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Crowd sourcing services, such as Amazon Mechanical Turk, allow for easy distribution of small tasks to a large number of workers. Unfortunately, since manually verifying the quality of the submitted results is hard, malicious workers often take advantage of the verification difficulty and submit answers of low quality. Currently, most requesters rely on redundancy to identify the correct answers. However, redundancy is not a panacea. Massive redundancy is expensive, increasing significantly the cost of crowd sourced solutions. Therefore, they need techniques that will accurately estimate the quality of the workers, allowing for the rejection and blocking of the low-performing workers and spammers.
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