个人简介
教育背景与工作经历 博士，计算机科学，澳门大学，2017-2022 硕士，电子商务技术，澳门大学，2014-2017 本科，计算机科学，马来西亚国立大学，2011-2014
研究领域
语义集成，商务文本分析，电子商务技术等
近期论文
1、Peng Qin, Weiming Tan, Jingzhi Guo and Bingqing Shen (2021). Intelligible Description Language Contract (IDLC) - A Novel Smart Contract Model. Information Systems Frontiers. (中科院二区)
2、Peng Qin and Jingzhi Guo (2020). A novel machine natural language mediation for semantic document exchange in smart city. Future Generation Computer Systems, 102, Jan., 2020, 810-826. (中科院二区, TOP)
3、Peng Qin, et al., (2021) Achieving Semantic Consistency for Multilingual Sentence Representation using an Explainable Machine Natural Language Parser (MParser). Applied Sciences, (Feature Paper). SCI(E)
4、Peng Qin, Chi-Man Pun (2017) Object tracking using distribution fields with correlation coefficients. Multimedia Tools and Applications, Page 1-24, 2017.
5、Peng Qin, Jingzhi Guo, Bingqing Shen, and Quanyi Hu. Towards self-automatable and unambiguous smart contracts: Machine natural language. In International Conference on e-Business Engineering, pp. 479-491. Springer, Cham, 2019.
6、Peng Qin, Jingzhi Guo, Yiling Xu and Longqi Wang. Semantic Document Exchange through Mediation of Machine Natural Language, In International Conference on e-Business Engineering, 2018.
7、Quanyi Hu, Peng Qin, Jie Yang, and Simon Fong. An enhanced particle swarm optimization with distribution fields appearance model for object tracking. International Journal of Wavelets, Multiresolution and Information Processing, 19, no. 01 (2021): 2050065.
8、Bingqing Shen, Weiming Tan, Jingzhi Guo, Peng Qin, and Bin Wang. (2020) An equity-based incentive mechanism for persistent virtual world content service. Service Oriented Computing and Applications. 14, no. 4 (2020): 227-241.
9、Quanyi Hu, Jie Yang, Peng Qin, and Simon Fong. (2020) Towards a Context-Free Machine Universal Grammar (CF-MUG) in Natural Language Processing. IEEE Access, 8: 165111-165129.
10、Quanyi Hu, Jie Yang, Peng Qin, Simon Fong, and Jingzhi Guo. Could or could not of Grid-Loc: grid BLE structure for indoor localisation system using machine learning. Service Oriented Computing and Applications 14, no. 3 (2020): 161-174.