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No more endless PDFs. Discover the core value of the latest top-tier research in one article.
Graph Based Local Risk Estimation in Large Scale Online Social Networks
What’s in Twitter, I know what parties are popular and who you are supporting now!
Educational Virtual-Wear Trial: More Than a Virtual Try-On Experience
Higher Education Students’ Perceptions Towards Using Facebook as a Learning Platform
Flexible Microwire Residence Times Difference Fluxgate Magnetometer
Scalable temporal latent space inference for link prediction in dynamic social networks (extended abstract)
Economic Aspects of Corporate Education and Use of Advanced Technologies
2359_Sensing and monitoring professional skiers.
Multi Layer Modeling of Socio-Technical Production Planning and Control Systems
Incentivizing crowdsourcing for radio environment mapping with statistical interpolation
Predicting Function Changes by Mining Revision History
From tweet to graph: Social network analysis for semantic information extraction
2366_Understanding User Behavior in Large Scale Internet Video Service.
Research on Group Social Function and User Differentiation – A Case Study of WeChat and QQ
Formal Modeling and Verification of Controllers for a Family of DRAM Caches
Socio-Natural Thought Semantic Link Network: A Method of Semantic Networking in the Cyber Physical Society
Towards Learning-Based, Content-Agnostic Detection of Social Bot Traffic
Exploring the Blocking Behavior Between Young Adults and Parents on WeChat Moments
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
Abstract-Artificial intelligence (AI) is a technology receiving significant attention from lawmakers, courts, and regulators. An aspect of this attention is an interest in understanding how AI works when applied to a process of law, or to a regulated application of technology such as driverless vehicles. One approach is to seek to understand what the AI technology does, with goals including ³transparency´ and ³explainability´. This paper considers these concepts from a law and technology perspective. Research in this area commonly examines the challenge of ³black box´ technologies, particularly the approach of ³post hoc explainability´. This paper points out that the post hoc approach provides an inference, rather than an actual description of AI behavior. It considers circumstances in which the post hoc approach may be satisfactory, and those involving arbitrary power in which it should not be used, as inconsistent with the principle of regularity in the rule of law. It recommends that the output of non-transparent AI technologies should necessarily be viewed critically. It concludes that human attention is required in determining whether or not to accept AI technology explanations
Modelling and Predicting the Data Availability in Decentralized Online Social Networks
2377_A Probabilistic Method for Estimating the Sharing of Identity by Descent for Populations with Migration.
2380_Economic incentives in information- centric networking implications for protocol design and public policy.
Development and Evaluation of Peer Feedback in the English Quiz Game Design in Social Network
Game-Theoretic Analysis on the Number of Participants in the Software Crowdsourcing Contest
A Proposal for Social Search System Design
The Development and Validation of the Social Network Sites (SNSs) Usage Questionnaire
Destination-Aware Task Assignment in Spatial Crowdsourcing: A Worker Decomposition Approach
2396_Robotics and Intelligent Systems in Support of Society.
Simulating Population Behavior: Transportation Mode, Green Technology, and Climate Change