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December 2014

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**SPECIAL** Communications/ICWS Seminar

Speaker Aditya Vempaty, Graduate Student; Department of EECS; Syracuse University
Date: 6/24/2014
Time: 4:00 pm
Location: 141 CSL
Event Contact: Denise Lewis
Sponsor: Cooridinated Science Lab
  **Abstract:** Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. Error-control codes and decoding algorithms can be used to design crowdsourcing systems for reliable classification despite unreliable and unskilled crowd workers, via redundant and easy-to-answer binary questions posed to the crowd. Three different crowdsourcing models are considered: systems with independent crowd workers, systems with peer-dependent reward schemes, and systems where workers have common sources of information. For each of these models, classification performance is analyzed with the proposed coding-based scheme. An ordering principle is developed for the quality of crowds. We show that pairing among workers and diversi?cation of the questions help in improving system performance. We demonstrate effectiveness of the proposed coding-based scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowdsourcing microtask platform. Results suggest that use of good codes may improve the performance of the crowdsourcing task over typical majority-voting approaches. Ongoing experiments to validate the superiority of the proposed approach, in collaboration with cognitive psychologists, are discussed, along with other applications of query codes for human computation. ****Bio**** Aditya Vempaty received the B. Tech. degree in electrical engineering from the Indian Institute of Technology (IIT), Kanpur, in 2011 with academic excellence awards for consecutive years. Since 2011, he has been working towards his Ph.D. degree in electrical engineering at Syracuse University. He is a Graduate Research Assistant at the Sensor Fusion Laboratory where he was also an Undergraduate Research Intern during summer of 2010. He was a Graduate Research Intern in the Data Systems Group at Intel Corporation in Santa Clara, CA in summer 2013. His research interests include statistical signal processing, decision making, security in sensor networks, and data fusion. He has been awarded the Syracuse University Graduate Fellowship award for the academic years 2013-15. He is a member of Phi Kappa Phi and Golden Key International Honor Society. He is currently visiting the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign.