Activity selection is critical for the smart environment and Cyber-Physical Systems (CPSs) that can provide timely and intelligent services, especially as the number of connected devices is increasing at an unprecedented speed. As it is important to collect labels by various agents in the CPSs, crowdsourcing inference algorithms are designed to help acquire accurate labels that involve high-level knowledge. However, there are some limitations in the algorithm in the existing literature such as incurring extra budget for the existing algorithms, inability to scale appropriately, requiring the knowledge of prior distribution, difficulties to implement these algorithms, or generating local optima. In this paper, we provide a crowdsourcing inference method with variational tempering that obtains ground truth as well as considers both the reliability of workers and the difficulty level of the tasks and ensure a local optimum. The numerical experiments of the real-world data indicate that our novel variational tempering inference algorithm performs better than the existing advancing algorithms. Therefore, this paper provides a new efficient algorithm in CPSs and machine learning, and thus, it makes a new contribution to the literature.

中文翻译：

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基于回火的网络物理系统众包推理方法：人工智能算法的进展

活动选择对于可以提供及时和智能服务的智能环境和网络物理系统（CPS）至关重要，尤其是随着连接设备的数量以前所未有的速度增长时。由于在CPS中由各种代理收集标签很重要，因此众包推理算法旨在帮助获得涉及高级知识的准确标签。但是，现有文献中的算法存在一些局限性，例如为现有算法带来额外预算，无法适当扩展，需要先验分布知识，难以实施这些算法或生成局部最优。在本文中，我们提供了一种采用变式回火的众包推论方法，该方法可获取基本事实，并同时考虑工人的可靠性和任务的难度级别，并确保局部最优。实际数据的数值实验表明，我们新颖的变分回火推理算法的性能要优于现有的先进算法。因此，本文为CPS和机器学习提供了一种新的高效算法，为文献研究做出了新的贡献。