KEYNOTE

Large Scale Metric Learning using Locality Sensitive Hashing

Tuesday June 14, 2016 keynote Speaker: Ramamohanarao Kotagiri Professor in the Department of Computer Science and Software Engineering, Melbourne.

Abstract:  Metric learning tries discover mapping of features such that objects belonging a particular class each other in the new space. However, the current methods of discovering such matric mappings are computationally in feasible when the data set is huge with large number of features. My talk will describe the state of the art algorithms for metric learning. will present our recent work on an efficient approach for discovering metric learning based mappings using Locality Sensitive Hashing (LSH). Our generic approach can accelerate state-of- the-art metric learning while achieving competitive classification accuracy, expanding feasibility by an order of magnitude. Our approach can accelerate Large Margin Nearest Neighbour (LMNN) to learn metrics on 1,000,000 samples in 3.6 minutes which is reduced from 5.8 hours.

Biography: Professor Ramamohanarao (Rao) Kotagiri received PhD from Monash University. He was awarded the Alexander von Humboldt Fellowship in 1983. He has been at the University Melbourne since 1980 and was appointed as a professor in computer science in 1989. Rao held several senior positions including Head of Computer Science and Software Engineering, Head of the School of Electrical Engineering and Computer Science at the University of Melbourne and Research Director for the Cooperative Research Centre for Intelligent Decision Systems. He served on the Editorial Boards of the Computer Journal Universal Computer Science, IEETKDE and VLDB (Very Large Data Bases) Journal. He was the program Co-Chair for VLDB, PAKDD, DASFAA and DOOD conferences. He is a steering committee member of IEEE ICDM, PAKDD and DASFAA. He received Distinguished Contribution Award by PAKDD for Data Mining; Distinguished Contribution Award in 2009 by the Computing Research and Education Association of Australasia; Distinguished Contribution Award by DASFAA for Database Research; Distinguished Service Award by IEEE ICDM for Data Mining. Rao is a Fellow of the Institute of Engineers Australia, a Fellow of Australian Academy Technological Sciences and Engineering and a Fellow of Australian Academy of Science.

Transparent-Computing based Intelligent Terminals and Their Applications.

Tuesday June 14, 2016 keynote Speaker: YaoXue Zhang President of the Central South University, China, Fellow of the Chinese Academy of Engineering, Professor in the Department of Computer Science and Technology at Tsinghua University, China.

Abstract: Recently, intelligent terminals, such as wearable devices and self-service terminals, have played an important role in daily life. This is an area with tremendous growth, primarily due to mobile use cases of terminals. However, current systems of intelligent terminals were designed for dedicated applications, not for their coming dominant use as mobile service. Such systems are prone to high power consumption, safety and cross-platform issues. Transparent computing is a promising technology that solves the urgent problems of intelligent terminals. In this talk, I will review the designs of current system and describes how transparent computing technology significantly improves the progress of intelligent terminals. I will share result of our research and our experience of deploying transparent-computing based intelligent terminals in user (and real) environments. I will also provide a wish list for "sensor+ network" — a large platform based on Transparent-Computing for connecting mass terminals and enhancing their applications.

Biography: Prof. Zhang Yaoxue received the B.S. degree from Northwest Institute of Telecommunication Engineering, China, and received the Ph.D. degree in computer networking from Tohoku University, Japan, in 1989. Currently he is a professor in the Department of Computer Science at Central South University, China, and also a professor in the Department of Computer Science and Technology at Tsinghua University, China. His current research interests include computer networking, operating systems, ubiquitous/pervasive computing, transparent computing, and active services. Because of his distinguished contributions, Prof. Zhang has won the National Award for Scientific and Technological Progress (2nd class) twice in 1998 and 2001, National Award for Technological Invention (2nd class) in 2004, National Award for Natural Science (1st class) in 2014, as well as 5 provincial or ministerial awards. He is a winner of the Prize of HLHL (Hong Kong) Foundation for Scientific and Technological Progress in 2005. Prof. Zhang is a fellow of the Chinese Academy of Engineering and the President of the Central South University, China.

From Big Data to Big Knowledge: Knowledge Engineering with Big Data

Wednesday June 15, 2016 keynote Speaker: Xindong Wu Professor of Computer Science at the University of Vermont (USA), Fellow of the IEEE and the AAAS, Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology (China).

Abstract: Big Data processing concerns large-volume, growing data sets with multiple, heterogeneous, autonomous sources, and explores complex and evolving relationships among data objects. This talk starts with a HACE theorem (http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6547630) that characterizes the features of the Big Data revolution, and presents BigKE, a big data knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demanddriven knowledge navigation. We discuss challenging issues and our ongoing research efforts with BigKE.

Biography: Xindong Wu is a Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology (China), and a Fellow of the IEEE and the AAAS. He holds a PhD in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, Big Data analytics, knowledge engineering, and Web systems. He is Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), and Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008, and has served as Program Committee Chair/Co-Chair for ICDM '03 (the 2003 IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ASONAM 2014 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining). Professor Wu is the 2004 ACM SIGKDD Service Award winner and the 2006 IEEE ICDM Outstanding Service Award winner. He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.

On Application-Aware Information Extraction for Big Data in Social Networks

Wednesday June 15, 2016 keynote Speaker: Ming-Syan Chen Distinguished Professor and Dean, College of EECS, National Taiwan Univ.

Abstract: Due to the paradigm shift to the Cloud computing, data has been accumulated at fast pace in various applications. Among others, the number of social network activities is increasing drastically. It has become very desirable to conduct various analyses for applications on social networks. However, as the scale of a social network has become prohibitively large, it is infeasible to scrutinize the data and extract the key essence from the entire social network. This issue becomes further complicated due to the heterogeneous nature of the data. As a result, a significant amount of research effort has been elaborated upon extracting the essential application-dependent information from a social network. In this talk, we shall examine some recent studies on data processing and information extraction for social networks. Explicitly, we shall explore the methods for three levels of information extraction in a social network, namely, parameter extraction, information extraction, and structure extraction, and interpret them from their respective objectives. We then comment on how to conduct application-aware information extraction for big data in social networks.

Biography: Ming-Syan Chen received the Ph.D. degrees in Computer, Information and Control Engineering from The University of Michigan, Ann Arbor, MI, USA. He is now the Dean of the College of Electrical Engineering and Computer Science and also a Distinguished Professor in EE Department at National Taiwan University. He was a research staff member at IBM Thomas J. Watson Research Center, NY, USA, the President/CEO of Institute for Information Industry (III), and the Director of Research Center of Information Technology Innovation (CITI) in the Academia Sinica. His research interests include databases, data mining, social networks, and IoT applications. He is a recipient of the National Chair Professorship and also the Academic Award of the Ministry of Education, the NSC (National Science Council) Distinguished Research Award, Y.Z. Hsu Science Chair Professor Award, Pan Wen Yuan Distinguished Research Award, Teco Award, Honorary Medal of Information, and K.-T. Li Research Breakthrough Award for his research work, and also the Outstanding Innovation Award from IBM Corporate for his contribution to a major database product. Dr. Chen is a Fellow of ACM and a Fellow of IEEE.