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Prof. Mengtian Cui 崔 梦 天 Visiting Professor |
Mengtian Cui is currently professor with the key laboratory of computer system, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, China. She received Ph.D. degree in computer software and theory from CAS( Chinese Academy of Sciences), China in 2010. She used to be a Research Fellow in Ryerson University, Canada and University of Fribourg, Switzerland. Her main research interests include trusted computing, formal methods and software defect detection and prediction. His current research activity, together with Prof. Dr Claudio J. Tessone, focuses on the analysis and prediction of open source software defect prediction from a complex systems perspective.
• Mengtian Cui, Manuel Sebastian Mariani, Matus Medo. Compounding algorithmic biases: The case of PageRank in evolving networks, Communications in Nonlinear Science and Numerical Simulation, 104 (2022) 1-13
• Yao Du, Mengtian Cui, Xiaochun Cheng, "A Mobile Malware Detection Method Based on Malicious Subgraphs Mining", Security and Communication Networks, vol. 2021, Article ID 5593178, 11 pages, 2021 https://doi.org/10.1155/2021/5593178
• Mengtian Cui, Yi-Cheng Zhang, “Application of Markov chain approach for multi-attributes dynamic software reliability assessment under both AHP and gray correlation methods”, International Journal of Modern Physics C, Vol. 29(5), 184000810(2018):1-8
• Rui Hou, Shuo Zhou, Mengtian Cui, Lingyun Zhou, Deze Zeng, Jiangtao Luo and Maode Ma,”Data Forwarding Scheme for Vehicle Tracking in Named Data Networking”, IEEE Transactions on vehicular technology, 2021, Vol. 70(7), pp. 6684-6695
• Mengtian Cui, Yue Sun, Yang Lu and Yue Jiang. Study on the Influence of the Number of Features on the Performance of Software Defect Prediction Model, In: the 2019 ACM International Conference Proceeding Series of the3rd International Conference on Deep Learning Technologies ( ICDLT 2019), p32-37, July 5-7, 2019. Xiamen, China.
• Weibang LI, Ling LI, Zhanhuai LI, Mengtian Cui. Statistical relational learning based automatic data cleaning, Frontiers of Computer Science, 2019,13(1): 215-217