scene flow estimation is the task to predict the point-wise 3D displacement vector between two consecutive frames of point clouds, which has important application in fields such as service robots and autonomous driving. Although many previous works have explored greatly on scene flow estimation based on point clouds, we point out two problems that have not been noticed or well solved before: 1) Points of adjacent frames in repetitive patterns may be wrongly associated due to similar spatial structure in their neighbourhoods; 2) Scene flow between adjacent frames of point clouds with long-distance movement may be inaccurately estimated. To solve the first problem, we propose a novel context-aware set conv layer to exploit contextual structure information of Euclidean space and learn soft aggregation weights for local point features. Our design is inspired by human perception of contextual structure information during scene understanding. We incorporate the context-aware set conv layer in a context-aware point feature pyramid module of 3D point clouds for scene flow estimation. For the second problem, we propose an explicit residual flow learning structure in the residual flow refinement layer to cope with long-distance movement. The experiments and ablation study on FlyingThings3D and KITTI scene flow datasets demonstrate the effectiveness of each proposed component and that we solve problem of ambiguous inter-frame association and long-distance movement estimation. Quantitative results on both FlyingThings3D and KITTI scene flow datasets show that our method achieves state-of-the-art performance, surpassing all other previous works to the best of our knowledge by at least 25%.",
        "published":"2021-09-10 06:15:18+00:00",
        "authors":"['Guangming Wang', 'Yunzhe Hu', 'Xinrui Wu', 'Hesheng Wang']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.85,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.08325
    },
    {
        "arxiv_id":"2109.04717v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04717v1",
        "title":"xBalloon: Animated Objects with Balloon Plastic Actuator",
        "summary":"Shape-changing interfaces are promising for users to change the physical properties of common objects. However, prevailing approaches of actuation devices require either professional equipment or materials that are not commonly accessible to non-professional users. In this work, we focus on the controllable soft actuators with inflatable structures because they are soft thus safe for human computer interaction. We propose a soft actuator design, called xBalloon, that is workable, inexpensive and easy-to-fabricate. It consists of daily materials including balloons and plastics and can realize bending actuation very effectively. For characterization, we fabricated xBalloon samples with different geometrical parameters and tested them regarding the bending performance and found the analytical model describing the relationship between the shape and the bending width. We then used xBalloons to animate a series of common objects and all can work satisfactorily. We further verified the user experience about the the fabrication and found that even those with no prior robotic knowledge can fabricate xBalloons with ease and confidence. Given all these advantages, we believe that xBalloon is an ideal platform for interaction design and entertainment applications.",
        "published":"2021-09-10 07:50:32+00:00",
        "authors":"['Haoran Xie', 'Takuma Torii', 'Aoshi Chiba', 'Qiukai Qi']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.85,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2109.04719v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04719v1",
        "title":"NaviChoker: Augmenting Pressure Sensation via Pneumatic Actuator",
        "summary":"Many technologies have been developed in recent years to present audiovisual information in new ways, but developing an information presentation interface to convey tactile information is still a challenge. We propose a tactile device using wearable technology that is an all-around pressure presentation system using pneumatic actuators. Specifically, we develop a system in which a choker equipped with a pneumatic actuator is worn around the neck, that applies pressure in any direction to indicate to the user the direction in which to walk and also when to start and stop walking. In this paper, we describe the construction of the device, evaluation experiments, our assessment of the prototype, and future plans for the device.",
        "published":"2021-09-10 07:54:07+00:00",
        "authors":"['Shogo Yoshida', 'Haoran Xie', 'Kazunori Miyata']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4999166667
    },
    {
        "arxiv_id":"2109.04740v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04740v1",
        "title":"How Does Fine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropy",
        "summary":"It is widely accepted that fine-tuning pre-trained language models usually brings about performance improvements in downstream tasks. However, there are limited studies on the reasons behind this effectiveness, particularly from the viewpoint of structural changes in the embedding space. Trying to fill this gap, in this paper, we analyze the extent to which the isotropy of the embedding space changes after fine-tuning. We demonstrate that, even though isotropy is a desirable geometrical property, fine-tuning does not necessarily result in isotropy enhancements. Moreover, local structures in pre-trained contextual word representations (CWRs), such as those encoding token types or frequency, undergo a massive change during fine-tuning. Our experiments show dramatic growth in the number of elongated directions in the embedding space, which, in contrast to pre-trained CWRs, carry the essential linguistic knowledge in the fine-tuned embedding space, making existing isotropy enhancement methods ineffective.",
        "published":"2021-09-10 08:58:59+00:00",
        "authors":"['Sara Rajaee', 'Mohammad Taher Pilehvar']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.84,
        "actuality":2.0,
        "controversy":2.0,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.1388333333
    },
    {
        "arxiv_id":"2109.04837v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04837v1",
        "title":"Human-Robot Interaction via a Joint-Initiative Supervised Autonomy (JISA) Framework",
        "summary":"In this paper, we propose and validate a Joint-Initiative Supervised Autonomy (JISA) framework for Human-Robot Interaction (HRI), in which a robot maintains a measure of its self-confidence (SC) while performing a task, and only prompts the human supervisor for help when its SC drops. At the same time, during task execution, a human supervisor can intervene in the task being performed, based on his\/her Situation Awareness (SA). To evaluate the applicability and utility of JISA, it is implemented on two different HRI tasks: grid-based collaborative simultaneous localization and mapping (SLAM) and automated jigsaw puzzle reconstruction. Augmented Reality (AR) (for SLAM) and two-dimensional graphical user interfaces (GUI) (for puzzle reconstruction) are custom-designed to enhance human SA and allow intuitive interaction between the human and the agent. The superiority of the JISA framework is demonstrated in experiments. In SLAM, the superior maps produced by JISA preclude the need for post processing of any SLAM stock maps; furthermore, JISA reduces the required mapping time by approximately 50 percent versus traditional approaches. In automated puzzle reconstruction, the JISA framework outperforms both fully autonomous solutions, as well as those resulting from on-demand human intervention prompted by the agent.",
        "published":"2021-09-10 12:44:49+00:00",
        "authors":"['Abbas Sidaoui', 'Naseem Daher', 'Daniel Asmar']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.81,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6111666667
    },
    {
        "arxiv_id":"2109.04867v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.04867v1",
        "title":"Studying word order through iterative shuffling",
        "summary":"As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax. This view rests upon a hypothesis that has not yet been empirically tested: that word order encodes meaning essential to performing these tasks. We refute this hypothesis in many cases: in the GLUE suite and in various genres of English text, the words in a sentence or phrase can rarely be permuted to form a phrase carrying substantially different information. Our surprising result relies on inference by iterative shuffling (IBIS), a novel, efficient procedure that finds the ordering of a bag of words having the highest likelihood under a fixed language model. IBIS can use any black-box model without additional training and is superior to existing word ordering algorithms. Coalescing our findings, we discuss how shuffling inference procedures such as IBIS can benefit language modeling and constrained generation.",
        "published":"2021-09-10 13:27:06+00:00",
        "authors":"['Nikolay Malkin', 'Sameera Lanka', 'Pranav Goel', 'Nebojsa Jojic']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.86,
        "actuality":1.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.2221666667
    },
    {
        "arxiv_id":"2109.05016v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05016v1",
        "title":"Neural Machine Translation Quality and Post-Editing Performance",
        "summary":"We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.",
        "published":"2021-09-10 17:56:02+00:00",
        "authors":"['Vil\u00e9m Zouhar', 'Ale\u0161 Tamchyna', 'Martin Popel', 'Ond\u0159ej Bojar']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.9,
        "actuality":3.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.2499166667
    },
    {
        "arxiv_id":"2109.05236v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05236v1",
        "title":"Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving",
        "summary":"News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models offline and provide online recommendation services. However, user data is usually highly privacy-sensitive, and centrally storing them may raise privacy concerns and risks. In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way. Following a widely used paradigm in real-world recommender systems, our framework contains two stages. The first one is for candidate news generation (i.e., recall) and the second one is for candidate news ranking (i.e., ranking). At the recall stage, each client locally learns multiple interest representations from clicked news to comprehensively model user interests. These representations are uploaded to the server to recall candidate news from a large news pool, which are further distributed to the user client at the ranking stage for personalized news display. In addition, we propose an interest decomposer-aggregator method with perturbation noise to better protect private user information encoded in user interest representations. Besides, we collaboratively train both recall and ranking models on the data decentralized in a large number of user clients in a privacy-preserving way. Experiments on two real-world news datasets show that our method can outperform baseline methods and effectively protect user privacy.",
        "published":"2021-09-11 09:36:47+00:00",
        "authors":"['Tao Qi', 'Fangzhao Wu', 'Chuhan Wu', 'Yongfeng Huang', 'Xing Xie']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.9,
        "actuality":5.0,
        "controversy":4.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.5555833333
    },
    {
        "arxiv_id":"2109.05278v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05278v1",
        "title":"Existence conditions for hidden feedback loops in online recommender systems",
        "summary":"We explore a hidden feedback loops effect in online recommender systems. Feedback loops result in degradation of online multi-armed bandit (MAB) recommendations to a small subset and loss of coverage and novelty. We study how uncertainty and noise in user interests influence the existence of feedback loops. First, we show that an unbiased additive random noise in user interests does not prevent a feedback loop. Second, we demonstrate that a non-zero probability of resetting user interests is sufficient to limit the feedback loop and estimate the size of the effect. Our experiments confirm the theoretical findings in a simulated environment for four bandit algorithms.",
        "published":"2021-09-11 13:30:08+00:00",
        "authors":"['Anton S. Khritankov', 'Anton A. Pilkevich']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.88,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2109.05658v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05658v1",
        "title":"Measurement as governance in and for responsible AI",
        "summary":"Measurement of social phenomena is everywhere, unavoidably, in sociotechnical systems. This is not (only) an academic point: Fairness-related harms emerge when there is a mismatch in the measurement process between the thing we purport to be measuring and the thing we actually measure. However, the measurement process -- where social, cultural, and political values are implicitly encoded in sociotechnical systems -- is almost always obscured. Furthermore, this obscured process is where important governance decisions are encoded: governance about which systems are fair, which individuals belong in which categories, and so on. We can then use the language of measurement, and the tools of construct validity and reliability, to uncover hidden governance decisions. In particular, we highlight two types of construct validity, content validity and consequential validity, that are useful to elicit and characterize the feedback loops between the measurement, social construction, and enforcement of social categories. We then explore the constructs of fairness, robustness, and responsibility in the context of governance in and for responsible AI. Together, these perspectives help us unpack how measurement acts as a hidden governance process in sociotechnical systems. Understanding measurement as governance supports a richer understanding of the governance processes already happening in AI -- responsible or otherwise -- revealing paths to more effective interventions.",
        "published":"2021-09-13 01:04:22+00:00",
        "authors":"['Abigail Z. Jacobs']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.87,
        "actuality":5.0,
        "controversy":4.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.7776666667
    },
    {
        "arxiv_id":"2109.05720v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05720v1",
        "title":"Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories",
        "summary":"For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistical validation algorithm that accurately estimates the F-score of binary classifiers for rare categories, where finding relevant examples to evaluate on is particularly challenging. Our key insight is that simultaneous calibration and importance sampling enables accurate estimates even in the low-sample regime (< 300 samples). Critically, we also derive an accurate single-trial estimator of the variance of our method and demonstrate that this estimator is empirically accurate at low sample counts, enabling a practitioner to know how well they can trust a given low-sample estimate. When validating state-of-the-art semi-supervised models on ImageNet and iNaturalist2017, our method achieves the same estimates of model performance with up to 10x fewer labels than competing approaches. In particular, we can estimate model F1 scores with a variance of 0.005 using as few as 100 labels.",
        "published":"2021-09-13 06:01:16+00:00",
        "authors":"['Fait Poms', 'Vishnu Sarukkai', 'Ravi Teja Mullapudi', 'Nimit S. Sohoni', 'William R. Mark', 'Deva Ramanan', 'Kayvon Fatahalian']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.05750v3",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05750v3",
        "title":"Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization",
        "summary":"Image harmonization aims to modify the color of the composited region with respect to the specific background. Previous works model this task as a pixel-wise image-to-image translation using UNet family structures. However, the model size and computational cost limit the performability of their models on edge devices and higher-resolution images. To this end, we propose a novel spatial-separated curve rendering network (S$^2$CRNet) for efficient and high-resolution image harmonization for the first time. In S$^2$CRNet, we firstly extract the spatial-separated embeddings from the thumbnails of the masked foreground and background individually. Then, we design a curve rendering module (CRM), which learns and combines the spatial-specific knowledge using linear layers to generate the parameters of the pixel-wise curve mapping in the foreground region. Finally, we directly render the original high-resolution images using the learned color curve. Besides, we also make two extensions of the proposed framework via the Cascaded-CRM and Semantic-CRM for cascaded refinement and semantic guidance, respectively. Experiments show that the proposed method reduces more than 90% parameters compared with previous methods but still achieves the state-of-the-art performance on both synthesized iHarmony4 and real-world DIH test set. Moreover, our method can work smoothly on higher resolution images in real-time which is more than 10$\\times$ faster than the existing methods. The code and pre-trained models will be made available and released at https:\/\/github.com\/stefanLeong\/S2CRNet.",
        "published":"2021-09-13 07:20:16+00:00",
        "authors":"['Jingtang Liang', 'Xiaodong Cun', 'Chi-Man Pun']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.82,
        "actuality":2.6666666667,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.05789v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05789v2",
        "title":"ARGO: Modeling Heterogeneity in E-commerce Recommendation",
        "summary":"Nowadays, E-commerce is increasingly integrated into our daily lives. Meanwhile, shopping process has also changed incrementally from one behavior (purchase) to multiple behaviors (such as view, carting and purchase). Therefore, utilizing interaction data of auxiliary behavior data draws a lot of attention in the E-commerce recommender systems. However, all existing models ignore two kinds of intrinsic heterogeneity which are helpful to capture the difference of user preferences and the difference of item attributes. First (intra-heterogeneity), each user has multiple social identities with otherness, and these different identities can result in quite different interaction preferences. Second (inter-heterogeneity), each item can transfer an item-specific percentage of score from low-level behavior to high-level behavior for the gradual relationship among multiple behaviors. Thus, the lack of consideration of these heterogeneities damages recommendation rank performance. To model the above heterogeneities, we propose a novel method named intra- and inter-heterogeneity recommendation model (ARGO). Specifically, we embed each user into multiple vectors representing the user's identities, and the maximum of identity scores indicates the interaction preference. Besides, we regard the item-specific transition percentage as trainable transition probability between different behaviors. Extensive experiments on two real-world datasets show that ARGO performs much better than the state-of-the-art in multi-behavior scenarios.",
        "published":"2021-09-13 09:12:12+00:00",
        "authors":"['Daqing Wu', 'Xiao Luo', 'Zeyu Ma', 'Chong Chen', 'Minghua Deng', 'Jinwen Ma']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":3.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0000833333
    },
    {
        "arxiv_id":"2109.05792v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.05792v1",
        "title":"Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making",
        "summary":"Automated decision systems (ADS) have become ubiquitous in many high-stakes domains. Those systems typically involve sophisticated yet opaque artificial intelligence (AI) techniques that seldom allow for full comprehension of their inner workings, particularly for affected individuals. As a result, ADS are prone to deficient oversight and calibration, which can lead to undesirable (e.g., unfair) outcomes. In this work, we conduct an online study with 200 participants to examine people's perceptions of fairness and trustworthiness towards ADS in comparison to a scenario where a human instead of an ADS makes a high-stakes decision -- and we provide thorough identical explanations regarding decisions in both cases. Surprisingly, we find that people perceive ADS as fairer than human decision-makers. Our analyses also suggest that people's AI literacy affects their perceptions, indicating that people with higher AI literacy favor ADS more strongly over human decision-makers, whereas low-AI-literacy people exhibit no significant differences in their perceptions.",
        "published":"2021-09-13 09:14:15+00:00",
        "authors":"['Jakob Schoeffer', 'Yvette Machowski', 'Niklas Kuehl']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.9,
        "actuality":3.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.3611666667
    },
    {
        "arxiv_id":"2109.06007v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06007v2",
        "title":"Visualization for Villainy",
        "summary":"Visualization has long been seen as a dependable and trustworthy tool for carrying out analysis and communication tasks -- a view reinforced by the growing interest in applying it to socially positive ends. However, despite the benign light in which visualization is usually perceived, it carries the potential to do harm to people, places, concepts, and things. In this paper, we capitalize on this negative potential to serve an underrepresented (but technologically engaged) group: villains. To achieve these ends, we introduce a design space for this type of graphical violence, which allows us to unify prior work on deceptive visualization with novel data-driven dastardly deeds, such as emotional spear phishing and unsafe data physicalization. By charting this vile charting landscape, we open new doors to collaboration with terrifying domain experts, and hopefully, make the world just a bit worse.",
        "published":"2021-09-13 14:31:25+00:00",
        "authors":"['Andrew M McNutt', 'Lilian Huang', 'Kathryn Koenig']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.88,
        "actuality":3.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.9721666667
    },
    {
        "arxiv_id":"2109.06037v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06037v1",
        "title":"Correcting the User Feedback-Loop Bias for Recommendation Systems",
        "summary":"Selection bias is prevalent in the data for training and evaluating recommendation systems with explicit feedback. For example, users tend to rate items they like. However, when rating an item concerning a specific user, most of the recommendation algorithms tend to rely too much on his\/her rating (feedback) history. This introduces implicit bias on the recommendation system, which is referred to as user feedback-loop bias in this paper. We propose a systematic and dynamic way to correct such bias and to obtain more diverse and objective recommendations by utilizing temporal rating information. Specifically, our method includes a deep-learning component to learn each user's dynamic rating history embedding for the estimation of the probability distribution of the items that the user rates sequentially. These estimated dynamic exposure probabilities are then used as propensity scores to train an inverse-propensity-scoring (IPS) rating predictor. We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods. The results show the superiority of our approach.",
        "published":"2021-09-13 15:02:55+00:00",
        "authors":"['Weishen Pan', 'Sen Cui', 'Hongyi Wen', 'Kun Chen', 'Changshui Zhang', 'Fei Wang']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.87,
        "actuality":3.6666666667,
        "controversy":3.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3888333333
    },
    {
        "arxiv_id":"2109.06306v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06306v1",
        "title":"BERT for Target Apps Selection: Analyzing the Diversity and Performance of BERT in Unified Mobile Search",
        "summary":"A unified mobile search framework aims to identify the mobile apps that can satisfy a user's information need and route the user's query to them. Previous work has shown that resource descriptions for mobile apps are sparse as they rely on the app's previous queries. This problem puts certain apps in dominance and leaves out the resource-scarce apps from the top ranks. In this case, we need a ranker that goes beyond simple lexical matching. Therefore, our goal is to study the extent of a BERT-based ranker's ability to improve the quality and diversity of app selection. To this end, we compare the results of the BERT-based ranker with other information retrieval models, focusing on the analysis of selected apps diversification. Our analysis shows that the BERT-based ranker selects more diverse apps while improving the quality of baseline results by selecting the relevant apps such as Facebook and Contacts for more personal queries and decreasing the bias towards the dominant resources such as the Google Search app.",
        "published":"2021-09-13 20:34:17+00:00",
        "authors":"['Negin Ghasemi', 'Mohammad Aliannejadi', 'Djoerd Hiemstra']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.89,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.9445
    },
    {
        "arxiv_id":"2109.06424v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06424v1",
        "title":"Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse",
        "summary":"This paper calls attention to the missing component of the recommender system evaluation process: Statistical Inference. There is active research in several components of the recommender system evaluation process: selecting baselines, standardizing benchmarks, and target item sampling. However, there has not yet been significant work on the role and use of statistical inference for analyzing recommender system evaluation results. In this paper, we argue that the use of statistical inference is a key component of the evaluation process that has not been given sufficient attention. We support this argument with systematic review of recent RecSys papers to understand how statistical inference is currently being used, along with a brief survey of studies that have been done on the use of statistical inference in the information retrieval community. We present several challenges that exist for inference in recommendation experiment which buttresses the need for empirical studies to aid with appropriately selecting and applying statistical inference techniques.",
        "published":"2021-09-14 04:22:01+00:00",
        "authors":"['Ngozi Ihemelandu', 'Michael D. Ekstrand']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":1.3333333333,
        "controversy":2.0,
        "relevance_magnitude":1.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":1.5834166667
    },
    {
        "arxiv_id":"2109.06440v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06440v1",
        "title":"Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems",
        "summary":"The ubiquitous use of IoT and machine learning applications is creating large amounts of data that require accurate and real-time processing. Although edge-based smart data processing can be enabled by deploying pretrained models, the energy and memory constraints of edge devices necessitate distributed deep learning between the edge and the cloud for complex data. In this paper, we propose a distributed AI system to exploit both the edge and the cloud for training and inference. We propose a new architecture, MEANet, with a main block, an extension block, and an adaptive block for the edge. The inference process can terminate at either the main block, the extension block, or the cloud. The MEANet is trained to categorize inputs into easy\/hard\/complex classes. The main block identifies instances of easy\/hard classes and classifies easy classes with high confidence. Only data with high probabilities of belonging to hard classes would be sent to the extension block for prediction. Further, only if the neural network at the edge shows low confidence in the prediction, the instance is considered complex and sent to the cloud for further processing. The training technique lends to the majority of inference on edge devices while going to the cloud only for a small set of complex jobs, as determined by the edge. The performance of the proposed system is evaluated via extensive experiments using modified models of ResNets and MobileNetV2 on CIFAR-100 and ImageNet datasets. The results show that the proposed distributed model has improved accuracy and energy consumption, indicating its capacity to adapt.",
        "published":"2021-09-14 05:03:54+00:00",
        "authors":"['Yinghan Long', 'Indranil Chakraborty', 'Gopalakrishnan Srinivasan', 'Kaushik Roy']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.9,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3055
    },
    {
        "arxiv_id":"2109.06466v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06466v1",
        "title":"Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding",
        "summary":"Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data. However, it's unclear whether they learn similar representations or they can be effectively combined. In this paper, we show that TAPT and ST can be complementary with simple TFS protocol by following TAPT -> Finetuning -> Self-training (TFS) process. Experimental results show that TFS protocol can effectively utilize unlabeled data to achieve strong combined gains consistently across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification. We investigate various semi-supervised settings and consistently show that gains from TAPT and ST can be strongly additive by following TFS procedure. We hope that TFS could serve as an important semi-supervised baseline for future NLP studies.",
        "published":"2021-09-14 06:24:28+00:00",
        "authors":"['Shiyang Li', 'Semih Yavuz', 'Wenhu Chen', 'Xifeng Yan']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.81,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.16675
    },
    {
        "arxiv_id":"2109.06474v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06474v1",
        "title":"Space Time Recurrent Memory Network",
        "summary":"We propose a novel visual memory network architecture for the learning and inference problem in the spatial-temporal domain. Different from the popular transformers, we maintain a fixed set of memory slots in our memory network and explore designs to input new information into the memory, combine the information in different memory slots and decide when to discard old memory slots. Finally, this architecture is benchmarked on the video object segmentation and video prediction problems. Through the experiments, we show that our memory architecture can achieve competitive results with state-of-the-art while maintaining constant memory capacity.",
        "published":"2021-09-14 06:53:51+00:00",
        "authors":"['Hung Nguyen', 'Fuxin Li']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2109.06573v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06573v1",
        "title":"The Impact of User Demographics and Task Types on Cross-App Mobile Search",
        "summary":"Recent developments in the mobile app industry have resulted in various types of mobile apps, each targeting a different need and a specific audience. Consequently, users access distinct apps to complete their information need tasks. This leads to the use of various apps not only separately, but also collaboratively in the same session to achieve a single goal. Recent work has argued the need for a unified mobile search system that would act as metasearch on users' mobile devices. The system would identify the target apps for the user's query, submit the query to the apps, and present the results to the user in a unified way. In this work, we aim to deepen our understanding of user behavior while accessing information on their mobile phones by conducting an extensive analysis of various aspects related to the search process. In particular, we study the effect of task type and user demographics on their behavior in interacting with mobile apps. Our findings reveal trends and patterns that can inform the design of a more effective mobile information access environment.",
        "published":"2021-09-14 10:42:13+00:00",
        "authors":"['Mohammad Aliannejadi', 'Fabio Crestani', 'Theo Huibers', 'Monica Landoni', 'Emiliana Murgia', 'Maria Soledad Pera']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.96,
        "actuality":2.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2109.06598v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06598v1",
        "title":"Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP",
        "summary":"A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.",
        "published":"2021-09-14 11:36:42+00:00",
        "authors":"['Anna Rogers', 'Tim Baldwin', 'Kobi Leins']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.92,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.72225
    },
    {
        "arxiv_id":"2109.06604v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06604v1",
        "title":"Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation",
        "summary":"Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, it heavily relies on high-quality in-domain parallel corpora, limiting its capability on unsupervised domain adaptation, where in-domain parallel corpora are scarce or nonexistent. In this paper, we propose a novel framework that directly uses in-domain monolingual sentences in the target language to construct an effective datastore for $k$-nearest-neighbor retrieval. To this end, we first introduce an autoencoder task based on the target language, and then insert lightweight adapters into the original NMT model to map the token-level representation of this task to the ideal representation of translation task. Experiments on multi-domain datasets demonstrate that our proposed approach significantly improves the translation accuracy with target-side monolingual data, while achieving comparable performance with back-translation.",
        "published":"2021-09-14 11:50:01+00:00",
        "authors":"['Xin Zheng', 'Zhirui Zhang', 'Shujian Huang', 'Boxing Chen', 'Jun Xie', 'Weihua Luo', 'Jiajun Chen']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.79,
        "actuality":3.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2109.06702v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06702v1",
        "title":"Learning Based Adaptive Force Control of Robotic Manipulation Based on Real-Time Object Stiffness Detection",
        "summary":"Force control is essential for medical robots when touching and contacting the patient's body. To increase the stability and efficiency in force control, an Adaption Module could be used to adjust the parameters for different contact situations. We propose an adaptive controller with an Adaption Module which can produce control parameters based on force feedback and real-time stiffness detection. We develop methods for learning the optimal policies by value iteration and using the data generated from those policies to train the Adaptive Module. We test this controller on different zones of a person's arm. All the parameters used in practice are learned from data. The experiments show that the proposed adaptive controller can exert various target forces on different zones of the arm with fast convergence and good stability.",
        "published":"2021-09-14 14:08:20+00:00",
        "authors":"['Zhaoxing Deng', 'Xutian Deng', 'Miao Li']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.87,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.111
    },
    {
        "arxiv_id":"2109.06783v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06783v1",
        "title":"Learning to Navigate Intersections with Unsupervised Driver Trait Inference",
        "summary":"Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits such as driving styles from observed vehicle trajectories. We use a variational autoencoder with recurrent neural networks to learn a latent representation of traits without any ground truth trait labels. Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning. Our pipeline enables the autonomous vehicle to adjust its actions when dealing with drivers of different traits to ensure safety and efficiency. Our method demonstrates promising performance and outperforms state-of-the-art baselines in the T-intersection scenario.",
        "published":"2021-09-14 15:54:35+00:00",
        "authors":"['Shuijing Liu', 'Peixin Chang', 'Haonan Chen', 'Neeloy Chakraborty', 'Katherine Driggs-Campbell']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.83,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.861
    },
    {
        "arxiv_id":"2109.06979v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.06979v1",
        "title":"CORNET 2.0: A Co-Simulation Middleware forRobot Networks",
        "summary":"We present a networked co-simulation framework for multi-robot systems applications. We require a simulation framework that captures both physical interactions and communications aspects to effectively design such complex systems. This is necessary to co-design the multi-robots' autonomy logic and the communication protocols. The proposed framework extends existing tools to simulate the robot's autonomy and network-related aspects. We have used Gazebo with ROS\/ROS2 to develop the autonomy logic for robots and mininet-WiFi as the network simulator to capture the cyber-physical systems properties of the multi-robot system. This framework addresses the need to seamlessly integrate the two simulation environments by synchronizing mobility and time, allowing for easy migration of the algorithms to real platforms.",
        "published":"2021-09-14 21:46:48+00:00",
        "authors":"['Srikrishna Acharya', 'Bharadwaj Amrutur', 'Mukunda Bharatheesha', 'Yogesh Simmhan']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.87,
        "actuality":3.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2109.07023v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07023v1",
        "title":"Embedding Node Structural Role Identity Using Stress Majorization",
        "summary":"Nodes in networks may have one or more functions that determine their role in the system. As opposed to local proximity, which captures the local context of nodes, the role identity captures the functional \"role\" that nodes play in a network, such as being the center of a group, or the bridge between two groups. This means that nodes far apart in a network can have similar structural role identities. Several recent works have explored methods for embedding the roles of nodes in networks. However, these methods all rely on either approximating or indirect modeling of structural equivalence. In this paper, we present a novel and flexible framework using stress majorization, to transform the high-dimensional role identities in networks directly (without approximation or indirect modeling) to a low-dimensional embedding space. Our method is also flexible, in that it does not rely on specific structural similarity definitions. We evaluated our method on the tasks of node classification, clustering, and visualization, using three real-world and five synthetic networks. Our experiments show that our framework achieves superior results than existing methods in learning node role representations.",
        "published":"2021-09-14 23:48:16+00:00",
        "authors":"['Lili Wang', 'Chenghan Huang', 'Weicheng Ma', 'Ying Lu', 'Soroush Vosoughi']",
        "arxiv_primary_category":"cs.si",
        "readability":0.87,
        "actuality":1.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":2.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":1.7221666667
    },
    {
        "arxiv_id":"2109.07035v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07035v1",
        "title":"Data Hunches: Incorporating Personal Knowledge into Visualizations",
        "summary":"The trouble with data is that often it provides only an imperfect representation of the phenomenon of interest. When reading and interpreting data, personal knowledge about the data plays an important role. Data visualization, however, has neither a concept defining personal knowledge about datasets, nor the methods or tools to robustly integrate them into an analysis process, thus hampering analysts' ability to express their personal knowledge about datasets, and others to learn from such knowledge. In this work, we define such personal knowledge about datasets as data hunches and elevate this knowledge to another form of data that can be externalized, visualized, and used for collaboration. We establish the implications of data hunches and provide a design space for externalizing and communicating data hunches through visualization techniques. We envision such a design space will empower users to externalize their personal knowledge and support the ability to learn from others' data hunches.",
        "published":"2021-09-15 01:06:33+00:00",
        "authors":"['Haihan Lin', 'Derya Akbaba', 'Miriah Meyer', 'Alexander Lex']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.87,
        "actuality":3.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.3611666667
    },
    {
        "arxiv_id":"2109.07194v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07194v1",
        "title":"Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference",
        "summary":"This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. We clarify whether the computational model can reproduce the following functions in a symbol emergence system, comprising two agents with different sensory modalities playing a naming game. (1) Function for forming a shared lexical system that comprises perceptual categories and corresponding signs, formed by agents through individual learning and semiotic communication between agents. (2) Function to improve the categorization accuracy in an agent via semiotic communication with another agent, even when some sensory modalities of each agent are missing. (3) Function that an agent infers unobserved sensory information based on a sign sampled from another agent in the same manner as cross-modal inference. We propose an interpersonal multimodal Dirichlet mixture (Inter-MDM), which is derived by dividing an integrative probabilistic generative model, which is obtained by integrating two Dirichlet mixtures (DMs). The Markov chain Monte Carlo algorithm realizes emergent communication. The experimental results demonstrated that Inter-MDM enables agents to form multimodal categories and appropriately share signs between agents. It is shown that emergent communication improves categorization accuracy, even when some sensory modalities are missing. Inter-MDM enables an agent to predict unobserved information based on a shared sign.",
        "published":"2021-09-15 10:20:54+00:00",
        "authors":"['Yoshinobu Hagiwara', 'Kazuma Furukawa', 'Akira Taniguchi', 'Tadahiro Taniguchi']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.75,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.2778333333
    },
    {
        "arxiv_id":"2109.07206v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07206v1",
        "title":"Signaling Design for Cooperative Resource Allocation and its Impact to Reliability",
        "summary":"Decentralized cooperative resource allocation schemes for robotic swarms are essential to enable high reliability in high throughput data exchanges. These cooperative schemes require control signaling with the aim to avoid half-duplex problems at the receiver and mitigate interference. We propose two cooperative resource allocation schemes, device sequential and group scheduling, and introduce a control signaling design. We observe that failure in the reception of these control signals leads to non-cooperative behavior and to significant performance degradation. The cause of these failures are identified and specific countermeasures are proposed and evaluated. We compare the proposed resource allocation schemes against the NR sidelink mode 2 resource allocation and show that even though signaling has an important impact on the resource allocation performance, our proposed device sequential and group scheduling resource allocation schemes improve reliability by an order of magnitude compared to sidelink mode 2.",
        "published":"2021-09-15 10:49:18+00:00",
        "authors":"['Rasmus Liborius Bruun', 'C. Santiago Morej\u00f3n Garc\u00eda', 'Troels B. S\u00f8rensen', 'Nuno K. Pratas', 'Tatiana Kozlova Madsen', 'Preben Mogensen']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.82,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2109.07324v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07324v1",
        "title":"PointManifoldCut: Point-wise Augmentation in the Manifold for Point Clouds",
        "summary":"Augmentation can benefit point cloud learning due to the limited availability of large-scale public datasets. This paper proposes a mix-up augmentation approach, PointManifoldCut, which replaces the neural network embedded points, rather than the Euclidean space coordinates. This approach takes the advantage that points at the higher levels of the neural network are already trained to embed its neighbors relations and mixing these representation will not mingle the relation between itself and its label. This allows to regularize the parameter space as the other augmentation methods but without worrying about the proper label of the replaced points. The experiments show that our proposed approach provides a competitive performance on point cloud classification and segmentation when it is combined with the cutting-edge vanilla point cloud networks. The result shows a consistent performance boosting compared to other state-of-the-art point cloud augmentation method, such as PointMixup and PointCutMix. The code of this paper is available at: https:\/\/github.com\/fun0515\/PointManifoldCut.",
        "published":"2021-09-15 14:31:42+00:00",
        "authors":"['Tianfang Zhu', 'Yue Guan', 'Anan Li']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":2.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":1.91675
    },
    {
        "arxiv_id":"2109.07409v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07409v1",
        "title":"Sporting the government: Twitter as a window into sportspersons' engagement with causes in India and USA",
        "summary":"With the ubiquitous reach of social media, influencers are increasingly central to articulation of political agendas on a range of topics. We curate a sample of tweets from the 200 most followed sportspersons in India and the United States respectively since 2019, map their connections with politicians, and visualize their engagements with key topics online. We find significant differences between the ways in which Indian and US sportspersons engage with politics online-while leading Indian sportspersons tend to align closely with the ruling party and engage minimally in dissent, American sportspersons engage with a range of political issues and are willing to publicly criticize politicians or policy. Our findings suggest that the ownership and governmental control of sports impact public stances on issues that professional sportspersons are willing to engage in online. It might also be inferred, depending upon the government of the day, that the costs of speaking up against the state and the government in power have different socio-economic costs in the US and India.",
        "published":"2021-09-15 16:30:43+00:00",
        "authors":"['Dibyendu Mishra', 'Ronojoy Sen', 'Joyojeet Pal']",
        "arxiv_primary_category":"cs.si",
        "readability":0.91,
        "actuality":4.6666666667,
        "controversy":4.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.6388333333
    },
    {
        "arxiv_id":"2109.07458v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07458v1",
        "title":"Comparing Text Representations: A Theory-Driven Approach",
        "summary":"Much of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how do we quantify and explain this effect? We adapt general tools from computational learning theory to fit the specific characteristics of text datasets and present a method to evaluate the compatibility between representations and tasks. Even though many tasks can be easily solved with simple bag-of-words (BOW) representations, BOW does poorly on hard natural language inference tasks. For one such task we find that BOW cannot distinguish between real and randomized labelings, while pre-trained MLM representations show 72x greater distinction between real and random labelings than BOW. This method provides a calibrated, quantitative measure of the difficulty of a classification-based NLP task, enabling comparisons between representations without requiring empirical evaluations that may be sensitive to initializations and hyperparameters. The method provides a fresh perspective on the patterns in a dataset and the alignment of those patterns with specific labels.",
        "published":"2021-09-15 17:48:19+00:00",
        "authors":"['Gregory Yauney', 'David Mimno']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.82,
        "actuality":4.0,
        "controversy":3.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.5278333333
    },
    {
        "arxiv_id":"2109.07582v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07582v1",
        "title":"How to Simplify Search: Classification-wise Pareto Evolution for One-shot Neural Architecture Search",
        "summary":"In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental. Most existing neural architecture search (NAS) methods utilize surrogates to predict the detailed performance (e.g., accuracy and model size) of a candidate architecture during the search, which however is complicated and inefficient. In contrast, we aim to learn an efficient Pareto classifier to simplify the search process of NAS by transforming the complex multi-objective NAS task into a simple Pareto-dominance classification task. To this end, we propose a classification-wise Pareto evolution approach for one-shot NAS, where an online classifier is trained to predict the dominance relationship between the candidate and constructed reference architectures, instead of using surrogates to fit the objective functions. The main contribution of this study is to change supernet adaption into a Pareto classifier. Besides, we design two adaptive schemes to select the reference set of architectures for constructing classification boundary and regulate the rate of positive samples over negative ones, respectively. We compare the proposed evolution approach with state-of-the-art approaches on widely-used benchmark datasets, and experimental results indicate that the proposed approach outperforms other approaches and have found a number of neural architectures with different model sizes ranging from 2M to 6M under diverse objectives and constraints.",
        "published":"2021-09-14 13:28:07+00:00",
        "authors":"['Lianbo Ma', 'Nan Li', 'Guo Yu', 'Xiaoyu Geng', 'Min Huang', 'Xingwei Wang']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.84,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2109.07602v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07602v1",
        "title":"Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series",
        "summary":"Time series models with recurrent neural networks (RNNs) can have high accuracy but are unfortunately difficult to interpret as a result of feature-interactions, temporal-interactions, and non-linear transformations. Interpretability is important in domains like healthcare where constructing models that provide insight into the relationships they have learned are required to validate and trust model predictions. We want accurate time series models where users can understand the contribution of individual input features. We present the Interpretable-RNN (I-RNN) that balances model complexity and accuracy by forcing the relationship between variables in the model to be additive. Interactions are restricted between hidden states of the RNN and additively combined at the final step. I-RNN specifically captures the unique characteristics of clinical time series, which are unevenly sampled in time, asynchronously acquired, and have missing data. Importantly, the hidden state activations represent feature coefficients that correlate with the prediction target and can be visualized as risk curves that capture the global relationship between individual input features and the outcome. We evaluate the I-RNN model on the Physionet 2012 Challenge dataset to predict in-hospital mortality, and on a real-world clinical decision support task: predicting hemodynamic interventions in the intensive care unit. I-RNN provides explanations in the form of global and local feature importances comparable to highly intelligible models like decision trees trained on hand-engineered features while significantly outperforming them. I-RNN remains intelligible while providing accuracy comparable to state-of-the-art decay-based and interpolation-based recurrent time series models. The experimental results on real-world clinical datasets refute the myth that there is a tradeoff between accuracy and interpretability.",
        "published":"2021-09-15 22:30:19+00:00",
        "authors":"['Asif Rahman', 'Yale Chang', 'Jonathan Rubin']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.82,
        "actuality":1.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.0555
    },
    {
        "arxiv_id":"2109.07745v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07745v1",
        "title":"Estimating Wildfire Evacuation Decision and Departure Timing Using Large-Scale GPS Data",
        "summary":"With increased frequency and intensity due to climate change, wildfires have become a growing global concern. This creates severe challenges for fire and emergency services as well as communities in the wildland-urban interface (WUI). To reduce wildfire risk and enhance the safety of WUI communities, improving our understanding of wildfire evacuation is a pressing need. To this end, this study proposes a new methodology to analyze human behavior during wildfires by leveraging a large-scale GPS dataset. This methodology includes a home-location inference algorithm and an evacuation-behavior inference algorithm, to systematically identify different groups of wildfire evacuees (i.e., self-evacuee, shadow evacuee, evacuee under warning, and ordered evacuee). We applied the methodology to the 2019 Kincade Fire in Sonoma County, CA. We found that among all groups of evacuees, self-evacuees and shadow evacuees accounted for more than half of the evacuees during the Kincade Fire. The results also show that inside of the evacuation warning\/order zones, the total evacuation compliance rate was around 46% among all the categorized people. The findings of this study can be used by emergency managers and planners to better target public outreach campaigns, training protocols, and emergency communication strategies to prepare WUI households for future wildfire events.",
        "published":"2021-09-16 06:40:23+00:00",
        "authors":"['Xilei Zhao', 'Yiming Xu', 'Ruggiero Lovreglio', 'Erica Kuligowski', 'Daniel Nilsson', 'Thomas Cova', 'Alex Wu', 'Xiang Yan']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.85,
        "actuality":4.6666666667,
        "controversy":1.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.5555833333
    },
    {
        "arxiv_id":"2109.07827v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07827v1",
        "title":"Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition",
        "summary":"Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, automotive or aerospace, it is often challenging to bridge the gap between an apparently optimal policy learnt by an agent and its real-world deployment, due to the uncertainties and risk associated with it. Broadly speaking RL agents face two kinds of uncertainty, 1. aleatoric uncertainty, which reflects randomness or noise in the dynamics of the world, and 2. epistemic uncertainty, which reflects the bounded knowledge of the agent due to model limitations and finite amount of information\/data the agent has acquired about the world. These two types of uncertainty carry fundamentally different implications for the evaluation of performance and the level of risk or trust. Yet these aleatoric and epistemic uncertainties are generally confounded as standard and even distributional RL is agnostic to this difference. Here we propose how a distributional approach (UA-DQN) can be recast to render uncertainties by decomposing the net effects of each uncertainty. We demonstrate the operation of this method in grid world examples to build intuition and then show a proof of concept application for an RL agent operating as a clinical decision support system in critical care",
        "published":"2021-09-16 09:36:53+00:00",
        "authors":"['Paul Festor', 'Giulia Luise', 'Matthieu Komorowski', 'A. Aldo Faisal']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.87,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2109.07861v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07861v1",
        "title":"Building an Ensemble of Classifiers via Randomized Models of Ensemble Members",
        "summary":"Many dynamic ensemble selection (DES) methods are known in the literature. A previously-developed by the authors, method consists in building a randomized classifier which is treated as a model of the base classifier. The model is equivalent to the base classifier in a certain probabilistic sense. Next, the probability of correct classification of randomized classifier is taken as the competence of the evaluated classifier. In this paper, a novel randomized model of base classifier is developed. In the proposed method, the random operation of the model results from a random selection of the learning set from the family of learning sets of a fixed size. The paper presents the mathematical foundations of this approach and shows how, for a practical application when learning and validation sets are given, one can determine the measure of competence and build a MC system with the DES scheme. The DES scheme with the proposed model of competence was experimentally evaluated on the collection of 67 benchmark datasets and compared in terms of eight quality criteria with two ensemble classifiers which use the previously-proposed concepts of randomized model. The proposed approach achieved the lowest ranks for almost all investigated quality criteria.",
        "published":"2021-09-16 10:53:13+00:00",
        "authors":"['Pawel Trajdos', 'Marek Kurzynski']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":2.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":2.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.1945
    },
    {
        "arxiv_id":"2109.07906v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07906v1",
        "title":"Ethics of AI: A Systematic Literature Review of Principles and Challenges",
        "summary":"Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assess the ethical capabilities of AI systems and provide best practices for further improvements.",
        "published":"2021-09-12 15:33:43+00:00",
        "authors":"['Arif Ali Khan', 'Sher Badshah', 'Peng Liang', 'Bilal Khan', 'Muhammad Waseem', 'Mahmood Niazi', 'Muhammad Azeem Akbar']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.92,
        "actuality":5.0,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.5276666667
    },
    {
        "arxiv_id":"2109.07971v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.07971v1",
        "title":"Do Language Models Know the Way to Rome?",
        "summary":"The global geometry of language models is important for a range of applications, but language model probes tend to evaluate rather local relations, for which ground truths are easily obtained. In this paper we exploit the fact that in geography, ground truths are available beyond local relations. In a series of experiments, we evaluate the extent to which language model representations of city and country names are isomorphic to real-world geography, e.g., if you tell a language model where Paris and Berlin are, does it know the way to Rome? We find that language models generally encode limited geographic information, but with larger models performing the best, suggesting that geographic knowledge can be induced from higher-order co-occurrence statistics.",
        "published":"2021-09-16 13:28:16+00:00",
        "authors":"['Bastien Li\u00e9tard', 'Mostafa Abdou', 'Anders S\u00f8gaard']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.92,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2109.08183v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08183v1",
        "title":"Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise",
        "summary":"People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model. Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people's trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model. Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people's trust perceptions.",
        "published":"2021-09-16 18:38:19+00:00",
        "authors":"['Jeroen Ooge', 'Katrien Verbert']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.9,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3888333333
    },
    {
        "arxiv_id":"2109.08259v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08259v1",
        "title":"Self-training with Few-shot Rationalization: Teacher Explanations Aid Student in Few-shot NLU",
        "summary":"While pre-trained language models have obtained state-of-the-art performance for several natural language understanding tasks, they are quite opaque in terms of their decision-making process. While some recent works focus on rationalizing neural predictions by highlighting salient concepts in the text as justifications or rationales, they rely on thousands of labeled training examples for both task labels as well as an-notated rationales for every instance. Such extensive large-scale annotations are infeasible to obtain for many tasks. To this end, we develop a multi-task teacher-student framework based on self-training language models with limited task-specific labels and rationales, and judicious sample selection to learn from informative pseudo-labeled examples1. We study several characteristics of what constitutes a good rationale and demonstrate that the neural model performance can be significantly improved by making it aware of its rationalized predictions, particularly in low-resource settings. Extensive experiments in several bench-mark datasets demonstrate the effectiveness of our approach.",
        "published":"2021-09-17 00:36:46+00:00",
        "authors":"['Meghana Moorthy Bhat', 'Alessandro Sordoni', 'Subhabrata Mukherjee']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.87,
        "actuality":3.6666666667,
        "controversy":4.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.6111666667
    },
    {
        "arxiv_id":"2109.08482v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08482v1",
        "title":"An Optimization-based Approach for Flow Table Capacity Bottleneck Mitigation in Software-Defined Networks",
        "summary":"Flow delegation is a flexible technique to mitigate flow table capacity bottlenecks in Software-defined Networks (SDN). Such bottlenecks occur when SDN switches provide insufficient flow table capacity which leads to performance degradation and network failures. Flow delegation addresses this problem by automatically relocating flow rules from a bottlenecked switch to neighboring switches with spare capacity. This paper introduces a new algorithm to efficiently perform flow delegation based on a novel delegation template abstraction and multi-period multi-objective optimization. Different from existing work, our approach can include estimated knowledge about future network situations and deal with different optimization criteria such as link and control overhead. We discuss the problem decomposition for the new algorithm and introduce an efficient two-step heuristic. Results show, that our approach performs significantly better than the simple greedy algorithm used in earlier work and is capable of handling flow delegation for networks with hundreds of switches.",
        "published":"2021-09-17 11:52:10+00:00",
        "authors":"['Robert Bauer', 'Martina Zitterbart']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.87,
        "actuality":2.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9999166667
    },
    {
        "arxiv_id":"2109.08532v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08532v1",
        "title":"PAPIR: Practical RIS-aided Localization via Statistical User Information",
        "summary":"The integration of advanced localization techniques in the upcoming next generation networks (B5G\/6G) is becoming increasingly important for many use cases comprising contact tracing, natural disasters, terrorist attacks, etc. Therefore, emerging lightweight and passive technologies that allow accurately controlling the propagation environment, such as reconfigurable intelligent surfaces (RISs), may help to develop advance positioning solutions relying on channel statistics and beamforming. In this paper, we devise PAPIR, a practical localization system leveraging on RISs by designing a two-stage solution building upon prior statistical information on the target user equipment (UE) position. PAPIR aims at finely estimating the UE position by performing statistical beamforming, direction-of-arrival (DoA) and time-of-arrival (ToA) estimation on a given three-dimensional search space, which is iteratively updated by exploiting the likelihood of the UE position.",
        "published":"2021-09-17 13:13:03+00:00",
        "authors":"['Antonio Albanese', 'Placido Mursia', 'Vincenzo Sciancalepore', 'Xavier Costa-P\u00e9rez']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.8,
        "actuality":4.3333333333,
        "controversy":4.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":4.0278333333
    },
    {
        "arxiv_id":"2109.08651v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08651v1",
        "title":"A Tutorial on Mathematical Modeling of Millimeter Wave and Terahertz Cellular Systems",
        "summary":"Millimeter wave (mmWave) and terahertz (THz) radio access technologies (RAT) are expected to become a critical part of the future cellular ecosystem providing an abundant amount of bandwidth in areas with high traffic demands. However, extremely directional antenna radiation patterns that need to be utilized at both transmit and receive sides of a link to overcome severe path losses, dynamic blockage of propagation paths by large static and small dynamic objects, macro- and micromobility of user equipment (UE) makes provisioning of reliable service over THz\/mmWave RATs an extremely complex task. This challenge is further complicated by the type of applications envisioned for these systems inherently requiring guaranteed bitrates at the air interface. This tutorial aims to introduce a versatile mathematical methodology for assessing performance reliability improvement algorithms for mmWave and THz systems. Our methodology accounts for both radio interface specifics as well as service process of sessions at mmWave\/THz base stations (BS) and is capable of evaluating the performance of systems with multiconnectivity operation, resource reservation mechanisms, priorities between multiple traffic types having different service requirements. The framework is logically separated into two parts: (i) parameterization part that abstracts the specifics of deployment and radio mechanisms, and (ii) queuing part, accounting for details of the service process at mmWave\/THz BSs. The modular decoupled structure of the framework allows for further extensions to advanced service mechanisms in prospective mmWave\/THz cellular deployments while keeping the complexity manageable and thus making it attractive for system analysts.",
        "published":"2021-09-17 17:15:18+00:00",
        "authors":"['Dmitri Moltchanov', 'Eduard Sopin', 'Vyacheslav Begishev', 'Andrey Samuylov', 'Yevgeni Koucheryavy', 'Konstantin Samouylov']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.8,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.08815v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08815v1",
        "title":"Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation",
        "summary":"To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified. We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements. By extending the commonly used Gaussian likelihood model for trajectories via the multiple-shooting formulation, our chosen particle-based inference algorithm Stein Variational Gradient Descent is able to identify highly nonlinear, underactuated systems. We leverage GPU code generation and differentiable simulation to evaluate the likelihood and its gradient for many particles in parallel. Our algorithm infers non-parametric distributions over simulation parameters more accurately than comparable baselines and handles constraints over parameters efficiently through gradient-based optimization. We evaluate estimation performance on several physical experiments. On an underactuated mechanism where a 7-DOF robot arm excites an object with an unknown mass configuration, we demonstrate how our inference technique can identify symmetries between the parameters and provide highly accurate predictions. Project website: https:\/\/uscresl.github.io\/prob-diff-sim",
        "published":"2021-09-18 03:05:44+00:00",
        "authors":"['Eric Heiden', 'Christopher E. Denniston', 'David Millard', 'Fabio Ramos', 'Gaurav S. Sukhatme']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.73,
        "actuality":1.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2109.08973v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.08973v1",
        "title":"Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search",
        "summary":"Non-prehensile multi-object rearrangement is a robotic task of planning feasible paths and transferring multiple objects to their predefined target poses without grasping. It needs to consider how each object reaches the target and the order of object movement, which significantly deepens the complexity of the problem. To address these challenges, we propose a hierarchical policy to divide and conquer for non-prehensile multi-object rearrangement. In the high-level policy, guided by a designed policy network, the Monte Carlo Tree Search efficiently searches for the optimal rearrangement sequence among multiple objects, which benefits from imitation and reinforcement. In the low-level policy, the robot plans the paths according to the order of path primitives and manipulates the objects to approach the goal poses one by one. We verify through experiments that the proposed method can achieve a higher success rate, fewer steps, and shorter path length compared with the state-of-the-art.",
        "published":"2021-09-18 17:24:37+00:00",
        "authors":"['Fan Bai', 'Fei Meng', 'Jianbang Liu', 'Jiankun Wang', 'Max Q. -H. Meng']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.86,
        "actuality":3.3333333333,
        "controversy":3.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2109.09214v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09214v1",
        "title":"A Conformal Mapping-based Framework for Robot-to-Robot and Sim-to-Real Transfer Learning",
        "summary":"This paper presents a novel method for transferring motion planning and control policies between a teacher and a learner robot. With this work, we propose to reduce the sim-to-real gap, transfer knowledge designed for a specific system into a different robot, and compensate for system aging and failures. To solve this problem we introduce a Schwarz-Christoffel mapping-based method to geometrically stretch and fit the control inputs from the teacher into the learner command space. We also propose a method based on primitive motion generation to create motion plans and control inputs compatible with the learner's capabilities. Our approach is validated with simulations and experiments with different robotic systems navigating occluding environments.",
        "published":"2021-09-19 20:17:47+00:00",
        "authors":"['Shijie Gao', 'Nicola Bezzo']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.87,
        "actuality":4.3333333333,
        "controversy":3.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.9165833333
    },
    {
        "arxiv_id":"2109.09409v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09409v1",
        "title":"IEEE 802.1AS Clock Synchronization Performance Evaluation of an Integrated Wired-Wireless TSN Architecture",
        "summary":"Industrial control systems present numerous challenges from the communication systems perspective: clock synchronization, deterministic behavior, low latency, high reliability, flexibility, and scalability. These challenges are mostly solved with standard technologies over Ethernet, e.g., Time-Sensitive Networking (TSN). As a research trend, it is expected that TSN will converge with wireless, leading to the Wireless TSN paradigm. Also, Wireless TSN is expected to be integrated with Ethernet TSN to create large-scale wired-wireless (Hybrid) TSN networks. The first step towards Hybrid TSN is the distribution of the clock reference from the wired to the wireless domain. In this paper, we leverage existing Ethernet TSN and wireless technologies implementations (Wi-Fi and w-SHARP) and we present two hardware architectures specifically engineered to enable the clock synchronization distribution among the network domains. The hardware architectures have been implemented over a System-on-Chip (SoC) Field Programmable Gate Array (FPGA) platform. We demonstrate through several experiments that the implementation is able to fulfill the synchronization performance required by TSN.",
        "published":"2021-09-20 10:11:51+00:00",
        "authors":"['Inaki Val', 'Oscar Seijo', 'Raul Torrego', 'Armando Astarloa']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.77,
        "actuality":4.0,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2109.09410v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09410v1",
        "title":"Background-Foreground Segmentation for Interior Sensing in Automotive Industry",
        "summary":"To ensure safety in automated driving, the correct perception of the situation inside the car is as important as its environment. Thus, seat occupancy detection and classification of detected instances play an important role in interior sensing. By the knowledge of the seat occupancy status, it is possible to, e.g., automate the airbag deployment control. Furthermore, the presence of a driver, which is necessary for partially automated driving cars at the automation levels two to four can be verified. In this work, we compare different statistical methods from the field of image segmentation to approach the problem of background-foreground segmentation in camera based interior sensing. In the recent years, several methods based on different techniques have been developed and applied to images or videos from different applications. The peculiarity of the given scenarios of interior sensing is, that the foreground instances and the background both contain static as well as dynamic elements. In data considered in this work, even the camera position is not completely fixed. We review and benchmark three different methods ranging, i.e., Gaussian Mixture Models (GMM), Morphological Snakes and a deep neural network, namely a Mask R-CNN. In particular, the limitations of the classical methods, GMM and Morphological Snakes, for interior sensing are shown. Furthermore, it turns, that it is possible to overcome these limitations by deep learning, e.g.\\ using a Mask R-CNN. Although only a small amount of ground truth data was available for training, we enabled the Mask R-CNN to produce high quality background-foreground masks via transfer learning. Moreover, we demonstrate that certain augmentation as well as pre- and post-processing methods further enhance the performance of the investigated methods.",
        "published":"2021-09-20 10:12:40+00:00",
        "authors":"['Claudia Drygala', 'Matthias Rottmann', 'Hanno Gottschalk', 'Klaus Friedrichs', 'Thomas Kurbiel']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.88,
        "actuality":2.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2109.09489v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09489v1",
        "title":"Struct-MRT: Immersive Learning and Teaching of Design and Verification in Structural Civil Engineering using Mixed Reality",
        "summary":"Our goal is to transform traditional paper-based instruction into an immersive lesson. This paper presents the conception, workflow and deployment of two MR applications for verification of typical yet geometrically complex structural members: a reinforced concrete corbel and a steel frame. The aim of this research is threefold: (i) to develop and implement the technological feasibility of such applications, (ii) to demonstrate possible use cases in the context of structural engineering lectures and (iii) to evaluate the presented MR examples and the future potential of such MR applications in structural engineering lectures through a survey. The workflow and MR teaching applications were developed with Apple's ARKit. The verification process was reproduced in the MR applications based on conventional exercises taught on paper. Users can navigate independently through the applications and review every single step, including a true-to-scale, spatial representation of the specific component as well as associated verification formulas in the respective step. The applications were used to assess the demand and expectations for immersive teaching techniques among students and instructors through a survey. The participants were asked to test the MR applications on their devices or watch pre-recorded video demonstrations, afterwards perception was elicited through a questionnaire. The results of subsequent data analysis show generally positive judgement of the MR application over the six questioned categories (style, usefulness, ease of use, enjoyment, attitude as well as intention towards using). The statistical analysis revealed (positivity) biases for users with prior XR experience w.r.t. to usage and navigation, while inexperienced users underlined increased enjoyment or excitement with this learning format. The outlook covers identified shortcomings and future developments in this field.",
        "published":"2021-09-20 12:46:52+00:00",
        "authors":"['Michael Kraus', 'Irfan Custovic', 'Walter Kaufmann']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.89,
        "actuality":3.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.47225
    },
    {
        "arxiv_id":"2109.09532v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09532v1",
        "title":"Characterizing User Susceptibility to COVID-19 Misinformation on Twitter",
        "summary":"Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 \"misinfodemic\", it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer {\\it who} constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.",
        "published":"2021-09-20 13:31:15+00:00",
        "authors":"['Xian Teng', 'Yu-Ru Lin', 'Wen-Ting Chung', 'Ang Li', 'Adriana Kovashka']",
        "arxiv_primary_category":"cs.si",
        "readability":0.86,
        "actuality":5.0,
        "controversy":4.3333333333,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":4.1945
    },
    {
        "arxiv_id":"2109.09618v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09618v1",
        "title":"Automatic Y-axis Rescaling in Dynamic Visualizations",
        "summary":"Animated and interactive data visualizations dynamically change the data rendered in a visualization (e.g., bar chart). As the data changes, the y-axis may need to be rescaled as the domain of the data changes. Each axis rescaling potentially improves the readability of the current chart, but may also disorient the user. In contrast to static visualizations, where there is considerable literature to help choose the appropriate y-axis scale, there is a lack of guidance about how and when rescaling should be used in dynamic visualizations. Existing visualization systems and libraries adapt a fixed global y-axis, or rescale every time the data changes. Yet, professional visualizations, such as in data journalism, do not adopt either strategy. They instead carefully and manually choose when to rescale based on the analysis task and data. To this end, we conduct a series of Mechanical Turk experiments to study the potential of dynamic axis rescaling and the factors that affect its effectiveness. We find that the appropriate rescaling policy is both task- and data-dependent, and we do not find one clear policy choice for all situations.",
        "published":"2021-09-20 15:20:51+00:00",
        "authors":"['Jacob Fisher', 'Remco Chang', 'Eugene Wu']",
        "arxiv_primary_category":"cs.hc",
        "readability":0.85,
        "actuality":3.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.6111666667
    },
    {
        "arxiv_id":"2109.09696v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09696v1",
        "title":"Configuring Multiple Instances with Multi-Configuration",
        "summary":"Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.",
        "published":"2021-09-20 17:04:56+00:00",
        "authors":"['Alexander Felfernig', 'Andrei Popescu', 'Mathias Uta', 'Viet-Man Le', 'Seda Polat-Erdeniz', 'Martin Stettinger', 'M\u00fcsl\u00fcm Atas', 'Thi Ngoc Trang Tran']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.86,
        "actuality":4.6666666667,
        "controversy":3.0,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.97225
    },
    {
        "arxiv_id":"2109.09777v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09777v1",
        "title":"DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection",
        "summary":"This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo, is a Transformer-based neural classifier which enhances contextualized word embeddings (CWEs) with hand-crafted features, relying on tokenwise sequence tagging for discourse segmentation and connective detection, and a feature-rich, encoder-less sentence pair classifier for relation classification. Our results for the first two tasks outperform SOTA scores from the previous 2019 shared task, and results on relation classification suggest strong performance on the new 2021 benchmark. Ablation tests show that including features beyond CWEs are helpful for both tasks, and a partial evaluation of multiple pre-trained Transformer-based language models indicates that models pre-trained on the Next Sentence Prediction (NSP) task are optimal for relation classification.",
        "published":"2021-09-20 18:11:05+00:00",
        "authors":"['Luke Gessler', 'Shabnam Behzad', 'Yang Janet Liu', 'Siyao Peng', 'Yilun Zhu', 'Amir Zeldes']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.77,
        "actuality":3.3333333333,
        "controversy":2.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.09832v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09832v1",
        "title":"Weak Signals in the Mobility Landscape: Car Sharing in Ten European Cities",
        "summary":"Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities. From the point of view of demand modelling, car sharing is a weak signal in the city landscape: only a small percentage of the population uses it, and thus it is difficult to study reliably with traditional techniques such as households travel diaries. In this work, we depart from these traditional approaches and we leverage web-based, digital records about vehicle availability in 10 European cities for one of the major active car sharing operators. We discuss which sociodemographic and urban activity indicators are associated with variations in car sharing demand, which forecasting approach (among the most popular in the related literature) is better suited to predict pickup and drop-off events, and how the spatio-temporal information about vehicle availability can be used to infer how different zones in a city are used by customers. We conclude the paper by presenting a direct application of the analysis of the dataset, aimed at identifying where to locate maintenance facilities within the car sharing operation area.",
        "published":"2021-09-20 20:37:25+00:00",
        "authors":"['Chiara Boldrini', 'Raffaele Bruno', 'Haitam Laarabi']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.92,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.4445
    },
    {
        "arxiv_id":"2109.09853v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09853v1",
        "title":"StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations",
        "summary":"This demonstration paper presents StreamSide, an open-source toolkit for annotating multiple kinds of meaning representations. StreamSide supports frame-based annotation schemes e.g., Abstract Meaning Representation (AMR) and frameless annotation schemes e.g., Widely Interpretable Semantic Representation (WISeR). Moreover, it supports both sentence-level and document-level annotation by allowing annotators to create multi-rooted graphs for input text. It can open and automatically convert between several types of input formats including plain text, Penman notation, and its own JSON format enabling richer annotation. It features reference frames for AMR predicate argument structures, and also concept-to-text alignment. StreamSide is released under the Apache 2.0 license, and is completely open-source so that it can be customized to annotate enriched meaning representations in different languages (e.g., Uniform Meaning Representations). All StreamSide resources are publicly distributed through our open source project at: https:\/\/github.com\/emorynlp\/StreamSide.",
        "published":"2021-09-20 21:36:22+00:00",
        "authors":"['Jinho D. Choi', 'Gregor Williamson']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.75,
        "actuality":2.3333333333,
        "controversy":1.3333333333,
        "relevance_magnitude":2.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":2.1945
    },
    {
        "arxiv_id":"2109.09876v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09876v1",
        "title":"Context-Specific Representation Abstraction for Deep Option Learning",
        "summary":"Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration. One promising approach that learns these options end-to-end is the option-critic (OC) framework. We examine and show in this paper that OC does not decompose a problem into simpler sub-problems, but instead increases the size of the search over policy space with each option considering the entire state space during learning. This issue can result in practical limitations of this method, including sample inefficient learning. To address this problem, we introduce Context-Specific Representation Abstraction for Deep Option Learning (CRADOL), a new framework that considers both temporal abstraction and context-specific representation abstraction to effectively reduce the size of the search over policy space. Specifically, our method learns a factored belief state representation that enables each option to learn a policy over only a subsection of the state space. We test our method against hierarchical, non-hierarchical, and modular recurrent neural network baselines, demonstrating significant sample efficiency improvements in challenging partially observable environments.",
        "published":"2021-09-20 22:50:01+00:00",
        "authors":"['Marwa Abdulhai', 'Dong-Ki Kim', 'Matthew Riemer', 'Miao Liu', 'Gerald Tesauro', 'Jonathan P. How']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.87,
        "actuality":2.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.4721666667
    },
    {
        "arxiv_id":"2109.09904v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09904v1",
        "title":"Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems",
        "summary":"Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as (system-produced) abstractions use in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. Symbols, like emotions, may well not be sine qua non for intelligence per se, but they will be crucial for AI systems to interact with us humans--as we can neither turn off our emotions nor get by without our symbols. In particular, in many human-designed domains, humans would be interested in providing explicit (symbolic) knowledge and advice--and expect machine explanations in kind. This alone requires AI systems to at least do their I\/O in symbolic terms. In this blue sky paper, we argue this point of view, and discuss research directions that need to be pursued to allow for this type of human-AI interaction.",
        "published":"2021-09-21 01:30:06+00:00",
        "authors":"['Subbarao Kambhampati', 'Sarath Sreedharan', 'Mudit Verma', 'Yantian Zha', 'Lin Guan']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.91,
        "actuality":3.3333333333,
        "controversy":4.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.6665833333
    },
    {
        "arxiv_id":"2109.09979v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.09979v1",
        "title":"When expertise gone missing: Uncovering the loss of prolific contributors in Wikipedia",
        "summary":"Success of planetary-scale online collaborative platforms such as Wikipedia is hinged on active and continued participation of its voluntary contributors. The phenomenal success of Wikipedia as a valued multilingual source of information is a testament to the possibilities of collective intelligence. Specifically, the sustained and prudent contributions by the experienced prolific editors play a crucial role to operate the platform smoothly for decades. However, it has been brought to light that growth of Wikipedia is stagnating in terms of the number of editors that faces steady decline over time. This decreasing productivity and ever increasing attrition rate in both newcomer and experienced editors is a major concern for not only the future of this platform but also for several industry-scale information retrieval systems such as Siri, Alexa which depend on Wikipedia as knowledge store. In this paper, we have studied the ongoing crisis in which experienced and prolific editors withdraw. We performed extensive analysis of the editor activities and their language usage to identify features that can forecast prolific Wikipedians, who are at risk of ceasing voluntary services. To the best of our knowledge, this is the first work which proposes a scalable prediction pipeline, towards detecting the prolific Wikipedians, who might be at a risk of retiring from the platform and, thereby, can potentially enable moderators to launch appropriate incentive mechanisms to retain such `would-be missing' valued Wikipedians.",
        "published":"2021-09-21 06:05:07+00:00",
        "authors":"['Paramita Das', 'Bhanu Prakash Reddy Guda', 'Debajit Chakraborty', 'Soumya Sarkar', 'Animesh Mukherjee']",
        "arxiv_primary_category":"cs.si",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8889166667
    },
    {
        "arxiv_id":"2109.10061v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10061v1",
        "title":"Graph Neural Networks for Graph Drawing",
        "summary":"Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiable loss functions has paved the road to the massive usage of Gradient Descent and related optimization algorithms. In this paper, we propose a novel framework for the development of Graph Neural Drawers (GND), machines that rely on neural computation for constructing efficient and complex maps. GND are Graph Neural Networks (GNNs) whose learning process can be driven by any provided loss function, such as the ones commonly employed in Graph Drawing. Moreover, we prove that this mechanism can be guided by loss functions computed by means of Feedforward Neural Networks, on the basis of supervision hints that express beauty properties, like the minimization of crossing edges. In this context, we show that GNNs can nicely be enriched by positional features to deal also with unlabelled vertexes. We provide a proof-of-concept by constructing a loss function for the edge-crossing and provide quantitative and qualitative comparisons among different GNN models working under the proposed framework.",
        "published":"2021-09-21 09:58:02+00:00",
        "authors":"['Matteo Tiezzi', 'Gabriele Ciravegna', 'Marco Gori']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.82,
        "actuality":2.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.5555
    },
    {
        "arxiv_id":"2109.10147v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10147v1",
        "title":"Knowledge Distillation with Noisy Labels for Natural Language Understanding",
        "summary":"Knowledge Distillation (KD) is extensively used to compress and deploy large pre-trained language models on edge devices for real-world applications. However, one neglected area of research is the impact of noisy (corrupted) labels on KD. We present, to the best of our knowledge, the first study on KD with noisy labels in Natural Language Understanding (NLU). We document the scope of the problem and present two methods to mitigate the impact of label noise. Experiments on the GLUE benchmark show that our methods are effective even under high noise levels. Nevertheless, our results indicate that more research is necessary to cope with label noise under the KD.",
        "published":"2021-09-21 13:00:22+00:00",
        "authors":"['Shivendra Bhardwaj', 'Abbas Ghaddar', 'Ahmad Rashid', 'Khalil Bibi', 'Chengyang Li', 'Ali Ghodsi', 'Philippe Langlais', 'Mehdi Rezagholizadeh']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2109.10159v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10159v1",
        "title":"From MANET to people-centric networking: milestones and open research challenges",
        "summary":"In this paper we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies we analyze four successful ad hoc networking paradigms, mesh, opportunistic, vehicular networks, and sensor networks that emerged from the MANET world. We also present the new research directions in the multi-hop ad hoc networking field: people-centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications.",
        "published":"2021-09-21 13:16:11+00:00",
        "authors":"['Marco Conti', 'Chiara Boldrini', 'Salil S. Kanhere', 'Enzo Mingozzi', 'Elena Pagani', 'Pedro M. Ruiz', 'Mohamed Younis']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.89,
        "actuality":4.6666666667,
        "controversy":4.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":4.1665833333
    },
    {
        "arxiv_id":"2109.10217v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10217v1",
        "title":"Shape Inference and Grammar Induction for Example-based Procedural Generation",
        "summary":"Designers increasingly rely on procedural generation for automatic generation of content in various industries. These techniques require extensive knowledge of the desired content, and about how to actually implement such procedural methods. Algorithms for learning interpretable generative models from example content could alleviate both difficulties. We propose SIGI, a novel method for inferring shapes and inducing a shape grammar from grid-based 3D building examples. This interpretable grammar is well-suited for co-creative design. Applied to Minecraft buildings, we show how the shape grammar can be used to automatically generate new buildings in a similar style.",
        "published":"2021-09-21 14:41:56+00:00",
        "authors":"['Gillis Hermans', 'Thomas Winters', 'Luc De Raedt']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.89,
        "actuality":2.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2109.10274v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10274v1",
        "title":"The Trade-offs of Domain Adaptation for Neural Language Models",
        "summary":"In this paper, we connect language model adaptation with concepts of machine learning theory. We consider a training setup with a large out-of-domain set and a small in-domain set. As a first contribution, we derive how the benefit of training a model on either set depends on the size of the sets and the distance between their underlying distribution. As a second contribution, we present how the most popular data selection techniques -- importance sampling, intelligent data selection and influence functions -- can be presented in a common framework which highlights their similarity and also their subtle differences.",
        "published":"2021-09-21 15:54:31+00:00",
        "authors":"['Dan Iter', 'David Grangier']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.94,
        "actuality":4.0,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.1388333333
    },
    {
        "arxiv_id":"2109.10410v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10410v1",
        "title":"RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing",
        "summary":"While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering. In this paper, we are applying retrieval-based modeling ideas to the problem of multi-domain task-oriented semantic parsing for conversational assistants. Our approach, RetroNLU, extends a sequence-to-sequence model architecture with a retrieval component, used to fetch existing similar examples and provide them as an additional input to the model. In particular, we analyze two settings, where we augment an input with (a) retrieved nearest neighbor utterances (utterance-nn), and (b) ground-truth semantic parses of nearest neighbor utterances (semparse-nn). Our technique outperforms the baseline method by 1.5% absolute macro-F1, especially at the low resource setting, matching the baseline model accuracy with only 40% of the data. Furthermore, we analyze the nearest neighbor retrieval component's quality, model sensitivity and break down the performance for semantic parses of different utterance complexity.",
        "published":"2021-09-21 19:30:30+00:00",
        "authors":"['Vivek Gupta', 'Akshat Shrivastava', 'Adithya Sagar', 'Armen Aghajanyan', 'Denis Savenkov']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.78,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":2.5000833333
    },
    {
        "arxiv_id":"2109.10501v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10501v1",
        "title":"Third-party Evaluation of Robotic Hand Designs Using a Mechanical Glove",
        "summary":"A robotic hand design suitable for dexterity should be examined using functional tests. To achieve this, we designed a mechanical glove, which is a rigid wearable glove that enables us to develop the corresponding isomorphic robotic hand and evaluate its hardware properties. Subsequently, the effectiveness of multiple degrees-of-freedom (DOFs) was evaluated by human participants. Several fine motor skills were evaluated using the mechanical glove under two conditions: one- and three-DOF conditions. To the best of our knowledge, this is the first extensive evaluation method for robotic hand designs suitable for dexterity. (This paper was peer-reviewed and is the full translation from the Journal of the Robotics Society of Japan, Vol.39, No.6, pp.557-560, 2021.)",
        "published":"2021-09-22 03:31:24+00:00",
        "authors":"['Takayuki Kanai', 'Yoshiyuki Ohmura', 'Akihiko Nagakubo', 'Yasuo Kuniyoshi']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.85,
        "actuality":2.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":2.9443333333
    },
    {
        "arxiv_id":"2109.10593v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10593v2",
        "title":"Emulating Aerosol Microphysics with Machine Learning",
        "summary":"Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average $R^2$ score of $89\\%$. On a GPU we achieve a speed-up of 120 compared to the original model.",
        "published":"2021-09-22 08:42:19+00:00",
        "authors":"['Paula Harder', 'Duncan Watson-Parris', 'Dominik Strassel', 'Nicolas Gauger', 'Philip Stier', 'Janis Keuper']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.88,
        "actuality":3.3333333333,
        "controversy":1.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.1389166667
    },
    {
        "arxiv_id":"2109.10892v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.10892v1",
        "title":"The Design of Stretch: A Compact, Lightweight Mobile Manipulator for Indoor Human Environments",
        "summary":"Mobile manipulators for indoor human environments can serve as versatile devices that perform a variety of tasks, yet adoption of this technology has been limited. Reducing size, weight, and cost could facilitate adoption, but risks restricting capabilities. We present a novel design that reduces size, weight, and cost, while still performing a variety of tasks. The core design consists of a two-wheeled differential-drive mobile base, a lift, and a telescoping arm configured to achieve Cartesian motion at the end of the arm. Design extensions include a 1 degree-of-freedom (DOF) wrist to stow a tool, a 2-DOF dexterous wrist to pitch and roll a tool, and a compliant gripper. We justify our design with mathematical models of static stability that relate the robot's size and weight to its workspace, payload, and applied forces. We also provide empirical support by teleoperating and autonomously controlling a commercial robot based on our design (the Stretch RE1 from Hello Robot Inc.) to perform tasks in real homes.",
        "published":"2021-09-22 17:56:51+00:00",
        "authors":"['Charles C. Kemp', 'Aaron Edsinger', 'Henry M. Clever', 'Blaine Matulevich']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.85,
        "actuality":4.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.33325
    },
    {
        "arxiv_id":"2109.11023v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.11023v1",
        "title":"An Investigation And Insight Into Terrorism In Nigeria",
        "summary":"Terrorism is one of the most serious life-challenging threat facing humanity around the world. The activities of terrorist organization threatens peace, disrupts progress and halts all-round development of any nation. Terrorist activities in Nigeria in the last decades has negatively affected the economic growth and has drastically reduced the possibilities of foreign investments in Nigeria. In this paper, statistical and inferential insights are applied to the terrorist activities in Nigeria between 1970 to 2019. Using the Global Terrorism Database (GTD), insights are made on the occurrences of terrorist attacks, the localities of target and the successful and unsuccessful rates of such attacks. The Apriori algorithm is also used in this paper to draw hidden patterns from the GTD in order to aid in the generation of strong rules through database mining, resulting in relevant insights. This understanding of terrorist activities will provide security agencies with the needed information to be one step ahead of terrorist, hence assisting in curbing terrorism in Nigeria.",
        "published":"2021-09-22 20:22:43+00:00",
        "authors":"['Aamo Iorliam', 'Raymond U. Dugeri', 'Beatrice O. Akumba', 'Samera Otor']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.91,
        "actuality":4.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.3055833333
    },
    {
        "arxiv_id":"2109.11268v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.11268v1",
        "title":"Cyber Resilience in IoT network: Methodology and example of assessment through epidemic spreading",
        "summary":"Cyber Resilience is an important property of complex systems and is important consideration in developing specific IoT applications. This work aims at introducing a novel approach to assess IoT resilience adopting the risk perception in network based epidemic spreading approach. In particular IoT has been considered a network of devices where the probability of infection and interactions (communication), needs to be balanced in order to reduce the malware outbreack while maintaining the network functionalities at an acceptable level. The mathematical model and the simulation results reveal the benefit of a shift from a risk-based to a resilience based approach to threat management in IoT.",
        "published":"2021-09-23 10:12:26+00:00",
        "authors":"['Emanuele Bellini', 'Franco Bagnoli', 'Alexander A. Ganin', 'Igor Linkov']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.91,
        "actuality":3.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.11319v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.11319v1",
        "title":"Active Learning for Argument Strength Estimation",
        "summary":"High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining. However, the annotation effort for this task is exceptionally high. Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled. Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.",
        "published":"2021-09-23 12:05:16+00:00",
        "authors":"['Nataliia Kees', 'Michael Fromm', 'Evgeniy Faerman', 'Thomas Seidl']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.91,
        "actuality":4.0,
        "controversy":4.0,
        "relevance_magnitude":3.0,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.4445
    },
    {
        "arxiv_id":"2109.11642v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.11642v1",
        "title":"Bayesian Inference of a Social Graph with Trace Feasibility Guarantees",
        "summary":"Network inference is the process of deciding what is the true unknown graph underlying a set of interactions between nodes. There is a vast literature on the subject, but most known methods have an important drawback: the inferred graph is not guaranteed to explain every interaction from the input trace. We consider this an important issue since such inferred graph cannot be used as input for applications that require a reliable estimate of the true graph. On the other hand, a graph having trace feasibility guarantees can help us better understand the true (hidden) interactions that may have taken place between nodes of interest. The inference of such graph is the goal of this paper. Firstly, given an activity log from a social network, we introduce a set of constraints that take into consideration all the hidden paths that are possible between the nodes of the trace, given their timestamps of interaction. Then, we develop a nontrivial modification of the Expectation-Maximization algorithm by Newman [1], that we call Constrained-EM, which incorporates the constraints and a set of auxiliary variables into the inference process to guide it towards the feasibility of the trace. Experimental results on real-world data from Twitter confirm that Constrained-EM generates a posterior distribution of graphs that explains all the events observed in the trace while presenting the desired properties of a scale-free, small-world graph. Our method also outperforms established methods in terms of feasibility and quality of the inferred graph.",
        "published":"2021-09-23 20:48:19+00:00",
        "authors":"['Effrosyni Papanastasiou', 'Anastasios Giovanidis']",
        "arxiv_primary_category":"cs.si",
        "readability":0.86,
        "actuality":1.6666666667,
        "controversy":1.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":1.9721666667
    },
    {
        "arxiv_id":"2109.12038v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12038v1",
        "title":"Improving Standing Balance Performance through the Assistance of a Mobile Collaborative Robot",
        "summary":"This letter presents the design and development of a robotic system to give physical assistance to the elderly or people with neurological disorders such as Ataxia or Parkinson's. In particular, we propose using a mobile collaborative robot with an interaction-assistive whole-body interface to help people unable to maintain balance. The robotic system consists of an Omni-directional mobile base, a high-payload robotic arm, and an admittance-type interface acting as a support handle while measuring human-sourced interaction forces. The postural balance of the human body is estimated through the projection of the body Center of Mass (CoM) to the support polygon (SP) representing the quasi-static Center of Pressure (CoP). In response to the interaction forces and the tracking of the human posture, the robot can create assistive forces to restore balance in case of its loss. Otherwise, during normal stance or walking, it will follow the user with minimum\/no opposing forces through the generation of coupled arm and base movements. As the balance-restoring strategy, we propose two strategies and evaluate them in a laboratory setting on healthy human participants. Quantitative and qualitative results of a 12-subjects experiment are then illustrated and discussed, comparing the performances of the two strategies and the overall system.",
        "published":"2021-09-24 15:51:07+00:00",
        "authors":"['Francisco J. Ruiz-Ruiz', 'Alberto Giammarino', 'Marta Lorenzini', 'Juan M. Gandarias', 'Jesus M. Gomez-de-Gabriel', 'Arash Ajoudani']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.87,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":3.611
    },
    {
        "arxiv_id":"2109.12098v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12098v1",
        "title":"CLIPort: What and Where Pathways for Robotic Manipulation",
        "summary":"How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goals or quickly learn transferable concepts across tasks. In parallel, there has been great progress in learning generalizable semantic representations for vision and language by training on large-scale internet data, however these representations lack the spatial understanding necessary for fine-grained manipulation. To this end, we propose a framework that combines the best of both worlds: a two-stream architecture with semantic and spatial pathways for vision-based manipulation. Specifically, we present CLIPort, a language-conditioned imitation-learning agent that combines the broad semantic understanding (what) of CLIP [1] with the spatial precision (where) of Transporter [2]. Our end-to-end framework is capable of solving a variety of language-specified tabletop tasks from packing unseen objects to folding cloths, all without any explicit representations of object poses, instance segmentations, memory, symbolic states, or syntactic structures. Experiments in simulated and real-world settings show that our approach is data efficient in few-shot settings and generalizes effectively to seen and unseen semantic concepts. We even learn one multi-task policy for 10 simulated and 9 real-world tasks that is better or comparable to single-task policies.",
        "published":"2021-09-24 17:44:28+00:00",
        "authors":"['Mohit Shridhar', 'Lucas Manuelli', 'Dieter Fox']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4999166667
    },
    {
        "arxiv_id":"2109.12134v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12134v1",
        "title":"A 4-DoF Parallel Origami Haptic Device for Normal, Shear, and Torsion Feedback",
        "summary":"We present a mesoscale finger-mounted 4-degree-of-freedom (DoF) haptic device that is created using origami fabrication techniques. The 4-DoF device is a parallel kinematic mechanism capable of delivering normal, shear, and torsional haptic feedback to the fingertip. Traditional methods of robot fabrication are not well suited for designing small robotic devices because it is challenging and expensive to manufacture small, low-friction joints. Our device uses origami manufacturing principles to reduce complexity and the device footprint. We characterize the bandwidth, workspace, and force output of the device. The capabilities of the torsion-DoF are demonstrated in a virtual reality scenario. Our results show that the device can deliver haptic feedback in 4-DoFs with an effective operational workspace of 0.64cm$^3$ with $\\pm 30 ^ \\circ$ rotation at every location. The maximum forces and torques the device can apply in the x-, y-, z-, and $\\theta$-directions, are $\\pm$1.5N, $\\pm$1.5N, 2N, and 5N$\\cdot$mm, respectively, and the device has an operating bandwidth of 9Hz.",
        "published":"2021-09-24 18:02:11+00:00",
        "authors":"['Sophia R. Williams', 'Jacob M. Suchoski', 'Zonghe Chua', 'Allison M. Okamura']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.79,
        "actuality":3.6666666667,
        "controversy":3.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.0555
    },
    {
        "arxiv_id":"2109.12192v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12192v1",
        "title":"What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020",
        "summary":"Since the fatal shooting of 17-year old Black teenager Trayvon Martin in February 2012 by a White neighborhood watchman, George Zimmerman in Sanford, Florida, there has been a significant increase in digital activism addressing police-brutality related and racially-motivated incidents in the United States. In this work, we administer an innovative study of digital activism by exploiting social media as an authoritative tool to examine and analyze the linguistic cues and thematic relationships in these three mediums. We conduct a multi-level text analysis on 36,984,559 tweets to investigate users' behaviors to examine the language used and understand the impact of digital activism on social media within each social movement on a sentence-level, word-level, and topic-level. Our results show that excessive use of racially-related or prejudicial hashtags were used by the counter protests which portray potential discriminatory tendencies. Consequently, our findings highlight that social activism done by Black Lives Matter activists does not diverge from the social issues and topics involving police-brutality related and racially-motivated killings of Black individuals due to the shape of its topical graph that topics and conversations encircling the largest component directly relate to the topic of Black Lives Matter. Finally, we see that both Blue Lives Matter and All Lives Matter movements depict a different directive, as the topics of Blue Lives Matter or All Lives Matter do not reside in the center. These findings suggest that topics and conversations within each social movement are skewed, random or possessed racially-related undertones, and thus, deviating from the prominent social injustice issues.",
        "published":"2021-09-20 18:34:30+00:00",
        "authors":"['Jamell Dacon', 'Jiliang Tang']",
        "arxiv_primary_category":"cs.cl",
        "readability":0.89,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":1.667,
        "newsworthiness_crowd_sum":3.0834166667
    },
    {
        "arxiv_id":"2109.12292v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12292v1",
        "title":"Fully Differentiable and Interpretable Model for VIO with 4 Trainable Parameters",
        "summary":"Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While being fully interpretable, they rely on manual interference and empirical parameter tuning. On the other hand, learning-based approaches allow for end-to-end training but require a large number of training data to learn millions of parameters. However, the non-interpretable and heavy models hinder the generalization ability. In this paper, we propose a fully differentiable, interpretable, and lightweight monocular VIO model that contains only 4 trainable parameters. Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data. Second, the refined pitch and roll are adopted to retrieve a gravity-aligned BEV image of each frame using differentiable camera projection. Finally, a differentiable pose estimator is utilized to estimate the remaining 4 DoF poses between the BEV frames. Our method allows for learning the covariance matrices end-to-end supervised by the pose estimation loss, demonstrating superior performance to empirical baselines. Experimental results on synthetic and real-world datasets demonstrate that our simple approach is competitive with state-of-the-art methods and generalizes well on unseen scenes.",
        "published":"2021-09-25 06:54:09+00:00",
        "authors":"['Zexi Chen', 'Haozhe Du', 'Yiyi Liao', 'Yue Wang', 'Rong Xiong']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.78,
        "actuality":3.0,
        "controversy":3.3333333333,
        "relevance_magnitude":2.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.12321v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12321v1",
        "title":"Under the Skin of Foundation NFT Auctions",
        "summary":"Non Fungible Tokens (NFTs) have gained a solid foothold within the crypto community, and substantial amounts of money have been allocated to their trades. In this paper, we studied one of the most prominent marketplaces dedicated to NFT auctions and trades, Foundation. We analyzed the activities on Foundation and identified several intriguing underlying dynamics that occur on this platform. Moreover, We performed social network analysis on a graph that we had created based on transferred NFTs on Foundation, and then described the characteristics of this graph. Lastly, We built a neural network-based similarity model for retrieving and clustering similar NFTs. We also showed that for most NFTs, their performances in auctions were comparable with the auction performance of other NFTs in their cluster.",
        "published":"2021-09-25 09:01:44+00:00",
        "authors":"['MohammadAmin Fazli', 'Ali Owfi', 'Mohammad Reza Taesiri']",
        "arxiv_primary_category":"cs.si",
        "readability":0.84,
        "actuality":4.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.2221666667
    },
    {
        "arxiv_id":"2109.12372v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12372v1",
        "title":"Know it to Defeat it: Exploring Health Rumor Characteristics and Debunking Efforts on Chinese Social Media during COVID-19 Crisis",
        "summary":"Health-related rumors spreading online during a public crisis may pose a serious threat to people's well-being. Existing crisis informatics research lacks in-depth insights into the characteristics of health rumors and the efforts to debunk them on social media in a pandemic. To fill this gap, we conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site. Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type. We further explore how four kinds of social media users (i.e., government, media, organization, and individual) combat health rumors, and identify their preferred way of sharing debunking information and the key rhetoric strategies used in the process. We examine the relationship between debunking and rumor discussions using a Granger causality approach, and show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies across rumor types and debunkers. Our results can provide insights into crisis informatics and risk management on social media in pandemic settings.",
        "published":"2021-09-25 14:02:29+00:00",
        "authors":"['Wenjie Yang', 'Sitong Wang', 'Zhenhui Peng', 'Chuhan Shi', 'Xiaojuan Ma', 'Diyi Yang']",
        "arxiv_primary_category":"cs.si",
        "readability":0.9,
        "actuality":5.0,
        "controversy":4.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":4.5555833333
    },
    {
        "arxiv_id":"2109.12613v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12613v1",
        "title":"SimpleX: A Simple and Strong Baseline for Collaborative Filtering",
        "summary":"Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction encoder, loss function, and negative sampling. While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored. In this work, we show that the choice of loss function as well as negative sampling ratio is equivalently important. More specifically, we propose the cosine contrastive loss (CCL) and further incorporate it to a simple unified CF model, dubbed SimpleX. Extensive experiments have been conducted on 11 benchmark datasets and compared with 29 existing CF models in total. Surprisingly, the results show that, under our CCL loss and a large negative sampling ratio, SimpleX can surpass most sophisticated state-of-the-art models by a large margin (e.g., max 48.5% improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only serve as a simple strong baseline to foster future research on CF, but also shed light on the potential research direction towards improving loss function and negative sampling.",
        "published":"2021-09-26 14:09:25+00:00",
        "authors":"['Kelong Mao', 'Jieming Zhu', 'Jinpeng Wang', 'Quanyu Dai', 'Zhenhua Dong', 'Xi Xiao', 'Xiuqiang He']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":4.0,
        "controversy":2.6666666667,
        "relevance_magnitude":4.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.58325
    },
    {
        "arxiv_id":"2109.12730v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12730v1",
        "title":"Sequential Stochastic Network Structure Optimization with Applications to Addressing Canada's Obesity Epidemic",
        "summary":"In this work, we introduce a novel mathematical network model for community level preventative health interventions. We develop algorithms to approximately solve this novel formulation at large scale and we rigorously explore their theoretical properties. We create a realistic simulation environment for interventions designed to curb the prevalence of obesity occurring in the region of Montreal, Canada, and use this environment to empirically evaluate the performance of the algorithms we develop. We find that our algorithms significantly outperform all baseline interventions. Moreover, for fixed computational resources, our algorithms address problems of significantly greater size than the best existing alternative algorithm.",
        "published":"2021-09-27 00:27:53+00:00",
        "authors":"['Nicholas A. G. Johnson']",
        "arxiv_primary_category":"cs.si",
        "readability":0.88,
        "actuality":5.0,
        "controversy":2.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.2776666667
    },
    {
        "arxiv_id":"2109.12869v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12869v1",
        "title":"Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks",
        "summary":"This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications. We employ a (BNN), and evaluate two practical inference techniques to obtain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance increase using more reliable uncertainty estimates as unary potentials within a Conditional Random Field (CRF), which is able to incorporate contextual information as well. Furthermore, the obtained uncertainties are exploited to achieve domain adaptation in a semi-supervised manner, which requires less manual efforts in annotating data. We evaluate our approach on two public benchmark datasets that are relevant for robot perception tasks.",
        "published":"2021-09-27 08:40:19+00:00",
        "authors":"['Jianxiang Feng', 'Maximilian Durner', 'Zoltan-Csaba Marton', 'Ferenc Balint-Benczedi', 'Rudolph Triebel']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.78,
        "actuality":3.6666666667,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.3055
    },
    {
        "arxiv_id":"2109.12928v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12928v1",
        "title":"A Biologically Inspired Global Localization System for Mobile Robots Using LiDAR Sensor",
        "summary":"Localization in the environment is an essential navigational capability for animals and mobile robots. In the indoor environment, the global localization problem remains challenging to be perfectly solved with probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, it is intriguing and promising to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based re-localization approach. The experiment results show that the proposed method is competitive to Monte Carlo Localization, and the results demonstrate the high accuracy, applicability, and reliability of the proposed biologically-inspired localization system in different localization scenarios.",
        "published":"2021-09-27 10:25:17+00:00",
        "authors":"['Genghang Zhuang', 'Carlo Cagnetta', 'Zhenshan Bing', 'Kai Huang', 'Alois Knoll']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.82,
        "actuality":4.3333333333,
        "controversy":2.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4999166667
    },
    {
        "arxiv_id":"2109.12932v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12932v1",
        "title":"Sparse Spatial Transformers for Few-Shot Learning",
        "summary":"Learning from limited data is a challenging task since the scarcity of data leads to a poor generalization of the trained model. The classical global pooled representation is likely to lose useful local information. Recently, many few shot learning methods address this challenge by using deep descriptors and learning a pixel-level metric. However, using deep descriptors as feature representations may lose the contextual information of the image. And most of these methods deal with each class in the support set independently, which cannot sufficiently utilize discriminative information and task-specific embeddings. In this paper, we propose a novel Transformer based neural network architecture called Sparse Spatial Transformers (SSFormers), which can find task-relevant features and suppress task-irrelevant features. Specifically, we first divide each input image into several image patches of different sizes to obtain dense local features. These features retain contextual information while expressing local information. Then, a sparse spatial transformer layer is proposed to find spatial correspondence between the query image and the entire support set to select task-relevant image patches and suppress task-irrelevant image patches. Finally, we propose an image patch matching module to calculate the distance between dense local representations to determine which category the query image belongs to in the support set. Extensive experiments on popular few-shot learning benchmarks show that our method achieves the state-of-the-art performance. Our code is available at \\url{https:\/\/github.com\/chenhaoxing\/SSFormers}.",
        "published":"2021-09-27 10:36:32+00:00",
        "authors":"['Haoxing Chen', 'Huaxiong Li', 'Yaohui Li', 'Chunlin Chen']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.84,
        "actuality":4.0,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":2.778,
        "newsworthiness_crowd_sum":3.0278333333
    },
    {
        "arxiv_id":"2109.12982v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.12982v1",
        "title":"Sustainable Urban Mobility in the Post-Pandemic Era (position paper)",
        "summary":"COVID-19 is the first pandemic of the modern world causing significant changes to the everyday life of billions of people in all continents. To reduce its expansion, most governments decided to mitigate a great percentage of daily movements of their citizens. For instance, they enforced strict controls (in space, time, etc.) on urban movement whereas they selectively prohibited international air and ground connections. In this short study, we briefly discuss some lessons learned out of this process based on recorded mobility figures, and we raise challenges that are emerging in the post-pandemic era, in the intersection of the sustainable urban mobility and movement data science fields.",
        "published":"2021-09-27 12:17:27+00:00",
        "authors":"['Christos Theodoridis', 'Yannis Theodoridis']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.95,
        "actuality":5.0,
        "controversy":2.0,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.4165833333
    },
    {
        "arxiv_id":"2109.13046v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13046v1",
        "title":"The Spread of Propaganda by Coordinated Communities on Social Media",
        "summary":"Large-scale manipulations on social media have two important characteristics: (i) use of \\textit{propaganda} to influence others, and (ii) adoption of coordinated behavior to spread it and to amplify its impact. Despite the connection between them, these two characteristics have so far been considered in isolation. Here we aim to bridge this gap. In particular, we analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. We first propose and evaluate several metrics for measuring the use of propaganda on Twitter. Then, we investigate the use of propaganda by different coordinated communities that participated in the online debate. The combination of the use of propaganda and coordinated behavior allows us to uncover the authenticity and harmfulness of the different communities. Finally, we compare our measures of propaganda and coordination with automation (i.e., bot) scores and Twitter suspensions, revealing interesting trends. From a theoretical viewpoint, we introduce a methodology for analyzing several important dimensions of online behavior that are seldom conjointly considered. From a practical viewpoint, we provide new insights into authentic and inauthentic online activities during the 2019 UK general election.",
        "published":"2021-09-27 13:39:10+00:00",
        "authors":"['Kristina Hristakieva', 'Stefano Cresci', 'Giovanni Da San Martino', 'Mauro Conti', 'Preslav Nakov']",
        "arxiv_primary_category":"cs.si",
        "readability":0.89,
        "actuality":5.0,
        "controversy":3.3333333333,
        "relevance_magnitude":5.0,
        "relevance_valence":4.444,
        "newsworthiness_crowd_sum":4.4443333333
    },
    {
        "arxiv_id":"2109.13113v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13113v1",
        "title":"Can You See Me Now? A Measurement Study of Zoom, Webex, and Meet",
        "summary":"Since the outbreak of the COVID-19 pandemic, videoconferencing has become the default mode of communication in our daily lives at homes, workplaces and schools, and it is likely to remain an important part of our lives in the post-pandemic world. Despite its significance, there has not been any systematic study characterizing the user-perceived performance of existing videoconferencing systems other than anecdotal reports. In this paper, we present a detailed measurement study that compares three major videoconferencing systems: Zoom, Webex and Google Meet. Our study is based on 48 hours' worth of more than 700 videoconferencing sessions, which were created with a mix of emulated videoconferencing clients deployed in the cloud, as well as real mobile devices running from a residential network. We find that the existing videoconferencing systems vary in terms of geographic scope, which in turns determines streaming lag experienced by users. We also observe that streaming rate can change under different conditions (e.g., number of users in a session, mobile device status, etc), which affects user-perceived streaming quality. Beyond these findings, our measurement methodology can enable reproducible benchmark analysis for any types of comparative or longitudinal study on available videoconferencing systems.",
        "published":"2021-09-27 15:00:18+00:00",
        "authors":"['Hyunseok Chang', 'Matteo Varvello', 'Fang Hao', 'Sarit Mukherjee']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.88,
        "actuality":5.0,
        "controversy":2.0,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.889,
        "newsworthiness_crowd_sum":3.8889166667
    },
    {
        "arxiv_id":"2109.13197v2",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13197v2",
        "title":"Using Comics to Introduce and Reinforce Programming Concepts in CS1",
        "summary":"Recent work investigated the potential of comics to support the teaching and learning of programming concepts and suggested several ways $coding$ $strips$, a form of comic strip with its corresponding code, can be used. Building on this work, we tested the recommended use cases of $coding$ $strip$ in an undergraduate introductory computer science course at a large comprehensive university. At the end of the course, we surveyed students to assess their experience and found they benefited in various ways. Our work contributes a demonstration of the various ways comics can be used in introductory CS courses and an initial understanding of benefits and challenges with using comics in computing education gleaned from an analysis of students' survey responses and code submissions.",
        "published":"2021-09-27 17:17:11+00:00",
        "authors":"['Sangho Suh', 'Celine Latulipe', 'Ken Jen Lee', 'Bernadette Cheng', 'Edith Law']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.91,
        "actuality":5.0,
        "controversy":1.6666666667,
        "relevance_magnitude":5.0,
        "relevance_valence":5.0,
        "newsworthiness_crowd_sum":4.1666666667
    },
    {
        "arxiv_id":"2109.13211v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13211v1",
        "title":"Composite Resource Scheduling for Networked Control Systems",
        "summary":"Real-time end-to-end task scheduling in networked control systems (NCSs) requires the joint consideration of both network and computing resources to guarantee the desired quality of service (QoS). This paper introduces a new model for composite resource scheduling (CRS) in real-time networked control systems, which considers a strict execution order of sensing, computing, and actuating segments based on the control loop of the target NCS. We prove that the general CRS problem is NP-hard and study two special cases of the CRS problem. The first case restricts the computing and actuating segments to have unit-size execution time while the second case assumes that both sensing and actuating segments have unit-size execution time. We propose an optimal algorithm to solve the first case by checking the intervals with 100% network resource utilization and modify the deadlines of the tasks within those intervals to prune the search. For the second case, we propose another optimal algorithm based on a novel backtracking strategy to check the time intervals with the network resource utilization larger than 100% and modify the timing parameters of tasks based on these intervals. For the general case, we design a greedy strategy to modify the timing parameters of both network segments and computing segments within the time intervals that have network and computing resource utilization larger than 100%, respectively. The correctness and effectiveness of the proposed algorithms are verified through extensive experiments.",
        "published":"2021-09-27 17:32:57+00:00",
        "authors":"['Peng Wu', 'Chenchen Fu', 'Tianyu Wang', 'Minming Li', 'Yingchao Zhao', 'Chun Jason Xue', 'Song Han']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.82,
        "actuality":3.0,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.08325
    },
    {
        "arxiv_id":"2109.13337v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13337v1",
        "title":"DEBOSH: Deep Bayesian Shape Optimization",
        "summary":"Shape optimization is at the heart of many industrial applications, such as aerodynamics, heat transfer, and structural analysis. It has recently been shown that Graph Neural Networks (GNNs) can predict the performance of a shape quickly and accurately and be used to optimize more effectively than traditional techniques that rely on response-surfaces obtained by Kriging. However, GNNs suffer from the fact that they do not evaluate their own accuracy, which is something Bayesian Optimization methods require. Therefore, estimating confidence in generated predictions is necessary to go beyond straight deterministic optimization, which is less effective. In this paper, we demonstrate that we can use Ensembles-based technique to overcome this limitation and outperform the state-of-the-art. Our experiments on diverse aerodynamics and structural analysis tasks prove that adding uncertainty to shape optimization significantly improves the quality of resulting shapes and reduces the time required for the optimization.",
        "published":"2021-09-28 11:01:42+00:00",
        "authors":"['Nikita Durasov', 'Artem Lukoyanov', 'Jonathan Donier', 'Pascal Fua']",
        "arxiv_primary_category":"cs.lg",
        "readability":0.85,
        "actuality":3.3333333333,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2109.13499v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13499v1",
        "title":"Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning",
        "summary":"This paper presents a self-supervised method for learning reliable visual correspondence from unlabeled videos. We formulate the correspondence as finding paths in a joint space-time graph, where nodes are grid patches sampled from frames, and are linked by two types of edges: (i) neighbor relations that determine the aggregation strength from intra-frame neighbors in space, and (ii) similarity relations that indicate the transition probability of inter-frame paths across time. Leveraging the cycle-consistency in videos, our contrastive learning objective discriminates dynamic objects from both their neighboring views and temporal views. Compared with prior works, our approach actively explores the neighbor relations of central instances to learn a latent association between center-neighbor pairs (e.g., \"hand -- arm\") across time, thus improving the instance discrimination. Without fine-tuning, our learned representation outperforms the state-of-the-art self-supervised methods on a variety of visual tasks including video object propagation, part propagation, and pose keypoint tracking. Our self-supervised method also surpasses some fully supervised algorithms designed for the specific tasks.",
        "published":"2021-09-28 05:40:01+00:00",
        "authors":"['Zixu Zhao', 'Yueming Jin', 'Pheng-Ann Heng']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.85,
        "actuality":2.6666666667,
        "controversy":2.0,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.7221666667
    },
    {
        "arxiv_id":"2109.13527v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13527v1",
        "title":"Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation",
        "summary":"Recently, micro-video sharing platforms such as Kuaishou and Tiktok have become a major source of information for people's lives. Thanks to the large traffic volume, short video lifespan and streaming fashion of these services, it has become more and more pressing to improve the existing recommender systems to accommodate these challenges in a cost-effective way. In this paper, we propose a novel concept-aware denoising graph neural network (named CONDE) for micro-video recommendation. CONDE consists of a three-phase graph convolution process to derive user and micro-video representations: warm-up propagation, graph denoising and preference refinement. A heterogeneous tripartite graph is constructed by connecting user nodes with video nodes, and video nodes with associated concept nodes, extracted from captions and comments of the videos. To address the noisy information in the graph, we introduce a user-oriented graph denoising phase to extract a subgraph which can better reflect the user's preference. Despite the main focus of micro-video recommendation in this paper, we also show that our method can be generalized to other types of tasks. Therefore, we also conduct empirical studies on a well-known public E-commerce dataset. The experimental results suggest that the proposed CONDE achieves significantly better recommendation performance than the existing state-of-the-art solutions.",
        "published":"2021-09-28 07:02:52+00:00",
        "authors":"['Yiyu Liu', 'Qian Liu', 'Yu Tian', 'Changping Wang', 'Yanan Niu', 'Yang Song', 'Chenliang Li']",
        "arxiv_primary_category":"cs.ir",
        "readability":0.85,
        "actuality":3.0,
        "controversy":2.3333333333,
        "relevance_magnitude":3.0,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.9165833333
    },
    {
        "arxiv_id":"2109.13765v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13765v1",
        "title":"Exploring the spatiotemporal heterogeneity in the relationship between human mobility and COVID-19 prevalence using dynamic time warping",
        "summary":"Understanding where and when human mobility is associated with disease infection is crucial for implementing location-based health care policy and interventions. Previous studies on COVID-19 have revealed the correlation between human mobility and COVID-19 cases. However, the spatiotemporal heterogeneity of such correlation is not yet fully understood. In this study, we aim to identify the spatiotemporal heterogeneities in the relationship between human mobility flows and COVID-19 cases in U.S. counties. Using anonymous mobile device location data, we compute an aggregate measure of mobility that includes flows within and into each county. We then compare the trends in human mobility and COVID-19 cases of each county using dynamic time warping (DTW). DTW results highlight the time periods and locations (counties) where mobility may have influenced disease transmission. Also, the correlation between human mobility and infections varies substantially across geographic space and time in terms of relationship, strength, and similarity.",
        "published":"2021-09-28 14:38:29+00:00",
        "authors":"['Hoeyun Kwon', 'Kaitlyn Hom', 'Mark Rifkin', 'Beichen Tian', 'Caglar Koylu']",
        "arxiv_primary_category":"cs.si",
        "readability":0.86,
        "actuality":3.6666666667,
        "controversy":2.0,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.1665833333
    },
    {
        "arxiv_id":"2109.13859v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.13859v1",
        "title":"NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction",
        "summary":"Recent advances in object segmentation have demonstrated that deep neural networks excel at object segmentation for specific classes in color and depth images. However, their performance is dictated by the number of classes and objects used for training, thereby hindering generalization to never seen objects or zero-shot samples. To exacerbate the problem further, object segmentation using image frames rely on recognition and pattern matching cues. Instead, we utilize the 'active' nature of a robot and their ability to 'interact' with the environment to induce additional geometric constraints for segmenting zero-shot samples. In this paper, we present the first framework to segment unknown objects in a cluttered scene by repeatedly 'nudging' at the objects and moving them to obtain additional motion cues at every step using only a monochrome monocular camera. We call our framework NudgeSeg. These motion cues are used to refine the segmentation masks. We successfully test our approach to segment novel objects in various cluttered scenes and provide an extensive study with image and motion segmentation methods. We show an impressive average detection rate of over 86% on zero-shot objects.",
        "published":"2021-09-22 05:17:09+00:00",
        "authors":"['Chahat Deep Singh', 'Nitin J. Sanket', 'Chethan M. Parameshwara', 'Cornelia Ferm\u00fcller', 'Yiannis Aloimonos']",
        "arxiv_primary_category":"cs.cv",
        "readability":0.86,
        "actuality":4.3333333333,
        "controversy":3.3333333333,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.4721666667
    },
    {
        "arxiv_id":"2109.14138v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.14138v1",
        "title":"A simulation sandbox to compare fixed-route, flexible-route transit, and on-demand microtransit system designs",
        "summary":"With advances in emerging technologies, options for operating public transit services have broadened from conventional fixed-route service through semi-flexible service to on-demand microtransit. Nevertheless, guidelines for deciding between these services remain limited in the real implementation. An open-source simulation sandbox is developed that can compare state-of-the-practice methods for evaluating between the different types of public transit operations. For the case of the semi-flexible service, the Mobility Allowance Shuttle Transit (MAST) system is extended to include passenger deviations. A case study demonstrates the sandbox to evaluate and existing B63 bus route in Brooklyn, NY and compares its performance with the four other system designs spanning across the three service types for three different demand scenarios.",
        "published":"2021-09-29 02:00:34+00:00",
        "authors":"['Gyugeun Yoon', 'Joseph Y. J. Chow', 'Srushti Rath']",
        "arxiv_primary_category":"cs.cy",
        "readability":0.9,
        "actuality":4.6666666667,
        "controversy":2.6666666667,
        "relevance_magnitude":4.6666666667,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":3.83325
    },
    {
        "arxiv_id":"2109.14309v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.14309v1",
        "title":"Online Aggregation of Probability Forecasts with Confidence",
        "summary":"The paper presents numerical experiments and some theoretical developments in prediction with expert advice (PEA). One experiment deals with predicting electricity consumption depending on temperature and uses real data. As the pattern of dependence can change with season and time of the day, the domain naturally admits PEA formulation with experts having different ``areas of expertise''. We consider the case where several competing methods produce online predictions in the form of probability distribution functions. The dissimilarity between a probability forecast and an outcome is measured by a loss function (scoring rule). A popular example of scoring rule for continuous outcomes is Continuous Ranked Probability Score (CRPS). In this paper the problem of combining probabilistic forecasts is considered in the PEA framework. We show that CRPS is a mixable loss function and then the time-independent upper bound for the regret of the Vovk aggregating algorithm using CRPS as a loss function can be obtained. Also, we incorporate a ``smooth'' version of the method of specialized experts in this scheme which allows us to combine the probabilistic predictions of the specialized experts with overlapping domains of their competence.",
        "published":"2021-09-29 09:49:16+00:00",
        "authors":"[\"Vladimir V'yugin\", 'Vladimir Trunov']",
        "arxiv_primary_category":"cs.ai",
        "readability":0.87,
        "actuality":4.0,
        "controversy":1.6666666667,
        "relevance_magnitude":3.6666666667,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":2.8888333333
    },
    {
        "arxiv_id":"2109.14325v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.14325v1",
        "title":"Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning",
        "summary":"One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases. In this work, we propose a novel technique of unsupervised action planning to improve the safety of on-policy reinforcement learning algorithms, such as trust region policy optimization (TRPO) or proximal policy optimization (PPO). We design our safety-aware reinforcement learning by storing all the history of \"recovery\" actions that rescue the agent from dangerous situations into a separate \"safety\" buffer and finding the best recovery action when the agent encounters similar states. Because this functionality requires the algorithm to query similar states, we implement the proposed safety mechanism using an unsupervised learning algorithm, k-means clustering. We evaluate the proposed algorithm on six robotic control tasks that cover navigation and manipulation. Our results show that the proposed safety RL algorithm can achieve higher rewards compared with multiple baselines in both discrete and continuous control problems. The supplemental video can be found at: https:\/\/youtu.be\/AFTeWSohILo.",
        "published":"2021-09-29 10:26:29+00:00",
        "authors":"['Hao-Lun Hsu', 'Qiuhua Huang', 'Sehoon Ha']",
        "arxiv_primary_category":"cs.ro",
        "readability":0.84,
        "actuality":3.0,
        "controversy":1.6666666667,
        "relevance_magnitude":3.3333333333,
        "relevance_valence":3.333,
        "newsworthiness_crowd_sum":2.83325
    },
    {
        "arxiv_id":"2109.14535v1",
        "arxiv_url":"http:\/\/arxiv.org\/abs\/2109.14535v1",
        "title":"Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning",
        "summary":"Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting(EH) and have limited energy available to analyse acquired data. When edge infrastructure is available, a device has a choice: to perform analysis locally or offload the task to other resource-rich devices such as cloudlet servers. However, such a choice carries a price in terms of consumed energy and accuracy. On the one hand, transmitting raw data can result in a higher energy cost in comparison to the required energy to process data locally. On the other hand, performing data analytics on servers can improve the task's accuracy. Additionally, due to the correlation between information sent by multiple devices, accuracy might not be affected if some edge devices decide to neither process nor send data and preserve energy instead. For such a scenario, we propose a Deep Reinforcement Learning (DRL) based solution capable of learning and adapting the policy to the time-varying energy arrival due to EH patterns. We leverage two datasets, one to model energy an EH device can collect and the other to model the correlation between cameras. Furthermore, we compare the proposed solution performance to three baseline policies. Our results show that we can increase accuracy by 15% in comparison to conventional approaches while preventing outages.",
        "published":"2021-09-29 16:34:22+00:00",
        "authors":"['Jernej Hribar', 'Ryoichi Shinkuma', 'George Iosifidis', 'Ivana Dusparic']",
        "arxiv_primary_category":"cs.ni",
        "readability":0.92,
        "actuality":3.6666666667,
        "controversy":4.6666666667,
        "relevance_magnitude":4.0,
        "relevance_valence":2.222,
        "newsworthiness_crowd_sum":3.6388333333
    }
]