1. 科研论文
科研论文情况：Google Scholar： https://scholar.google.com/citations?hl=zh-CN&user=YevGUDgAAAAJ，gitee：https://gitee.com/qinkesci/paper-list/blob/master/Paper-list.md
[1] Hailin Wang, Dan Zhang, Guisong Liu, Li Huang, Ke Qin， Enhancing relation extraction using multi-task learning with SDP evidence， Information Sciences， 670, 2024, 120610.
[2] Yizhuo Ma, Ke Qin, Shuang Liang, Beta-LR: Interpretable Logical Reasoning based on Beta Distribution, 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Accepted. 2024.
[3] Qiuyi Qi, Tuo Shi, Ke Qin and Guangchun Luo, Completion Time Optimization in UAV-Relaying-Assisted MEC Networks with Moving Users, IEEE Transactions on Consumer Electronics, doi: 10.1109/TCE.2023.3278470, 2023.
[4] Wang H, Qin K , Duan G, et al. Denoising Graph Inference Network for Document-Level Relation Extraction[J]. Big Data Mining and Analytics, 2023, 6(2): 248-262.
[5] Wang H, Qin K, Lu G, et al. Deep neural network-based relation extraction: an overview[J]. Neural Computing and Applications, 2022: 1-21.
[6] Wang H, Qin K, Lu G, et al. Document-level relation extraction using evidence reasoning on RST-GRAPH[J]. Knowledge-Based Systems, 2021: 107274.link
[7] Yin J, Wang J, Jiang J, Sun Y, Chen X, Qin Ke. Research on the Construction and Application of Breast Cancer-specific Database System Based on Full Data Lifecycle[J]. Frontiers in Public Health, 2021, 9: 936. link
[8] Duan G, Yang H, Qin K , et al. Improving Neural Machine Translation Model with Deep Encoding Information[J]. Cognitive Computation, 2021: 1-9. link
[9] Min S, Gao Z, Peng J, Qin K et al. STGSN-A Spatial-Temporal Graph Neural Network framework for time-evolving social networks[J]. Knowledge-Based Systems, 106746.Link
[10] Ainam J P, Qin K, Owusu J W, Lu Guoming. Unsupervised domain adaptation for person re-identification with iterative soft clustering[J]. Knowledge-Based Systems, 2020: 106644. Link
[11] Zhongyang Xiong, Ke Qin, Haobo Yang, Guangchun Luo. Learning Chinese Word Representation Better By Cascade Morphological N-gram, Neural Computing and Applications. 2020:1-12. Link.
[12] Hailin Wang, Ke Qin, Guoming Lu, Guangchun Luo, Guisong Liu. Direction-sensitive relation extraction using Bi-SDP attention model. Knowledge-Based Systems, 2020, 198, 105928, 1-13. Link
[13] Jean-Paul Ainam，Ke Qin, Guisong Liu, Guangchun Luo, Brighter Agyemang. Enforcing Affinity Feature Learning through Self-attention for Person Re-identification, ACM Transactions on Multimedia Computing, Communications, and Applications, 2020, 16(1). Link
[14] Ke Qin. On Chaotic Neural Network Design — A New Framework. Neural Processing Letters, 45(1):243-261, 2017.02. link
[15]Guangchun Luo, Haifeng Sun, Ke Qin, Junbao Zhang. Greedy Zone Epidemic Routing in Urban VANETs. IEICE Transactions on Communications, 2015, E98-B(01): 219-230.
[16] K. Qin, B. J. Oommen. Logistic neural networks: Their chaotic and pattern recognition properties. Neurocomputing, 125:184–194, 2014.02. link
[17] Y. C. Shi, P. Y. Zhu, Ke Qin. Projective synchronization of different chaotic neural networks with mixed time delays based on an integral sliding mode controller. Neurucomputing, 123:443–449, 2014.
[18] Y. Ma, S. Z. Zhu, Ke Qin. Combining the requirement information for software defect estimation in design time. Information Processing Letters, 114(9): 469-474, 2014.
[19] Guangchun Luo, Junbao Zhang, Haojun Huang, Ke Qin, and Haifeng Sun. Exploiting Inter-contact Time for Routing in Delay Tolerant Networks. Transactions on Emerging Telecommunications Technologies, 2013, 24(6): 589-599.
[20] G. C. Luo, J. S. Ren, K. Qin. Dynamical associative memory: The properties of the new weighted chaotic adachi neural network. IEICE Transactions on Information and Systems, E95d(8):2158–2162, 2012. link
[21] Guangchun Luo, Ying Ma, Ke Qin. Active Learning for Software Defect Prediction. IEICE Transactions on Information & Systems, 2012, E95-D(6):1680-1683.
[22] Guangchun Luo, Junbao Zhang, Ke Qin, and Haifeng Sun. Location-Aware Social Routing in Delay Tolerant Networks. IEICE Transactions on Communications. 2012, E95-B(5), 1826-1829.
[23] Guangchun Luo, Ying Ma, Ke Qin. Asymmetric Learning Based on Kernel Partial Least Squares for Software Defect Prediction. IEICE Transactions on Information and Systems, 2012, E95-D(7):2006-2008.
[24] K. Qin, B. J. Oommen. Adachi-like chaotic neural networks requiring linear-time computations by enforcing a tree-shaped topology. IEEE Transactions on Neural Networks, 20(11):1797–1809, 2009. link
[25] Y. Ma, Ke Qin, S. Z. Zhu. Discrimination Analysis for Predicting Defect-Prone Software Modules. Journal of Applied Mathematics. http://dx.doi.org/10.1155/2014/675368, 2014.
[26] Ningduo Peng, Guangchun Luo, Ke Qin, Aiguo Chen. Query-Biased Preview over Outsourced and Encrypted Data. The Scientific World Journal, 2013, http://dx.doi.org/10.1155/2013/860621.
[27] Guangchun Luo, Ningduo Peng, Ke Qin, Aiguo Chen. A Layered Searchable Encryption Scheme with Functional Components Independent of Encryption Methods. The Scientific World Journal, 2014, http://dx.doi.org/10.1155/20- 14/153791.
[28] Ying Ma, Ke Qin, Shunzhi Zhu. Discrimination Analysis for Predicting Defect-Prone Software Modules. Journal of Applied Mathematics, 2014, http://dx.doi.org/10.1155/2014/675368.
[29] K. Qin, B. J. Oommen. Chaotic Neural Networks with a Random Topology Can Achieve Pattern Recognition. Chaotic Modeling and Simulation, 4:583-590, 2013 link
[30] K. Qin, B. J. Oommen. Ideal chaotic pattern recognition is achievable: The ideal-m-adnn – its design and properties. Transactions on Computational Collective Intelligence XI, 8065:22–51, 2013. link
[31] K. Qin, B. J. Oommen. The entire range of chaotic pattern recognition properties possessed by the Adachi neural network. Intelligent Decision Technologies, 6(1):27–41, 2012. link
[32] K. Qin, B. J. Oommen. Ideal chaotic pattern recognition using the modified Adachi neural network. Chaotic Modeling and Simulation, 4:701–710, 2012. link
[33] K. Qin, B. J. Oommen. An enhanced tree-shaped Adachi-like chaotic neural network requiring linear-time computations. Chaotic Systems: Theory and Applications, 284–293, 2010. link
[34] K. Qin, B. J. Oommen. Chaotic Neural Networks with a “Small-World” Topology Can Achieve Pattern Recognition, Chaotic Modeling and Simulation, 4:379–386, 2014. link
[35] J. S. Ren, K. Qin, G. C. Luo. On Software Defect Prediction Using Machine Learning. Journal of Applied Mathematics. http://dx.doi.org/10.1155/2014/785435, 2014. link
[36]Jean-Paul Ainam, K. Qin, Guisong Liu, Guangchun Luo. Person Re-identification through Clustering and Partial Label Smoothing Regularization. In proceedings of the 2nd International Conference on Software Engineering and Information Management (ICSIM'19), 189-193, January 10–13, 2019, Bali, Indonesia. ACM, New York, USA. link
[37] Jean-Paul Ainam, K. Qin, Guisong Liu, Guangchun Luo. Deep Residual Network with Self Attention Improves Person Re-Identification Accuracy. In proceedings of the 2019 11th International Conference on Machine Learning and Computing (ICMLC'19), 380-385, February 22–24, 2019, Zhuhai, China. ACM, New York, USA. link
[38] Haobo Yang, Zongyang Xiong, Jiexin Zhang, Ke Qin, Guoming Lu, Cascade Morphological n-gram can Improve Chinese Words Representation Learning. In proceedings of the 2019 IEEE Green Computing and Communications (GreenCom'19), 842-847, July 14-17, 2019, Atlanta, USA.
[39] K. Qin, B. J. Oommen. Chaotic Pattern Recognition Using the Adachi Neural Network Modified in a Small-World Way. In Proceedings of the 7th Chaotic Modeling and Simulation International Conference (Chaos2014), 391–398, Lisbon, Portugal, 2014
[40] K. Qin, B. J. Oommen. Networking logistic neurons can yield chaotic and pattern recognition properties. In Proceedings of the IEEE International Conference on Computational Intelligence for Measure Systems and Applications(ICMSA2011), 134–139, Ottawa, Canada, 2011. link
[41] K. Qin, B. J. Oommen. Chaotic and pattern recognition properties of a network of logistic neurons. In Proceedings of the 2nd International Conference on Computer Engineering and Technology (ICCET2010), vol.V3, 83–87, Chengdu, China, 2010. link
[42] K. Qin, M. T. Zhou, Y. Feng. A novel multicast key exchange algorithm based on extended chebyshev map. In Proceedings of the 4th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS2010), 643–648, Kracow, Poland, 2010. link
[43] K. Qin, B. J. Oommen. Cryptanalysis of a Cryptographic Algorithm that Utilizes Chaotic Neural Networks. In Proceedings of the 29th International Symposium on Computer and Information Sciences (ISCIS2014), 167–174, Kracow, Poland, 2014. link
[44] K. Qin, B. J. Oommen. Chaotic pattern recognition using the Adachi neural network modified in a random manner. In Proceedings of the 6th Chaotic Modeling and Simulation International Conference (Chaos2013)., Istanbul, Turkey, 2013.
[45] K. Qin, B. J. Oommen. Chaotic pattern recognition: The spectrum of properties of the Adachi neural network. In Proceedings of the International Conference on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition (SSSPR2008), Vol. 5342,540–550, Florida, USA, 2008. link
[46] K. Qin, M. T. Zhou, N. Q. Liu, et al. A novel group key management based on Jacobian Elliptic Chebyshev Rational Map. In Proceedings of the IFIP International Conference Network and Parallel Computing(NPC2007), 287–295, Dalian, China 2007. link
2. 专著
[1] B. J. Oommen, Ke Qin, Dragos Calitoiu.Handbook of Applications of Chaos Theory (Chap 36). CRC Press, 2016. Edited by:Christos H. Skiadas, Charilaos Skiadas. https://www.crcpress.com/Handbook-of-Applications-of-Chaos-Theory/Skiadas-Skiadas/p/book/9781466590434 （Google Books预览）
3. 教材
[1] 网络安全协议，秦科，电子科技大学出版社，2019.03.
[2] 信息安全概论，郝玉洁、刘贵松，秦科， 电子科技大学出版社，2007.03.
