2016 International Conference on Intelligent Computation
August 2-5,2016
Lanzhou,China
( http://www.ic-icc.cn/2016/index.htm )

Vladimir Cherkassky

Frank L. Lewis

Asoke K.Nandi

DeLiang Wang

David Zhang

	Vladimir Cherkassky IEEE Fellow University of Minnesota, USA
	Frank L. Lewis National Academy of Inventors, Fellow IEEE, InstMC, and IFAC University of Texas at Arlington, USA
	Asoke K.Nandi FREng, FIEEE, FIET, FIMA, FInstP, FRSA, FIMechE, FBCS Brunel University London, UK
	DeLiang Wang IEEE Fellow Ohio State University, USA
	David Zhang IEEE FIAPR Polytechnic University, Hong Kong

Abstract: there is a growing interest in data-analytic modeling for prediction and/or detection of epileptic seizures from EEG recording of brain activity. In spite of evidence that many patients have changes in EEG signal prior to seizures, development of robust predictive methods remains elusive. We argue that the main obstacle for development of effective EEG-based predictive models is an apparent disconnect between clinical and data-analytic modeling assumptions and constraints. We present an SVM-based system for reliable seizure prediction, where design choices and performance metrics are clearly related to clinical objectives. This system achieves very accurate classification / discrimination between preictal and interictal states in dogs with naturally occurring epilepsy. We argue that sound application of machine learning methods for predictive modeling requires proper formalization of clinical objectives in the context of statistical assumptions underlying existing machine learning algorithms. In this respect, the main promise of Big_Data (i.e., more data more knowledge) appears counterproductive, as it ignores the role of human intelligence and good engineering, which cannot be outsourced to statistical or machine learning software.

Bio-Sketch: Vladimir Cherkassky is Professor of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. He received his PhD in Electrical and Computer Engineering from the University of Texas at Austin in 1985. He has worked on theory and applications of statistical learning since late 1980’s and he has co-authored the monograph Learning from Data, Wiley-Interscience, now in its second edition. He is also the author of a new textbook Predictive Learning - see www.VCtextbook.com
    He has served on editorial boards of IEEE Transactions on Neural Networks (TNN), Neural Networks, Natural Computing, and Neural Processing Letters. He was a Guest Editor of the IEEE TNN Special Issue on VC Learning Theory and Its Applications published in September 1999. Dr. Cherkassky was organizer and Director of NATO Advanced Study Institute (ASI) From Statistics to Neural Networks: Theory and Pattern Recognition Applications held in France in 1993. He received the IBM Faculty Partnership Award in 1996 and 1997 for his work on learning methods for data mining. In 2007, he became Fellow of IEEE for ‘contributions and leadership in statistical learning’. In 2008, he received the A. Richard Newton Breakthrough Award from Microsoft Research for ‘development of new methodologies for predictive learning’.
    His current research interests include methodological aspects of predictive learning and advanced/non-standard formalizations for inductive inference.

Abstract: Co-robotics involves humans and robots working together safely in the same shared space as a team. This motivates physical Human-Robot Interaction (HRI) systems that adapt to different humans and have guaranteed robustness and stability properties. For modern interactive HRI systems to be capable of performing a wide range of tasks successfully, it is required to include the effects of both the robot dynamics and the human operator dynamics. In this talk we propose three adaptive HRI control systems that assist the human operator to perform a given task with minimum human workload demands and improved overall human-robot system performance.
    Human performance neuropsychological and human factors studies have shown that in coordinated motion with a robot, human learning has two components. The operator learns a robot-specific inverse dynamics model to compensate for the nonlinearities of the robot, and simultaneously learns a feedback control component that is specific to the successful performance of the task. These foundations can be incorporated in the design of HRI control systems that include the effects of both the robot dynamics and the human dynamics by using a 2-loop design procedure.
    In this talk, we develop an adaptive HRI control structure consisting of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to most existing neural network and adaptive impedance based control methods, no information of the task performance (e.g. specifically no reference trajectory information) is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the best parameters of the prescribed robot impedance model to adjust the robot’s dynamics to the operator’s skills to effectively perform a given task. The outer loop includes the human operator dynamics and all the task performance details. Given the inner-loop neuro-adaptive robot controller, three different outer loop designs are given for robot-assisted task performance. Experimental results on a PR2 robot demonstrate the effectiveness of this approach in using the robot to improve the human’s performance of a motion task.

Biosketch F.L. Lewis:Member, National Academy of Inventors. Fellow IEEE, Fellow IFAC, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer. UTA Distinguished Scholar Professor, UTA Distinguished Teaching Professor, and Moncrief-O’Donnell Chair at The University of Texas at Arlington Research Institute. Qian Ren Thousand Talents Consulting Professor, Northeastern University, Shenyang, China. IEEE Control Systems Society Distinguished Lecturer. Bachelor's Degree in Physics/EE and MSEE at Rice University, MS in Aeronautical Engineering at Univ. W. Florida, Ph.D. at Ga. Tech. He works in feedback control, reinforcement learning, intelligent systems, and distributed control systems. He is author of 6 U.S. patents, 316 journal papers, 406 conference papers, 20 books, 48 chapters, and 12 journal special issues. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, U.K. Inst. Measurement & Control Honeywell Field Engineering Medal 2009. Received IEEE Computational Intelligence Society Neural Networks Pioneer Award 2012. Distinguished Foreign Scholar at Nanjing Univ. Science & Technology. Project 111 Professor at Northeastern University, China. Distinguished Foreign Scholar at Chongqing Univ. China. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the Year by Ft. Worth IEEE Section. Listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Dean’s Excellence in Doctoral Mentoring Award. Elected to UTA Academy of Distinguished Teachers 2012. Texas Regents Outstanding Teaching Award 2013. He served on the NAE Committee on Space Station in 1995.

Abstract: Clustering techniques have been developed and applied in many areas for several decades. In particular, they have been used for gene clustering over the last two or three decades in bioinformatics and brain signal processing. New algorithms are being developed and applied to address many different problems. However, in applications with real data with little a priori knowledge, it is often difficult to select an appropriate clustering algorithm and evaluate the quality of clustering results due to the unknown ground truth. It is also the case that conclusions based on only one specific algorithm might be biased, since each algorithm has its own assumptions of the structure of the data, which might not correspond to the real data.
    Another important issue relates to multiple datasets, which may have been generated either in the same laboratory or different laboratories at different times and with different settings yet trying to conduct the similar experiments. In such a scenario, one has essentially a selection of heterogeneous datasets on similar experiments. The challenge is how to reach consensus conclusions in such scenarios.
    This plenary presentation will address both of the aforementioned issues and discuss how Bi-CoPaM and UNCLES can solve both the issues. This presentation will include examples of results from some bioinformatics and brain signal processing, although these can be applied to all applications areas involving clustering.

Bio-Sketch: Professor Asoke K. Nandi received the degree of Ph.D. in Physics from the University of Cambridge (Trinity College), Cambridge (UK). He held academic positions in several universities, including Oxford (UK), Imperial College London (UK), Strathclyde (UK), and Liverpool (UK) as well as Finland Distinguished Professorship in Jyvaskyla (Finland). In 2013 he moved to Brunel University (UK), to become the Chair and Head of Electronic and Computer Engineering. Professor Nandi is a Distinguished Visiting Professor at Tongji University (China) and an Adjunct Professor at University of Calgary (Canada).
    In 1983 Professor Nandi contributed to the discovery of the three fundamental particles known as W+,W- and Z0 (by the UA1 team at CERN), providing the evidence for the unification of the electromagnetic and weak forces, which was recognized by the Nobel Committee for Physics in 1984. His current research interests lie in the areas of signal processing and machine learning, with applications to communications, gene expression data, functional magnetic resonance data, and biomedical data. He has made many fundamental theoretical and algorithmic contributions to many aspects of signal processing and machine learning. He has much expertise in “Big Data”, dealing with heterogeneous data, and extracting information from multiple datasets obtained in different laboratories and different times. He has authored over 500 technical publications, including 200 journal papers as well as four books, entitled Automatic Modulation Classification: Principles, Algorithms and Applications (Wiley, 2015), Integrative Cluster Analysis in Bioinformatics (Wiley, 2015), Automatic Modulation Recognition of Communications Signals (Springer, 1996), and Blind Estimation Using Higher-Order Statistics (Springer, 1999),. Recently he published in Blood, BMC Bioinformatics, IEEE TWC, NeuroImage, PLOS ONE, Royal Society Interface, and     Signal Processing. The h-index of his publications is 63 (Google Scholar).
Professor Nandi is a Fellow of the Royal Academy of Engineering and also a Fellow of seven other institutions including the IEEE and the IET. Among the many awards he received are the Institute of Electrical and Electronics Engineers (USA) Heinrich Hertz Award in 2012, the Glory of Bengal Award for his outstanding achievements in scientific research in 2010, the Water Arbitration Prize of the Institution of Mechanical Engineers (UK) in 1999, and the Mountbatten Premium, Division Award of the Electronics and Communications Division, of the Institution of Electrical Engineers (UK) in 1998.

Abstract:Speech separation, or the cocktail party problem, is a widely acknowledged challenge in speech and signal processing. Motivated by the auditory masking phenomenon, we have suggested the ideal binary mask (IBM) as a main goal for speech separation. This leads to a new formulation of the separation problem as supervised classification where time-frequency (T-F) units are classified into two classes: those dominated by the target speech and the rest. This formulation opens speech separation to modern machine learning techniques, and deep neural networks (DNN) are particularly well-suited for this problem due to their strong representational capacity. DNN-based IBM estimation elevates speech separation performance to a new level, and produces the first demonstration of substantial speech intelligibility improvements for both hearing-impaired and normal-hearing listeners in background noise. DNN-based separation is not limited to binary masking, and we have examined a number of training targets and found that ratio masking can be preferable in terms of speech quality, and T-F masking in general outperforms spectral mapping.

Brief Biography: DeLiang Wang received the B.S. degree and the M.S. degree from Peking (Beijing) University and the Ph.D. degree in 1991 from the University of Southern California all in computer science. Since 1991, he has been with the Department of Computer Science & Engineering and the Center for Cognitive and Brain Sciences at The Ohio State University, where he is a Professor. He also holds a visiting appointment at the Center of Intelligent Acoustics and Immersive Communications, Northwestern Polytechnical University. He has been a visiting scholar to Harvard University, Oticon A/S (Denmark), and Starkey Hearing Technologies. Wang's research interests include machine perception and neurodynamics. He received the Office of Naval Research Young Investigator Award in 1996, the 2005 Outstanding Paper Award from IEEE Transactions on Neural Networks, and the 2008 Helmholtz Award from the International Neural Network Society. He was named the University Distinguished Scholar by Ohio State University in 2014. He is an IEEE Fellow, and currently serves as Co-Editor-in-Chief of Neural Networks.

Abstract:As one of the most powerful and reliable means of personal authentication, biometrics has been an area of particular interest. It has led to the extensive study of biometrics technologies and the development of numerous algorithms, applications, and systems, which could be defined as Advanced Biometrics. This presentation will be focused on this new biometrics research trend. As case studies, two new biometrics applications (medical biometrics and aesthetical biometrics) are developed. Some useful achievements could be given to illustrate their effectiveness.

Bio-Sketch:David Zhang graduated in Computer Science from Peking University. He received his MSc in 1982 and his PhD in 1985 in Computer Science from the Harbin Institute of Technology (HIT), respectively. From 1986 to 1988 he was a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second PhD in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. He is a Chair Professor since 2005 at the Hong Kong Polytechnic University where he is the Founding Director of the Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government in 1998. He also serves as Visiting Chair Professor in Tsinghua University, and Adjunct Professor in Peking University, Shanghai Jiao Tong University, HIT, and the University of Waterloo. He is Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG); Founder and and Series Editor, Springer International Series on Biometrics (KISB); Organizer, the 1st International Conference on Biometrics Authentication (ICBA); Associate Editor of more than ten international journals including IEEE Transactions and so on. So far, he has published over 10 monographs, 400 journal papers and 35 patents from USA/Japan/HK/China. According to Google Scholar, his papers have got over 34,000 citations and H-index is 85. He was listed as a Highly Cited Researcher in Engineering by Thomson Reuters in 2014 and in 2015, respectively. Professor Zhang is a Croucher Senior Research Fellow, Distinguished Speaker of the IEEE Computer Society, and a Fellow of both IEEE and IAPR.