Keynote&Plenary Speakers for ICCC 2017

 

Fellow of IEEE, Prof. Chenyang Lu, Washington University in St. Louis, USA

Speech title: Dependable Internet of Things

Abstract: IoT-driven control underpins many IoT applications in industries and smart cities.  In contrast to best-effort IoT often found in consumer markets, there remain daunting challenges to develop IoT systems that not only monitor but also control physical systems in a dependable fashion.  We will highlight the dependability challenges caused by communication delays, data loss and resource constraints of IoT.  We will further discuss cyber-physical co-design as a fundamental approach to achieve dependable control based on IoT. 

Biography: Chenyang Lu is the Fullgraf Professor in the Department of Computer Science and Engineering at Washington University in St. Louis. His research interests include Internet of Things, real-time systems, and cyber-physical systems and. He is Editor-in-Chief of ACM Transactions on Sensor Networks and chaired premier conferences such as ACM SenSys, IEEE RTSS, ACM/IEEE ICCPS and ACM/IEEE IoTDI'17. He is the author and co-author of over 150 research papers with over 17,000 citations and an h-index of 57. He received the Ph.D. degree from University of Virginia in 2001. He is a Fellow of IEEE.

Fellow of IEEE, Prof. Xuemin Lin, University of New South Wales, Australia

Speech Title: Advances in Big Graph Processing

 

Abstract: Graphs are very important parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications such as bioinformatics, web search, social network, road network, etc. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data. In this talk, I will first overview our recent research efforts in processing big graphs including scalable processing theory and techniques, distributed computation, and system framework. We will also look to the future of the area. 

Biography: Xuemin Lin is a UNSW Scientia Professor (a.k.a, Distinguished Professor) and the head of database group in the school of computer science and engineering at UNSW. Xuemin is also a current Professor at ECNU as specially appointed Chinese National Distinguished Professor (Thousands Distinguished Professors Program).  He is a fellow of IEEE. Xuemin's research interests lie in databases, data mining, algorithms, and complexities. Specifically, he is working in the areas of scalable processing and mining of various data, including graph, spatial-temporal, streaming, text and uncertain data. In these areas, Xuemin made a significant contributions by totally publishing over 270 papers (among them over 140 are published in the top tier journals and conferences). His papers are also nominated as one of the best papers in ICDE2010 (Spatial), SIGMOD2011 (Text), ICDE2012 (Spatial Temporal), and ICDE2013 (Graph). In ICDE2016, he received the best paper award, while in ICDE2007, he received the best student paper award. His papers in Keyword search is nominated as a Spotlight paper in TKDE, Dec, 2011. Xuemin Lin has been working with Key industry for the system development including Alibaba, Huawei etc.

Xuemin has been very frequently serving as a PC member in SIGMOD, VLDB, ICDE, ICDM, KDD, CIKM, and EDBT. He was a vice chair in ICDM2012, track chair in ICDE2013, and area chair in CIKM2014. He received the honour of outstanding reviewer in KDD2012. He was an associate editor of ACM TODS (2008-2014) and IEEE TKDE (Feb 2013- Jan 2015), and an associate editor-in-Chief of TKDE (2015-2016), respectively. Currently, he is the editor-in-Chief of TKDE (Jan 2017 - now) and an associate editor of WWW Journal (2013 - now).

Prof. Yanzhen Qu, Colorado Technical University, USA

Speech Title: Artificial Intelligence: Current Status, Challenges, and Opportunities

 

Abstract: Artificial Intelligence (AI) has been a hot subject in mainstream media in recent years due to two primary reasons: The first is that with the broad application of machine deep learning and big data analytics, many novel and successful applications, including Google’s Alpha Go, driverless cars, and IBM’s Watson, have been reported. The second aspect is that a number of leading intellectuals in the field, including Stephen Hawking, Elon Musk, and Bill Gates, have recently become very vocal on the risks that AI-created superintelligence poses to the human race. This talk will present a comprehensive analysis of the differences between natural bioprocess-based human intelligence and computer-based artificial intelligence. The purpose is not only to present a truthful status review on the AI field’s recent achievements, but also to identify the remaining challenges, meaningful research topics, and application opportunities of AI. 

Biography: Yanzhen Qu is currently a Professor of Computer Science and the Dean of the College of Computer Science and Technology at Colorado Technical University (CTU), USA. Over the course of his 20+ years in industry, he has served as a senior or executive manager of product R&D and IT in several multinational corporations. He was also the chief system architect and director of development for several of the world’s first very large real-time commercial software systems. At Colorado Technical University, Dr. Qu has led his faculty to create several new degree programs, including cybersecurity and data science at both undergraduate and graduate levels, and has also supervised many doctoral students to conduct their research work effectively and to complete their dissertations on time. Just in recent five years he and his students have published several dozen scholarly papers, several of which have won top awards at various international conferences. He has also coached CTU’s student teams to win two finalist and one 1st place awards at The USA’s Annual National Security Innovation Competition from 2012 to 2014. As a senior member of IEEE, Dr. Qu has served as general, program, and session chair at various meetings, and has been invited as a keynote speaker at many IEEE, ACM, ASIS, and IFIP international conferences, symposiums, and workshops. He is also an editorial board member of several professional peer-reviewed CS and IT journals, and has been a visiting professor at over 30 international universities. Dr. Qu’s broad research interests include internet of things, cyber security, affective computing, e-learning technologies, software engineering, cloud computing, mobile computing, artificial intelligence, data mining, machine learning, and big data analytics. He received his B.Eng. in Electronic Engineering from Anhui University, China, M. Eng. in Electrical Engineering from Chinese Academy of Sciences, China, and Ph.D. in Computer Science from Concordia University, Canada.

Prof. Yutaka Ishibashi, Nagoya Institute of Technology, Japan

Speech title: Remote Robot Control with Haptics

 

Abstract: In a remote robot system with force feedback, a user can operate a remote robot with a haptic sensor by using a haptic interface device while watching video. The user can perceive reaction force via the haptic interface device when the robot arm hits/touches some objects. Therefore, by using the system, we can do a various types work which only users cannot do. Research on remote robot control with haptics is mainly grouped into two categories: One is carried out by researchers in the robot & control research field, and the other is done by those in the communication & network research field. The first researchers tried to guarantee stability of robots at the expense of quality slightly (i.e., stabilization control). The second researchers tried to improve the quality under the condition that stability is kept (i.e., QoS (Quality of Service) control). 

In the keynote speech, first we outline our studies on remote robot control with haptics which we have been studying so far. We tried to integrate the two types of control (stabilization control and QoS control). Next, we explain collaborative work by multiple remote robot systems. At that time, we need to handle cooperative work among robots and users are needed. We can improve the cooperation by haptics as well as voice and video. We have to study collaboration between robots, that between robot and user, and that between users by using haptic sense. To realize stable and high-quality remote robot control with haptics, we need to enhance the stability control and QoS control which we have studied so far. For example, we are studying temporal and spatial synchronization control (like synchronized swimming) and stabilizing multi-bilateral control. 

Biography: Yutaka Ishibashi received the B.E., M.E., and Ph.D. degrees from Nagoya Institute of Technology, Nagoya, Japan, in 1981, 1983, and 1990, respectively.

In 1983, he joined the Musashino Electrical Communication Laboratory of NTT. From 1993 to 2001, he served as an Associate Professor of Department of Electrical and Computer Engineering, Faculty of Engineering, Nagoya Institute of Technology.

Currently, he is a Professor of Department of Computer Science, Graduate School of Engineering, Nagoya Institute of Technology.

His research interests include networked multimedia, QoS (Quality of Service) control, media synchronization, and remote robot control with haptics.

He was the Chair of the IEICE Communication Quality Technical Committee (2007-2009).

He served as TPC Chair of IEEE CQR (Communications Quality and Reliability) Workshop in 2011 and 2012.

He was IEEE Nagoya Section Secretary (2015-2016), and he is currently IEEE Nagoya Section Chair.

He is also a Steering Committee Member of NetGames (Network and Systems Support for Games).

He served as NetGames Workshop Co-Chair in 2006, 2010, 2014, and 2017.

He is a Fellow of IEICE, a Senior member of IEEE, and a Member of ACM, IPSJ, ITE, and VRSJ.

 

Prof. Yulin Wang, Wuhan University, China

Speech title: Image Authentication and Tamper Localization

 

Abstract: Image authentication can be used in many fields, including e-government, e-commerce, national security, news pictures, court evidence, medical image, engineering design, and so on. Since some content-preserving manipulations, such as JPEG compression, contrast enhancement, and brightness adjustment, are often acceptable—or even desired—in practical application, an authentication method needs to be able to distinguish them from malicious tampering, such as removal, addition, and modification of objects. Therefore, the traditional hash-based authentication is not suitable for the application. As for the semi-fragile watermarking technique, it meets the requirements of the above application at the expense of severely damaging image fidelity. In this talk, we propose a hybrid authentication technique based on what we call fragile hash value. The technique can blindly detect and localize malicious tampering, while maintaining reasonable tolerance to conventional content-preserving manipulations. The hash value is derived from the relative difference between each pair of the selected DCT AC coefficient in a central block and its counterpart which is estimated by the DC values of the center block and its adjacent blocks. In order to maintain the relative difference relationship when the image undergoes legitimate processing, we make a pre-compensation for the AC coefficients.

 

Biography: Prof. Yulin Wang is a full professor and PhD supervisor in International School of Software, Wuhan University, China. He got PhD degree in 2005 in Queen Mary, University of London, UK. Before that, he has worked in high-tech industry for more than ten years. He has involved many key projects, and hold 8 patents. He got his master and bachelor degree in 1990 and 1987 respectively from Xi-Dian University, and Huazhong University of Science and Technology(HUST), both in China. His research interests include digital rights management, digital watermarking, multimedia and network security, and signal processing. In recently 10 years, Prof. Wang has published as first author 3 books, 40 conference papers and 45 journal papers, including in IEEE Transactions and IEE proceedings and Elsevier Journals. Prof. Wang served as editor-in-chief for International Journal of Advances in Multimedia in 2010. He served as reviewer for many journals, including IEEE Transactions on Image Processing, IEEE Signal Processing Letters, Elsevier Journal of Information Sciences. He served as reviewer for many research funds, including National High Technology Research and Development Program of China ( ‘863’ project). Prof. Wang was the external PhD adviser of Dublin City University, Ireland during 2008-2010. He was the keynote speakers in many international conferences. He bas been listed in Marcus ‘who’s who in the world’ since 2008.

Prof. Sheng-Uei Guan, Xi'an Jiaotong-Liverpool University, China

Speech title: Incremental Hyperplane Partitioning toward Classification

 

Abstract: An incremental hyperplane partitioning approach is proposed for classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. We solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, a recursive approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA.

 

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.

Prof. Tianrui Li, Southwest Jiaotong University, China

Speech Title: Data-Driven Intelligence: Challengues and our Solutions

 

Abstract: Data-Driven Intelligence has become a hot research topic in the area of information science. This talk aims to outline the  challengues on Data-Driven Intelligence. Then our solutions for Data-Driven Intelligence are provided, which cover the following aspects. 1) A hierarchical entropy-based approach is demonstrated to evaluate the effectiveness of data collection, the first step of Data-Driven Intelligence. 2) A multi-view-based method is illustrated for filling missing data, the preprocessing step on Data-Driven Intelligence. 3) A unified framework is outlined for Parallel Large-scale Feature Selection to manage Big Data with high dimension. 4) A MapReduce-based parallel method together with three parallel strategies are presented for computing rough set approximations, which is a fundamental part in rough set-based data analysis similar to frequent pattern mining in association rules. 5) Incremental learning-based approaches are shown for updating approximations and knowledge in dynamic data environments, e.g., the variation of objects, attributes or attribute values, which improve the computational efficiency by using previously acquired learning results to facilitate  knowledge maintenance without re-implementing the original data mining algorithm. 6) A deep-learning-based model to deal with multiple different sources of data is developed.  

Biography: Tianrui Li received his  B.S. degree, M.S. degree and P h.D. degree from the Southwest  Jiaotong University, China  in 1992, 1995 and 2002 respect ively. He was a Post-Doctoral  Researcher at Belgian Nuclear  Research Centre (SCK • CEN),  Belgium from 2005-2006, a visiting professor at Hasselt  University, Belgium in 2008, t he University of Technology, Sydney, Australia in 2009 and the University of Regina, Canad a in 2014. And, he is presentl y a Professor and the Director  of the Key Lab of Cloud Computing and Intelligent  Technique of Sichuan Province,  Southwest Jiaotong  University, China. Since 2000,  he has co-edited 6 books, 10 special issues of internationa l journals, 15 proceedings,  received 5 Chinese invention  patents and published over  240 research papers (e.g., IEEE  TKDE, IEEE TEC, IEEE TFS, IEEE TIFS, IEEE ASLP, IEEE TIE, IEEE TC, IEEE TVT) in refereed journals  and conferences (e.g., KDD,  IJCAI, UbiComp). Three papers  were ESI Hot Papers and Ten papers was ESI Highly Cited  Papers. His Google H-index is 32. He serves as the area editor of International Journal of Computational Intell igence Systems (SCI), editor  of Knowledge-based Systems ( SCI) and Information Fusion ( SCI), etc. He is an IRSS fellow, a  distinguished member of CCF,  a senior member of ACM, IEEE,  CAAI, ACM SIGKDD member,  Chair of IEEE CIS Chengdu  Chapter, Treasurer of ACM  SIGKDD China Chapter and CCF  YOCSEF Chengdu Chair (2013- 2014). Over fifty graduate  students (including 8 Post- Docs, 12 Doctors) have been  trained. Their employment  units include Microsoft  Research Asia, Sichuan  University, Baidu, Alibaba,  Tencent and Huawei. They have  received 2 "Si Shi Yang Hua"  Medals, Best Papers/ Dissertation Awards 13 times,  Champion of Sina Weibo Interac tion-prediction at Tianchi Big Data Competition (Bonus  200,000 RMB), Second Place of  Social Influence Analysis  Contest of IJCAI-2016  Competitions.

Prof. Ai Bo, Beijing Jiaotong University, China

 Speech Title: Scatterer Modeling for Wireless Channel @ mmWave

 

Abstract: Mmwave communication has been regarded as one of key technology in 5G. FCC has allocated 28 GHz, 37/39 GHz and 64-71 GHz frequencies as licensed or unlicensed bands for 5G mobile radio services (MRS). As is known, the sensitivity of mmWave links to blockage is due to their weak diffraction characteristics and the scatterers in real physical scenarios are usually neglected. In this talk, we will make an analysis of scatterer modeling for wireless channel at mmWave, which is of great importance to the precise modeling for wireless channels.

 

Biography: Prof. Bo Ai received his Master degree and Ph. D. degree from Xidian University in China. He graduated from Tsinghua University with the honor of Excellent Postdoctoral Research Fellow at Tsinghua University in 2007. He was a visiting professor at EE Department, Stanford University in 2015. He is now working in State Key Lab of Rail Traffic Control and Safety at Beijing Jiaotong University as a full professor and Ph. D. candidate advisor. He is the Deputy Director of State Key Lab of Rail Traffic Control and Safety, and the Deputy Director of Modern Telecommunication Institute. 
He has authored/co-authored 6 books and published over 260 academic research papers in his research area. He has hold 26 invention patents. He has been the research team leader for 30 national projects and has won some important scientific research prizes. He has been notified by Council of Canadian Academies (CCA) that, based on Scopus database, Prof. Bo Ai has been listed as one of the Top 1% authors in his field all over the world. Prof. Bo Ai has also been Feature Interviewed by IET Electronics Letters. His interests include the research and applications of channel measurement and channel modeling, dedicated mobile communications for rail traffic.
Prof. Bo Ai is a Fellow of The Institution of Engineering and Technology (IET Fellow). He was as a Co-chair or a Session/Track Chair for many international conferences. He is an Editor of IEEE Transactions on Consumer Electronics and an Editorial Committee Member of the Wireless Personal Communications journal. He is the Lead Guest Editor for Special Issues on IEEE Transactions on Vehicular Technology, IEEE Antennas and Propagations Letters, International Journal on Antennas and Propagations. He has received many awards such as the Qiushi Outstanding Youth Award by Hong Kong Qiushi Foundation, the New Century Talents by the Chinese Ministry of Education, the Zhan Tianyou Railway Science and Technology Award by the Chinese Ministry of Railways, and the Science and Technology New Star by the Beijing Municipal Science and Technology Commission.

Prof. Yan Yang, Southwest Jiaotong University, China

Speech Title: Data-driven Train Condition Recognition

 

Abstract: Real time monitoring of the running status of train and detecting their hidden failures accurately have great significance. Train bogie is an important part to guarantee the safe operation of High Speed Train (HST) and the comfort of passengers. The main techniques for recognizing conditions of HST are to collect the vibration signals by mounting sensors, analyze data features and build fault diagnosis model. Deep learning, ensemble learning and multi-view learning have attracted considerable attention in recent years. In this talk, I will discuss condition recognition of HST with Deep Belief Networks (DBNs), Empirical Mode Decomposition (EMD), Multi-view Clustering Ensemble, Multi-view Classification Ensemble, Feature Fusion, and etc.

 

Biography: Dr. Yan Yang is currently Professor and vice dean of Information Science and Technology, Southwest Jiaotong University. She worked as a visiting scholar at the Center of Pattern Analysis and Machine Intelligence (CPAMI) in Waterloo University of Canada for one and half year. Her research interests include artificial intelligence, big data analysis and mining, ensemble learning, cloud computing and service. Prof. Yang has participated in more than 10 high-level projects recently. And have taken charge of two programs supported by the National Natural Science Foundation of China (NSFC), one NSFC International (Regional) Cooperation and Exchanges program, one Project of National Science and Technology Support Program, and one Supporting Program for Science and Technology of Sichuan Province. She has authored and co-authored over 150 papers in journals and international conference proceedings, 1 special issue of international journal, 1 proceeding and 2 books. She also serves as the Vice Chair of ACM Chengdu Chapter, Member of IEEE, Senior Member of CCF and CAAI, Member of CCF Education Work, Artificial Intelligence and Pattern Recognition, Theoretical Computer Science Committee, Member of CAAI Machine Learning, Rough Set and Soft Computing Committee, Deputy Secretary General of Sichuan Province Computer Society and Vice Chair of Big Data Industry University Research Council of Sichuan Institute of Electronics.

Invited Speakers for ICCC 2017

Prof. Takanori Miyoshi, Toyohashi University of Technology, Japan

Speech Title: Haptics will Change the Social Communication

 

Abstract: Social communication based on visual and auditory has enabled communication unlike any humankind has experienced before. However, Internet communication based on haptic interface has not generally spread yet. In this talk, I present the three elements that all people connecting the Internet share the sense of force and enjoy the Internet games such as tug of war by stabilizing the whole system in spite of Internet communication latency. 1. The device in which a force sense is good and safe. 2. The bidirectional real time protocol which is not barred by the firewall. 3. Stabilizing control for any communication latency.

Prof. Yinglei Song, Jiangsu University of Science and Technology, China

 

Dr. Pingguo Huang, Seijoh University, Japan

 

Keynote, Plenary&Invited Speakers for ICCC 2018

coming soon...