Abstract
Online examinations gradually become popular due to Covid 19 pandemic. Environmentally friendly, saving money, and convenient,... are some of the advantages when taking exams online. Besides its major benefits, online examinations also have some serious adversities, especially integrity and cheating. There are some existing proctoring systems that support anti-cheating, but most of them have a low probability of predicting fraud based on students’ gestures and posture. As a result, our article will introduce an online examination called ExamEdu that supports integrity, in which the accuracy of detecting cheating behaviors is 96.09% using transfer learning and fine-tuning for ResNet50 Convolutional Neural Network.
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References
Morawska, L., Cao, J.: Airborne transmission of sars-cov-2: the world should face the reality. Environ. Int. 139, 105730 (2020)
Nations, U.: Policy brief: Education during covid-19 and beyond. United Nations (2020)
Hermanto, Y.B., Srimulyani, V.A.: The challenges of online learning during the covid-19 pandemic. Jurnal Pendidikan dan Pengajaran 54(1), 46–57 (2021)
Nguyen, Q.N., Duong-Trung, N., Le Ha, D.N., Ha, X.S., Phan, T.T., Pham, H.X., Huynh, H.X.: Movie recommender systems made through tag interpolation. In: Proceedings of the 4th International Conference on Machine Learning and Soft Computing. ICMLSC 2020, pp. 154–158. Association for Computing Machinery, New York (2020)
Huynh, H.T., Duong-Trung, N., Ha, X.S., Quynh Thi Tang, N., Huynh, H.X., Quoc Truong, D.: Automatic keywords-based classification of Vietnamese texts. In: 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 1–3 (2020)
Duong-Trung, N., Nguyen, Q.N., Ha, D.N.L., Ha, X.S., Phan, T.T., Huynh, H.X.: Genres and actors/actresses as interpolated tags for improving movie recommender systems. Int. J. Adv. Comput. Sci. Appl. 11(2), 67–74 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016)
Gourier, N., Crowley, J.: Estimating face orientation from robust detection of salient facial structures. FG Net Workshop on Visual Observation of Deictic Gestures (2004)
Ahmad, I., AlQurashi, F., Abozinadah, E., Mehmood, R.: A novel deep learning-based online proctoring system using face recognition, eye blinking, and object detection techniques. Int. J. Adv. Comput. Sci. Appl. 12(10), 847–854 (2021)
Jadi, A.: New Detection Cheating Method of Online-Exams during COVID-19 Pandemic. Int. J. Comput. Sci. Network Secur. 21(4), 123–130 (2021)
El Kohli, S., Jannaj, Y., Maanan, M., Rhinane, H.: Deep learning: new approach for detecting scholar exams fraud. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 46(4), 103–107 (2021)
Bawarith, R., Basuhail, D.A., Fattouh, D.A., Gamalel-Din, P.D.S.: Exam cheating detection system. Int. J. Adv. Comput. Sci. Appl. 8(4), 176–181 (2017)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv (2017)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015)
Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs/1608.06993 (2016)
Behera, A., Gidney, A.G., Wharton, Z., Robinson, D., Quinn, K.: A CNN model for head pose recognition using wholes and regions. In: 2019 14th IEEE International Conference on Automatic Face &Gesture Recognition (FG 2019), pp. 1–2 (2019)
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Luong, H.H., Khanh, T.T., Ngoc, M.D., Kha, M.H., Duy, K.T., Anh, T.T. (2022). Detecting Exams Fraud Using Transfer Learning and Fine-Tuning for ResNet50. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_56
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DOI: https://doi.org/10.1007/978-981-19-8069-5_56
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