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Purpose: The purpose of this study is to design and implement an intelligent online proctoring system (IOPS) by using the advantage of artificial intelligence technology in order to monitor the online exam, which is urgently needed in online learning settings worldwide. As a pilot application, the authors used this system in an authentic university online exam and checked the proctoring result. Design/methodology/approach: The IOPS adopts the B/S (Browser/Server) architecture. The server side is implemented with programming language C and Python and stores the identification data of all examinees and their important behavior change status, including facial expression, eye and mouth movement and speech. The browser side collects and analyzes multimodal data of the examinee writing the online test locally and transfers the examinee's most important behavior status change data to the server. Real-time face recognition and voice detection are implemented with the support of open-source software. Findings: The system was integrated into a Web-based intelligent tutoring system for school mathematics education. As a pilot application, the system was also used for online proctored exam in an undergraduate seminar in Peking University during the epidemic period in 2020. The recorded log data show that all students concentrated themselves on the exam and did not leave the camera and did not speak. Originality/value: During the epidemic period of the novel coronavirus outbreak, almost all educational institutions in the world use online learning as the best way to maintain the teaching and learning schedule for all students. However, current online instruction platforms lack the function to prevent the learners from cheating in online exams and cannot guarantee the integrity and equality for all examinees as in traditional classroom exams. The literature review shows that the online proctoring system should become an important component of online exams to tackle the growing online cheating problem. Although such proctoring systems have been developed and put on the market, the practical usage of such systems in authentic exams and its effect have not been reported. Those systems are heavyweight and commercial product and cannot be freely used in education. The light-weight IOPS developed by the authors could meet the need for online exam as a stable and practical approach and could contribute to the growing online learning and distance learning.

Background: The complications of large‐volume fat grafting (LVFG) for breast augmentation remain unpredictable and include palpable breast nodules, oil cysts, and calcifications. Aims: This study was aimed to provide an optimal treatment option for breast nodules after LVFG and evaluate their pathological characteristics. Patients/Methods: We effectively performed complete resection of breast nodules in 29 patients after LVFG using a minimal skin incision with the vacuum‐assisted breast biopsy (VABB) system under ultrasound guidance. And we further carried on histologic examination of excised nodules and evaluated their pathological characteristics. Results: The breast nodules were excised thoroughly with cosmetic effect satisfactorily. Interestingly, subsequent histologic examination showed that type I and VI collagens were strongly expressed in the fibrotic area and type IV collagen were positively expressed around the blood vessel. Furthermore, we found that the type VI collagen+ area appeared around mac2+ macrophages and α‐SMA+ myofibroblasts. Conclusions: The VABB system may be the optimal treatment option for breast nodules after LVFG. And type VI collagens may serve as a biomarker of grafted adipose tissue fibrosis. The relationship between macrophages, fibroblasts, and collagen formation may be therapeutic targets for regulating fibrosis. [ABSTRACT FROM AUTHOR]