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A privacy-preserving student status monitoring system
Complex & Intelligent Systems ( IF 5.8 ) Pub Date : 2022-07-26 , DOI: 10.1007/s40747-022-00796-5
Haopeng Wu, Zhiying Lu, Jianfeng Zhang

Timely feedback of students’ listening status is crucial for teaching work. However, it is often difficult for teachers to pay attention to all students at the same time. By leveraging surveillance cameras in the classroom, we are able to assist the teaching work. However, the existing methods either lack the protection of students’ privacy, or they have to reduce the accuracy of success, because they are concerned about the leakage of students’ privacy. We propose federated semi-supervised class assistance system to evaluate the listening status of students in the classroom. Rather than training the semi-supervised model in a centralized manner, we train a semi-supervised model in a federated manner among various monitors while preserving students’ privacy. We also formulate a new loss function according to the difference between the pre-trained initial model and the expected model to restrict the training process of the unlabeled data. By applying the pseudo-label assignment method on the unlabeled data, the class monitors are able to recognize the student class behavior. In addition, simulation and real-world experimental results demonstrate that the performance of the proposed system outperforms that of the baseline models.



中文翻译:

保护隐私的学籍监控系统

及时反馈学生的听力状态对教学工作至关重要。然而,教师往往很难同时关注所有学生。通过利用教室中的监控摄像头,我们能够协助教学工作。然而,现有的方法要么缺乏对学生隐私的保护,要么不得不降低成功的准确性,因为它们担心学生隐私的泄露。我们提出了联邦半监督课堂辅助系统来评估学生在课堂上的听力状态。我们不是以集中方式训练半监督模型,而是在各种监视器之间以联合方式训练半监督模型,同时保护学生的隐私。我们还根据预训练的初始模型与预期模型的差异制定了新的损失函数,以限制未标记数据的训练过程。通过对未标记数据应用伪标签分配方法,班级监视器能够识别学生的班级行为。此外,仿真和实际实验结果表明,所提出系统的性能优于基线模型。

更新日期:2022-07-26
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