IEEE Toronto Section

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Archive for the ‘Systems’ Category

Fingerprints of Black-Box Optimization in Science and Engineering

Friday, October 27th, 2017

Monday November 13, 2017 at 3:00 p.m. Dr. Shahryar Rahnamayan will be presenting “Fingerprints of Black-Box Optimization in Science and Engineering”.

Day & Time: Monday November 13, 2017
3:00 p.m. – 5:00 p.m.

Speaker: Dr. Shahryar Rahnamayan

Location: Room ENG 210
George Vari Engineering and Computing Centre
245 Church Street, Toronto, ON M5B 1Z4

Contact: Mehrdad Tirandazian

Organizers: IEEE Toronto Systems Chapter

Abstract: In this research seminar, the speaker will discuss his recent optimization research works and accomplishments, categorized in the following two main groups of contributions: theoretical/developmental and practical. The first group will cover his contributions in large-scale optimization, opposition-based computation, many-objective optimization, image-based large-scale visualization and interaction, incremental cooperative coevolution, micro-differential evolution, 3D visualization of many-objective Pareto-front, innovation, preserving constraint handling, decision making in high-dimensional objective space, and multi-modal optimization. In the practical category, the speaker will explain several real-world applications to demonstrate contributions of optimization in medical image processing, renewable energy systems, forensic science, vibration, scheduling, and wireless sensors network. In this talk, the essential role of complex black-box optimization in since and engineering will be highlighted. This seminar would be beneficial for faculty members and students who conduct “research in optimization” or “optimization in research”.

Biography: Dr. Shahryar Rahnamayan received his B.Sc. and M.Sc. degrees both with honors in software engineering. In 2007, he received his Ph.D. degree in the field of evolutionary computation from the University of Waterloo (UW), Canada. Since 2008, he is an associate professor in the Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology. He is a faculty member of the BEACON Center (an NSF center for study of evolution in action) since 2014; and also adjunct professor at the Systems Design Engineering, University of Waterloo, since 2009. Dr. Rahnamayan was a postdoctoral fellow at the School of Engineering, Simon Fraser University, in 2008. His research is mainly focused on evolutionary computation and its real-world applications. Dr. Rahnamayan has 139 peer-reviewed publications mostly in evolutionary optimization areas, which received 3700 citations (h-index: 24); one of his high-impact journal papers in optimization ranked 23rd out of 194,000 in term of number of citations, 2008-2017. Dr. Rahnamayan co-founded Segasist Technologies Inc., which develops segmentation solutions for medical image analysis and radiation planning; the company raised over $2M and secured the FDA approval. Dr. Rahnamayan has been awarded several prestigious research grants, including, NSERC Discovery Grant and Applied Research and Commercialization Initiative Fund. He recently conducted research as a visiting associate professor at Michigan State University (MI, USA) for two years (2014-2016). Dr. Rahnamayan is an active reviewer for more than thirty international conference and journal papers. He has been awarded the UOIT Research Excellence Award in 2017.

System of Systems Engineering – Systems Analysis and Policy Optimization

Wednesday, October 25th, 2017

Monday November 27, 2017 at 3:00 p.m. Kyarash Shahriari will be presenting “System of Systems Engineering – Systems Analysis and Policy Optimization”.

Day & Time: Monday November 27, 2017
3:00 p.m. – 5:00 p.m.

Speaker: Kyarash Shahriari

Location: Room ENG 210
George Vari Engineering and Computing Centre
245 Church Street, Toronto, ON M5B 1Z4

Contact: Mehrdad Tirandazian

Organizers: IEEE Toronto Systems Chapter, IEEE Toronto Aerospace & Electronic Systems Chapter

Abstract: The new social/economical/environmental context we are living in necessitates ever-increasing complex and collaborative systems. This has given birth to a new category of systems called System of Systems (SoS). SoS is a collection of interconnected complex systems each of which are independent in structure and governance, occasionally competitors in their activities, but collaborate together, by force or in a volunteer basis, to achieve specific objectives and to look for emergent properties which are not otherwise achievable. Examples of the SoS are System of financial institutions in a country; a regional electrical grid including distributed power generators operating together in an open energy market; or transportation network in provincial, federal, or international level. Treating the previously known complex systems in SoS context implies new modeling, simulation, and analysis engineering tools together with new optimization methodologies. The main benefits, especially for policy makers and authorities, would then be the simplicity of analysis and adjustments of policies which results in costs reduction for both authorities and stakeholders. In this talk we review the concept of SoS, the differences between SoS and previously known complex systems, and the state of the engineering tools for these systems.

Biography: Kyarash received his B.Sc.’2000 in Electronics Engineering, and MSc’2003 and PhD’2007 in Control Systems Engineering respectively from Institute National Polytechnique de Grenoble (INPG) and Universite Joseph Fourier, Grenoble, France. He started his professional career with Atkins Rail, London, UK, as Systems Research Engineer where he worked on developing integrated system-oriented frameworks for Safety, Security, and Sustainability Analysis. After moving to Canada in 2008, he joined LACM laboratory, Laval University, as research fellow and Centre de recherche industrielle du Quebec (CRIQ), Quebec City, a year after, as Research Officer with the main focus on Complex Dynamic Systems Control, System of Systems Engineering, Energy Efficiency and Continuous Improvement in energy intensive industries. To accept new challenges, Kyarash moved to aerospace industry in 2013 and joined Aversan Inc. / Honeywell Aerospace as Control Systems Design Engineer where he work on Environmental Control Systems (ECS) in aircrafts.

Kyarash is a Senior Member of the IEEE, he was the founder chair of Young Professional Affinity Group, Quebec City Section, and is currently holding Aerospace and Electronic Systems Society (AESS) chapter chair, Toronto Section. He is also registered professional engineer in Quebec and in Ontario Provinces.

Kyarash’s main field or interests are System of Systems, Advanced Control Systems, and Energy Efficiency.

Who Are We Studying in Social Media: Bots or Humans?

Friday, November 4th, 2016

Thursday November 24, 2016 at 12:00 p.m. Dr. Anatoliy Gruzd, Associate Professor of Ted Rogers School of Management and Canada Research Chair in Social Media Data Stewardship, will be presenting “Who Are We Studying in Social Media: Bots or Humans?”.

Speaker: Dr. Anatoliy Gruzd
Associate Professor
Ted Rogers School of Management, Ryerson University
Canada Research Chair in Social Media Data Stewardship

Day & Time: Thursday, November 24, 2016
12:00 p.m. – 1:00 p.m.

Location: Room ENG 288, George Vari Centre for Computing and Engineering, 245 Church Street
Ryerson University, Toronto, Ontario, M5B 2K3
Map: http://www.ryerson.ca/maps – Look for ENG

Organizers: IEEE Toronto Systems Chapter, Alexei Botchkarev
IEEE Toronto WIE, Magnetics, Measurement/Instrumentation-Robotics, Computer Science Department of Ryerson University
Maryam Davoudpour

Registration: Registration is free, but space is limited. Please register via http://tinyurl.com/systemsChapterEvent24

Abstract: Researchers studying various online and computer-mediated communities used to be able to argue that the online is an extension of the offline, and that offline and online are just different slices of real life. But the increasing number of bots in our datasets and the increasing use of algorithmic filtering by social media giants are widening the gap between online and offline, and between computer-mediated and algorithm-driven communication. This in turn makes some online data less reliable, at least for those of us studying human behavior. It also begs the question, if we are using data from social media for modelling, are we modelling human behavior in social media or simply reverse engineering how bots and other algorithms operate? Therefore, there is an urgent need to better understand the nature of bots and algorithmic filtering, and their influence on users’ online interactions, not just from a computational, but also from sociological perspective. This talk will discuss some of the key challenges and possible solutions to detecting social bots in the context of conducting social media research.

Biography: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor in the Ted Rogers School of Management at Ryerson University. He is also the Director of the Social Media Lab and a co-editor of a multidisciplinary journal on Big Data and Society published by Sage. Dr. Gruzd’s research initiatives explore how the advent of social media and the growing availability of social big data are changing the ways in which people communicate, collaborate and disseminate information and how these changes impact the social, economic and political norms and structures of modern society. Dr. Gruzd and his lab are also actively developing and evaluating new approaches and tools to support social media data analytics and stewardship.

His research and commentaries have been reported across Canada and internationally in various mass media outlets such as Foreign Affairs, Los Angeles Times, Nature.com, The Atlantic, The Globe and Mail, The National Post, The Canadian Press, CBC TV, CBC Radio, CTV and Global TV.

Operational-Log Analysis for Big Data Systems: Challenges and Solutions

Monday, October 24th, 2016

Friday November 18, 2016 at 12:00 p.m. Dr. Andriy Miranskyy, Assistant Professor at the Department of Computer Science, Ryerson University, will be presenting “Operational-Log Analysis for Big Data Systems: Challenges and Solutions”.

Speaker: Dr. Andriy Miranskyy
Assistant Professor, Department of Computer Science, Ryerson University

Day & Time: Friday, November 18, 2016
12:00 p.m. – 1:00 p.m.

Location: George Vari Centre for Computing and Engineering
Ryerson University
Room: ENG 288
245 Church Street, Toronto, Ontario M5B 2K3
Map – http://www.ryerson.ca/maps – Look for ENG

Registration: Registration is free, but space is limited. Please register via this link: http://tinyurl.com/systemsEvent

Organizers: IEEE Toronto Systems Chapter, Alexei Botchkarev albot@ieee.org
IEEE Toronto WIE, Magnetics, Measurement/Instrumentation-Robotics and Computer Science Department of Ryerson University
IEEE Toronto WIE Chair: Maryam Davoudpour maryam.davoudpour@ieee.org

Abstract: Big data systems (BDSs) are complex, consisting of multiple interacting hardware software components, such as distributed compute nodes, networking, databases, middleware, business intelligence layer, and high availability infrastructure. Any of these components can fail. Finding the failures’ root causes is extremely laborious. Analysis of BDS-generated logs can speed up this process. The logs can also help improve testing processes, detect security breaches, customize operational profiles, and aid with any other tasks requiring runtime-data analysis.

However, practical challenges hamper log analysis tools’ adoption. The logs emitted by a BDS can be thought of as big data themselves. When working with large logs, practitioners face seven main issues: scarce storage, unscalable log analysis, inaccurate capture and replay of logs, inadequate log-processing tools, incorrect log classification, a variety of log formats, and inadequate privacy of sensitive data. This talk describes the challenges and practical solutions faced while building and institutionalizing dynamic analysis tools in the industry.

Biography: Andriy Miranskyy is an assistant professor at the Department of Computer Science, Ryerson University. His research interests are in the area of mitigating risk in software engineering, focusing on software quality assurance, program comprehension, software requirements, project risk management, Big Data, and Green IT. Andriy received his Ph.D. in Applied Mathematics at the University of Western Ontario. He has 17 years of software engineering experience in information management and pharmaceutical industries. Prior to joining Ryerson, Andriy worked as a software engineer in the IBM Information Management division at the IBM Toronto Software Laboratory; currently, he is the Faculty Fellow of the IBM Centre for Advanced Studies. He has served as Guest Editor for a special edition of IEEE Software as well as organizer, committee member, and reviewer for several software engineering workshops and conferences.

Modeling Semantics of Content on Twitter (What did you mean when you said Yoyo!)

Tuesday, August 11th, 2015

October 22, 2015 at 12:00 p.m. Dr. Ebrahim Bagheri, Associate Professor and the Director for the Laboratory for Systems, Software and Semantics (LS3) at Ryerson University, will be presenting “Modeling Semantics of Content on Twitter (What did you mean when you said Yoyo!)”.

Speaker: Dr. Ebrahim Bagheri
Associate Professor and the Director for the Laboratory for Systems, Software and Semantics (LS3) at Ryerson University.

Day & Time: Thursday, October 22, 2015
12:00 p.m. – 1:00 p.m.

Location: Kerr Hall West
379 Victoria Street, Toronto, Ontario
Ryerson University
Room: KHW057
Map – http://www.ryerson.ca/maps – Look for KHW

Organizer: IEEE Systems Chapter – Toronto Section

Contact: E-mail: Alexei Botchkarev

Registration: Registration is free, but space is limited. Please register via this link: http://tinyurl.com/systems-Oct-22

Abstract: The microblogging service, Twitter, has gained wide popularity with over 300M active users and over 500M tweets per day. The unique characteristic of Twitter, only allowing short length messages to be communicated, has brought about interesting changes to how information is expressed and communicated by the users, i.e., the semantics of information when expressed on Twitter differ from when expressed on other medium. For instance, the word ‘metal’ when observed on Twitter carries a different semantic meaning, most likely referring to heavy metal music, as opposed to when used in other contexts where its predominant sense is the metal material. In this talk, I will discuss how the meaning and senses of words can be captured and modeled on Twitter to enable better and more efficient search, retrieval and recommendation of content.

Biography: Ebrahim Bagheri is an Associate Professor and the Director for the Laboratory for Systems, Software and Semantics (LS3) at Ryerson University, and has been active in the areas of the Semantic Web and Software Engineering. He was one of the research theme leaders of the national project on Radiation Emission Monitoring at the National Research Council Canada and was responsible for leading the development of the Semantic Web and Knowledge Engineering components of that project. In 2011, he co-chaired the Canadian Semantic Web Conference in Vancouver, BC (http://ceur-ws.org/Vol-774/). His work on Semantic-Driven Information Extraction has resulted in two provisionally patented technologies namely Denote and Derive. Denote is a semantic annotation platform based on Linked Open Data and Derive is an extensible architecture for unsupervised knowledge extraction and object (concept and property-value pair) population from the Web. He has been involved in projects that encompass the use of Semantic Web technologies in the areas of e-commerce and business process modeling funded by NSERC, AIF and IBM. Over the past 5 years, he has led projects worth over $5M CAD including various NSERC research and development projects with over 12 industrial partners. He is a senior member of IEEE, an IBM Faculty Fellow and a member of PEO.

Geographic Partitioning Techniques for the Anonymization of Health Care Data (Big data and advanced analytics methods to ensure privacy).

Sunday, August 2nd, 2015

September 29, 2015 at 1:30 p.m. Dr. Wei Shi, Assistant professor at the faculty of Business and I.T. in the University of Ontario Institute of Technology and an adjunct professor in the School of Computer Science at Carleton University, will be presenting “Geographic Partitioning Techniques for the Anonymization of Health Care Data (Big data and advanced analytics methods to ensure privacy)”.

Speaker: Dr. Wei Shi
Assistant professor at the faculty of Business and I.T. in the University of Ontario Institute of Technology and an adjunct professor in the School of Computer Science at Carleton University.

Day & Time: Tuesday, September 29, 2015
1:30 p.m. – 2:30 p.m.

Location: Eric Palin Hall
Ryerson University
Room: EPH207
87 Gerrard Street East, Toronto, Ontario
Click here to see the Map – Look for EPH

Organizer: IEEE Toronto Systems Chapter

Contact: E-mail: Alexei Botchkarev

Registration: Registration is free, but space is limited. Please register via this link: http://tinyurl.com/systemsEvent

Abstract: Hospitals and health care organizations collect large amounts of detailed health care data that is in high demand by researchers. Thus, the possessors of such data are in need of methods that allow for this data to be released without compromising the confidentiality of the individuals to whom it pertains. As the geographic aspect of this data is becoming increasingly relevant for research being conducted, it is important for an anonymization process to pay due attention to the geographic attributes of such data. In this talk, a novel system for health care data anonymization is presented. At the core of the system is the aggregation of an initial regionalization guided by the use of a Voronoi diagram. We conduct a comparison with another geographic-based system of anonymization, GeoLeader. We show that our system is capable of producing results of a comparable quality with a much faster running time.

Biography: Dr. Wei Shi is an assistant professor at the faculty of Business and I.T. in the University of Ontario Institute of Technology and an adjunct professor in the School of Computer Science at Carleton University. Dr. Shi received her BEng. in Computer Engineering from Harbin Institute of Technology in China and MSC and Ph.D. in Computer Science from Carleton University in Ottawa, Canada. Her research interests include big data analytics, algorithm design and analysis for distributed environments such as the cloud, wireless sensor network, mobile network as well as vehicular network. She has published over 40 technical papers in top conferences and journals. Her research work is supported by IBM and Natural Sciences and Engineering Research Council (NSERC) of Canada.