Keynote Speakers

Cyber-Enabled and Data-Driven Smart Health Control

Qun Jin
Professor and Department Chair of Human Informatics and Cognitive Sciences,
Waseda University, Japan

Abstract
In recent years, cyber technologies, such as ubiquitous cloud computing, mobile Internet, smartphones, wireless sensors, Internet of Things (IoT), big data, enable healthcare related services to enhance quality of life (QoL) and promote well-being for all of the people. In this talk, after briefly introducing the concept of smart health in terms of control systems and the cyber-enabled framework for data-driven smart health control, our vision and work on big data driven personal analytics and individual modeling for smart health will be described and explained. Furthermore, quality control and sustainable use of health big data as well as opportunities and issues of cyber-enabled smart health will be addressed and discussed.

Short Bio

JinQun Jin is currently a tenured full professor and the chair of the Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He has been engaged extensively 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. Dr. Jin has published more than 200 refereed papers in the academic journals, such as ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Learning Technologies, IEEE Systems Journal, and Information Sciences (Elsevier), and international conference proceedings in the related research areas. He has served as a general chair, program chair and TPC member for numerous international conferences, and editor-in-chief, associate editor, editorial board member and guest editor for a number of scientific journals. His recent research interests cover human-centric ubiquitous computing, behavior and cognitive informatics, data analytics and big data security, personal analytics and individual modeling, cyber-enabled applications in e-learning and e-health, and computing for well-being. He is a member of IEEE, IEEE CS, and ACM, USA, IEICE, IPSJ, and JSAI, Japan, and CCF, China.

Person Re-identification: Benchmarks and Our Solutions

Qi Tian
The University of Texas at San Antonio (UTSA), USA

Abstract
Person re-identification (re-id) is a promising way towards automatic video surveillance. As research hotspot in recent years, there has been an urgent demand for building a solid benchmarking framework, including comprehensive datasets and effective baselines.
To benchmark a large scale person re-id dataset, we propose a new high quality frame-based dataset for person re-identification titled “Market-1501”, which contains over 32,000 annotated bounding boxes, plus a distractor set of over 500K images. Different from traditional datasets which use hand-drawn bounding boxes that are unavailable under realistic settings, we produce the dataset with Deformable Part Model (DPM) as pedestrian detector. Moreover, this dataset is collected in an open system, where each identity has multiple images under each camera. We propose an unsupervised Bag-of-Words representation and treat the person re-identification as a special task of image search, which is demonstrated very efficient and effective.
To further push the person re-identification to practical applications, we propose a new video based dataset titled “MARS”, which is the largest video re-id dataset to date. Containing 1,261 identities and over 20,000 tracklets, it provides rich visual information compared to image-based datasets. The tracklets are automatically generated by the DPM as pedestrian detector and the GMMCP tracker. Extensive evaluation of the state-of-the-art methods including the space-time descriptors are presented. We further show that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity.
Finally, we present “Person Re-identification in the Wild (PRW)” dataset for evaluating end-to-end re-id methods from raw video frames to the identification results. We address the performance of various combinations of detectors and recognizers, mechanisms for pedestrian detection to help improve overall re-identification accuracy and assessing the effectiveness of different detectors for re-identification. A discriminatively trained ID-discriminative Embedding (IDE) in the person subspace using convolutional neural network (CNN) features and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement are introduced to aid the identification.

Short Bio
0Qi Tian is currently a Full Professor in the Department of Computer Science, the University of Texas at San Antonio (UTSA). He was a tenured Associate Professor from 2008-2012 and a tenure-track Assistant Professor from 2002-2008. During 2008-2009, he took one-year Faculty Leave at Microsoft Research Asia (MSRA) as Lead Researcher in the Media Computing Group.
Dr. Tian received his Ph.D. in ECE from University of Illinois at Urbana-Champaign (UIUC) in 2002 and received his B.E. in Electronic Engineering from Tsinghua University in 1992 and M.S. in ECE from Drexel University in 1996, respectively. Dr. Tian’s research interests include multimedia information retrieval, computer vision, pattern recognition and bioinformatics and published over 320 refereed journal and conference papers. He was the co-author of a Best Paper in ACM ICMR 2015, a Best Paper in PCM 2013, a Best Paper in MMM 2013, a Best Paper in ACM ICIMCS 2012, a Top 10% Paper Award in MMSP 2011, a Best Student Paper in ICASSP 2006, and co-author of a Best Student Paper Candidate in ICME 2015, and a Best Paper Candidate in PCM 2007.
Dr. Tian research projects are funded by ARO, NSF, DHS, Google, FXPAL, NEC, SALSI, CIAS, Akiira Media Systems, HP, Blippar and UTSA. He received 2014 Research Achievement Awards from College of Science, UTSA. He received 2010 ACM Service Award. He is the associate editor of IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Multimedia System Journal (MMSJ), and in the Editorial Board of Journal of Multimedia (JMM) and Journal of Machine Vision and Applications (MVA).  Dr. Tian is the Guest Editor of IEEE Transactions on Multimedia, Journal of Computer Vision and Image Understanding, etc. Dr. Tian is a Fellow of IEEE (2016). URL: http://www.cs.utsa.edu/~qitian, Email: qi.tian@utsa.edu

Searchable Symmetric Encryption: Potential Attacks, Practical Constructions and Extensions
Jinjun Chen
University of Technology Sydney (UTS), Australia

Abstract:
Data outsourcing has become one of the most successful applications of cloud computing, as it significantly reduces data owners' costs on data storage and management. To prevent privacy disclosure, sensitive data has to be encrypted before outsourcing. Traditional encryption tools such as AES, however, destroy the data searchability because keyword searches cannot be performed over encrypted data. Though the above issue has been addressed by an advanced cryptographic primitive, called searchable symmetric encryption (SSE), we observe that existing SSE schemes still suffer security, efficiency or functionality flaws. In this research, we further study SSE on three aspects. Firstly, we address the search pattern leakage issue. We demonstrate that potential attacks are exist as long as an adversary with some background knowledge learns users' search pattern. We then develop a general countermeasure to transform an existing SSE scheme to a new scheme where the search pattern is hidden. Secondly, motivated by the practical phenomenon in data outsourcing scenarios that user data is distributed over multiple data sources, we propose efficient SSE constructions which allow each data source to build a local index individually and enable the storage provider to merge all local indexes into a global one. Thirdly, we extend SSE into graph encryption with support for specific graph queries. E.g., we investigate how to perform shortest distance queries on an encrypted graph.

Short Bio:
Chen.jpgDr Jinjun Chen is a Professor from Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. He is the Director of Lab for Data Systems and Visual Analytics in the Global Big Data Technologies Centre at UTS. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, data intensive systems, cloud computing, workflow management, privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Cloud Computing, IEEE Transactions on Computers (TC), IEEE Transactions on Service Computing, and IEEE TKDE.

He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor¡¯s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences, JNCA. He is the Chair of IEEE Computer Society¡¯s Technical Committee on Scalable Computing (TCSC), Vice Chair of Steering Committee of Australasian Symposium on Parallel and Distributed Computing, Founder and Coordinator of IEEE TCSC Technical Area on Big Data and MapReduce, Founder and Steering Committee Co-Chair of IEEE International Conference on Big Data and Cloud Computing, IEEE International Conference on Big Data Science and Engineering, and IEEE International Conference on Data Science and Systems.

1 International Conference on Information Science and Control Engineering