Crowdsourcing is a paradigm that utilizes human intelligence to solve problems that computers cannot yet solve. However, the introduction of human intelligence into computations has also resulted in new challenges in quality control. These challenges originate from the malicious behaviors of crowd workers. Malicious workers are workers with hidden motives, who either simply sabotage a task or provide arbitrary responses to attain some monetary compensation. Recently, many studies have tried to detect and reduce the impact of malicious workers. The mechanisms vary from using ground truth to peer review by experts. Although the use of such mechanisms may increase the overall quality of outputs, it also imposes overhead costs in terms of money and/or time, with such costs being often remarkable and contradictory to the philosophy of crowdsourcing. In this paper, a novel dynamic weighted majority method is introduced to detect malicious workers based on a new malicious metric. Effectiveness of the proposed methodology is then showed by presenting the experimental results.

中文翻译：

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用于检测恶意人群的动态加权多数方法

众包是一种利用人类智能来解决计算机尚无法解决的问题的范式。然而，将人类智能引入计算也给质量控制带来了新的挑战。这些挑战源于人群工作者的恶意行为。恶意工人是具有隐藏动机的工人，他们要么简单地破坏任务，要么提供任意响应以获得一些金钱补偿。最近，许多研究试图检测和减少恶意工人的影响。这些机制从使用真实情况到专家的同行评审各不相同。尽管使用此类机制可能会提高产出的整体质量，但也会增加金钱和/或时间方面的间接成本，此类成本通常非常显着，并且与众包理念相矛盾。在本文中，引入了一种新颖的动态加权多数方法来基于新的恶意度量来检测恶意工作者。然后通过展示实验结果来展示所提出方法的有效性。