Prof. Hussein T. Mouftah   (Fellow of: IEEE, Canadian Academy of Engineering, Engineering Institute of Canada, and RSC Academy of Sciences )
University of Ottawa

Panel Tutorial #3: Deploying Autonomous Vehicles in Smart Cites

Abstract- The transformation of our current cities into smarter cities will bring challenges in diverse areas such as the transportation system, the electricity system, and wearable systems, just to name a few. In smart cities, Information and Communication Technologies (ICT) will play a vital role for providing services in the urban environment.

These services include real time monitoring and reaction in time through wireless sensor and actuator networks. Smart Grids (SGs), Intelligent Transportation Systems (ITS), Internet of Things (IoT), Electric Vehicles (EVs), and Wireless Sensor Networks (WSNs) will be the building blocks of futuristic smart cities. Smart grid refers to the modernization of traditional power grid by incorporating two-way digital communication support at generation, transmission, and distribution level.

Intelligent transportation system refers to making the vehicular traffic smarter by reducing congestion, optimized fuel consumption, shorter routes, and better safety, self-driving cars by using communication and sensing technologies. Internet of things refer to a world-wide network of interconnected objects uniquely addressable, employing M2M communications, based on standard communication protocols and allows people and things to be connected Anytime, Anyplace, with Anything and Anyone, ideally using Any path/network and Any service.

IoT can be very useful for resource management in the context of smart cities. Wireless sensor networks are composed of sensor nodes capable of performing sensing and implementing the M2M communications. All these technologies will help to build smart cities. In this presentation we will address technology trends with a focus on a use case of connected electric vehicles in smart cities.

Please check Prof. Mouftah's keynote speech titled: Smart Cities


Biography- After four years of industrial experience mainly at Bell-Northern Research (BNR), Dr. Hussein T. Mouftah started his academic career as an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University in 1979. In 1988 he became full professor there and from 1998 until 2002 he was Associate Head for the Department. Since 2002 he has been a Tier 1 Canada Research Chair at the University of Ottawa, SITE and in 2006 he was appointed Distinguished University Professor. During his sabbatical leaves, he did consulting work for BNR and Nortel Networks (1986-87; 1993-94; and 2000-01). Dr. Mouftah has published over 1000 technical papers, 7 books and 48 book chapters. To his credit he has 12 patents and 140 industrial reports. He has received research grants and contracts totaling close to $40 million and he has supervised more than 300 highly qualified personnel of which 95 are Master's and 63 are PhD graduates and 30 are post-doctoral fellows. Dr. Mouftah has served the Institute of Electrical and Electronic Engineering (IEEE) Communications Society as Editor-in-Chief of the Communications Magazine (1995-97), Director of Magazines (1998-99), Chair of the Awards Committee (2002-03), Director of Education (2006-07), and Member of the Board of Governors (1997-99 and 2006-07). Also, he is the founding Chair of two of IEEE Communications Society's Technical Committees (TCs): Optical Networking TC (2002-04) and Ad Hoc and Sensor Networks TC (2005-07). He has been a Distinguished Speaker of the IEEE Communications Society (2000-2008). Dr. Mouftah is the recipient of the 1989 Engineering Medal for Research and Development from the Association of Professional Engineers of Ontario and of the 2002 Ontario Distinguished Researcher Award of the Ontario Innovation Trust. He has also received 12 Outstanding/Best Paper Awards (ISMVL'1984; IEEE Communications Magazine in 1993; SPECTS'2002; HPSR'2002; CITO Innovators2004; ICC'2005; 2 at ISCC2008; CCECE2009; IST-AWSN'09; WiSense2010; and EPEC2010), the IEEE Canada Outstanding Service Award (1995), and the CSIM Distinguished Service Award of the IEEE Communications Society (2006). In 2004 Dr. Mouftah received the IEEE Communications Society Edwin Howard Armstrong Achievement Award and the George S. Glinski Award for Excellence in Research from the Faculty of Engineering, University of Ottawa. In 2006 he was honored with the IEEE McNaughton Gold Medal and the Engineering Institute of Canada Julian Smith Medal. In 2007 he was the recipient of the Royal Society of Canada Thomas W. Eadie Medal. He has also received the 2007-2008 University of Ottawa Award for Excellence in Research, and the 2008 ORION Leadership Award of Merit. Dr. Mouftah is a Fellow of the IEEE (1990), Fellow of the Canadian Academy of Engineering (2003), Fellow of the Engineering Institute of Canada (2005), and Fellow of the Royal Society of Canada RSC Academy of Sciences (2008).

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Prof. H. J. Siegel   (Fellow of: IEEE and ACM)
Colorado State University

Tutorial #4: Resource Management for High-Performance Computing Systems

Abstract- To use a high-performance computing system efficiently, the resources in the system must be assigned to computational tasks in an effective way. Furthermore, there is a need for energy-efficient computing at many different levels. This tutorial focuses on resource management in a heterogeneous high-performance distributed computing system that is oversubscribed and has an energy-constraint.

Topics with broad applicability are presented, including:
1) Why it is impossible to solve scheduling problems optimally;
2) A general system performance measure for many environments;
3) How to analyze the trade-offs between two conflicting performance goals;
4) Examples of energy-aware heuristics to allocate heterogeneous resources;
5) How genetic algorithms for optimization problems are constructed.

The resource management problem of assigning dynamically-arriving tasks to a shared heterogeneous collection of machines with different computational capabilities and energy-usage behaviors is addressed. These machines execute a workload composed of different tasks, where the tasks have diverse computational and energy characteristics. The execution time and energy consumption of each task on each machine is based on how the task's computational requirements interact with the machine's capabilities.

To measure system performance, each task has its own utility function that represents the value of completing that task, and this utility decreases the longer it takes a task to complete. The goal of the resource manager is to maximize the sum of the utilities earned by all tasks arriving in the system over a given interval of time, while satisfying a constraint on how much energy is consumed. Example resource management heuristics to accomplish this goal for both serial and parallel tasks are described and compared.

Minimizing system energy consumption and maximizing system computing performance (utility) are two conflicting goals. A method is given to analyze the trade-offs between these two goals as a bi-objective optimization problem, where a set of different serial task resource allocations are created that demonstrate the possibilities.

The resource management approaches presented can be applied to a variety of computing and communication system environments, including parallel, distributed, cluster, grid, Internet, cloud, embedded, multicore, content distribution networks, wireless networks, and sensor networks. In addition, the approaches can be used with many different system performance metrics and constraints.


Please check Prof. Siegel's keynote speech here: Robust Computing systems


Biography- H. J. Siegel is a Professor Emeritus and Senior Research Scientist/Scholar at Colorado State University (CSU). From 2001 to 2017, he was the George T. Abell Endowed Chair Distinguished Professor of Electrical and Computer Engineering at CSU, where he was also a Professor of Computer Science. He was a professor at Purdue University from 1976 to 2001. He received two B.S. degrees from the Massachusetts Institute of Technology (MIT), and the M.A., M.S.E., and Ph.D. degrees from Princeton University. He is a Fellow of the IEEE and a Fellow of the ACM. Prof. Siegel has co-authored over 450 published technical papers in the areas of parallel and distributed computing and communications, which have been cited over 17,000 times. He was a Coeditor-in-Chief of the Journal of Parallel and Distributed Computing, and was on the Editorial Boards of the IEEE Transactions on Parallel and Distributed Systems and the IEEE Transactions on Computers. For more information, please see www.engr.colostate.edu/~hj

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Prof. Amir Atiya   (Winner of The KFAS Award for Scientific Achievements and Contributions)
Cairo University

Tutorial #1: Machine Learning in Natural Language Processing

Abstract- The abundance of text in electronic form nowadays in the Internet or otherwise allows us to perform intelligent tasks and automate many processes, such as sentiment analysis, text summarization, automated translation, etc. This led to the field of natural language processing (NLP) achieving much prominence lately. We will review the various applications of natural language processing, and how they can be solved using machine learning, such as neural networks, and support vector machines.

A recent direction is the so-called word-to-vector modeling. In such an approach a word is modeled as a vector of numbers. The similarity between two words is represented by the proximity of the two corresponding vectors. This way we model the relations between the words in a quantitative way, and it allows better text understanding, and better implementation of such applications as sentiment analysis.

In this talk I will review the state of the art in word to vector modeling. Moreover, I propose a new approach that is based on embedding the words into a sphere, whereby the dot product of the corresponding vectors represents the similarity between any two words. Embedding the vectors into a sphere enables us to take into consideration the polarity or the words (like a word and its opposite can be modeled as a vector and its minus). Such fundamental problem was never solved in previous approaches. The proposed approach offers other benefits, such as simplicity, and flexibility.




Please check Prof. Amir's keynote speech here: Machine Learning


Biography- Dr. Amir Atiya received his B.S. and M.S. degrees from Cairo University in1982 and1985 respectively, and the M.S. and Ph.D. degrees from Caltech (California Institute of Technology), in 1986 and 1991 respectively, all in electrical engineering. Dr. Atiya is currently a Professor in the Department of Computer Engineering, Cairo University. During1997-2001 he held the position of Visiting Associate at Caltech, and during the summer of 2005 he was a Visiting Professor at Chonbuk National University, South Korea. He also held various appointments in the finance industry and in the data mining industry. His research interests are in the areas of statistics, machine learning, theory of forecasting, neural networks, computational finance, and pattern recognition, in which he published over 50 journal papers and 80 conference papers. His work is highly cited and one of his models has been included in Matlab as a function in the financial toolbox. Dr. Atiya received the highly regarded Kuwait Prize in 2005. He also received the Egyptian State Prize for Best Research in Science and Engineering, in 1994, and received the Young Investigator Award from the International Neural Network Society, in 1996. Currently, he is an Associate Editor for IEEE Transactions on Neural Networks, since 1998. He was a Guest Co-editor of the special issue of IEEE Transactions on Neural Networks on 'Neural Networks in Financial Engineering', July 2001, and was a Guest Co-editor of the special issue of IEEE Transactions on Neural Networks on 'Adaptive Learning Systems in Communications Networks', September 2005.

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Dr. Yasser Rasheed  
Intel Corporation

Tutorial #2: Robust Endpoint Security Strategy

Agenda of the workshop-

Duration: 3 hours
1-hr Presentation on the four priorities (Identity, data, threats and disaster recovery);
50-min Breakout sessions (ideally 4 groups, each tackling one area of the above);
10-min Break;
45-min Report out and discussion;
15-min Wrap-up/closing comments


Please check Dr. yasser's keynote speech here: Cyber security: current status and challenges


Biography- Yasser Rasheed is the Global Director of Endpoint Security Products at Intel. He leads worldwide sales for Intel's HW-based end-point security solutions and products, such as Intel® Authenticate and Intel® Data Guard. Prior to this role, Yasser was the CTO for Intel's Business Client Platform Division, responsible for product definition, architecture and execution of key innovations for Intel's business client platforms. Dr. Rasheed has been with Intel since 2000, where he led various R&D projects and played an instrumental role in establishing Intel's early vision for Digital and Connected Home. Yasser holds a Ph.D. in Electrical and Computer Engineering from the University of Toronto, and an Executive MBA from the University of Oregon.

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