Zheyi Fan

Zheyi Fan, Zhaohui Li, Jingyan Wang et al.

Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task. Methods to overcome this problem often involve robust least-squares regression, but few methods perform well when confronted with severe adaptive adversarial attacks. In many applications, prior knowledge is often available from historical data or engineering experience, and by incorporating prior information into a robust regression method, we develop an effective robust regression method that can resist adaptive adversarial attacks. First, we propose the novel TRIP (hard Thresholding approach to Robust regression with sImple Prior) algorithm, which improves the breakdown point when facing adaptive adversarial attacks. Then, to improve the robustness and reduce the estimation error caused by the inclusion of priors, we use the idea of Bayesian reweighting to construct the more robust BRHT (robust Bayesian Reweighting regression via Hard Thresholding) algorithm. We prove the theoretical convergence of the proposed algorithms under mild conditions, and extensive experiments show that under different types of dataset attacks, our algorithms outperform other benchmark ones. Finally, we apply our methods to a data-recovery problem in a real-world application involving a space solar array, demonstrating their good applicability.

Shaojie Qu, Kan Li, Zheyi Fan et al.

With the development of MOOCs massive open online courses, increasingly more subjects can be studied online. Researchers currently show growing interest in the field of MOOCs, including dropout prediction, cheating detection and achievement prediction. Previous studies on achievement prediction mainly focused on students' video and forum behaviors, and few researchers have considered how well students perform their assignments. In this paper, we choose a C programming course as the experimental subject, which involved 1528 students. This paper mainly focuses on the students' accomplishment behaviors in programming assignments and compiled information from programming assignments. In this paper, feature sequences are extracted from the logs according to submission times, submission order and plagiarism. The experimental results show that the students who did not pass the exam had obvious sequence patterns but that the students who passed the test did not have an obvious sequence pattern. Then, we extract 23 features from the compiled information of students' programming assignments and select the most distinguishing features to predict the students' performances. The experimental results show that we can obtain an accuracy rate of 0.7049 for predicting students' performances.