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Full-Text Articles in Physical Sciences and Mathematics
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Active Crowdsourcing For Annotation, Shuji Hao, Chunyan Miao, Steven C. H. Hoi, Peilin Zhao
Research Collection School Of Computing and Information Systems
Crowdsourcing has shown great potential in obtaining large-scale and cheap labels for different tasks. However, obtaining reliable labels is challenging due to several reasons, such as noisy annotators, limited budget and so on. The state-of-the-art approaches, either suffer in some noisy scenarios, or rely on unlimited resources to acquire reliable labels. In this article, we adopt the learning with expert~(AKA worker in crowdsourcing) advice framework to robustly infer accurate labels by considering the reliability of each worker. However, in order to accurately predict the reliability of each worker, traditional learning with expert advice will consult with external oracles~(AKA domain experts) …
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Faitcrowd: Fine Grained Truth Discovery For Crowdsourced Data Aggregation, Fenglong Ma, Yaliang Li, Qi Li, Minghui Qiu, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Jiawei Han
Research Collection School Of Computing and Information Systems
In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we …
Multi-Agent Task Assignment For Mobile Crowdsourcing Under Trajectory Uncertainties, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra
Multi-Agent Task Assignment For Mobile Crowdsourcing Under Trajectory Uncertainties, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra
Research Collection School Of Computing and Information Systems
In this work, we investigate the problem of mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually browse and filter tasks to perform, we intend to automatically make task recommendations based on workers' historical trajectories and desired time budgets. However, predicting workers' trajectories is inevitably faced with uncertainties, as no one will take exactly the same route every day; yet such uncertainties are oftentimes abstracted away in the known literature. In this work, we depart from the deterministic modeling and study the stochastic task recommendation problem where …
Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han
Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han
Research Collection School Of Computing and Information Systems
In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.