Keynote Speaker 1

Professor Anthony T.S. Ho (University of Surrey, UK)

Professor Anthony T.S. Ho holds a Personal Chair in Multimedia Security and served as Head of Department of Computer Science, University of Surrey from 2010 to 2015. He is also a Tianjin Distinguished Professor, Guest Professor of Tianjin University of Science and Technology and of Wuhan University of Technology, China, as well as Visiting Professor of University of Malaya, Malaysia. He was the recipient of the prestigious Institution of Engineering and Technology (IET) Innovation in Engineering Award under the Security category for his research and commercialization work on digital watermarking in 2006.

Professor Ho obtained his MSc in Applied Optics from Imperial College London in 1980 and his PhD in Digital Image Processing from King's College London, University of London in 1983. After graduation, he worked in technical management positions in industry for 11 years in the UK and Canada. From 1994 to 2005, He was a Senior Lecturer and then Associate Professor at Nanyang Technological University (NTU), Singapore. He has published more than 160 articles in international journals and conference proceedings as well as 8 International patents granted related to watermarking and steganography.

Professor Ho is Founding Editor-in-Chief of the International Journal of Information Security and Applications (JISA), Area Editor for Signal Processing: Image Communication, and Associate Editor for Array Open Access Journal, all published by Elsevier. He serves as Associate Editor for IEEE Transactions on Information Forensics and Security (TIFS) and Associate Editor for IEEE Signal Processing Letters (SPL) (2014-2016), as well as Associate Editor for EURASIP Journal of Image and Video Processing published by Springer. He is a Fellow of Institution of Engineering and Technology (FIET), Fellow of Institute of Physics (FInstP) and Fellow of British Computer Society (FBCS).

Title: “Anomaly Detection and Identification of Natural Data Using Benford’s Law”

This talk will present an overview of the theory and applications of Benford’s law for anomaly detection in natural data. Some examples will be highlighted including the detection of glare effect in images and classification of biometric images for privacy protection, as well as security attacks related to network traffic data. Recent research based on this law has further shown that consistent anomaly patterns could be achieved for different network attacks, leading to the potential identification/pattern recognition of various types of attacks. Moreover, Benford’s law has also been successfully applied for the detection of Alzheimer’s Disease based on Electroencephalogram (EEG) data and this will be highlighted in the presentation.

Keynote Speaker 2

Professor Guoyin Wang (Chongqing University of Posts and Telecommunications, China)

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 Xi’an Jiaotong University, Xi’an, China, in 1992, 1994, and 1996, respectively. He worked at the University of North Texas, USA, and the University of Regina, Canada, as a Visiting Scholar during 1998–1999. Since 1996, he has been working at the Chongqing University of Posts and Telecommunications, Chongqing, China, where he is currently a Professor and a Ph.D. supervisor, the Director of the Chongqing Key Laboratory of Computational Intelligence, and the Dean of the Graduate School. His research interests include data mining, machine learning, rough set, granular computing, cognitive computing, etc. Dr. Wang was the President of the International Rough Set Society (IRSS) 2014-2017. He is a Vice-President of the Chinese Association for Artificial Intelligence (CAAI), and a council member of the China Computer Federation (CCF).

Title: Data-driven Granular Cognitive Computing

Inspired by human’s granularity thinking, problem solving mechanism and the cognition law of ‘‘global precedence’’, a cognitive computing model, data-driven granular cognitive computing (DGCC), is proposed in this talk. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism. The HD3 characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data and interpretation of deep learning.