Prof. Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong
Title: Cross-Domain Big Data Fusion and Analytics
Abstract: Big data analytics using cross-domain multi-source datasets allow us to study the phenomena of our interest by fusing views from multiple angles, facilitating us to identify meaningful problems and discover new insights. However, we need methods and techniques to solve the challenges like heterogeneity, uncertainty and high dimensionality in analyzing cross-domain datasets. In this talk, I will describe a general framework of cross-domain big data analytics and share our work of fusing and analyzing datasets from multiple domains to uncover the underlying patterns, correlations and interactions. Example applications include human and urban dynamics like predicting traffic congestions, optimize demand dispatching in emerging on-demand services, and designing wireless networks.

Prof. Weijia Jia, University of Macau, Macau
Title: Smart City Research at The University of Macau
Abstract: We would like to introduce the research status on smart city at the University of Macau. Especially the newly set up of state key lab and their research activity.

Prof. Xiaohua Jia, City University of Hong Kong, Hong Kong
Title: Privacy-Preserving Service Matching Platforms on Public Clouds
Abstract: As smart phones and other mobile devices are popularly used nowadays, users can use mobile phone to subscribe services, as well as publish services they would like to offer. There are many platforms to facilitate users to publish or subscribe their services over the Internet. Most of such platforms are hosted on public clouds where security and privacy is a major concern of users. It’s essential to make the trading platforms secure and privacy-preserving for users. In this talk, we first present pMatch: a privacy-preserving task matching scheme for crowdsourcing systems. With pMatch, task requesters encrypt the information of tasks and publish the encrypted task to the platform, and workers encrypt their interests and submit them to the platform for task subscription. The platform can match the tasks to the most suitable workers over the encrypted data. Thus, no private information about either the tasks or the workers is leaked to the cloud server. Then, we present pRide: a privacy-preserving ride-hailing system. With pRide, both riders and drivers submit their encrypted location information to the platform, and the platform is able to match a rider with its closest driver without learning any location information of either the rider or the drivers.

Prof. Qun Jin, Waseda University, Japan
Title: Personal analytics and individual modeling for data-driven and AI-enhanced healthcare
Abstract: In this talk, after briefly introducing the basic concept of data-driven and AI-enhanced healthcare, our vision and work on personal analytics and individual modeling for smart health to enhance quality of life (QoL) and promote well-being for all of the people will be described and explained. Furthermore, quality control and sustainable use of health data as well as opportunities and issues of data-driven smart health will be addressed and discussed.

Prof. Qing Li, City University of Hong Kong, Hong Kong
Title: Event Management and Multi-dimensional Analysis through EC Mechanism
Abstract: The last 3 decades have witnessed the big changes of data types, scales, and links with neighboring areas, from simple data with closed-world assumption to more complex objects with semi-closed/open assumption, from MB/GB/PB scale to PB/TB/EB/ZB scale, and from loose coupling to tight coupling with areas like Programming, Cloud Computing, IoT, and AI (machine learning in particular). In this talk, I will discuss several aspects of data management from a historical perspective, and through a joint collaboration we initiated, elaborate on the recent and complex types of data like (multi-modal) events for management. In particular, I will start with overviewing techniques of discovering events from multi-modal big data, and elaborate on building an event cube (EC) model to support event queries and analysis. Based on the essential event elements of 5W1H, the discovered events can be organized w.r.t. the dimensions and operated at various levels of granularity through the EC model. In addition, this model greatly facilitates analyzing and mining hidden/inherent relationships among the events, thereby enabling the system to answer the challenging questions of "how" and "why", thereby facilitating the analysis and mining of hidden/inherent relationships among the events effectively.

Prof. Junzhou Luo, Southeast University, China
Title: Industrial Transparent Computing: Architecture and Key Technologies
Abstract: The industrial internet is a highly cooperative global network, which connects machines and humans together for smart manufacturing. While smart manufacturing heavily relies on intelligent, speedy and secure control, novel computing paradigms that support real-time decision with secure execution have garnered much attention from the industrial internet research community. This talk introduces industrial transparent computing (ITC), a promising paradigm for the industrial internet. With ITC, industrial control programs are stored by ITC servers but executed by industrial terminals, hence it can be viewed as a special application scenario of transparent computing. ITC offers a high level of security and reduces complexity and cost of terminals. Moreover, ITC smoothly coordinates the computation and communications between ITC servers and industrial terminals through intelligent optimization, in order to achieve real-time decision and control. In this talk, I will introduce ITC's architecture, elaborate the opportunities and challenges of transparent computing in the industrial environments, and at last, identify future research directions.

Prof. Geyong Min, University of Exeter, UK
Title: Network Big Data Analysis for Future Intelligent Internet
Abstract: Research and development of Next-Generation Internet (NGI) have become a global endeavour. With an overwhelming amount of data pouring into the Internet, network domains are embracing an unprecedented wave of traffic flows and are stepping into the era of network big data. To achieve high performance and high availability of NGI, our vision is to conduct efficient data analysis in order to dig valuable insights and knowledge hidden in network big data for improving the design, operation, and management of NGI. We will present the innovative big data processing technologies, real-time incremental data analysis tools, and cost-effective distributed platform we have recently developed to support better decision-making for network design, anomaly detection, fault localization, resource management and optimization.

Prof. Chengzhong Xu, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences, China
Title: Intelligent Scheduling and QoS-aware Resource Management based on Machine Learning
Abstract: Scheduling and resource management is a key to success of cloud datacenter. Today’s cloud DC is characteristic of deep heterogeneity in nodal architecture and group dynamics in workloads. Applications tend to be deployed in a mixed way between online and offline jobs. Traditional scheduling and resource management become hard to meet the requirements for efficiency, job completion time, and fairness in a fine-grained manner. Machine learning is by nature able to deal with uncertainty. The non-deterministic setting of Cloud DCs renders ML plausible. The vast log data accumulated in the management of cloud DCs paves a way for deep learning approaches. This talk will discuss the opportunities and challenges of ML for the management of cloud DCs. Our past experience will be presented as well.

Biography. Dr. Cao is currently a Chair Professor of Department of Computing at The Hong Kong Polytechnic University, Hong Kong. He is also the director of the Internet and Mobile Computing Lab in the department and the director of University’s Research Facility in Big Data Analytics.
Dr. Cao’s research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over 500 papers in major international journals and conference proceedings. He received Best Paper Awards from conferences including DSAA’2017, IEEE SMARTCOMP 2016, ISPA 2013, IEEE WCNC 2011, etc.
Dr. Cao served the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society 2012-2014, a member of IEEE Fellows Evaluation Committee of the Computer Society and the Reliability Society, a member of IEEE Computer Society Education Awards Selection Committee, a member of IEEE Communications Society Awards Committee, and a member of Steering Committee of IEEE Transactions on Mobile Computing. Dr. Cao has served as chairs and members of organizing and technical committees of many international conferences, and as associate editor and member of the editorial boards of many international journals, including IEEE TPDS, IEEE TCC, IEEE TC, IEEE Network, ACM TOSN, Elsevier Pervasive and Mobile Computing Journal (PMCJ), and Springer Peer-to-Peer Networking and Applications.
Dr. Cao is a fellow of IEEE and ACM distinguished member. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.

Biography. Jie Li is a Chair Professor of Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China where he is a chair professor. His current research interests are in big data, cloud computing, mobile distributed computing and networking, network security, OS, modeling and performance evaluation of information systems. He was a full professor in Department of Computer Science, University of Tsukuba, Japan. He was a visiting Professor in Yale University, USA, Inria Sophia Antipolis and Inria Grenoble-Rhone-Aples, France. He is the co-chair of IEEE Big Data Technical Community and the founding Chair of IEEE ComSoc Technical Committee on Big Data and the Co-Chair of IEEE Big Data Community. He serves as an associated editor for many IEEE journals and transactions. He has also served on the program committees for several international conferences.
He received the B.E. degree in computer science from Zhejiang University, Hangzhou, China, the M.E. degree in electronic engineering and communication systems from China Academy of Posts and Telecommunications, Beijing, China. He received the Dr. Eng. degree from the University of Electro-Communications, Tokyo, Japan.

Biography. Weijia Jia is currently a Chair Professor at University of Macau while he is taking no-pay leave from the position of Zhiyuan Chair Prof from Shanghai Jiaotong University, China (he received 2013 China 1000 Talent Award). He received BSc/MSc from Center South University, China in 82/84 and Master of Applied Sci./PhD from Polytechnic Faculty of Mons, Belgium in 92/93, respectively, all in computer science. For 93-95, he joined German National Research Center for Information Science (GMD) in Bonn (St. Augustine) as research fellow. From 95-13, he worked in City University of Hong Kong as a full professor in Computer Science Dept. His research interests include smart city; next generation IoT, knowledge graph constructions; multicast and anycast QoS routing protocols, wireless sensor networks and distributed systems. In these fields, he has over 400 publications in the prestige international journals/conferences and research books and book chapters (H-inedx 46 as at 2018). His past research contributions can be summaried from the aspects of vertex cover and efficient anycast for optimal placement and routing of severs/sensors in many applications of mobile/sensor/wireless networks and the Internet. He received Best Product Awards from the Internatonal Science&Tech. Expos (Shenzhen) in 2011/2012 and 1st- Prize of Scientific Research Awards from Ministry of Education of PR China in 2017 (list 2). He has served as area editor for various prestige international journals, chair and PC member/keynote speaker for many prestige international conferences. He is the Senior Member of IEEE and the Member of ACM.

Biography. Xiaohua Jia received his BSc (1984) and MEng (1987) from University of Science and Technology of China, and DSc (1991) in Information Science from University of Tokyo. He is currently Chair Professor with Dept of Computer Science at City University of Hong Kong. His research interests include cloud computing and distributed systems, data security and privacy, computer networks and mobile computing. Prof. Jia is an editor of IEEE Internet of Things, IEEE Trans. on Parallel and Distributed Systems (2006-2009), Wireless Networks, Journal of World Wide Web, Journal of Combinatorial Optimization, etc. He is the General Chair of ACM MobiHoc 2008, TPC Co-Chair of IEEE GlobeCom 2010 – Ad Hoc and Sensor Networking Symp, Area-Chair of IEEE INFOCOM 2010, 2015-2017. He is Fellow of IEEE.

Biography. Qun Jin is a professor at the Networked Information Systems Laboratory, Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. Dr. Jin has been extensively engaged in research works in the fields of computer science, information systems, and social and human informatics. He seeks to exploit the rich interdependence between theory and practice in his work with interdisciplinary and integrated approaches. His recent research interests cover human-centric ubiquitous computing, behavior and cognitive informatics, big data, data quality assurance and sustainable use, personal analytics and individual modeling, intelligence computing, blockchain, cyber security, cyber-enabled applications in healthcare, and computing for well-being. Dr. Jin is a senior member of Association of Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), and Information Processing Society of Japan (IPSJ).

Biography. Qing Li is a Professor at the Department of Computer Science, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong, where he joined as a faculty member since Sept 1998. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media and Web services, and e-learning systems. He has authored/co-authored over 300 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data and Knowledge Engineering (DKE), World Wide Web (WWW), and Journal of Web Engineering, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA, ACM RecSys, IEEE U-MEDIA, ER, and ICWL. Prof. Li is a Fellow of IET (UK), a senior member of IEEE (US) and a distinguished member of CCF (China).

Biography. Dr. Junzhou Luo received his B.S. degree in applied mathematics from Southeast University in 1982, and then got his M.S. and Ph.D. degree in computer networks from Southeast University in 1992 and in 2000 respectively. From 1982 he has been a faculty member at the School of Computer Science and Engineering, Southeast University. His research interests include network architecture, protocol engineering, network security, cloud computing and big data. In the past 30 years, he finished 35 research projects supported by Natural Science Foundation of China and the other China national or Jiangsu Provincial science and technology programs, and published over 450 journal and conference papers on computer networks. He has participated in the AMS physics experiment 14 years led by Professor Samuel C. C. Ting, the Nobel Prize Laureate in physics, and has set up AMS Science Operation Center at Southeast University for the AMS data processing. Now he is a professor and the dean of the School of Computer Science and Engineering, Southeast University. He is IEEE member and ACM member, and he is Co-Chair of IEEE SMC Technical Committee on Computer Supported Cooperative Work in Design and the Chair of ACM SIGCOMM China.

Biography. Professor Geyong Min is a Chair in High Performance Computing and Networking and the academic lead of Computer Science in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. His recent research has been supported by European Horizon-2020, FP6/FP7, UK EPSRC, Royal Society, Royal Academy of Engineering, and industrial partners including British Telecom, IBM, Huawei Technologies, INMARSAT, Motorola, and InforSense Ltd. His research interests include Future Internet, Wireless Networks, Mobile and Ubiquitous Computing, Cloud Computing, High Performance Computing, and Big Data. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and at reputable international conferences, such as SIGCOMM-IMC, ICDCS, IPDPS, GLOBECOM, and ICC. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Computers, and IEEE Transactions on Cloud Computing. He served as the General Chair/Program Chair of a number of international conferences in the area of Information and Communications Technologies.

Biography. Chengzhong Xu, IEEE Fellow, is the Director and Chief Scientist of the Institute of Advanced Computing and Digital Engineering, Shenzhen Institutes of Advanced Technology of Chinese Academy of Sciences. He is also the Director of Cloud Security Engineering Center of Guangdong Province. He was in the faculty of Wayne State University. His research interest is mainly in distributed and parallel systems, cloud computing and big data applications, and data-driven intelligence, with an emphasis on resource management for performance, availability, reliability, energy efficiency, and security. He published more than 250 papers in journals and conferences in these areas and received more than 8500 citations with H-index of 46. He was the author of book “Scalable and Secure Internet Services and Architecture” (Chapman & Hall/CRC Press, 2005) and the leading co-author of book “Load Balancing in Parallel Computers: Theory and Practice” (Kluwer Academic/Springer, 1997). He received the Best Paper Nominee Award of HPCA’2012, HPDC’2013, Cluster’2016, and ICPP’2015. He served on a number of journal editorial boards, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, Journal of Parallel and Distributed Computing, and China Science. He is the Chair of IEEE Technical Committee on Distributed Processing. He was a recipient of Career Development Chair Award, and the President’s Award for Excellence in Teaching of WSU. He was also a recipient of the “National Thousands Talent Program” (千人计划)　“Outstanding Young Oversea Chinese Scholar”(海外杰青) award. He received his B.S. and M.S. degrees from Nanjing University in 1986 and 1989, respectively, and his Ph.D. degree from the University of Hong Kong in 1993, all in Computer Science. For more information, visit http://www.ece.eng.wayne.edu/~czxu.

Title: Cyber-Physical-Social Sensing for the Future Smart World

15:50 p.m. to 18:30 p.m., October 10, 2018.

Abstract. Sensing is the ability of any living beings to collect data and perceive the physical world. It is the foundation of knowledge acquisition and formation of intelligence. With the advancement of information and communication technologies, the notion of sensing grows beyond the physical world with data coming from cyber world. Advanced technologies such as social and crowd computing, smart wearables and implants, cognitive computing, and brain informatics, while achieving fruitful results in their own arena, also bring tremendous challenges on the interoperability, scalability, dependability, security and privacy for different Smart World applications. The heterogeneous and transdisciplinary nature of modern Cyber-Physical-Social sensing need to be addressed carefully to facilitate the development of the future Smart World, which aims to provide intelligent, interactive, collaborative and safe services to human beings. In this session, we have six distinguished panellists who will offer their insights on fusing, interpreting, reasoning, security and privacy preservation aspects of the Cyber-Physical-Social sensing for the future Smart World.

Chair:
Kevin I-Kai Wang, The University of Auckland, New Zealand
Panelists:
Liming Chen, De Montfort University, UK
Paolo Nesi, University of Florence, Italy
Kim-Kwang Raymond Choo, The University of Texas at San Antonio, USA
Richard Hill, University of Huddersfield, UK
Md Zakirul Alam Bhuiyan, Fordham University, USA
Hao Wang, Norwegian University of Science and Technology, Norway