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Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms.

, , , , and . HCOMP, page 178-187. AAAI Press, (2015)

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Optimal Worker Quality and Answer Estimates in Crowd-Powered Filtering and Rating., , and . HCOMP, page 12-13. AAAI, (2014)Towards Globally Optimal Crowdsourcing Quality Management: The Uniform Worker Setting., , and . SIGMOD Conference, page 47-62. ACM, (2016)ClusterJoin: A Similarity Joins Framework using Map-Reduce., , and . Proc. VLDB Endow., 7 (12): 1059-1070 (2014)Globally Optimal Crowdsourcing Quality Management: Full technical report., , and . CoRR, (2015)Understanding Workers, Developing Effective Tasks, and Enhancing Marketplace Dynamics: A Study of a Large Crowdsourcing Marketplace., , , and . Proc. VLDB Endow., 10 (7): 829-840 (2017)Advancing the use of crowdsourcing for data-intensive tasks.. Stanford University, USA, (2017)Aggregating Crowdsourced Image Segmentations., , and . HCOMP (WIP&Demo), volume 2173 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Multi-Armed Bandit Mechanisms for Multi-Slot Sponsored Search Auctions, , and . CoRR, (2010)Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management., , and . IEEE Data Eng. Bull., 39 (4): 26-37 (2016)Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms., , , , and . HCOMP, page 178-187. AAAI Press, (2015)