Speaker

Qiang Yang

Keynote Title: Transfer Learning for Sentiment Analysis Abstract: Sentiment analysis is an important task in natural language processing and data mining. Aiming to identify the overall sentiment polarity of a natural language document, it is essential in understanding user opinions in social networks. Traditional machine-learning approaches and black-box style deep-learning models both have major shortcomings, especially when dealing with cross-domain cold start problems. In this talk, we introduce a new end-to-end adversarial memory network approach for cross-domain sentiment classification. Our approach can automatically capture connections between domains using an attention mechanism without the manual selection. Our framework consists of two parameter-shared memory networks, where one network is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features can minimize the classification error and maximise domain classification error. In addition, the attention mechanism is used to make the features found to be interpretable. We show the background, the proposed approach and experimental results in this talk. The work is jointly done with PhD student Zheng Li and Dr. Yu Zhang of HKUST. Brief Biography: Qiang Yang is a chair professor of Computer Science and Engineering (CSE) Department at Hong Kong University of Science and Technology (HKUST). He is also the director of HKUST’s Big Data Institute from 2015 to 2017. Between 2012 and 2014, he was a founding director of the Huawei Noah's Ark Research Lab. His research interests are artificial intelligence including machine learning and data mining. He is a fellow of ACM, AAAI, IEEE, IAPR and AAAS. He received his PhD from Computer Science Department of the University of Maryland, College Park in 1989. He had been an assistant/associate professor at the University of Waterloo between 1989 and 1995, and a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001. He has been an invited speaker at IJCAI 2009, ACL 2009, ACML 2009 and ADMA 2008 and 2012, SDM 2012, WSDM 2013, etc. He was elected as a vice chair of ACM SIGART/SIGAI between 2010 and 2014. He was the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST) and the founding EiC of IEEE Transactions on Big Data. He is on the editorial board of IEEE Intelligent Systems and several other international journals（IEEE, TKDE (2005-2009), AI Magazine, etc.). He has served as a PC co-chair and general co-chair of several international conferences, including ACM KDD 2010 and 2012, ACM RecSys 2013, ACM IUI 2010, ICCBR 2001, etc. He was the Program Committee Chair for IJCAI 2015 and was elected to be an AAAI Executive Council member in 2016. In 2017, he receives the SIGKDD Distinguished Service Award. He currently serves as the president of IJCAI’s board of trustees.

Speaker

Anthony Hunter

Keynote Title: A Brief Introduction to Probabilistic Argumentation Abstract: Argumentation can be modelled at an abstract level using an argument graph (i.e. a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another). Since argumentation involves uncertainty, it is potentially valuable to consider how this can be quantified in argument graphs. Two key approaches to capturing uncertainty in argumentation are the constellations approach and the epistemic approach. The former can be used to represent uncertainty over the topology of the argument graph, and the latter can be used to represent belief in arguments. Theoretical foundations, and studies with participants, are being developed for both approaches. Brief Biography: Anthony Hunter is a Professor of Artificial Intelligence, and Head of the Intelligent Systems Group, in the Department of Computer Science, University College London. His research interests focus on computational models of argument. This is a field aimed to formalizing and harnessing aspects of the human ability to handle incomplete and inconsistent information through the use of argumentation. He has authored numerous papers on aspects of monological and dialogical argumentation, including deductive argumentation and probabilistic argumentation, and with Philippe Besnard, he co-authored the book “Elements of Argumentation” which was published by MIT Press in 2008. He has had funding for investigating the use of argumentation for decision making and sense making, and currently he is principal investigator on a couple of UK government-funded projects on computational persuasion. He is an associate editor of the Artificial Intelligence journal, an area chair of the International Journal of Approximate Reasoning, and an editorial member of the Journal of Artificial Intelligence Research.

Speaker

Meikang Qiu

Keynote Title: Smart Energy-Aware Data Allocation for Heterogeneous Memory Abstract: Heterogeneous memory is an emerging concept that uses multiple kinds of memories to balance the contradictions between the performance and cost. Many prior researches have addressed the heterogeneous memory designs, which attracted both researchers and practitioners. However, the main challenge in memory design is that the moving costs between memories cannot be ignored, such that forming an effective data allocation plan is a critical part for reducing total costs. This paper focuses on this issue and proposes an adaptive energy-aware approach that is designed for efficiently generating adaptive data allocation plans in heterogeneous memories. Two crucial variables are considered the condition constraints, including time and energy consumptions. The proposed method is called Smart Data Allocation Model (SDAM), which is supported by our algorithm, Smart Switch Allocation Algorithm (SA2). The experimental results have depicted that our approach is superior to prior developed methods. Brief Biography: Meikang Qiu is a professor of Columbia University, USA. He is also the Chair of IEEE Smart Computing STC. Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, he is a faculty member at Columbia University. He is an IEEE Senior member and ACM Senior member. He is the Chair of IEEE Smart Computing Technical Committee. His research interests include Cyber Security, Big Data Analysis, Cloud Computing, Smarting Computing, Intelligent Data, Embedded systems, etc. A lot of novel results have been produced and most of them have already been reported to research community through high-quality journal and conference papers. He has published 4 books, 400 peer-reviewed journal and conference papers (including 200+ journal articles, 200+ conference papers, 70+ IEEE/ACM Transactions papers). His paper published in IEEE Transactions on Computers about privacy protection for smart phones has been selected as a Highly Cited Paper in 2017. His paper about embedded system security published in Journal of Computer and System Science (Elsevier) have been recognized as Highly Cited Papers in both 2016 and 2017. His paper about data allocation for hybrid memory has been published in IEEE Transactions on Computers has been selected as hot paper (1 in 1000 papers) in 2017. His paper on Tele-health system has won IEEE System Journal 2018 Best Paper Award. He also won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. He has won another 10+ Conference Best Paper Awards in recent years. Currently he is an associate editor of 10+ international journals, including IEEE Transactions on Computers and IEEE Transactions on Cloud Computing. He has served as leading guest editor for IEEE Transactions on Dependable and Secure Computing (TDSC), special issue on Social Network Security. He is the General Chair/Program Chair of a dozen of IEEE/ACM international conferences, such as IEEE TrustCom, IEEE BigDataSecurity, IEEE CSCloud, and IEEE HPCC. He has won Navy Summer Faculty Award in 2012 and Air Force Summer Faculty Award in 2009. His research is supported by US government such as NSF, NSA, Air Force, Navy and companies such as GE, Nokia, TCL, and Cavium.