Keynote Speakers

Keynote Speakers


Prof. Alex X. Liu,

IEEE Fellow, Michigan State University, USA

Speech Title: Signal Processing Approaches to Sensing without Sensors


Biography

Alex Liu received his Ph.D. degree in Computer Science from The University of Texas at Austin in 2006, and is a professor at the Department of Computer Science and Engineering, Michigan State University. He received the IEEE & IFIP William C. Carter Award in 2004, a National Science Foundation CAREER award in 2009, and the Michigan State University Withrow Distinguished Scholar Award in 2011. He has served as an Editor for IEEE/ACM Transactions on Networking, and he is currently an Associate Editor for IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Mobile Computing, and an Area Editor for Computer Communications. He has served as the TPC Co-Chair for ICNP 2014 and IFIP Networking 2019. He received Best Paper Awards from ICNP-2012, SRDS-2012, and LISA-2010. His research interests focus on networking and security. He is a Fellow of the IEEE.

 

Abstract

Human activity recognition is the core technology that enables a wide variety of applications such as health care, smart homes, fitness tracking, and building surveillance. We recognize human activities using signals from commercial WiFi devices. Human bodies reflect wireless signals as they are mostly made of water. Different human activities cause different changes on wireless signals. Thus, by analyzing the changes in wireless signals, we can recognize the corresponding human activities that cause the changes. We classify human activities into macro activities, which involve mostly arm, leg, or body scale movements, and micro activities, which involve mostly finger or hand scale movements. Human activity recognition and monitoring is the enabling technology for various applications such as elderly/health care, building surveillance, human-computer interaction, health care, smart homes, and fitness tracking. In this talk, I will present our research results on this topic.


 


Prof. Sheng-Uei Guan

Xi'an Jiaotong-Liverpool University, China

Speech Title : Input Space Partitioning for Machine Learning


Biography

Steven Guan received his M.Sc. & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a Professor and the Director for Research Institute of Big Data Analytics at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK. Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. After leaving the industry, he joined Yuan-Ze University for three and half years. He served as deputy director for the Computing Center and the chairman for the Department of Information & Communication Technology. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor. Prof. Guan’s research interests include: machine learning, intelligent systems, computational intelligence, big data analytics, data mining, personalization, modeling, security, networking, electronic commerce, mobile commerce, coding theory, and pseudorandom number generation. He has published extensively in these areas, with 130+ journal papers and 180+ book chapters or conference papers. He has chaired and delivered keynote speeches for 30+ international conferences and served in 170+ international conference committees and 20+ editorial boards.  

Abstract

This talk introduces an input attribute grouping method to improve the performance of learning. During training for a specific problem, the input attributes are partitioned into groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive or negative interactions between attributes. After partitioning, multiple sub-networks are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each sub-network. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.


 


Plenary Speakers


Prof. Yinglei Song

Jiangsu University of Science and Technology, China

Speech Title : Finding Accurate Solutions for Optimization Problems with Parameterized Methods


Biography

Yinglei Song received his B.S. from Tsinghua University in 1998 and his Ph.D. in Computer Science from the University of Georgia in 2006. From 2007 to 2012, he was an assistant professor of computer science at the University of Maryland Eastern Shore, USA. He is now a Professor of Electronics and Information Science at Jiangsu University of Science and Technology. He was selected to be one of the Specially Appointed Professors of Jiangsu Province in 2015. He has published over 80 refereed research papers and has served in the editorial boards and program committees of a few well known international journals and conferences. His research is in the design, analysis, implementation and applications of parameterized algorithms for optimization problems. His work spans theory and practice, including both algorithm development and real-world applications.

Abstract

A large number of problems in computer and information science can be formulated as optimization problems and finding exact solutions for these problems is often computationally challenging. In practice, it is often the case that one or a number of parameters are closely associated with the inherent structure of such a problem. It is therefore possible to develop efficient algorithms or methods that can efficiently find accurate solutions for such a problem when these parameters are small. In this talk, a new perspective will be presented on finding accurate solutions for many optimization problems in practice. Specifically, instead of developing algorithms or methods for the generic instances of these problems, parameters that are closely associated with the inherent structure of such a problem are identified. Based on a few different techniques, it is possible to develop parameterized methods to compute an accurate solution for an optimization problem. As an example,an exact solution for the frequency assignment problem in wireless communication can be computed in linear time when the tree width of the underlying graph is bounded from above by a small integer. In addition, I will show that parameterized algorithms or methods also exist for a few problems from different areas in information science, such as machine learning, data mining and image processing. Finally, the limitations of this type of methods are presented and a number of hardness results for parameterized problems are shown.


Prof. Jianpo Li

Northeast Electric Power University, China

Speech Title: Wireless Sensor Network Node Localization Technology


Biography

Jianpo Li received his B.S., M.S., and Ph.D. from the Department of Communication Engineering, Jilin University, China, in 2002, 2005, and 2008, respectively. In 2008, he joined the School of Information Engineering, Northeast Electric Power University (NEEPU). He was a visiting scholar with New York University in 2013 and University of Ottawa in 2016. Now he is the full professor and Vice-dean of the School of Computer Science, NEEPU. He has published more than 70 research papers, and has 16 patents. His research interests focus on wireless sensor networks, intelligent signal processing, 5G, wireless power transmission. 

Abstract

Node localization is one of the core technologies in wireless sensor networks. It is widely used in many fields such as target tracking, event monitoring, localization routing, and battlefield localization. To improve the localization performance, we construct the transmission model in real environment, which can help us build up the connection between transmission distance and signal strength. Then we study the static node localization and dynamic node localization in 2D and 3D environment. In this talk, I will talk about our research methods and results on node localization for WSN.