By Last Name
HE QingProfessor
Research Areas:
Machine Learning;DataMining;Intelligence
Department:
Key Laboratory Of Intelligent Information Processing
Tutor Category:
Contact:
heqing@ict.ac.cn
Personal Page:
https://people.ucas.edu.cn/~heqing
Qing He, Male, Chinese, Professor, Ph. D. Supervisor,
The Key Laboratory of Intelligent Information Processing,
Institute of Computting Technology,
Chinese Academay of Sciences,
Beijing,China
100190
Email: heqing@ict.ac.cn
Telephone: 8610-62600543
Address: Kexueyuan Nan Road No.6, Haidian District, Beijing,China
Webpage: http://mldm.ict.ac.cn/
Research Areas
Machine Learning; Data Mining Based Cloud Computing; Web Mining; Artificial Intelligence
Education
1. Date Degree University Mayjor
2. 1981.9-1985.7 Bachelor Hebei Normal University Mathematics
3. 1985.8-1987.7 Master Zhengzhou University Mathematics
4. 1997.8-2000.7 Ph. D. Beijing Normal University Fuzzy Mathematics and Artificial Intelligent
5. 2000.8-2002.8 Post Ph. D. Institute of Computing and Technology, CAS Computer Software and Theory
6. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Visiting Professor, 2001.11
7. University of Technology, SydneyUniversity of Technology, Sydney, Visiting Professor, 2003.10
2. 1981.9-1985.7 Bachelor Hebei Normal University Mathematics
3. 1985.8-1987.7 Master Zhengzhou University Mathematics
4. 1997.8-2000.7 Ph. D. Beijing Normal University Fuzzy Mathematics and Artificial Intelligent
5. 2000.8-2002.8 Post Ph. D. Institute of Computing and Technology, CAS Computer Software and Theory
6. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, Visiting Professor, 2001.11
7. University of Technology, SydneyUniversity of Technology, Sydney, Visiting Professor, 2003.10
Experience
Work Experience
1. Qing He is a Professor at the Institute of Computing Technology, Chinese Academy of Sciences (CAS), and he is a Professor of University of Chinese Academy of Sciences (UCAS). He is also the Vice Secretary of Chinese Association for Artificial Intelligence,
2. the Member of China Computer Federation Artificial Intelligence and Pattern Recognition Committee, the Member of Chinese Institute of Electronics and Clouding Computing and Big Data Experts Committee. He received the B.S. degree from Hebei Normal University, Shijiazhuang, P. R. C., in 1985, and the M.S. degree from Zhengzhou University, Zhengzhou, P. R. C., in 1987, both in Mathematics. He received the Ph.D. degree in 2000 from Beijing Normal University in Fuzzy Mathematics and Artificial and Intelligence, Beijing, P. R. C. Since 1987 to 1997, he had been teaching at Hebei University of Science and Technology. He is currently a doctoral supervisor at the Institute of Computing and Technology, CAS. His interests include data mining, machine learning, classification, fuzzy clustering, cloud computing, big data. A series of achievements have been gained in fuzzy information processing, fuzzy clustering, knowledge representation, text information processing, and in big data mining based on cloud computing. More than 300 papers have been published. Multi-strategy data mining platform MSMiner, Web Intelligent Information Processing software GHunt, Hypersurface Classifier HSC, have been organized and developed. Recently, in cloud computing and big data mining applications, Qing He led his machine learning and data mining team (http://mldm.ict.ac.cn/Home.html), commissioned by the China Mobile Research Institute, developed cloud-based parallel data mining platform by the end of 2008 for mining TB level actual data and achieving high-performance, low-cost data mining. Through this innovation, the country received a proprietary cloud-based data mining technique. Assembly invited he made a technical report in the second, third, sixth China Cloud Computing Conference. He has presided and completed a number of relevant data mining project supported by the National Natural Science Foundation and 863 projects. Moreover, the projects were rated excellent. He proposed a series of effective data mining algorithms and multiple parallel machine learning algorithms. He organized his team developing forty parallel machine learning algorithms. Multiple big data mining software such as PDMiner, COMS, CWMS and WMCS developed by his team have gotten the software copyright and practical applying to telecommunications, electricity, information security, environmental protection, the financial insurance, and dozens of companies, enterprises with the considerable economic and social benefits.
2. the Member of China Computer Federation Artificial Intelligence and Pattern Recognition Committee, the Member of Chinese Institute of Electronics and Clouding Computing and Big Data Experts Committee. He received the B.S. degree from Hebei Normal University, Shijiazhuang, P. R. C., in 1985, and the M.S. degree from Zhengzhou University, Zhengzhou, P. R. C., in 1987, both in Mathematics. He received the Ph.D. degree in 2000 from Beijing Normal University in Fuzzy Mathematics and Artificial and Intelligence, Beijing, P. R. C. Since 1987 to 1997, he had been teaching at Hebei University of Science and Technology. He is currently a doctoral supervisor at the Institute of Computing and Technology, CAS. His interests include data mining, machine learning, classification, fuzzy clustering, cloud computing, big data. A series of achievements have been gained in fuzzy information processing, fuzzy clustering, knowledge representation, text information processing, and in big data mining based on cloud computing. More than 300 papers have been published. Multi-strategy data mining platform MSMiner, Web Intelligent Information Processing software GHunt, Hypersurface Classifier HSC, have been organized and developed. Recently, in cloud computing and big data mining applications, Qing He led his machine learning and data mining team (http://mldm.ict.ac.cn/Home.html), commissioned by the China Mobile Research Institute, developed cloud-based parallel data mining platform by the end of 2008 for mining TB level actual data and achieving high-performance, low-cost data mining. Through this innovation, the country received a proprietary cloud-based data mining technique. Assembly invited he made a technical report in the second, third, sixth China Cloud Computing Conference. He has presided and completed a number of relevant data mining project supported by the National Natural Science Foundation and 863 projects. Moreover, the projects were rated excellent. He proposed a series of effective data mining algorithms and multiple parallel machine learning algorithms. He organized his team developing forty parallel machine learning algorithms. Multiple big data mining software such as PDMiner, COMS, CWMS and WMCS developed by his team have gotten the software copyright and practical applying to telecommunications, electricity, information security, environmental protection, the financial insurance, and dozens of companies, enterprises with the considerable economic and social benefits.
Teaching Experience
1. Professor of Graduate of Chinese Academey of Sciences
2. Course:Fuzzy Mathematics and Application in Computing
Honors & Distinctions
1. 2015 Wenjun Wu Artificial Intelligence Innovation Award
2. 2006 Beijing Science Technology Award
3. Publish Paper List
2. 2006 Beijing Science Technology Award
3. Publish Paper List
Conference Paper List
1. Zixian Zhou, Mengda Huang，Feiyang Pan, Jia He, Xiang Ao, Dandan Tu, Qing He. Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning，in the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23)
2. Fuwei Zhang, Zhao Zhang, X他iang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei and Qing He. Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement，In the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)，Vol. 36 No. 4: 4345-4353
3. Fuwei Zhang，Zhao Zhang，Xiang Ao，Fuzhen Zhuang，Yongjun Xu，Qing He，Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion, CIKM2022
4. Kuan Li, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He. Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN, SIGKDD, 2022
5. Linfeng Dong, Yang Liu, Xiang Ao, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He: Bi-Level Selection via Meta Gradient for Graph-Based Fraud Detection. DASFAA (1) 2022: 387-394 2.0
6. Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He: AUC-oriented Graph Neural Network for Fraud Detection. WWW 2022: 1311-1321
7. Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, Qing He: User-Centric Conversational Recommendation with Multi-Aspect User Modeling. SIGIR 2022: 223-233
8. Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He and Hui Xiong. Customized Conversational Recommender Systems, ECML PKDD 22
9. Yang Liu, Xiang Ao, Fuli Feng, Qing He. UD-GNN: Uncertainty-aware Debiased Training on Semi-Homophilous Graphs, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), Pages 1131–1140, 2022
10. Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He: Selective Fairness in Recommendation via Prompts. SIGIR2022: 2657-2662 Short paper.
11. Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Qing He: Multi-view Multi-behavior Contrastive Learning in Recommendation. DASFAA (2) 2022: 166-182
12. Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, Qing He: Personalized Transfer of User Preferences for Cross-domain Recommendation. WSDM 2022: 1507-1515
13. Yuyao Guo, Haoming Li, Xiang Ao, Min Lu, Dapeng Liu, Lei Xiao, Jie Jiang, Qing He . Calibrated Conversion Rate Prediction via Knowledge Distillation under Delayed Feedback in Online Advertising. CIKM2022
14. Zidi Qin, Yang Liu, Qing He and Xiang Ao. Explainable Graph-based Fraud Detection via Neural Meta-graph Search, CIKM2022，short paper
15. Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He: Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection. AAAI 2021，virtually February 2-9, 2021.
16. Feiyang Pan, Haoming Li, Xiang Ao, Wei Wang, Yanrong Kang, Ao Tan, Qing He. GuideBoot: Guided Bootstrap for Deep Contextual Bandits in Online Advertising, WWW21，April 12-16, 2021，Ljubljana,Slovenia
17. Guoxin Yu, Xiang Ao, Ling Luo, Min Yang, Xiaofei Sun, Jiwei Li and Qing He. Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis. ACL 2021, findings, virtually August 1-6, 2021, Bangkok, Thailand.
18. Haoming Li, Feiyang Pan, Xiang Ao, Zhao Yang, Min Lu, Junwei Pan, Dapeng Liu, Lei Xiao, Qing He. Follow the Prophet: Accurate Online Conversion Rate Prediction In the Face of Delayed Feedback，SIGIR’21，Online，July 11-15, 2021
19. Qingping Yang, Yixuan Cao, Hongwei Li, Ping Luo. Numerical formula recognition from tables. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2021.
20. Rongyu Cao, Hongwei Li, Ganbin Zhou, Ping Luo. Towards Document Panoptic Segmentation with Pinpoint Accuracy: Method and Evaluation, in J. Llad′os et al. (Eds.): ICDAR 2021, LNCS 12822, pp. 3–18, 2021.
21. Rongyu Cao, Ping Luo. 2021. Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers. AAAI 2021，virtually February 2-9, 2021.
22. Runchuan Wang, Zhao Zhang, Fuzhen Zhuang, Dehong Gao, Yi Wei, Qing He, Adversarial Domain Adaptation for Cross-lingual Information Retrieval with Multilingual BERT, CIKM’21, November 1–5, 2021, Virtual Event, Australia.
23. Tianxin Wang,Fuzhen Zhuang,zhiqiang Zhang,Daixin Wang, Jun Zhou,Qing He. Low-dimensional Alignment for Cross-Domain Recommendation, CIKM’21, November 1–5, 2021, Virtual Event, Australia.
24. Xiang Ao, Xiting Wang，Ling Luo, Ying Qiao, Qing He and Xing Xie. PENS: A Dataset and Generic Framework for Personalized News Headline Generation ， ACL-IJCNLP 2021 main conference，82-92
25. Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang and Qing He. Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection, WWW21，April 12-16, 2021，Ljubljana,Slovenia
26. Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Qing He and Hui Xiong. Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning, WWW21，April 12-16, 2021，Ljubljana, Slovenia
27. Ying Sun, Hengshu Zhu;_, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong. Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation accepted for a poster presentation at NeurIPS 2021.
28. Yixuan Cao, Feng Hong, Hongwei Li, Ping Luo: A Bottom-Up DAG Structure Extraction Model for Math Word Problems. AAAI 2021，virtually February 2-9, 2021.
29. Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin and Qing He. Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users，SIGIR’21，Online，July 11-15, 2021
30. Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin and Juan Cao. Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks, SIGIR ’21, July 11–15, 2021, Virtual Event, Canada
31. Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin and Juan Cao. Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising，KDD’21，Online，August 14-18, 2021
32. Zhengqi Xu, Yixuan Cao, Rongyu Cao, Guoxiang Li. Xuangqiang Liu, Yan Pang, Yangbin Wang, Tianfer Zhang, Allie Cheung, Matten Tan, Lukas Petrikas, Ping Luo. Jura Towards Automatic Compliance Assessment, CIKM’21, November 1–5, 2021, Virtual Event, Australia.
33. Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He. Neural Hierarchical Factorization Machines for User's Event Sequence Analysis. SIGIR20 short paper, July 25-30, 2020, Xi'an, China.
34. Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou, Xiaohu Cheng, Fen Lin, Qing He. Domain Adaptation with Category Attention Network for Deep Sentiment Analysis. WWW’20, April 20–24, 2020, Taipei，pp.: 3133-3139.
35. Feiyang Pan, Xiang Ao, Pingzhong Tang, Min Lu, Dapeng Liu, Lei Xiao and Qing He. Field-aware calibration: a simple and empirically strong method for reliable probabilistic predictions. WWW’20, April 20–24, 2020, Taipei
36. Qiwei Zhong，Yang Liu，Xiang Ao，Binbin Hu，Jinghua Feng，Jiayu Tang，Qing He. Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network，WWW’20, April 20–24, 2020, Taipei
37. Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Gu Xi, and Qing He. Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection. Proceedings of the Web Conference 2020. pp.: 928-938. WWW’20, April 20–24, 2020, Taipei
38. Zhao Zhang, Fuzhen Zhuang*, Hengshu Zhu, Zhiping Shi, Hui Xiong, Qing He, Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion. AAAI 2020, Feb.7-12,Newyork USA.
39. Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He. Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection, WWW’20, April 20–24, 2020, Taipei
40. Dongbo Xi, Fuzhen Zhuang*, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He: Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification. AAAI 2019.
41. Feiyang Pan, Qingpeng Cai, An-Xiang Zeng, Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang. Policy Optimization with Model-based Explorations. AAAI 2019.
42. Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He. Warm Up Cold-start Advertisements : Improving CTR Predictions via Learning to Learn ID Embeddings. To appear in the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019).
43. Pan feiyang, Cai, Qi,Tang, Pingzhong, Zhuang, Fuzhen., He, Qing. Policy gradients for contextual recommendations, WWW2019
44. Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, Hui Xiong. A Structure-Aware Convolutional Neural Network Approach, KDD2019
45. Ling Luo, Xiang Ao,Yan Song,Jinyao Li,Xiaopeng Yang,Qing He,Dong Yu, Unsupervised Neural Aspect Extraction with Sememes,IJCAI2019
46. Ying Sun, Hengshu Zhu, Fuzhen Zhuang, Jingjing Gu and Qing He Exploring the Urban Region-of-Interest through the Analysis of Online Map Search Queries，KDD2018
47. Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen, Qing He. Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation，The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) ，February 2–7, 2018，New Orleans, Lousiana, USA
48. Ganbin Zhou, Ping Luo, Yijun Xiao, Fen Lin, Bo Chen, Qing He. Elastic Responding Machine for Dialog Generation with Mechanism Dynamically Selecting，The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) ，February 2–7, 2018，New Orleans, Lousiana, USA
49. Jingwu Chen, Fuzhen Zhuang, Xin Hong, Xiang Ao, Xing Xie and Qing He: Attention-driven Factor Model for Explainable Personalized Recommendation. SIGIR 2018
50. Xiang Ao, Yang Liu, Zhen Huang, Luo Zuo, Qing He. Free-rider Episode Screening via Dual Partition Model. The 23rd International Conference on Database Systems for Advanced Applications (DASFAA), 2018.
51. Ling Luo, Xiang Ao, Feiyang Pan, Tong Zhao, Ningzi Yu, Qing He. Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention. The 27th International Joint Conference on Artificial Intelligence (IJCAI2018).
52. Jia He, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long.Nonlinear Maximum Margin Multi-view Learning with Adaptive Kernel, IJCAI17
53. Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, Qing He. Mechanism-Aware Neural Machine for Dialogue Response Generation，AAAI2017
54. Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang, Qing He. Mining Precise-positioning Episode Rules from Event Sequences，ICDE2017
55. Fuzhen Zhuang, Jing Zheng, Chuan Shi and Qing He.Transfer Collaborative Filtering from Multiple Sources via Consensus Regularization,WSDM2017
56. Qing He, Yunlong Ma, Qun Wang, Fuzheng Zhuang, Zhongzhi Shi. Parallel Outlier Detection Using KD-Tree Based on MapReduce, IEEE CloudCom 2011,Washington, DC, USA, 4-9 July, 2011
57. Qing He, Zhongzhi Shi, Lian Ren.The Classification Method Based on Hyper Surface，2002 International Joint Conference on Neural Networks，2002.5:1499-1503, Honolulu, Hawaii,USA, May 12-17, 2002
58. Qing He, Xiurong Zhao, Sulan Zhang. Multi-modal services for web information collection based on multi-agent techniques, Lecture Notes in Computer Science, v 4088 LNAI, Agent Computing and Multi-Agent Systems: 9th Pacific Rim International Workshop on Multi-Agents, PRIMA 2006, p 129-137, Guilin, China, in August 2006
59. Jia He, Changying Du, Fuzhen Zhuang,Yin Xin, Qing He*, Guoping Long. Online Bayesian Max-margin Subspace Multi-view Learning, IJCAI-16，July 9–15, 2016, New York
60. Ping Luo, Ganbin Zhou, Qing He*. Browsing Regularities in Hedonic Content Systems: the More the Merrier? IJCAI-16，July 9–15, 2016, New York
61. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He*. Online Frequent Episode Mining, ICDE 2015 : International Conference on Data Engineering (ICDE15), Seoul, Korea, April 13-17, 2015
62. Changying Du, Shandian Zhe, Fuzhen Zhuang, Alan Qi, Qing He*, Zhongzhi Shi. Bayesian Maximum Margin Principal Component Analysis, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15)，Austin, Texas, USA, January 25–30, 2015,
63. Xinyu Wu, Ping Luo, Qing He*, Tianshu Feng. Festival, Date and Limit Line: Predicting Vehicle Accident Rate in Beijing, SDM15, British Columbia, Canada, April 30-May 2
64. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He*, Zhongzhi Shi.Discovering and learning sensational episodes of news events. The 23rd international conference on World Wide Web, WWW2014, Seoul, Korea, April 7-11,
65. Wenjuan Luo, Fuzhen Zhuang, Xiaohu Cheng, Qing He*, Zhongzhi Shi. Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews, IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China,December 14-17, 2014
66. Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo and Qing He*. Multi-task Multi-view Learning for Heterogeneous Tasks, CIKM’14, November 03–07, 2014, Shanghai, China
67. Fuzhen Zhuang, Xiaohu Cheng, Sinno Jialin Pan, Wenchao Yu, Qing He*, Zhongzhi Shi. Transfer Learning with Multiple Sources via Consensus Regularized Autoencoders, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML14/PKDD14), Nancy, France, September 15th to 19th, 2014.
68. Changying Du, Jia He, Fuzhen Zhuang, Yuan Qi, Qing He*. Nonparametric Bayesian Multi-Task Large-margin Classification, 21st European Conference on Artificial intelligence (ECAI14), Prague, Czech, 18-22 Aug. 2014.
69. Shuo Han, Fuzhen Zhuang, Qing He*, Zhongzhi Shi. Balanced Seed Selection for Budgeted Influence Maximization in Social Networks, PAKDD 2014: Pacific-Asia Conference on Knowledge Discovery and Data Mining , 2014-05-13, Tainan, Taiwan, China
70. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He*, Zhongzhi Shi. Discovering and learning sensational episodes of news events. The 23rd international conference on World Wide Web, WWW2014, Seoul, Korea,April 7-11
71. Wenjuan Luo, Fuzhen Zhuang, Xiaohu Cheng, Qing He*, Zhongzhi Shi. Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews, IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China / December 14-17, 2014
72. Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo and Qing He*. Multi-task Multi-view Learning for Heterogeneous Tasks, CIKM’14, Shanghai, China, November 03–07, 2014
73. Shuo Han, Fuzhen Zhuang, Qing He*, Zhongzhi Shi. Balanced Seed Selection for Budgeted Influence Maximization in Social Networks, PAKDD 2014 : Pacific-Asia Conference on Knowledge Discovery and Data Mining, Tainan, Taiwan, China,2014-05-13
74. Fuzhen Zhuang, Ping Luo, Changying Du, Qing He*, Zhongzhi Shi. Triplex Transfer Learning: Exploiting both Shared and Distinct Concepts for Text Classification, WSDM’13, Rome, Italy, February 4–8, 2013
75. Fuzhen Zhuang, Ping Luo, Peifeng Yin, Qing He*, Zhongzhi Shi. Concept Learning for Cross-domain Text Classification: a General Probabilistic Framework, 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013). Beijing, China, August 3-9, 2013
76. Tianfeng Shang, Qing He*, Fuzhen Zhuang and Zhongzhi Shi. A New Similarity Measure Based on Preference Sequence for Collaborative Filtering. Web Technologies and Applications. 15th Asia-Pacific Web Conference, APWeb 2013,Sydney, NSW, Australia, 4-6 April 2013
77. Tianfeng Shang, Qing He*, Fuzhen Zhuang, Zhongzhi Shi. Extreme Learning Machine Combining Matrix Factorization for Collaborative Filtering. IEEE The 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, USA, August 4-9, 2013.
78. Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He*, and Zhongzhi Shi. Shared Structure Learning for Multiple Tasks with Multiple Views, ECML/PKDD13, Prague, September 23-27, 2013
79. Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang,Qing He*, and Zhongzhi Shi. Embedding with Autoencoder Regularization, ECML/PKDD13, Prague,September 23-27, 2013
80. Changying Du, Fuzhen Zhuang, Qing He* and Zhongzhi Shi. Multi-Task Semi-Supervised Semantic Feature Learning for Classification, ICDM2012，pp. 191-200, Brussels, Belgium, 2012 (12/10-12/13)
81. Wenjuan Luo Fuzhen Zhuang, Qing He*, and Zhongzhi Shi. Quad-tuple PLSA: Incorporating Entity and Its Rating in Aspect Identification, The 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), PAKDD 2012, pp. 392–404, Kuala Lumpur, Malaysia, 29 May - 1 June,2012
82. Xudong Ma, Ping Luo, FuzhenZhuang, Qing He*, Zhongzhi Shi and ZhiyongShen. Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding, Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI 11，pp.1396-1401C,Barcelona in July 2011
83. Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He*. Yuhong Xiong. Exploiting Associations between Word Clusters and Document Classes for Cross-domain Text Categorization, 2010 SIAM International Conference on Data Mining (SDM'2010), pp.13-24, Columbus, Ohio, April 19, 2010(EI,被大会推荐的十二篇最佳论文提名之一)
84. Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He*, Yuhong Xiong, and Zhongzhi Shi. D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification, accepted as a regular paper at the IEEE International Conference on Data Mining (ICDM 2010) to be held in Sydney Australia, December 14-17，2010, pp.709-718, (EI )
85. Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He*, Yuhong Xiong, Zhongzhi Shi1, Hui Xiong. Collaborative Dual-PLSA: Mining Distinction and Commonality across Multiple Domains for Classification, The 19th ACM International Conference on Information and Knowledge Management( CIKM’10), October 26-30, 2010, Toronto, Canada. (获得八篇最佳论文提名之一, 并获得Student Travel Awards)
86. Qing Tan, Qing He*, Zhongzhi Shi. Nonparametric Curve Extraction Based on Ant Colony System, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), pp.599-604, Atlanta, USA, July 10-15, 2010
87. Weizhong Zhao, Huifang Ma, Qing He.Parallel k-means clustering based on mapreduce, Cloud Computing, 2009
88. Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, Qing He*. Transfer Learning from Multiple Source Domains via Consensus Regularization, full paper in CIKM 2008 , Napa Valley, California October 26-30, 2008 (EI)
89. Qiuge Liu, Qing He*, Zhongzhi Shi. Extreme Support Vector Machine Classify, Lecture Notes in Computer Science, v 5012 LNAI, Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Proceedings, 2008, p 222-233,Osaka,Japan,May 20-23,2008(EI)
90. Luo, Ping; Lu, Kevin; He, Qing*; Shi, Zhongzhi. A heterogeneous computing system for data mining workflows, Lecture Notes in Computer Science, v 4042 LNCS, Flexible and Efficient Information Handling - 23rd British National Conference on Databases, BNCOD 23, Proceedings, 2006, p 177-189, Belfast, Northern Ireland, UK, July 18-20, 2006
91. Zheng, Zheng; He, Qing*; Shi, Zhongzhi. Granule sets based bilevel decision model, Lecture Notes in Computer Science, v 4062, Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings, 2006, p 530-537, Chongqing, China, July 24-26, 2006
92. Zhao, Xiu-Rong; He, Qing*; Shi, Zhong-Zhi. HyperSurface Classifiers ensemble for high dimensional data sets, Lecture Notes in Computer Science, v 3971, Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, p 1299-1304, Chengdu, China, May 28 - June 1, 2006
93. Ping Luo, Qing He*, Rui Huang, Fen Lin, Zhongzhi Shi. Execution Engine of Meta-learning System for KDD in Multi-agent Environment. Lecture Notes in Computer Science. Springer-Verlag, Volume 3505 / 2005, 149-160. AIS-ADM 2005, St. Petersburg, Russia, June 6-8, 2005
94. Ping Luo, Su Yan, Zhiqiang Liu, Zhiyong Shen, Shengwen Yang, Qing He. From Online Behaviors to Offline Retailing, the ACM KDD 2016 Conference as a full presentation（CCF A）
Journal Paper List
1. Feiyang Pan, Shuokai Li, Xiang Ao, Qing He: Relation Reconstructive Binarization of word embeddings. Frontiers Comput. Sci. 16(2): 162307 (2022)
2. Shuokai Li, Xiang Ao, Feiyang Pan, Qing He: Learning policy scheduling for text augmentation. Neural Networks 145: 121-127 (2022)
3. Tianxin Wang, Fuzhen Zhuang, Ying Sun, Xiangliang Zhang, Leyu Lin, Feng Xia, Lei He, Qing He: Adaptively sharing multi-levels of distributed representations in multi-task learning. Inf. Sci. 591:226-234 (2022)
4. Yang Liu, Xiang Ao, Linfeng Dong, Chao Zhang, Jin Wang, Qing He. Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding. IEEE Transactions on Knowledge and Data Engineering.34(1): 462-474 (2022)
5. Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi, and Qing He. Combat Data Shift in Few-shot Learning with Knowledge Graph, Frontiers of Computer Science, 2023, 17(1):171305
6. Feiyang Pan,Shuokai Li,Xiang Ao,Qing He. Relation Reconstructive Binarization of Word Embeddings, Frontiers of Computer Science, 2022, 16(2): 162307Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchuan Zhu, Hengshu Zhu, Hui Xiong, Qing He, A Comprehensive Survey on Transfer Learning, PROCEEDINGS OF THE IEEE, Vol. 109, No. 1, January 2021, pp.43-76
7. Jingwu Chen, Fuzhen Zhuang, Tianxin Wang, Leyu Lin, Feng Xia, Lihuan Du, Qing He. Follow the Title then Read the Article: Click-guide Network for Dwell Time Prediction, IEEE Transactions on Knowledge and Data Engineering, VOL. 33, NO. 7, JULY 2021, pp.2903-2913
8. Shuokai Li, Xiang Ao, Feiyang Pan, Qing He. Learning Policy Scheduling for Text Augmentation, Neural Networks,145(2022):121-127
9. Xiang Ao, Yang Liu, Zidi Qin, Yi Sun and Qing He. Temporal High-order Proximity Aware Behavior Analysis on Ethereum. World Wide Web ，(2021) 24:1565–1585.
10. Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Qi Zhang, Qing He, and Hui Xiong. Market-oriented Job Skill Valuation with Cooperative Composition Neural Network，NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-22215-y.
11. Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He. Deep Subdomain Adaptation Network for Image Classification. IEEE Transactions on Neural Networks and Learning Systems, VOL. 32, NO. 4, APRIL 2021,pp. 1713-1722
12. Zhao Zhang, Fuzhen Zhuang, Meng Qu, Zheng-Yu Niu, Hui Xiong, Qing He, Knowledge graph embedding with shared latent semantic units, Neural Networks 139 (2021) 140–148
13. Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Hengshu Zhu, Pengpeng Zhao, Chang Tan, Qing He: Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification，Neural Networks 132 (2020) 75–83.
14. Fuzhen Zhuang, Yingmin Zhou, Haochao Ying, Fuzheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong: Sequential Recommendation via Cross-domain Novelty Seeking Trait Mining. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY ,Vol.35, ？No.？ 2, SI: ？ 305-319, MAR 2020
15. Jia He, Changying Du,Fuzhen Zhuang, Xin Yin, Qing He. Guoping Long. Online Bayesian Max-margin Subspace Learning for Multi-view Classification and Regression, Machine Learning, (2020) 109:219–249.
16. Jia He, Fuzhen Zhuang, Yanchi Liu, Qing He, Fen Lin:
17. Bayesian dual neural networks for recommendation. Frontiers Comput. Sci. 13(6): 1255-1265 (2019)
18. Ming Huang, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang, Qing He. Supervised representation learning for multi-label classification. Machine Learning 108(5): 747-763 (2019)
19. Thapana Boonchooa, Xiang Ao, Yang Liu, Weizhong Zhao, Fuzhen Zhuang, Qing He. Grid-based DBSCAN: Indexing and Inference. Pattern Recognition (PR), 90 : 271-284, 2019
20. Thapana Boonchoo, Xiang Ao , Qing He: Multi-Aspect Embedding for Attribute-Aware Trajectories. Symmetry 11(9): 1149 (2019)
21. Xiang Ao, Haoran Shi, Jin Wang, Luo Zuo, Hongwei Li, Qing He:Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies. ACM TIST 10(4): 36:1-36:26 (2019)
22. Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He: Multi-Representation Adaptation Network for Cross-domain Image Classification. Neural Networks 119:214-221 (2019).
23. Zhao Zhang, Fuzhen Zhuang, Xuebing Li, Zhengyu Niu, Jia He, Qing He, Hui Xiong: Knowledge Triple Mining via Multi-Task Learning. Information Systems, Information Systems 80 (2019): 64–75
24. Fuzhen Zhuang, Jing Zheng, Jingwu Chen, Xiangliang Zhang, Chuan Shi, Qing He. Transfer collaborative filtering from multiple sources via consensus regularization. Neural Networks 108: 287-295 (2018)
25. Fuzhen Zhuang, Xuebing Li, Xin Jin, Dapeng Zhang, Lirong Qiu, Qing He. Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix Factorization. IEEE Trans. Cybernetics 48(8): 2284-2293 (2018)
26. X. Ao, P. Luo, J. Wang, F. Zhuang and Q. He, "Mining Precise-Positioning Episode Rules from Event Sequences," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 3, pp. 530-543, 2018.
27. Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He. Discovering and learning sensational episodes of news events[J]. Information Systems,Volume 78, November 2018, Pages 68-80
28. Zhou Ganbin Luo Ping He Qing.Predicting Compositional Time Series via Autoregressive Dirichlet Estimation[J]. Science China Information Sciences, 2018, 61(9):098-106.
29. Fuzhen Zhuang，Zhiqiang Zhang，Mingda Qiana，Chuan Shi，Xing Xie，Qing He. Representation learning via Dual-Autoencoder for recommendation，Neural Networks，Volume 90, June 2017, Pages 83-89
30. Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He: Supervised Representation Learning with Double Encoding-layer Autoencoder for Transfer Learning. ACM Transactions on Intelligent Systems and Technology (TIST) ISSN:2157-6904 Volume:9 Issue:2 Page:1-17 .
31. Haocheng Wang, Fuzhen Zhuang, Xin Jin, Xiang Ao, and Qing He. Bag of little bootstraps on features for enhancing classification performance. Intelligent Data Analysis 20 (2016) 1085–1099.
32. Qing He, Haocheng Wang, Fuzhen Zhuang, Tianfeng Shang, Zhongzhi Shi. Parallel sampling from big data with uncertainty distribution, Fuzzy Sets and Systems 258 (2015) 117–133
33. Jie Lu, Zheng Zheng, Guangquan Zhang, Qing He and Zhongzhi Shi. A new solution algorithm for solving rule-sets based bilevel decision problems, CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE. ，2015，Vol: 27, No: 4,pages: 830-54
34. Wenjuan Luo, Fuzhen Zhuang, Weizhong Zhao, Qing He, Zhongzhi Shi. QPLSA: Utilizing quad-tuples for aspect identification and rating, Information Processing and Management 51 (2015) 25–41
35. Wenchao Yu, Fuzhen Zhuang, Qing He and Zhongzhi Shi. Learning Deep Representations via Extreme Learning Machine, Neurocomputing, Volume 149, Part A, 3 February 2015, Pages 308-315
36. Qing He, Xin Jin, Changying Du, Fuzhen Zhuang and Zhongzhi Shi. Clustering in extreme learning machine feature space. Neurocomputing 128 : 88-95 (2014). (SCI).
37. Qing He, Tianfeng Shang, Fuzhen Zhuang and Zhongzhi Shi. Parallel Extreme Learning Machine for Regression based on MapReduce, Neurocomputing 102(2013)52–58 (SCI\EI)
38. He, Qing; Zhao, Weizhong; Shi, Zhongzhi. CHSMST: A clustering algorithm based on hyper surface and minimum spanning tree, Soft Computing, v 15, n 6, p 1097-1103, June 2011(SCI\EI)
39. Qing He, Changying Du, Qun Wang, FuzhenZhuang, Zhongzhi Shi. A Parallel Incremental Extreme SVM Classifier, Neurocomputing，74 (2011) 2532–2540 (SCI\EI )
40. Qing He, Xiurong Zhao, Zhongzhi Shi.Minimal consistent subset for Hyper Surface Classification method. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE Volume: 22 Issue: 1 Pages: 95-108, FEB 2008.(SCI\EI)
41. Qing He, Xiurong Zhao, Zhongzhi Shi. Classification based on dimension transposition for high dimension data，International Journal Soft Computing 11(4),2007, pp: 329 - 334(SCI)
42. Xiang Ao; Ping Luo; Xudong Ma; Fuzhen Zhuang; Qing He*; Zhongzhi Shi; Zhiyong Shen. Combining Supervised and Unsupervised Models via Unconstrained Probabilistic Embedding, Information Sciences, 257 (2014) 101–114. (SCI impact factor (2012): 3.643)
43. Fuzhen Zhuang, Ping Luo, Changying Du, Qing He*, Zhongzhi Shi, Hui Xiong: Triplex transfer learning: exploiting both shared and distinct concepts for text classification, IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 7, 1191-1203, JULY 2014 (impact factor (2012): 3.236) (SCI\EI)
44. Shuo Han, Fuzhen Zhuang, Qing He*, Zhongzhi Shi, & Xiang Ao. Energy model for rumor propagation on social networks. Physica A: Statistical Mechanics and its Applications，394 (2014) 99–109.
45. Wenjuan Luo, Fuzhen Zhuang, Qing He*, Zhongzhi Shi Exploiting relevance, coverage, and novelty for query-focused multi-document summarization，Knowledge-Based Systems. Volume 46, July 2013, Pages 33–42 . (SCI\EI)
46. Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He*, Yuhong Xiong, Zhongzhi Shi and Hui Xiong. Mining Distinction and Commonality across Multiple Domains using Generative Model for Text Classification, IEEE Transactions on Knowledge and Data Engineering, VOL. 24, NO. 11, NOVEMBER 2012,2025-2039(SCI\EI )
47. Zhiping Shi, Xi Liu, Qingyong Li, Qing He*, Zhongzhi Shi, Extracting Discriminative Features for CBIR, MULTIMEDIA TOOLS AND APPLICATIONS， Volume 61, Number 2 (2012), 263-279(SCI)
48. Fuzhen Zhuang, George Karypis, Xia Ning, Qing He*, Zhongzhi Shi. Multi-view learning via probabilistic latent semantic analysis, Information Sciences,199 (2012) 20–30(SCI\EI)
49. Weizhong Zhao, Qing He*, Huifang Ma, Zhongzhi Shi. Effective Semi-supervised Document Clustering via Active Learning with Instance-level Constraints, Knowledge and Information Systems (2012) 30:569–587 (SCI\EI)
50. Tan, Qing; He, Qing*; Zhao, Weizhong; Shi, Zhongzhi; Lee, E.S. An improved FCMBP fuzzy clustering method based on evolutionary programming, Computers and Mathematics with Applications, v 61, n 4, p 1129-1144, February 2011(SCI\EI)
51. Guang-Quan Zhang, ZhengZheng, Jie Lu, Qing He*. An Algorithm for Solving Rule-Sets Based Bilevel Decision Problems, COMPUTATIONAL INTELLIGENCE Vol.27 No.2 pp.235-259, 2011 (SCI\EI)
52. Fuzhen Zhuang, Ping Luo, Hui Xiong, Yuhong Xiong, Qing He*, and Zhongzhi Shi. Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, December 2010 (vol. 22 no. 12) ,pp. 1664-1678 (SCI\EI)
53. Zheng, Z., Lu, J, Zhang G, He Q*, Rule sets based bilevel decision model and algorithm, Expert Systems with Applications, 2009. Vol. 36, No. 1, 18-26(SCI)
54. Shifei Ding, Yongping Zhang, Xiaofeng Lei, Xinzheng Xu, Xin Wang, Li Wang, Qing He*. Research on a principal components decision algorithm based on information entropy, Journal of Information Science, Vol. 35, No. 1, 120-127 (2009) (SCI)
55. Zhuang F Z, Luo P, He Q, et al. Inductive transfer learning for unlabeled target-domain via hybrid regularization. Chinese Sci Bull, 2009, 54: 2470―2478 (SCI)
56. Zhiping Shi, Qing He*, Zhongzhi Shi. An Index and Retrieval Framework Integrating Perceptive Features and Semantics for Multimedia Database. Multimedia Tools and Application (2009) 42:207–231 Springer (SCI)
57. Zheng, Z., Lu, J, Zhang G, He Q*, Rule sets based bilevel decision model and algorithm, Expert Systems with Applications, 2009. Vol. 36, No. 1, 18-26(SCI)
58. Ping Luo, Guoxing Zhan, Qing He*, Zhongzhi Shi, and Kevin Lu, On Defining Partition Entropy by Inequalities. IEEE TRANSACTIONS ON INFORMATION THEORY, v53, n 9, SEPTEMBER 2007, p 3233-3239.（SCI）
59. Ping Luo; Lu, Kevin; Shi, Zhongzhi; He, Qing*. Distributed data mining in grid computing environments. Future Generation Computer Systems, v 23, n 1, Jan 1, 2007, p 84-91(SCI\EI)
60. Zhongzhi Shi; Huang, Youping; He, Qing*; Xu, Lida; Liu, Shaohui; Qin, Liangxi; Jia, Ziyan; Li, Jiayou; Huang, Huijing; Zhao, Lei. MSMiner-a developing platform for OLAP. Decision Support Systems, v 42, n 4, January, 2007, Decision Support Systems in Emerging Economies, pp. 2016-2028(SCI)
61. Luo, Ping; Lu, Kevin; Shi, Zhongzhi; He, Qing*. Distributed data mining in grid computing environments, Future Generation Computer Systems, v 23, n 1, Jan 1, 2007, p 84-91
62. Luo, Ping; Lu, Kevin; Huang, Rui; He, Qing*; Shi, Zhongzhi. A heterogeneous computing system for data mining workflows in multi-agent environments, Expert Systems, v 23, n 5, November, 2006, p 258-271(SCI\EI)
63. Qing He, Zhongzhi Shi,Li-an Ren, E.S. Lee. A Novel Classification Based on Hypersurface. International Journal of Mathematical and Computer Modeling 38(2003),395-407 (SCI)
64. Qing He, Hongxing Li, Zhongzhi Shi, E.S.Lee. On Fuzzy Clustering Method Based on Perturbation. Computers and Mathematics with Applications, v 46, n 5-6, September, 2003, p 929-946 (SCI\EI)
65. Qing He, Hongxing Li, C.L.P. Chen, E.S. Lee. Extension Principles and Fuzzy Set Categories. International Journal of Computers and Mathematics with Applications 2000, 39: 45-53(SCI)