7TH ICMCT | ICMCT.org | ICMCT History

Prof. Dr. Guoqiang Zhong, Ocean University of China, China

Guoqiang Zhong received his B.S. degree in Mathematics from Hebei Normal University, Shijiazhuang, China, his M.S. degree in Operations Research and Cybernetics from Beijing University of Technology (BJUT), Beijing, China, and his Ph.D. degree in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China, in 2004, 2007 and 2011, respectively. Between October 2011 and July 2013, he was a Postdoctoral Fellow with the Synchromedia Laboratory for Multimedia Communication in Telepresence, University of Quebec, Montreal, Canada. Between March 2014 and December 2020, he was an associate professor at Department of Computer Science and Technology, Ocean University of China, Qingdao, China. Since January 2021,he has been a full professor at Department of Computer Science and Technology (from April, 2021, Colleage of Computer Science and Technology), Ocean University of China. He has published 4 books, 4 book chapters and more than 90 technical papers in the areas of artificial intelligence, pattern recognition, machine learning and computer vision. His research interests include pattern recognition, machine learning and computer vision. He has served as Chair/PC member/reviewer for many international conferences and top journals, such as IEEE TNNLS, IEEE TKDE, IEEE TCSVT, Pattern Recognition, Knowledge-Based Systems, Neurocomputing, ACM TKDD, AAAI, AISTATS, ICPR, IJCNN, ICONIP and ICDAR. He has been awarded outstanding reviewer by several journals, such as Pattern Recognition, Knowledge-Based Systems, Neurocomputing and Cognitive Systems Research. He has won the Best Paper Award of BICS2019 and the APNNS Young Researcher Award. He is senior member of CCF, CAAI and CSIG, member of ACM, IEEE, IAPR and APNNS, professional committee member of CAAI-PR, CAA-PRMI and CSIG-DIAR, and trustee of Shandong Association of Artificial Intelligence.
仲国强于2004年、2007年和2011年在河北师范大学获得理学学士学位,在北京工业大学获得理学硕士学位,在中国科学院自动化研究所获得工学博士学位,2011年10月至2013年7月,他在加拿大魁北克大学Synchromedia实验室从事博士后研究。2014年3月至2020年12月,他任中国海洋大学计算机科学与技术系副教授。自2021年1月起,他任中国海洋大学计算机科学与技术系(2021年4月起,中国海洋大学计算机科学与技术学院)教授。他在人工智能、模式识别、机器学习和计算机视觉领域出版了4本专著、发表4篇专著章节和90多篇学术论文。他的研究兴趣包括模式识别、机器学习和计算机视觉。他曾担任许多国际会议和顶级期刊的主席/程序委员会委员/审稿人,如IEEE TNNLS、IEEE TKDE、IEEE TCSVT、Pattern Recognition、Knowledge-Based Systems、Neurocomputing、ACM TKDD、AAAI、AISTATS、ICPR、IJCNN、ICONIP和ICDAR。他曾被Pattern Recognition、Knowledge-Based Systems、Neurocomputing 和 Cognitive Systems Research等多家期刊评为优秀审稿人。他曾荣获BICS2019最佳论文奖和APNNS青年研究者奖。他是CCF、CAAI和CSIG的高级会员,ACM、IEEE、IAPR和APNNS的会员,CAAI-PR、CAA-PRMI和CSIG-DIAR专业委员会委员和山东省人工智能学会理事。

Speech Title: Automatic Design of Deep Neural Networks.
Deep neural networks (DNNs) have been widely used in many applications, such as pattern recognition and computer vision. However, design of DNNs needs expertise to deal with lots of hyperparameters and select a proper structure from many possible configurations. Hence, the architecture design of DNNs is transferring from hand-crafted to automatic methods. In this talk, I will present some novel automatic design methods of DNNs, including DNA Computing Inspired Deep Networks Design, Automatic Design of Deep Networks with Neural Blocks, AutoML for DenseNet Compression, Differentiable Light-Weight Architecture Search, and Generative Neural Architecture Search. These methods comprehensively present the state-of-the-art of the neural architecture search area.


Assoc. Prof. Xujian Zhao, Southwest University of Science and Technology, China

Dr. Zhao is an associate professor in the School of Computer Science and Technology, Southwest University of Science and Technology, China. He received his Ph.D. degree in computer science from the University of Science and Technology of China (USTC) in 2012. And he was a visiting scientist (guest researcher) in 2011 at the Spoken Language Systems Laboratory, Saarland University, Germany. His main research interests include Statistical Natural Language Processing, Machine Learning, Information Extraction (IE), and Web Search. In recent years, he has published more than 70 papers in journals and conferences and compiled two academic monographs. He serves on several reviewer boards for several international conferences and journals. Dr. Zhao has served as the Chair of the Text Analysis Forum on WAIM/APWEB'19, PC member of WAIM/APWEB’13, and WAIM/APWEB’19. Dr. Zhao is a member of IEEE and ACM, a senior member of the China Computer Society (CCF), and a committee member of the CCF Database Society.
赵旭剑,男,1984.5出生,博士,副教授,硕导,德国萨尔大学客座研究员,IEEE、ACM会员、中国计算机学会高级会员,数据库专委会委员,CCF YOCSEF 成都学术委员会委员。2012年获中国科学技术大学计算机应用技术博士学位。近年来主持或参与了多项国家863项目、国家自然科学基金项目、教育部人文社科基金、德国德意志学术交流中心(DAAD)科研项目、四川省教育厅科研项目等纵向课题以及微软联合项目、华为创新研究计划等企业合作项目。近几年在国内外期刊和会议上发表论文70余篇,参与学术专著编著两部,国家发明专利一项。主要研究包括大数据分析、机器学习、数据挖掘、自然语言处理等方向。

Title: A Survey of Web-Oriented Storyline Mining
The complex Web information makes it difficult for people to quickly and accurately obtain the storyline of news events. Therefore, “storyline mining” has become a valid research issue in recent years, with the purpose to extract the evolutionary stages of events and further explore the evolution model of events by analyzing the correlation between news events and subsequent related events. Storyline mining can be applied to many applications, such as web news retrieval, text summarization, and public opinion monitoring. This proposal first outlines the definition, process, and main tasks of storyline mining. Next, from the aspects of storyline generation and event evolution analysis, the main signs of progress of the current studies on this task are introduced in detail. Finally, several future research directions and technical frameworks for storyline mining are discussed in the proposal.


Assoc. Prof. Gaige Wang, Ocean University of China, China

Gai-Ge Wang is an associate professor at Ocean University of China, China. His entire publications have been cited over 10000 times (Google Scholar). The latest Google h-index and i10-index are 58 and 109, respectively. Fifteen and sixty-six papers are selected as Highly Cited Paper by Web of Science, and Scopus, respectively. He was selected as one of “2021 Highly Cited Researchers” by Clarivate. He was selected as one of “2021 and 2020 Highly Cited Chinese Researchers” in computer science and technology by Elsevier. He was selected as one of “MDPI 2021 Most Influential Author Award” in Computer Science and Mathematics by MDPI. He was selected as World’s Top 2% Scientists 2020. One of his papers was selected as “100 Most Influential International Academic Papers in China”, one of his papers ranks 1 in the selection of the latest high-impact publications in computer science by Chinese researchers from Springer Nature in 2019. He is senior member of SAISE, SCIEI, a member of IEEE, IEEE CIS, and ISMOST. He served as Editors-in-Chief of International Journal of Artificial Intelligence and Soft Computing, Early Career Advisory Board Member of IEEE/CAA Journal of Automatica Sinica (SCI), Editorial Advisory Board Member of Communications in Computational and Applied Mathematics (CCAM), Associate Editor of IJCISIM, an Editorial Board Member of Journal of Computational Design and Engineering, Mathematics, IJBIC, Karbala Int J of Modern Science, and Journal of AIS. He served as Guest Editor for many journals including Mathematics, IJBIC, FGCS, Memetic Computing and Operational Research. His research interests are swarm intelligence, evolutionary computation, and big data optimization.
王改革,中国海洋大学,博士,副教授,主要致力于进化计算、群体智能和大数据优化等方面研究。发表学术论文130余篇,其中,SCI检索82篇,1区Top 20篇,2区25篇。主持国家自然科学基金等重要科研项目4项。获教育部高等学校科学研究优秀成果奖二等奖1项,江苏省自然科学奖二等奖1项。论文总引用10000多次,他引9000多次。22篇论文引用达100次以上,15篇论文被ESI选为前1%高引论文,4篇论文被ESI选为前0.1%热点论文,66篇论文分别被Scopus列为高被引论文。入选2021年科睿唯安“全球高被引学者”和爱思唯尔(Elsevier) 2020“中国高被引学者”(Highly Cited Chinese Researchers)计算机科学与技术榜单;入选MDPI 2021高影响力作者奖(计算机和数学)和全球前2%顶尖科学家榜单(位居国内计算机科学与技术第二)。1篇论文入选2019年“中国百篇最具影响国际学术论文”;位列2019年度Springer Nature中国学者高影响力论文-计算机科学领域榜首;1篇论文入选科技部“精品期刊顶尖论文平台--领跑者5000”数据平台。h-index和i10-index分别为55和106。担任International Journal of Artificial Intelligence and Soft Computing主编,Journal of Computational Design and Engineering、IEEE/CAA Journal of Automatica Sinica、Mathematics和IJBIC等SCI期刊的副主编或编委。

Speech Title: Improving Metaheuristic Algorithms Using Information Feedback Model 
Abstract: In most metaheuristic algorithms, the individual update process does not (fully) utilize the individual information generated in previous iterations. If this useful information can be fully utilized in subsequent optimization processes, the quality of the feasible solutions produced by the algorithm will be greatly improved. Based on this, a method of reusing available information of previous individuals to guide subsequent search is proposed. In this method, the previous useful information is fed back to the individual update process, and then six information feedback models are proposed. In these models, the individuals of previous iterations are selected in a fixed or random way, and then the useful information of the selected individuals is applied to the individual update process. Then, based on the individuals generated and selected by the basic algorithm, a simple fitness weighting method is used to generate new individuals. Six different information feedback models are applied to 10 metaheuristic algorithms to generate new algorithms and verify the performance of the proposed information feedback model. Experiments show that these new algorithms are significantly better than the basic algorithms on 14 standard test functions and 10 CEC 2011 real world problems, and further prove the effectiveness of the proposed information feedback model. At the same time, the model is applied to solve many-objective optimization methods (MOEA/D and NSGA-III), and good results are achieved.

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