INVITED TALKS

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Speaker:   Xiaosong Zhang , The Yangtze river scholar, University of Electronic Science and Technology of China,China

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Multi-Granularity Cognitive Computing for Network Security

Speaker:   Guoyin Wang , The National Science Fund for Distinguished Young Scholars, The Yangtze river scholar,Chongqing University of Posts and Telecommunications, China

Abstract

Most intelligent computing models are inspired by various human/natural/social intelligence mechanisms. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind’s capabilities. Inspired by human’s granularity thinking, problem solving mechanism and the cognition law of ‘‘global precedence’’, a new cognitive computing model, multi-granularity cognitive computing, is proposed in 2017. It provides a granular cognitive computing framework for efficient knowledge discovery from big data. High efficiency and timeliness models and algorithms are required to ensure the smooth execution of the security tasks in huge scale network space. It should also be consistent with human’s understanding of the network space. Multi-granularity cognitive computing could be used to develop such efficient and robust models and algorithms.

To address the above problems, we have studied two issues. The first is to degrade the effects of large scale data and noise data in datasets. Granular ball based classifying, clustering and sampling methods are designed, which can deal with large scale datasets efficiently. In order to avoid the influence of noises in collected datasets, such as fake accounts and machine netizens, a random space granularity based framework is proposed to check label noises. The second is to study the technology of social network alignment. An improved embedding model via pseudo anchors based on granular cognitive computing is developed to provide effective support for social network alignment. On the perspective of fine granularity, we add virtual alignment users as pseudo anchors in the two social networks to be aligned. Furthermore, a meta-learning based fine-tuning strategy is designed to enlarge the scale of the two network spaces, and generate sparse representation vectors for users. Thus, the mismatch and inaccuracy ratios of the alignment model caused by the density of network spaces could be reduced. These models and algorithms have been applied in many practical network security application tasks successfully.

Short Bio:

Guoyin Wang received the B.E. degree in computer software, the M.S. degree in computer software, and the Ph.D. degree in computer organization and architecture from Xian Jiaotong University, China, in 1992, 1994, and 1996, respectively. He worked with the University of North Texas, USA, and the University of Regina, Canada, as a Visiting Scholar from 1998 to 1999. Since 1996, he has been working with the Chongqing University of Posts and Telecommunications, Chongqing, China, where he is currently a Professor, a Vice-President of the University, and the Director of the Chongqing Key Laboratory of Computational Intelligence. He was the director of the Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, China, 2011-2017. His research interests include data mining, machine learning, rough sets, granular computing, and cognitive computing. He is the author of over 20 books, the editor of dozens of proceedings of international and national conferences, and has more than 300 reviewed research publications. He was the President of International Rough Set Society (IRSS, 2014-2017). He is currently a Vice-President of the Chinese Association for Artificial Intelligence (CAAI, 2014-), a Council Member of the China Computer Federation (CCF, 2008-), and the President of Chongqing Association for Artificial Intelligence (CQAAI, 2018-).

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Speaker:  Xingquan (Hill) Zhu, Florida Atlantic University, USA

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Speaker:  Toshiyuki Amagasa,University of Tsukuba, Japan

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