Important Dates

January 31, 2018

Workshop/Special Session Proposal Due


April 15, 2018

Paper Submission Deadline


May 31, 2018

Author Notification:


June 17, 2018

Camera-ready Submission


Congress Keynotes

Smart Sensors, IoT and Data Analytics - Research, Trends and Opportunities

M. Jamal Deen

McMaster University, Canada







Abstract
: Several of the grand challenges in engineering for current and future societal needs require smart sensors and possibly Internet-of-Things (IoTs). In the health area, we will discuss some major healthcare issues related to aging and several examples of smart sensor systems. We will discuss the use of sensor systems to measure your walking signals and sleep quality, and their customization to an individual’s needs. Smart sensors are also used for a living diary and in a smart home server that functions as the “brain” of a smart medical home. In addition, we will discuss what are the trends and opportunities in smart sensors and IoTs. This will include various high- impact applications of smart sensors, the pervasiveness of smart phones and their feasible applications in sensing and monitoring, what are some healthcare market drivers, the growing demand for personal health and wellness monitoring systems, IoTs, and wearable technologies. The presentation will include the use of data analytics to provide important customizable information to the users based on data collected from a variety of sensors and IoT devices. Finally, we will discuss the applications of smart sensors, IoT and data analytics, and what are some important research issues in sensors, home networks, autonomic systems and healthcare in the context of a futuristic smart medical home.

Biography: Prof. M. Jamal Deen is Distinguished University Professor, Senior Canada Research Chair in Information Technology, and Director of the Micro- and Nano-Systems Laboratory, McMaster University. His current research interests are nanoelectronics, optoelectronics, nanotechnology, data analytics and their emerging applications to health and environmental sciences. Dr. Deen’s research record includes more than 560 peer-reviewed articles (about 20% are invited and with an h-index of 57), two textbooks on “Silicon Photonics- Fundamentals and Devices” and” Fiber Optic Communications: Fundamentals and Applications”, 12 awarded patents of which 6 were extensively used in industry, and 18 best paper/poster/presentation awards. Over his career, he has won more than fifty-five awards and honors.

As an undergraduate student at the University of Guyana, Dr. Deen was the top ranked mathematics and physics student and the second ranked student at the university, winning the Chancellor’s gold medal and the Irving Adler prize. As a graduate student, he was a Fulbright-Laspau Scholar and an American Vacuum Society Scholar. He is a Distinguished Lecturer of the IEEE Electron Device Society for more than a decade. His awards and honors include the Callinan Award as well as the Electronics and Photonics Award from the Electrochemical Society; a Humboldt Research Award from the Alexander von Humboldt Foundation; the Eadie Medal from the Royal Society of Canada; McNaughton Gold Medal (highest award for engineers), the Fessenden Medal and the Ham Education Medal, all from IEEE Canada IEEE Canada In addition, he was awarded the four honorary doctorate degrees in recognition of his exceptional research and scholarly accomplishments, professionalism and service. Dr. Deen has also been elected Fellow status in ten national academies and professional societies including The Royal Society of Canada - The Academies of Arts, Humanities and Sciences (the highest honor for academics, scholars and artists in Canada), IEEE, APS (American Physical Society) and ECS (Electrochemical Society). He served as the elected President of the Academy of Science, The Royal Society of Canada in 2015-2017.

From Data to Information Granules: A Data Science Perspective

Witold Pedrycz

University of Alberta, Canada
Polish Academy of Sciences, Poland







Abstract
: To capture the essence of data, facilitate building their essential descriptors and reveal key relationships (associations), we advocate a need for transforming data into more abstract constructs - information granules. In this setting, information granules are regarded as conceptually sound knowledge tidbits over which various models could be developed.

The paradigm shift implied by the engagement of information granules becomes manifested in several tangible ways including (i) a stronger dependence on data when building structure-free and versatile models spanned over selected numeric or granular representatives of experimental data, (ii) emergence of models at various levels of abstraction being promoted by the specificity/generality of information granules, and (iii) building a collection of individual local models and supporting their efficient aggregation and consensus building.

A framework of Granular Computing along with a diversity of its formal settings offers a critically needed conceptual and algorithmic environment. A suitable perspective built with the aid of information granules is advantageous in realizing a suitable level of abstraction. It also becomes instrumental when forming sound and pragmatic problem-oriented and user-oriented tradeoffs among precision of results, their easiness of interpretation, value, and stability.

The functional scheme advocated in this talk is outlined as follows: data -> numeric prototypes -> granular prototypes -> granular models. Numeric prototypes are formed through invoking clustering algorithms, which quite commonly gives rise to a collection of the representatives. Two ways of generalization of prototypes are considered: (i) symbolic and (ii) granular. In the symbolic generalization, one moves away from the numeric values of the prototypes and regards them as sequences of integer indexes (labels). Along this line, developed are concepts of (symbolic) stability and (symbolic) resemblance of data structures. The second generalization motivates the buildup of granular prototypes, which arise as a direct consequence for a more comprehensive representation of the data. This entails that information granules (including their level of abstraction), have to be prudently formed to achieve the required quality of the granular model. Subsequently two modes of aggregation of models (sources of knowledge) such as passive and active aggregation are also discussed.


Biography: Witold Pedrycz (IEEE Fellow, 1998) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.

His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data science, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 17 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering.

Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer). He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.

Network Slicing for Service Provisioning in 5G Communication Systems

Weihua Zhuang

University of Waterloo, Canada







Abstract: The fifth generation (5G) communication network is expected to accommodate a wide range of emerging services with diverse service quality requirements. The network will integrate a variety of network resources and technologies to support high transmission rate and to enhance quality of experience to mobile users. The traditional one-size-fits-all network architecture cannot efficiently meet the needs of different services, due to the poor scalability, limited adaptability, and inflexibility. Network function virtualization (NFV) over software defined networking (SDN) technology is a promising approach for an agile and flexible 5G networking infrastructure. In this presentation, we will provide an overview of several recent studies for 5G networks, including dynamic radio resource slicing in wireless network virtualization, computing and transmission resource allocation in the core network, and how to establish a customized virtual network topology for multicast services. We will conclude this presentation with a brief discussion of some open research topics.

Biography
: Dr. Weihua Zhuang has been with the Department of Electrical and Computer Engineering, University of Waterloo, Canada, since 1993, where she is a Professor and a Tier I Canada Research Chair in Wireless Communication Networks. She is the recipient of 2017 Technical Recognition Award from IEEE Communications Society Ad Hoc & Sensor Networks Technical Committee, one of 2017 ten N2Women (Stars in Computer Networking and Communications), and a co-recipient of several best paper awards from IEEE conferences. Dr. Zhuang was the Editor-in-Chief of IEEE Transactions on Vehicular Technology (2007-2013), Technical Program Chair/Co-Chair of IEEE VTC Fall 2017 and Fall 2016, and the Technical Program Symposia Chair of the IEEE Globecom 2011. She is a Fellow of the IEEE, the Royal Society of Canada, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Dr. Zhuang is an elected member in the Board of Governors and VP Publications of the IEEE Vehicular Technology Society.

AI and Robotics: Scenario Intelligence

Max Qing Hu Meng

The Chinese University of Hong Kong, China







Abstract
: Robotics and artificial intelligence are attracting more and more public attentions and research efforts lately. Recent revolutionary development and drastic progress in robotic technology and artificial intelligence in terms of both hardware capability and software power have made it possible for researchers to redefine what robotics and artificial intelligence can achieve with their joint force in accomplishing complicated human tasks, exploring new applications, and expanding envelops of possibilities. We will use our own research case studies to initiate discussions on how the artificial intelligence shall be combined with or integrated in robotics to tackle tasks that could not be accomplished to our satisfaction. Personal thoughts and outlook on future research efforts and potentials in robotics and artificial intelligence will be outlined to conclude the talk.

Biography: Max Q.-H. Meng received his Ph.D. degree in Electrical and Computer Engineering from the University of Victoria, Canada, in 1992. He joined the Chinese University of Hong Kong in 2001 and is currently Professor and Chairman of Department of Electronic Engineering. He was with the Department of Electrical and Computer Engineering at the University of Alberta in Canada, serving as the Director of the Advanced Robotics and Teleoperation Lab and holding the positions of Assistant Professor (1994), Associate Professor (1998), and Professor (2000), respectively. He is affiliated with the State Key Laboratory of Robotics and Systems at Harbin Institute of Technology as a Distinguished Chair Professor via the One Thousand Talent Program and the Honorary Dean of the School of Control Science and Engineering at Shandong University, in China. His research interests include robotics, perception, intelligent robots and agents, and medical robotics and devices. He has published some 600 journal and conference papers and led more than 50 funded research projects to completion as PI. He has served as an editor of several journals and General and Program Chair of many conferences including General Chair of IROS 2005 and General Chair of ICRA 2021 to be held in Xi’an, China. He is an elected member of the Administrative Committee (AdCom) of the IEEE Robotics and Automation Society. He is a recipient of the IEEE Millennium Medal, a Fellow of IEEE, a Fellow of HKIE, and a Fellow of the Canadian Academy of Engineering.

Machine Learning for Complex Networks

Ljiljana Trajkovic

Simon Fraser University, Canada







Abstract
: Collection and analysis of data from deployed networks is essential for understanding modern networks. Traffic traces collected from various deployed communication networks and the Internet have been used to characterize and model network traffic, analyze Internet topologies, and classify network anomalies. Data mining and statistical analysis of network data are often employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic. Spectral graph theory has been applied to analyze various topologies of complex networks and capture historical trends in their development. Recent machine learning techniques have proved valuable for predicting anomalous traffic behavior and for classifying anomalies in complex networks. Further applications of these tools will help improve our understanding of the underlying mechanisms that govern the behavior of complex networks such as the Internet, social networks (Facebook, LinkedIn, Twitter, Internet blogs, forums, and websites), power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms. They will also help improve performance of these networks and enhance their security.

Biography: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, in 1974, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, in 1979 and 1981, respectively, and the Ph.D. degree in electrical engineering from University of California at Los Angeles, in 1986.

She is currently a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada. From 1995 to 1997, she was a National Science Foundation (NSF) Visiting Professor in the Electrical Engineering and Computer Sciences Department, University of California, Berkeley. She was a Research Scientist at Bell Communications Research, Morristown, NJ, from 1990 to 1997, and a Member of the Technical Staff at AT&T Bell Laboratories, Murray Hill, NJ, from 1988 to 1990. Her research interests include high-performance communication networks, control of communication systems, computer-aided circuit analysis and design, and theory of nonlinear circuits and dynamical systems.

Dr. Trajkovic serves as IEEE Division X Delegate-Elect/Director-Elect (2018), IEEE Division X Delegate/Director (2019–2020). She serves as Senior Past President (2018–2019) of the IEEE Systems, Man, and Cybernetics Society and served as Junior Past President (2016–2017), President (2014–2015), President-Elect (2013), Vice President Publications (2012–2013, 2010–2011), Vice President Long-Range Planning and Finance (2008–2009), and a Member at Large of its Board of Governors (2004–2006). She served as 2007 President of the IEEE Circuits and Systems Society. She was a member of the Board of Governors of the IEEE Circuits and Systems Society (2001–2003, 2004–2005). She is Chair of the IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections. She was Chair of the IEEE Technical Committee on Nonlinear Circuits and Systems (1998). She is General Co-Chair of SMC 2020 and SMC 2018 Workshop on BMI Systems and served as General Co-Chair of SMC 2016 and HPSR 2014, Special Sessions Co-Chair of SMC 2017, Technical Program Chair of SMC 2017 and SMC 2016 Workshops on BMI Systems, Technical Program Co-Chair of ISCAS 2005, and Technical Program Chair and Vice General Co-Chair of ISCAS 2004. She served as an Associate Editor of the IEEE Transactions on Circuits and Systems (Part I) (2004–2005, 1993–1995), the IEEE Transactions on Circuits and Systems (Part II) (2018–, 1999–2001, 2002-2003), and the IEEE Circuits and Systems Magazine (2001–2003). She was a Distinguished Lecturer of the IEEE Circuits and Systems Society (2010–2011, 2002–2003). She is a Professional Member of IEEE-HKN and a Fellow of the IEEE.

Computational Intelligence for Dependable and Resilient Cloud Computing

Vincenzo Piuri

Università degli Studi di Milano, Italy







Abstract
: Recent years have seen a growing interest among users in the migration of their applications to the Cloud computing environments. However, due to high complexity, Cloud-based services often experience a large number of failures and security breaches, and consequently, impose numerous challenges on the dependability and resilience of users’ applications.

Unfortunately, current dependability and resilience solutions focus either on the infrastructure itself or on application analysis, but fail to consider the complex inter-dependencies between system components and application tasks.

This aspect is highly crucial especially when Cloud environments are used, as it is increasingly considered nowadays, in critical applications.

Besides, definition of application requirements, allocations of resources to application tasks, and optimization of global management parameters usually are based either on statistical approaches or on heuristics strategies typical of operating research. Computational intelligence may give additional opportunities and flexibility in specifying the requirements especially when they are defined by non-experts and in optimizing the resource allocation and the global management parameters.

This talk will discuss a user-centric, dependability- and resilience-driven framework that considers deploying and protecting users’ applications in the Cloud infrastructure so as to minimize their exposure to the vulnerabilities in the network, as well as offering fault tolerance and resilience as a service to the users who need to deploy their applications in the Cloud.

In this scenario, the talk analyzes the opportunities offered by computational intelligence to specify the characteristics and the requirements of these environments and support their management in the presence of many local optimization minima.

Biography: Professor Vincenzo Piuri has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. He is Full Professor in computer engineering at the Università degli Studi di Milano, Italy (since 2000).

His main research interests are: intelligent systems, cloud computing, fault tolerance, signal and image processing, machine learning, pattern analysis and recognition, theory and industrial applications of neural networks, biometrics, intelligent measurement systems, industrial applications, digital processing architectures, embedded systems, and arithmetic architectures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters.

He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Past Vice President for Technical Activities (2016), IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19) and Associate Editor of the IEEE Transactions on Computers and the IEEE Transactions on Cloud Computing, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.

He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications. He is Honorary Professor at the Obuda University, Budapest, Hungary, Guangdong University of Petrochemical Technology, China, the Northeastern University, Shenyang, China, the Muroran Institute of Technology, Japan, and the Amity University, India.

Deep Learning for Big Data Applications - Challenges and Future Directions

Yi Pan

Georgia State University, USA







Abstract
: Due to improvements in mathematical formulas and increasingly powerful computers, we can now model many more layers of virtual neurons (deep neural networks or deep learning) than ever before. Deep learning is now producing many remarkable recent successes in computer vision, automatic speech recognition, natural language processing, audio recognition, and medical imaging processing. Although various deep learning architectures and novel algorithms have been applied to many big data applications, extending deep learning into more complicated applications such as bioinformatics or medical images will require more conceptual and software breakthroughs, not to mention many more advances in processing power. In this talk, I will outline the challenges and problems in deep learning research. They include design of new architectures, handling high dimensional data, encoding schemes, mathematical proofs, optimization of hyperparameters, logic and reasoning, result explanation and hardware support for deep learning. Some solutions and preliminary results in these areas will be presented.

Biography: Yi Pan is currently a Regents’ Professor and Chair of Computer Science at Georgia State University, USA. He has served as an Associate Dean and Chair of Biology Department during 2013-2017 and Chair of Computer Science during 2006-2013. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's research interests include parallel and cloud computing, wireless networks, and bioinformatics. Dr. Pan has published more than 250 journal papers with over 80 papers published in various IEEE journals. In addition, he has published over 150 papers in refereed conferences. He has also co-authored/co-edited 43 books. His work has been cited more than 8000 times. Dr. Pan has served as an editor-in-chief or editorial board member for 15 journals including 7 IEEE Transactions. He is the recipient of many awards including IEEE Transactions Best Paper Award, several other conference and journal best paper awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized many international conferences and delivered keynote speeches at over 60 international conferences around the world.

Blockchain: Benefits, Limitations, and a Use Case on Enforcing Data Privacy

Wenjing Lou

Virginia Tech, USA







Abstract
: Blockchain, the technology behind Bitcoin, has drawn widespread attention in recent years. As a popular “secure by design” technology, Blockchain demonstrates the great potential to enable a wide range of distributed applications across a broad spectrum of industries. With a blockchain in place, applications that could previously run only through a trusted intermediary, can now operate in a fully decentralized fashion while achieving the same security objectives with the same amount of certainty.

In this talk, we will discuss a few use cases of blockchain and examine its fundamental properties, both desirable and undesirable. While blockchain promises user anonymity, transaction irreversibility, public verifiability, and transparency, etc., some of these properties are not guaranteed and they come at a very high price. At the same time, excessive overhead and performance deficits may place a fundamental limit on some security uses. We will also introduce “PrivacyGuard”, a blockchain-based privacy policy enforcement framework that explores the combination of blockchain with the trusted execution environment (TEE) such as Intel SGX to empower individual data owners to define their privacy policy using “smart contract,” track their data usage on the blockchain, and enforce their policy compliance through a hardware-assisted TEE.

Biography: Wenjing Lou is the W. C. English Professor of Computer Science at Virginia Tech and a Fellow of the IEEE. She holds a Ph.D. in Electrical and Computer Engineering from the University of Florida. Her research interests cover many topics in the cybersecurity field, with her current research interest focusing on privacy protection techniques in networked information systems and cross-layer security enhancement in the Internet of Things (IoT) systems.

Prof. Lou is currently on the editorial boards of IEEE Transactions on Dependable and Secure Computing (TDSC), ACM/IEEE Transactions on Networking (ToN), IEEE Transactions on Mobile Computing (TMC), and Journal of Computer Security. She is the Steering Committee Chair of IEEE Conference on Communications and Network Security (IEEE CNS), which is a conference series in IEEE Communications Society (ComSoc) core conference portfolio and the only ComSoc conference focusing solely on cybersecurity.

Smart Healthcare

Tomoaki Ohtsuki

Keio University, Japan







Abstract
: With rapid aging society in developed countries particularly Japan, social costs for nursing care and medical expenses are also rising. Meanwhile, the size of the average family has continued to shrink, which results in the increase of elderly people living alone. Smart healthcare is expected to support the aging society where people can live healthy and peacefully, while reducing the costs for support dramatically. To realize such a society, smart technologies are necessary. Smart sensor is one of the smart technologies where it is expected to collect information about people and environments while keeping privacy. For instance, monitoring a person living alone is an important problem in which the use of cameras is not normally permitted or preferred. In this talk I will introduce smart healthcare and smart sensors that realize it. I will also introduce some of our developed smart sensors based on wireless communications technologies. I will present some ideas how to securely share personal health data and cnclude.

Biography: Tomoaki Ohtsuki (Otsuki) is currently a Professor at Keio university, Japan. He received the B.E., M.E., and Ph. D. degrees in Electrical Engineering from Keio University, Yokohama, Japan in 1990, 1992, and 1994, respectively. From 1995 to 2005 he was with Science University of Tokyo. In 2005 he joined Keio University. He is engaged in research on wireless communications, optical communications, signal processing, and information theory. Dr. Ohtsuki is a recipient of the 1997 Inoue Research Award for Young Scientist, the 1997 Hiroshi Ando Memorial Young Engineering Award, Ericsson Young Scientist Award 2000, 2002 Funai Information and Science Award for Young Scientist, IEEE the 1st Asia-Pacific Young Researcher Award 2001, the 5th International Communication Foundation (ICF) Research Award, 2011 IEEE SPCE Outstanding Service Award, the 27th TELECOM System Technology Award, ETRI Journal’s 2012 Best Reviewer Award, and 9th International Conference on Communications and Networking in China 2014 (CHINACOM ’14) Best Paper Award.

He has published more than 165 journal papers and 380 international conference papers.

He served a Chair of IEEE Communications Society, Signal Processing for Communications and Electronics Technical Committee. He served a technical editor of the IEEE Wireless Communications Magazine and an editor of Elsevier Physical Communications. He is now serving an Area Editor of the IEEE Transactions on Vehicular Technology and an editor of the IEEE Communications Surveys and Tutorials. He has served general-co chair and symposium co-chair of many conferences, including IEEE GLOBECOM 2008, SPC, IEEE ICC2011, CTS, IEEE GCOM2012, SPC, IEEE SPAWC, and IEEE APWCS. He gave tutorials and keynote speech at many international conferences including IEEE VTC, IEEE PIMRC, and so on. He was a Vice President of Communications Society of the IEICE, Japan and is the elected President of Communications Society of the IEICE, Japan. He is a senior member of the IEEE and a fellow of the IEICE.

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