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IEEE
Computational Intelligence Society /
Systems, Man & Cybernetics
Ottawa
Joint Chapter
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Past Meetings
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2019
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Chris Bildfell, Ali Wytsma and Rafael Falcon
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Understanding the data scientist transition from academia to industry
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Alex Teske
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2018
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Plamen Angelov
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Hani Hagras
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Rafael Falcon
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Nicolas Primeau
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2017
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Fatemeh Cheraghchi
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Hava Siegelmann
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Alex Plachkov
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Hisao Ishibuchi
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Fred Ma and Slawo Wesolkowski
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2016
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David Bissessar
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Howard Schwartz
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Benjamin Desjardins
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Mahmoud Gad & Ahmed Shah
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Witold Pedrycz
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George Yee
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Chung Horng Lung
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2015
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Jim Bezdek, CIS DL
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Catherine Cheung
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Cesare Alippi, CIS DL
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Rafael Falcon
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Farud Hadziomerovic
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Qinghan Xiao
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2014
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Gary Yen
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Chris Drummond
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2013
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Nathalie Japkowicz
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Diana Inkpen
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2012
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John Verdon
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Giovanni Acampora, Vincenzo Loia &
Autilia Vitiello
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Julio Valdes
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Russell C. Eberhart
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Alfredo Vaccaro
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2011
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James Bezdek
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Erik Blasch
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Emilio Miguelanez Martin
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Jerry Mendel
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Hani Hagras
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2010
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Rafael Falcon
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Alan Barton
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Qinghan Xiao
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Genci Capi
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Slawo Wesolkowski
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2009
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Sylvain Chartier
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Russel Eberhart
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Simon Haykin
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Qing Chen
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Q. J. Zhang
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2008
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James M. Keller
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Marcel Turcotte
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Moufid Harb
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2007
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Oana Frunza
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Jacek M. Zurada
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Stan Matwin
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Monique Frize
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2006
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Evangelia Micheli-Tzanakou
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Diana Inkpen
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Jian Pei
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Jean-Philippe Thivierge
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2005
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Mark Fiala
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Adrian D.C. Chan
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Wail Gueaieb
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B. John Oommen
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2004
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Dmitry Gorodnichy
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Julio J. Valdés
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Ana-Maria Cretu
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Peter Turney
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2003
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Emil M. Petriu
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Nicolas D. Georganas
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Date
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Wednesday March 13th, 2019
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Time
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6:15 PM Arrival and networking (light snacks
available)
6:45 PM Approximate start of talk (40-60 mins)
7:45 - 8:00 PM Q&A period
8:00 - 8:30 PM Post-talk networking and discussion
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Location
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SITE 5084
School of Electrical Engineering and Computer Science
University of Ottawa
800 King Edward Ave, Ottawa, K1N 6N5
registration is
required; please register
here
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Title
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Big Data Techniques for Maritime Risk
Management:
A Parallel & Distributed Risk Management
Framework with Natural Language Processing and Genetic Fuzzy Systems
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Speaker
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MSc. Alexander Teske
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Larus Technologies Corporation
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Abstract
Risk management is an essential tool for ensuring
the safety and timeliness of maritime operations and transportation.
Some of the many risk factors that can compromise the smooth operation
of maritime activities include harsh weather and pirate activity.
However, identifying and quantifying the extent of these risk factors
for a given vessel is not a trivial process. To do so automatically
requires both large volumes of data and the algorithms to exploit it.
In this talk, three big data techniques are applied
to an existing Risk Management Framework (RMF). A parallel/distributed
version of two of the RMF’s modules efficiently process large volumes
of data and assess the risk levels of the corresponding maritime
vessels in near-real-time. Natural language processing is used to keep
tabs on pirate activity by ingesting newspaper and magazine articles. A
genetic fuzzy system is used within the RMF's risk assessment module in
order to automatically learn the fuzzy rule base governing the risk
assessment process. Additionally, a geovisualization tool is presented
which displays the position and risk levels of ships at sea. Together,
these techniques enable a maritime risk management process powered by
big data.
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Speaker Biography
Alex
Teske received a bachelor’s degree in Software Engineering in 2016 and
a master’s degree in computer science in 2019, both from the University
of Ottawa. His research interests include optimization with genetic
algorithms, risk management, and natural language processing. He is
currently working as a software developer at Larus Technologies.
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Date
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Monday April 23th, 2018
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Time
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12:00 PM Arrival and networking (light snacks
available)
12:20 PM Approximate start of talk (30-40 mins)
1:00 - 1:15 PM Q&A period
1:15 - 1:30 PM Post-talk networking and discussion
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Location
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SITE 5084
School of Electrical Engineering and Computer Science
University of Ottawa
800 King Edward Ave, Ottawa, K1N 6N5
registration is
required; please register
here
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Title
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Type-2 fuzzy logic systems and their
applications
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Speaker
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Professor Hani Hagras
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University of Essex, United Kingdom
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IEEE Computational Intelligence Society Distinguished
Lecturer
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Abstract
The talks will cover the following topics
* Introduction to Type-2 Fuzzy Logic Sets and systems and their
theoretical basis
* Practical Implementation of Interval Type-2 Fuzz Logic Systems and
their various applications
* Emerging areas of type-2 fuzzy logic systems.
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Speaker Biography
Prof.
Hani Hagras is a Professor of Computational Intelligence, Director of
Research. Director of the Computational Intelligence Centre, Head
of the FuzzySystems Research Group and Head of the
Intelligent Environments Research Group in the University of Essex, UK.
He is a Fellow of Institute of Electrical and Electronics
Engineers (IEEE) and he is also a Fellow of the Institution
of Engineering and Technology (IET). and Principal
Fellow of the UK Higher Education Academy. His major research
interests are in computational intelligence,
notably type-2 fuzzy systems, fuzzy logic,
neural networks, genetic algorithms, and evolutionary
computation. His research interests also include ambient intelligence,
pervasive computing and intelligent buildings. He is also
interested in embedded agents, robotics and intelligent
control. He has authored more than 300 papers in international
journals, conferences and books. His work has received
funding that totalled to about £5 Million in the last five years from
the European Union, the UK Technology Strategy Board (TSB), the UK
Department of Trade and Industry (DTI), the UK
Engineering and Physical Sciences Research Council (EPSRC),
the UK Economic and Social Sciences Research Council (ESRC)
as well as several industrial companies including. He has also Five
industrial patents in the field of computational
intelligence andintelligent control. His research has won numerous
prestigious international awards where most recently he was awarded by
the IEEE Computational Intelligence Society (CIS), the 2013 Outstanding
Paper Award in the IEEE Transactions
on Fuzzy Systems and also he has won the 2004
Outstanding Paper Award in the IEEE Transactions
on Fuzzy Systems. He was also awarded the 2015 Global
Telecommunications Business award for his joint project with British
Telecom. In 2016, he was elected as Distinguished Lecturer by the IEEE
Computational Intelligence Society. He was also the Chair of the IEEE
CIS Chapter that won the 2011 IEEE CIS Outstanding Chapter award. His
work with IP4 Ltd has won the 2009 Lord Stafford Award for Achievement
in Innovation for East of England. His work has also won the 2011 Best
Knowledge Transfer Partnership Project for London and the
Eastern Region. His work has also won best paper awards in several
conferences including the 2014 and 2006 IEEE International
Conference on Fuzzy Systems and the 2012 UK
Workshop on Computational Intelligence. He served as the Chair of IEEE
Computational Intelligence Society (CIS) Senior Members Sub-Committee.
He served also as the chair of the IEEE CIS Task Force on Intelligent
Agents. He is currently the Chair of the IEEE CIS Task Force on
Extensions to Type-1 Fuzzy Sets. He is also a Vice Chair of
the IEEE CIS Technical Committee on Emergent Technologies. He is a
member of the IEEE Computational Intelligence Society
(CIS) Fuzzy Systems Technical Committee. He served also
as a member of the IEEE CIS Fellows Committee. He serves also as a
member of the IEEE CIS conferences committee. He is an Associate Editor
of the IEEE Transactions on Fuzzy Systems. He is also an
Associate Editor of the International Journal of
Robotics and Automation. Prof. Hagras chaired several
international conferences where he will act as the Programme Chair of
the 2017 IEEE International Conference on Fuzzy Systems.
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Date
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Monday February 26th, 2018
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Time
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6:45 PM Arrival and networking (light snacks
available)
7:10 PM Approximate start of talk (30-40 mins)
8:00 - 8:30 PM Post-talk networking and discussion
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Location
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Invest Ottawa
7 Bayview Road
registration is
required; please register
here
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Title
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Machine Learning meets Granular Computing:
the emergence of granular models in the Big Data era
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Speaker
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Dr. Rafael Falcon
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Research Scientist, Larus Technologies
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Adjunct Professor, Faculty of Engineering, University
of Ottawa
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Abstract
Traditional
Machine Learning (ML) models are unable to effectively cope with the
challenges posed by the many V’s (volume, velocity, variety, etc.)
characterizing the Big Data phenomenon. This has triggered the need to
revisit the underlying principles and assumptions ML stands upon.
Dimensionality reduction, feature/instance selection, increased
computational power and parallel/distributed algorithm implementations
are well-known approaches to deal with these large volumes of data.In
this talk we will introduce Granular Computing (GrC), a vibrant
research discipline devoted to the design of high-level information
granules and their inference frameworks. By adopting more symbolic
constructs such as sets, intervals or similarity classes to describe
numerical data, GrC has paved the way for a more human-centric manner
of interacting with and reasoning about the real world. We will go over
several granular models that address common ML tasks such as
classification/clustering and will outline a methodology to
appropriately design information granules for the problem at hand.
Though not a mainstream concept yet, GrC is a promising direction for
ML systems to harness Big Data.
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Speaker Biography
Rafael
received his PhD degree in Computer Science from the School of
Information Technology and Engineering at the University of Ottawa in
2012. Previously, he attained his Bachelor and MSc degrees in Computer
Science from the Central University of Las Villas, Cuba. He currently
works as a Research Scientist for Larus Technologies Corporation, an
Ottawa-based firm that specializes in high-level information fusion and
decision support from a Computational Intelligence angle. He is also an
Adjunct Professor with the School of Electrical Engineering and
Computer Science, University of Ottawa and chairs the IEEE
Computational Intelligence/ Systems, Man and Cybernetics Ottawa
chapter. His research interests are in the area of Computational
Intelligence with applications to security and defense, including
wireless sensor networks, robotics, maritime domain awareness and
multi-sensor data fusion.
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Date
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Thursday January 18th, 2018
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Time
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6:30 - 6:45 PM Pizza / drinks and networking
6:45 - 8:15 PM Technical talk
8:15 - 8:30 PM Q&A and networking
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Location
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School of Electrical Engineering and Computer Science
Room 5084
University of Ottawa
800 King Edward Ave
registration is
required; please register
here
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Title
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Risk-Aware Decision Support for Critical
Infrastructure Protection using Multi-Objective Optimization
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Speaker
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Nicolas Primeau
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Amazon.com Inc
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Abstract
Uncertainty
is the root of all situational risk. Yet uncertainty remains an
ambiguous, fuzzy, far from solved concept. Multiple approaches exist to
reduce the consequences of unwanted events, such as reducing their
likeliness, predicting their likelihood, or mitigating their effects.
However, all of these approaches require computational methods that can
handle ambiguous situations that may or may not have a single correct
solution. In the end, these methods must be combined into closed loop
systems that can be used by organizations to reduce their own
situational uncertainty.
This talk will discuss decision support systems for risk management,
multi-objective optimization, and interfaces through which these
systems can interact with the physical world, such as wireless sensor
networks and the internet of things. It will first present important
concepts on each of these topics, then delve into three scenarios of
increasing complexity pertaining to critical infrastructure protection
where these tools are used. As research in many of these topics is
nascent, these is no clear roadmap on how these tools can be properly
used. Consequently, the talk will end with lessons learnt in the
research endeavours that yielded these scenarios, and advice for future
research.
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Speaker Biography
Nicolas
Primeau received his
B.A.Sc. (2015) and M.A.Sc. (2017) in Computer and Electrical
Engineering from
the School of Information Technology and Engineering at the University
of
Ottawa. His research interests included wireless sensor networks,
computational
intelligence, decision support systems for risk management, and
multi-objective
optimization. He joined Amazon.com, Inc. in 2017 where he is currently
involved
in developing Amazon's Alexa services.
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Date
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Wednesday November 22nd, 2017
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Time
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6:30 - 6:45 PM Pizza / drinks and networking
6:45 - 8:15 PM Technical talk
8:15 - 8:30 PM Q&A and networking
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Location
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School of Electrical Engineering and Computer Science
Room 5084
University of Ottawa
800 King Edward Ave
registration is
required; please register
here
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Title
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Metaheuristic Optimization in Maritime
Disruption Management: Big-Data-Enabled Multi-Objective Modeling of
Vessel Scheduling Recovery Problem
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Speaker
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Fatemeh Cheraghchi
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PhD Student
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School of Electrical Engineering and Computer
Science, University of Ottawa
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Abstract
The international seaborne
trade constitutes nearly 90% of the volume of global trade and is
linked to
almost every international supply chain. Thus, it has a major impact on
the
global economy. Due to limited
differentiation of services, the main competition between stakeholders
in this
industry is cost-based. Therefore, in order to be able to sustain
competitiveness, efficient and optimized services should reduce the
cost and
present reliable services. However, such services are vulnerable to
many
uncertainty factors. Disruption management refers to dynamically
recovering
from various disruption events that prevent the original operational
plan from
being seamlessly executed.
In this talk,
we will introduce a multi-objective
modeling of the Speed-based Vessel
Schedule Recovery Problem (S-VSRP)
to mitigate the impact of disruptions in a vessel’s schedule by
adjusting the
vessel’s speed along the route. Automatic Identification System
(AIS) real-world data is brought to
S-VSRP in order to turn it into a Big-Data-enabled optimization problem. We propose meta-heuristic optimization
methods to find Pareto-optimal solutions. The main decision objectives
are concerned
with the minimization of the total loss
and delay while maximizing the compliance with
historical navigational patterns. Several evolutionary multi-objective
optimizers
are utilized to approximate the Pareto-optimal solutions providing the
vessel
voyage speeds. The Pareto front gives the stakeholders the ability to
inspect
the tradeoff among these three conflicting objectives.
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Speaker Biography
Fatemeh Cheraghchi is a Ph.D. student in Computer Science at the School
of Electrical Engineering and Computer Science of the University of
Ottawa.
Previously, she attained her Bachelor and MSc degrees in Computer
Science from
the University of Tehran, Iran. She currently works as a Research
Assistant at Larus
Technologies Corporation, an Ottawa-based firm that specializes in
high-level
information fusion and decision support from a Computational
Intelligence angle and for the Knowledge Discovery and Data (KDD) lab at the University
of Ottawa
on the NSERC project entitled “Big
Data Analytics for Maritime Internet of Things”. Her research
interests are
in the area of data mining, machine learning, evolutionary computing,
and
algorithm design, especially
with Big Data applications.
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Date
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Monday October 2nd, 2017
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Time
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6:30 - 6:45 PM Pizza / drinks and networking
6:45 - 8:15 PM Technical Talk
8:15 - 8:30 PM Q&A and networking
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Location
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School of Electrical Engineering and Computer Science
Room 5084
University of Ottawa
800 King Edward Ave
registration is
required; please register
here
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Title
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Turing and Super-Turing Computation
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Speaker
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Professor Hava Siegelmann
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University of Massachusetts Amherst and DARPA
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IEEE CIS Distinguished Lecturer
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Abstract
All current computers
are based on the Turing Machine mathematical model, invented by one of
the greatest minds of the last century: English mathematician and
computer scientist, Alan Turing (1912-1954). And while many biological
systems or aspects of systems are appropriately described in this
manner, Turing computation can only compute what it has been programmed
for; it lacks the ability to learn or adapt - suggesting that a
different form of computation must exist. It is possible then, that
current biological models may be fundamentally incorrect - holding
significant implications for Artificial Intelligence (which looks to
biology for answers on how to accomplish intelligent behavior) and the
accurate modeling and thus our understanding of biological computation.
We introduce the Super-Turing computational model - capable of
describing adaptation and computation found in living organisms. We
explain the relationship between the two models, where Super-Turing
computation can be thought of as an adaptive program calling
non-adaptive Turing sub-routines. Turing himself predicted a superior
computational system that would mimic natural systems and make possible
more intelligent, human-like computation; Super-Turing computation may
be the answer Turing sought.
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Speaker Biography
Hava
Siegelmann is a professor of computer science, and a world leader in
the fields of Artificial Intelligence, Machine Learning, and
Computational Neuroscience. Her academic position is in the school of
Computer Science and the Program of Neuroscience and Behavior at the
University of Massachusetts Amherst; she is the director of the
school's Biologically Inspired Neural and Dynamical Systems Lab.
Siegelmann is an
American computer scientist who founded the field of super-Turing
computation. For her lifetime contribution to the field of Neural
Networks she is the recipient of the 2016 Donald Hebb Award. She earned
her PhD at Rutgers University, New Jersey, in 1993
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Date
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Wednesday May 10th, 2017
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Time
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6:30 - 6:45 PM Pizza / drinks and networking
6:45 - 8:15 PM Technical Talk
8:15 - 8:30 PM Q&A and networking
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Location
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School of Electrical Engineering and Computer Science
Room 5084
University of Ottawa
800 King Edward Ave
registration is
required; please register
here
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Title
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Automatic, Soft-Data-Augmented Course of
Action Generation for Concurrent Security Operations in the Maritime
Domain
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Speaker
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Mr. Alex Plachkov
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Nav Canada
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Abstract
Automatic
Course of Action (CoA) generation, a key element of Level 3 High-Level
Information Fusion, is a fundamental component of effective risk
management and mitigation.
This talk shall present an extension of a framework capable of
integrating physics-based (hard) and people-generated (soft) data, for
the purpose of achieving increased situational assessment and automatic
CoA generation upon risk event detection. The ingested soft data
provides the framework with important subject matter expertise and
mission-specific requirements, allowing for superior resource (response
asset) management and increased overall mission effectiveness. This
talk will present an analysis conducted on experimental data gathered
from a combination of real-world and synthetically-generated scenarios
situated in the maritime world.
Keywords - course of action recommendation; decision support systems;
multi-criteria decision making; high-level information fusion;
hard-soft data fusion
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Speaker Biography
Alex
Plachkov received his Bachelor and Master of Applied Science in
Computer Engineering from the University of Ottawa in 2013 and 2016,
respectively. He completed his Master’s degree in the area of
situational awareness for maritime security operations in
collaboration with Larus Technologies Corporation, while working on
projects funded by
Defence Research & Development Canada (DRDC) and the Natural
Sciences and Engineering Research Council of Canada (NSERC). Parts of
his research work have been published in two IEEE and one ACM
conferences. Since the completion of his graduate studies, Alex has
been working as a Software Developer at NAV Canada, where he is leading
the development of a system providing services in the air traffic
control domain.
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Date
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Wednesday March 15th, 2017
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Time
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6:30 - 6:45 PM Pizza / drinks and networking
6:45 - 8:15 PM Technical Talk
8:15 - 8:30 PM Q&A and networking
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Location
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School of Electrical Engineering and Computer Science
Room 5084
University of Ottawa
800 King Edward Ave
registration is
required; please register
here
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Title
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Evolutionary Many-Objective Optimization:
Algorithms, Test Problems and Performance Indicators
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Speakers
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Professor Hisao Ishibuchi
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Graduate School of Engineering, Osaka Prefecture
University, Japan
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IEEE Computational Intelligence Society Distinguished
Lecturer
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Abstract
Evolutionary
many-objective optimization is a hot topic in the evolutionary
computation community. A large number of new algorithms as well as a
variety of modifications of existing algorithms have been proposed for
the efficient handling of multi-objective problems with four or more
objectives. In this presentation, first we check the increasing
popularity of evolutionary many-objective optimization. Next, after
briefly explaining the basic idea of evolutionary multi-objective
optimization, we overview some well-known difficulties in
many-objective optimization: search for Pareto optimal solutions,
approximation of the entire Pareto front, presentation of a large
number of non-dominated solutions, choice of a single final solution,
and examination of the search behavior of evolutionary algorithms. Then
we explain representative approaches to evolutionary many-objective
optimization: modification of Pareto dominance, dimensionality
reduction, incorporation of preference information, indicator-based
algorithms, and decomposition-based algorithms. Our main focus is on
the current trend in the field of evolutionary many-objective
optimization, which is the proposal of new decomposition-based
algorithms and their performance evaluation using DTLZ and WFG test
problems. Finally, for discussing promising future research directions,
we examine the following two issues: (i) choice of many-objective test
problems and (ii) specifications in performance indicators. More
specifically, we explain that the performance of recently proposed
decomposition-based algorithms strongly depends on the choice of test
problems as well as the specifications of reference points for the
hypervolume (HV) and inverted generational distance (IGD) indicators.
Examinations of these issues clearly show the importance of the
following future research directions: proposal of a wide variety of
many-objective test problems, examination of the reality of test
problems (i.e., their relevance to real-world problems), development of
a robust search mechanism with good performance over a wide variety of
test problems with no adaptation, and development of an efficient
adaptation mechanism of search strategies to a different test problem.
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Speaker Biography
Dr.
Ishibuchi received the BS and MS degrees from Kyoto University in 1985
and 1987, respectively. In 1992, he received the Ph. D. degree from
Osaka Prefecture University where he has been a professor since 1999.
He received a Best Paper Award from GECCO 2004, HIS-NCEI 2006,
FUZZ-IEEE 2009, WAC 2010, SCIS & ISIS 2010, FUZZ-IEEE 2011 and
ACIIDS 2015. He also received a 2007 JSPS Prize
(https://www.jsps.go.jp/english/e-jsps-prize/awards_4th_02.html). He
was the IEEE CIS Vice-President for Technical Activities (2010-2013),
the General Chair of ICMLA 2011, the Program Chair of IEEE CEC 2010 and
IES 2014, and a Program/Technical Co-Chair of FUZZ-IEEE 2006,
2011-2013, 2015 and IEEE CEC 2013-2014, 2018. Currently, he is the
Editor-in-Chief of IEEE CI Magazine (2014-2017), an IEEE CIS AdCom
member (2014-2019), and an IEEE CIS Distinguished Lecturer (2015-2017).
He is also an Associate Editor of IEEE TEVC (2007-2016), IEEE Access
(2013-2016) and IEEE TCyb (2013-2016). He is an IEEE Fellow. His
current research interests include fuzz classifier design, evolutionary
multi-objective and many-objective optimization, and evolutionary
games. According
to Google Scholar, the total number of citations of his publications is
about
20,000 and his h-index is 62. According to the Most Cited Researchers
List
developed for ShanghaiRanking's Global Ranking of Academic Subjects
2016 by
Elsevier, he
is included in the most cited 300 researchers in the field of Computer
Science
and Engineering.
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Date
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Thursday February 2nd, 2017
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Time
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11:30-12:00 Refreshments and networking
12:00-1:00 Technical Talk / Q&A Period
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Location
|
School of Electrical Engineering and Computer Science
Room 5084
University of Ottawa
800 King Edward Ave
registration is
required; please register
here
|
Title
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Capability-based force mix analysis with a
multi-objective optimization example
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Speakers
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Fred Ma and Slawomir Wesolkowski, Defense Scientists
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Center for Operational Research and Analysis
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Defense Research and Development Canada (DRDC)
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Abstract
A
major financial expense for any military is the acquisition, operation,
and
maintenance of assets (e.g., vehicles, ships and aircraft), as well as
training
systems for those assets (e.g., combination of vehicles, vehicle
simulators and
other training systems). Even slight improvements in force structure
efficiency
can save governments large amounts of money or, using the same budget,
can buy
more capable or greater quantities of equipment. The uncertainty,
multi-objectivity, and temporal dependence of military missions
increase the
complexity of military force structure problems. This complexity,
coupled with
high equipment costs, has driven the development (and application) of
capability-based models using a spectrum of methods – from force
structure computation
to force structure evaluation. Models are capability-based, in that
capabilities provided by assets are matched to capabilities required by
scenarios (representing a variety of military missions) to be carried
out by
those assets (e.g., cargo and passenger capacities for airlift;
anti-submarine
warfare, anti-air warfare or surveillance for maritime fleets). First,
the
merits of various models on the simulation-optimization continuum
developed at
Defence Research and Development Canada’s Centre for Operational
Research and
Analysis (DRDC CORA) are discussed, and how those methods can be useful
for
detailed asset mix evaluations or broad asset mix computations. Second,
we
describe a multiobjective evolutionary algorithm generate force mix
options that
trade-off between lower bounds for multiple objectives. The lower bound
comes
from the assumption that a nation has complete flexibility to engage in
scenarios at times that minimize simultaneous demand on FEs. The
results are
compared with the results from Tyche, a discrete event Simulator, which
provides a more realistic, though pessimistic, point estimates. Results
confirm
the expected relative behavior of both models.
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Speakers Bios
Dr.
Fred Ma worked with
experimental CCD imagers before studying methods to efficiently
simulate light
propagation in dielectric waveguides for his Masters. His doctorate was
in
evolutionary optimization of digital communications systems designs
targeting
reconfigurable hardware platforms. At Defence R&D Canada's
Centre for
Operational Research and Analysis, he has studied service oriented
network
architectures for interconnecting command and control systems,
enterprise
architecture, project prioritization methods, systems of systems
reliability,
cost estimation, and various aspects of strategic planning for defence.
Dr.
Slawomir Wesolkowski is a
Scientist at Defence R&D Canada and an Adjunct Professor at both
the School
of Electrical Engineering and Computer Science at the University of
Ottawa and
in the Systems Design Engineering Department at the University of
Waterloo. He
holds five US patents, and one Canadian/EU patent. He has published
over 50
papers in refereed journals and conferences on operations research,
image
processing and technology introduction, as well as over 30 internal
reports
advising the Government of Canada. He obtained BASc, MASc and PhD
degrees in
Systems Design Engineering from the University of Waterloo, Canada. His
research interests include operations research and risk analysis.
|
Date
|
Thursday October 20th, 2016
|
Time
|
6:30-7:00 Refreshments and networking
7:00-8:30 Technical Talk / Q&A Period
|
Location
|
Grad Student House
University of Ottawa
601 Cumberland St
registration is
required; please register
here
|
Title
|
Privacy, security and convenience: biometric
encryption for smartphone-based electronic travel documents
|
Speaker
|
David Bissessar
|
| |
Advanced Analytics Division
|
| |
Canada Border Services Agency
|
Abstract
This talk discusses a previously published
book chapter where the authors propose a new paradigm for issuing,
storing and verifying travel
documents that features entirely digital documents which are bound to
the individual by virtue of a privacy–respecting biometrically derived
key, and which make use of privacy-respecting digital credentials
technology. Currently travel documentation rely either on paper
documents or electronic systems requiring connectivity to core servers
and databases at the time of verification. If biometrics are used in
the traditional way, there are accompanying privacy implications. We
present a smartphone-based approach which enables a new kind of
biometric checkpoint to be placed at key points throughout the
international voyage. These lightweight verification checkpoints would
not require storage of biometric information, which can reduce the
complexity and risk of implementing these systems from a policy and
privacy perspective. Our proposed paradigm promises multiple benefits
including increased security in airports, on airlines and at the
border, increased traveller convenience.
|
|
Speaker Bio
David
Bissessar is a Research Scientist at the CBSA in the Advanced Analytics
Section. David’s areas of expertise are Biometrics, Privacy, Machine
Learning and Cryptography. Prior to joining the CBSA in 2010, he worked
at numerous positions in industry including positions as a Solutions
Principal for EMC, a Technical Director for IBM, the CTO of 21
Communications in China and as a software engineer at Nortel.
|
Date
|
Monday May 30th, 2016
|
Time
|
6:30-7:00 Light dinner and networking
7:00-9:00 Technical Talk / Q&A Period
|
Location
|
Raven Telemetry
6 Hamilton N Suite 250
free admission; light dinner will be served
registration is
required; please register
here
|
Title
|
Multi-agent machine learning for mobile
robots: a reinforcement approach
|
Speaker
|
Prof. Howard Schwartz
|
| |
Systems and Computer Engineering
|
| |
Carleton University
|
Abstract
This
talk presents methods of Multi-Agent Machine Learning for mobile
robots. We demonstrate how reinforcement learning can be used as the
underlying learning algorithm for mobile robots. The mobile robots
learn how to work as either cooperating or competing vehicle teams. We
are targeting applications associated with infrastructure security,
border security and surveillance. The research focusses on the
pursuer-evader game and the guarding a territory game. We examine
several versions of these games, including the cases of high speed
super invaders and super evaders that are captured by multiple
cooperating slower pursuers and guards. The research uses reinforcement
learning algorithms that are based on Fuzzy Q-Learning and Fuzzy Actor
Critic learning. These algorithms allow the mobile robots to adapt to
their changing environments. We show how the robots can achieve their
Nash Equilibrium strategies and how they can take advantage of other
robots or agents that play poor (non-Nash Equilibrium) strategies. We
further demonstrate the idea of adaptive personalities, whereby agents
can be cooperative or aggressive and can change with time as
environmental conditions evolve. Video of simulation results and video
of experimental results are shown to demonstrate the effectiveness of
the proposed methods.
|
|
Speaker Bio
Professor
Schwartz received his BEng. degree in Civil Engineering from McGill
University, Montreal, Canada, and his MSc. and Ph.D. degrees from MIT
in Aerospace Engineering and Mechanical Engineering, respectfully. He
is currently Professor of Electrical and Computer Engineering at
Carleton University. He served as Department Chairman of the Department
of Systems and Computer Engineering, Carleton University from 2009 to
2013.
Dr. Schwartz developed early versions of high performance GPS receivers
while employed with the Canadian Marconi Co. (1982-1984). He has spent
sabbaticals at March Networks (2001-2002) developing real time video
analytics, at CMC Electronics working on software quality control
(2008) and most recently he spent a sabbatical year at Espial Inc.
(2014-2015) doing software development for TV set top boxes.
Professor Schwartz’s research is in the field of Multi-Agent Machine
Learning for mobile robots. He has published over 100 technical papers
in leading journals and international conferences. He is Associate
Editor of the IEEE Transactions on Cybernetics and is the author of the
book Multi-Agent Machine Learning: A Reinforcement Approach, Wiley, 2014
|
Date
|
Thursday May 12th, 2016
|
Time
|
6:30-6:45 Refreshments and networking
6:45-8:00 Technical Talk / Q&A Period
|
Location
|
Graduate Student House, Room 307
601 Cumberland St
free admission; refreshments will be served
registration is
required; please register
here
|
Title
|
Sensor relocation by mobile robots using
multiobjective optimization
|
Speaker
|
Mr. Benjamin Desjardins
|
| |
School of Electrical Engineering and Computer Science
|
| |
University of Ottawa
|
Abstract
Optimization
is one of the core focuses within computer science yet it can be
difficult to apply it to real-world problems. This is in part due to
the difficulty of mathematically modelling real world phenomena such
that it can be described as a function with a single output. In order
to circumvent this problem, we introduce the concept of modelling a
problem more realistically using evolutionary multi-objective
optimization algorithms.
Wireless
sensor networks (WSNs) provide a method for monitoring a region of
interest. Within the WSN domain there is a type of network referred to
as a Wireless Sensor and Robot Network (WSRN) in which a robot is added
to the network in order to provide some task. We look at the case where
said robot is responsible for relocating sensors to provide maximum
network coverage. This problem has been previously examined as a
traditional optimization problem but we provide a more realistic
real-world model allowing for more appropriate responses. In this talk,
we examine the steps required to intelligently transform a problem into
a multi-objective version in such a way that the end result is more
beneficial to the user. Tangentially, we will also examine the
performance of current evolutionary multi-objective optimization
algorithms with respect to this multi-objective formulation.
|
|
Speaker Bio
Ben
Desjardins is currently a Master of Computer Science student at the
University of Ottawa. He completed his undergraduate degree at the
University of Ottawa in 2013, and worked as a software developer for
IBM until 2014 when he returned to schools to pursue research interests.
His
research involves the applications of multi-objective optimization to
the wireless sensor network domain. He has published papers as well as
a book chapter in this area.
|
Date
|
Monday Apr 25th, 2016
|
Time
|
6:30-7:00 Refreshments and networking
7:00-8:30 Technical Talk / Q&A Period
|
Location
|
Raven Telemetry
6 Hamilton Ave N #250
Ottawa
free admission; a light dinner will be served
registration is
required; please register
here
|
Title
|
Machine Learning and Cybersecurity
|
Speaker
|
Dr. Mahmoud Gad and Mr. Ahmed Shah
|
| |
Venus Cybersecurity, Ottawa
|
| |
|
Abstract
Although
machine learning has been successfully applied to complex problem
domains such as speech recognition, image recognition and text
analysis, there are unique challenges for applying machine learning in
the cybersecurity domain. Machine learning works well in situations
where patterns and large volumes of data exist; unfortunately, this is
not the case in cyber-attacks where the attacker generally try their
best to be unpredictable by camouflaging within the massive volumes of
data.
The presenters conducted a literature review on the state of the art
applications of Machine Learning applications in Intrusion Detection.
They will be discussing the outcomes of this literature review
including; ML applications in cybersecurity, challenges, the
availability and quality of public datasets,and finally their
recommendations.
|
Date
|
Tuesday Apr 19th, 2016
|
Time
|
6:30-6:45 Refreshments and networking
6:45-8:00 Technical Talk / Q&A Period
|
Location
|
Faculty of Social Sciences, Room 1006
University of Ottawa
120 University
free admission; refreshments will be served
registration is
required; please register
here
|
Title
|
Granular Computing: Pursuing New Avenues of
Computational Intelligence
|
Speaker
|
Professor Witold Pedrycz
|
| |
|
| |
University of Alberta, Edmonton
|
| |
IEEE CIS Distinguished Lecturer
|
Abstract
In
numerous real-world
problems including a broad range of modeling tasks, we are faced with a
diversity of locally available distributed sources of data and expert
knowledge, with which one has to interact, reconcile and form a global
and
user-oriented model of the system under consideration.
While the technology of Computational
Intelligence (CI) has been playing a vital role with this regard, there
are
still a number of challenges inherently manifesting in these problems.
To
prudently address
these challenges, in this talk, we introduce a concept of information
granules
embracing a plethora of formal constructs such as intervals (sets),
fuzzy sets,
rough sets, etc. We highlight an
emergence of higher type and higher order information granules in the
analysis
and synthesis of granular models. The fundamental problem that becomes
central
to all investigations is concerned with the formation of information
granules.
We elaborate on the principle of justifiable granularity and discuss
its role
as a key design vehicle facilitating a construction of information
granules
realized on a basis of available experimental evidence (which could be
either
numeric or granular).
We
elaborate on a number of
conceptual and design issues of granular models. In particular, it is
demonstrated that granular models developed on a basis of existing
numeric
models of CI lead to their substantial augmentations and result in
interesting
and comprehensive ways of evaluation of their performance. Two general
approaches under investigation are associated with a formation of
granular
parameter spaces and granular output spaces. The proposed assessment of
the
quality of the model embraces two generic criteria, namely a coverage
criterion
of experimental data and a specificity criterion. It is shown that a
hierarchy
of information granules gives rise to granular models both of higher
type and
higher order.
The
detailed
investigations are focused on selected problems of rule-based models,
building
auto-encoders in architectures of deep learning, and evolution of
information
granules when describing dynamics of data streams.
.
|
|
Speaker Bio
Witold
Pedrycz
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 Council. He is a recipient of the
IEEE
Canada Computer Engineering Medal 2008. In 2009 he has received a
Cajastur Prize
for Soft Computing from the European Centre for Soft Computing for “pioneering
and multifaceted contributions to Granular Computing”. In 2013 he
was
awarded a Killam Prize and received a Fuzzy Pioneer Award 2013 from the
IEEE
Computational Intelligence Society.
His main
research
directions involve Computational Intelligence, fuzzy modeling and
Granular
Computing, knowledge discovery and data mining, 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 15 research monographs covering various aspects of
Computational
Intelligence, data mining, and Software Engineering.
Dr.
Pedrycz is intensively 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
Co-Editor-in Chief of Granular Computing
(Springer). He also currently serves as
an Associate Editor of IEEE Transactions
on Fuzzy Systems and is a member of a number of editorial
boards of a
number of international journals in the area of Computational
Intelligence and
intelligent systems.
|
Date
|
Thursday Mar 17th, 2016
|
Time
|
6:30-6:45 Refreshments and networking
6:45-8:00 Technical Talk / Q&A Period
|
Location
|
Room 213B
Algonquin College Building P
1385 Woodroffe Ave
free admission; free parking after 6 pm
refreshments will be served
|
Title
|
Threat Modeling
|
Speaker
|
Dr. George Yee
|
| |
|
| |
Aptusinnova Inc.
|
| |
Adjuct Professor, Carleton University
|
Abstract
Threat
modeling refers to the identification of possible attack paths against
a system, where "system" is primarily recognized as a computer system
but can include other types of systems such as business processes (e.g.
a supply chain). Threat modeling includes the identification of
remedial actions that can prevent an attack or attenuate the
consequences of an attack. As well, it covers prioritizing which attack
paths are more likely than others. Threat modeling has been around for
many years and research on this topic can focus on how to do it more
effectively or how to apply it to new areas. In this talk, I will give
an introduction to threat modeling, followed by an account of some
threat modeling application areas, including how it can be applied to
the development of secure software as well as to defending against
insider attacks. I will conclude the talk by describing some ways to
make threat modeling more effective.
.
|
|
Speaker Bio
George
Yee is a research scientist with his own company Aptusinnova Inc.,
which conducts research into the latest "hot" technologies (e.g. the
Internet of Things). Previously he was an IT Research Analyst
with the Office of the Privacy Commissioner of Canada, and a Senior
Research Officer in the Information Security Group of the National
Research Council Canada (NRC). Prior to joining the NRC, he spent over
20 years at Bell-Northern Research and Nortel Networks. George received
his Ph.D. (Electrical Engineering) from Carleton University, Ottawa,
Canada, where he is an Adjunct Research Professor. He is a Senior
Member of IEEE, and member of ACM and Professional Engineers Ontario.
His research interests include the application of computational
intelligence techniques (e.g. optimization) to improve security and
privacy.
|
Date
|
Wednesday Jan 20th, 2016
|
Time
|
6:45-7:00 Refreshments and networking
7:00-8:30 Technical Talk / Q&A Period
|
Location
|
Lab T-129
Algonquin College Building T
1385 Woodroffe Ave
free admission; free parking after 6 pm
refreshments will be served
|
Title
|
Experience of building Internet of Things
(IoT) applications
|
Speaker
|
Professor Chung-Horng Lung
|
| |
|
| |
Department of Systems and Computer Engineering
|
| |
Carleton University
|
Abstract
Internet of Things (IoT) is an emerging technology that has the
potential to further advance our societies by creating smart
environments to enable intelligent interactions between humans and
objects. Applications built around IoT are constantly growing in
variety and quantity. Technologies in IoT have been evolving rapidly
and the alternatives also have increased quickly. As a result, it
becomes challenging to conduct system and software trade-off analysis
or to select suitable IoT technologies for applications. This talk is
to share our practical experience as well as some challenges in
building various IoT applications.
.
|
|
Speaker Bio
Chung-Horng Lung received his
Ph.D. degree in Computer Science and Engineering from Arizona State
University in 1994. In 1995, he joined Nortel's Software Engineering
Analysis Lab (SEAL) as the lead for the Software Architecture Analysis
group to work with various Nortel product teams. In 1999, he joined
Optical Packet Interworking (OPi) to work on IP/MPLS network traffic
engineering. In September 2001, he joined the Department of Systems and
Computer Engineering, Carleton University, where he is now a full
professor.
His research interests include: Software Engineering, Computer
Networks, Cloud Computing, Big Data Analytics, and Wireless Ad- Hoc and
Sensor Networks.
|
Date
|
Wednesday Dec 9th, 2015
|
Time
|
9:00-9:30 Refreshments and networking
9:30-11:30 Chapter Elections and Technical Talk
|
Location
|
Room 5084
School of Electrical Engineering and Computer Science
University of Ottawa
800 King Edward Ave
free admission
refreshments will be served
|
Title
|
Every picture tells a story: visual cluster
assessment and rectangular relational data
|
Speaker
|
Jim Bezdek
|
| |
|
| |
Retired Professor
|
| |
IEEE Computational Intelligence Society Distinguished
Lecturer
|
Abstract
The
VAT/iVAT, algorithms are the parents of a large family of visual
assessment models. This talk is divided into two pieces that are best
covered in about 1 hour each.
Part 1. Definitions of the three canonical problems of cluster
analysis: tendency assessment, clustering, and cluster validity.
History of Visual Clustering. Applications: role-based compliance
assessment, eldercare time series data, and anomaly detection in
wireless sensor networks.
Part 2. Five Easy Pieces:
asiVAT: non-symmetric data. Application: Social Networks (Monastery
data)
impVAT: missing data. Application: Social Networks (Karate club data)
clusiVAT: big data. Applications: clustering in big (synthetic)
data, MIT video trajectories
inciVAT: streaming data. Application: anomaly detection in Heron Island
data.
LOFiVAT: immunization of iVAT and Single linkage to inlier
contamination: Application: Grand St. Bernard weather station.
.
|
|
Speaker Bio
Jim
received the PhD in Applied Mathematics from Cornell University in
1973. Jim is past president of NAFIPS (North American Fuzzy Information
Processing Society), IFSA (International Fuzzy Systems Association) and
the IEEE CIS (Computational Intelligence Society): founding editor the
Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy
Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE
3rd Millennium, IEEE CIS Fuzzy Systems Pioneer, and IEEE technical
field award Rosenblatt medals.
Jim's
interests: woodworking, optimization, motorcycles, pattern recognition,
cigars, clustering in very large data, fishing, co-clustering, blues
music, wireless sensor networks, poker and visual clustering. And of
course, clustering in big data. Jim retired in 2007, and will be coming
to a university near you soon.
|
Date
|
Thursday Nov 12th, 2015
|
Time
|
10:00-10:30 Refreshments and networking
10:30-11:30 Technical Talk
|
Location
|
National Research Council
Building M-12, room 217
1200 Montreal Road, Ottawa
free admission and parking
NRC campus map here
|
Title
|
Load estimation and fatigue life tracking for
helicopter components using Computational Intelligence techniques
|
Speaker
|
Catherine Cheung
|
| |
|
| |
Research Officer, Aerospace Structural Integrity Group
|
| |
National Research Council
|
Abstract
The accurate estimation of helicopter component loads is an important
task in life cycle management and life extension efforts. Since the
operation of helicopter fleets has been increasingly extended to
additional or expanded roles from the original design usage, there is a
growing need to monitor individual aircraft usage and more accurately
determine any changes in the life of the critical components caused by
the change in usage.
While measuring component loads directly is possible using installed
sensors, the installation and operation of a sensor suite is
challenging and expensive, and consequently seldom implemented.
Therefore a robust and accurate process to indirectly estimate these
loads could be a practical alternative. The application of
computational intelligence techniques to this problem is a natural fit,
given the complexity of the load signals and influence of numerous
factors. Load estimation methods can utilize data obtained from
existing aircraft instrumentation, such as standard flight state and
control system parameters, to minimize the need for additional sensors.
This talk will describe the continued efforts at NRC to develop and
improve computational intelligence-based methodologies to enable the
estimation of helicopter loads and the tracking of load exceedances and
fatigue damage for a targeted helicopter component from existing flight
data. Computational intelligence algorithms, statistical and machine
learning techniques, such as artificial neural networks, evolutionary
algorithms, fuzzy sets, residual variance analysis and others, were
implemented as part of the data exploration and modelling stages of the
methodologies. Results for the Australian S-70A-9 Black Hawk and the
CH-146 Griffon will be presented.
.
|
|
Speaker Bio
Catherine
Cheung is a research officer in the Aerospace Structural Integrity
group at the National Research Council in Ottawa. She has worked with
NRC since 2003, first specializing in full-scale structural test and
their control systems, and more recently focusing on research in load
and usage monitoring of rotary-wing structures using various
computational intelligence techniques. She received a MASc from the
University of Toronto Institute for Aerospace Studies and a BASc from
the University of Toronto Engineering Science program.
|
Date
|
Wednesday October 7th, 2015
|
Time
|
10:00-11:00 Technical Talk
11:00 - 11:30 Networking
|
Location
|
Fauteax 137, University of Ottawa
57 Louis Pasteur St, Ottawa
free admission
|
Title
|
Learning in Non-Stationary Environments
|
Speaker
|
|
| |
Professor, Politecnico di Milano, Italy
|
| |
IEEE CIS Distinguished Lecturer
|
Abstract
Most
of machine learning applications assume the stationarity hypothesis for
the process generating the data. This amenable assumption is so widely
–and implicitly- accepted that sometimes we even forget that it does
not generally hold in the practice due to concept drift (i.e., a
structural change in the process generating the acquired datastreams).
The
ability to detect concept drift and react accordingly is hence a major
achievement for intelligent learning machines and constitutes one of
the hottest research topics for embedded systems. This ability allows
the machine for actively tuning the application to maintain high
performance, changing online the operational strategy, detecting and
isolating possible occurring faults to name a few relevant tasks.
The
talk will focus on “Learning in a non-stationary environments”, by
introducing both passive and active approaches. The active approach
will be deepened by presenting triggering mechanisms based on Change
point methods and Change detection tests. Finally, the just-in-time
detect&react mechanism is introduced where, following a detected
change, the system immediately reacts with a strategy depending on the
available information.
|
| |
Speaker Bio
CESARE
ALIPPI received the degree in electronic engineering cum laude in 1990
and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is
a Full Professor of information processing systems with the Politecnico
di Milano. He has been a visiting researcher at UCL (UK), MIT (USA),
ESPCI (F), CASIA (RC), A*STAR (SIN).
Alippi
is an IEEE Fellow, Distinguished lecturer of the IEEE CIS, Member of
the Board of Governors of INNS, Vice-President education of IEEE CIS,
Associate editor (AE) of the IEEE Computational Intelligence Magazine,
past AE of the IEEE-Trans. Instrumentation and Measurements,
IEEE-Trans. Neural Networks, and member and chair of other IEEE
committees.
In
2004 he received the IEEE Instrumentation and Measurement Society Young
Engineer Award; in 2013 he received the IBM Faculty Award. He was
awarded the 2016 IEEE TNNLS outstanding paper award.
Among
the others, Alippi was General chair of the International Joint
Conference on Neural Networks (IJCNN) in 2012, Program chair in 2014,
Co-Chair in 2011. He was General chair of the IEEE Symposium Series on
Computational Intelligence 2014.
Current research activity addresses adaptation and learning in
non-stationary environments and Intelligence for embedded systems.
Alippi holds 5 patents, has published in 2014 a
monograph with Springer on “Intelligence for embedded systems” and
(co)-authored more than 200 papers in international journals and
conference proceedings.
|
Date
|
Wednesday May 13th, 2015
|
Time
|
19:00-19:30 Refreshments and networking
19:30-20:30 Technical Talk
20:30 - 21:30 Networking
|
Location
|
The Crowsnest, Naval Officer's Mess
78 Lisgar St, Ottawa
free admission
|
Title
|
Robot-Assisted Wireless Sensor Networks:
Recent Developments and Future Challenges
|
Speaker
|
|
| |
Research Scientist
|
| |
Larus Technologies
|
Abstract
This talk will focus
on Wireless Sensor and Robot Networks, an emerging class of
cyber-physical systems stemming from the integration of wireless sensor
networks and multi-robot systems. Recently proposed solutions to two
representative problems in this field (robot-assisted sensor placement
and sensor relocation) will be discussed from an algorithmic perspective.
The talk will outline some challenges that lie ahead
in regards to real-world implementation of these systems.
|
| |
Speaker Bio
Rafael Falcon received
his PhD degree in Computer Science from the School of Information
Technology and Engineering at the University of Ottawa in 2012. He
currently works as a Research Scientist for Larus Technologies
Corporation, an Ottawa-based firm that specializes in high-level
information fusion and decision support from a Computational
Intelligence angle. His research interests are in the area of
Computational Intelligence with applications to security and defense,
including wireless sensor networks, robotics, maritime domain awareness
and multi-sensor data fusion.
|
|
|
Thursday April 16th, 2015
|
Time
|
18:00-18:30 Refreshments and networking
18:30-20:00 Technical Talk
|
Location
|
Algonquin College, Building T, Room T129
1385 Woodroffe Ave, Ottawa
(free admission;
registration required; free parking after 5 pm)
|
Title
|
Applications of Petri Nets in Communications:
Calculation
of Probability Distributions of Performance Variable in Petri Net Models
|
Speaker
|
|
| |
Independent Consultant
|
Abstract
A
brief overview of Petri nets, as a powerful modeling tool for many
systems, will be given. Models with Stochastic Petri Nets (SPN) enable
evaluating system performance. Every SPN can be reduced to a
corresponding Markov Chain. This presentation deals with
telecommunication networks where, in addition to average packet delays,
it is necessary to know the probability distribution of packet delays
exceeding given values. In provisioning the input buffer it is
important to know statistics to be able to determine probability of
buffer overflow and underflow. An original method to calculate
statistics (and percentiles) of traversing time in Markov chains will
be presented. Markov chains can be used to model the traffic in any
network. They can model packet traffic in stored and forward networks
like Internet; the parts moving across the production network; or
patients moving through the health network. Conventionally the
traversing time is given by the average values. However, the
percentiles are of great importance. In Internet traffic, the
percentiles of packet delay enable the architect to properly provision
the receiving anti-jitter buffer. In production networks, percentile
might indicate to the manufacturer the number of rejecting parts. Since
the most powerful modeling tool – Petri nets – reduce to Markov chains,
this method is applicable to the Petri nets as it will be shown in an
example of alternating bit protocol. The importance of percentile
calculation is stressed by the fact that reliable percentile figures
are not possible to obtain by simulation or monitoring/measurementsince
they are rare events, and getting reliable figures requires very long
time.
|
| |
Speaker Bio
Faruk
Hadziomerovic received his B.Sc. from University of Zagreb, M.E.E. from
the Netherlands University Foundation for International Cooperation
(NUFFIC) in the Hague, and Ph.D. from University of Sarajevo, with the
thesis “Multiprocessor-Multimemory Computer Based on Microprocessors”.
He spent a part of his career teaching at University of Sarajevo,
Carleton University in Ottawa, Sarajevo School of Science and
Technology, etc., and the other part in industrial research at the
Institute for Control and Computer Sciences (IRCA), Sarajevo, Bell
Northern Research (BNR) and Nortel Networks in Ottawa, and other
companies in the telecommunication sector. His main teaching and
research areas include microprocessors, operating systems, computer
networks, and modeling for performance. His technicalcontributions are
in the field of microprocessor hardware, network protocols and Petri
nets.
|
|
|
Tuesday February 3rd, 2015
|
Time
|
10:00-11:30 (EDT)
|
Location
|
NRC Auditorium, 1200 Montreal Road, M-50 Building
(admission and
parking are free)
|
Title
|
Computational Intelligence and Biometric
Technologies: Application-driven Development
|
Speaker
|
|
| |
Defense Scientist, Ottawa Research Centre
|
| |
Defense Research & Development Canada (DRDC)
|
| |
|
| |
|
Abstract
The
research field of biometrics encompasses a wide range of disciplines
such as signal processing, pattern recognition, classification, and
decision making. Although various biometric algorithms are proposed,
tested, reviewed and implemented every year, challenges still remain
because of the existence of, among other issues, background noise,
signal distortion, biometric template aging, and measurement
environment variations.
Computational intelligence (CI) is primarily comprised of three
interrelated areas of study: artificial neural network (ANN), fuzzy
systems (FS), and evolutionary computation (EC), though a precise
definition of the field is impossible. Biometrics and CI are both fast
growing fields of research and application. The reason they are linked
together is because CI provides a powerful tool to deal with various
important and pressing challenges in biometrics.
This
keynote will highlight the IEEE CIS biometrics events and explore how
to use CI-based technologies in solving biometric problems, such as
CI-based biometric image processing and feature extraction, CI-based
biometric matching, and CI-based multi-modal biometric fusion. This
talk will give more emphasis to the use of CI methods in solving
challenging problems in real-life biometric applications. Biometric
technology has been used for many years and is a security technology of
the past, present, and future, while CI techniques make it possible for
biometric applications to be more reliable and accurate.
|
| |
Speaker Bio
Dr.
Qinghan Xiao, IEEE Senior Member, is a Defence Scientist at the Defence
R&D Canada - Ottawa Research Centre. He currently serves as the
Chair of Task Force on Biometrics of the IEEE CIS Technical Committee
on Intelligent Systems Applications. Dr. Xiao is an Associate Editor of
the International Journal of Biometrics (IJBM). His research interests
include biometrics, radio frequency identification (RFID), smart card,
and integrated electronic and cyber warfare technologies. He has been
invited as session chair and speaker to many international conferences,
co-chaired several special sessions, workshops, and symposiums for IEEE
on biometrics, and served as Guest Editor for two special issues on
biometrics standardization and computational intelligence in
biometrics. Dr. Xiao is the recipient of 2010 IEEE Ottawa Outstanding
Engineer Award for his contributions to the area of biometrics. He was
the technology lead of the anti-intrusion RFID project that received
the 2012 IEEE Ottawa Outstanding Technology Recognition Award.
|
|
|
Monday November 10, 2014
|
Time
|
18:00-19:30 (EDT)
|
Location
|
Algonquin College, Building P, room P-215
(admission and parking are free)
|
Title
|
State-of-the-Art Evolutionary Algorithms for
Many-Objective Optimization
|
Speaker
|
|
| |
Professor
|
| |
School of Electrical and Computer Engineering
|
| |
Oklahoma State University
|
Abstract
Evolutionary
computation is the study of biologically motivated computational
paradigms
which exert novel ideas and inspiration from natural evolution and
adaptation. The
applications of population-based heuristics in solving multiobjective
optimization problems have been receiving a growing attention. To search for a family of Pareto optimal
solutions
based on nature-inspiring problem solving paradigms, Evolutionary
Multiobjective
Optimization Algorithms have been successfully exploited to solve
optimization
problems in which the fitness measures and even constraints are
uncertain and changed
over time.
When
encounter optimization problems with many
objectives, nearly all designs performs poorly because of loss of
selection
pressure in fitness evaluation solely based upon Pareto optimality
principle. This
talk will survey recently
published literature along this line of research- evolutionary
algorithm for
many-objective optimization and its real-world applications. Based on performance metrics ensemble, we
will provide a comprehensive measure among all competitors and
more importantly reveal insight pertaining to specific problem
characteristics that
the underlying evolutionary algorithm could perform the best. The experimental results confirm
the finding from the No Free Lunch theorem: any algorithm’s elevated
performance
over one class of problems is exactly paid for in loss over another
class.
|
| |
Speaker Bio
Gary G. Yen
received the Ph.D. degree in
electrical and computer engineering from the University of Notre Dame
in 1992. He is currently a Regents
Professor in the
School of Electrical and Computer Engineering, Oklahoma State
University. His research interest includes
intelligent
control, computational intelligence, evolutionary multiobjective
optimization,
conditional health monitoring, signal processing and their
industrial/defense
applications.
Gary
was an
associate editor of the IEEE Transactions on Neural Networks
and IEEE
Control Systems Magazine during 1994-1999, and of the IEEE
Transactions
on Control Systems Technology, IEEE Transactions on Systems,
Man and
Cybernetics and IFAC Journal on Automatica and
Mechatronics during 2000-2010. He is currently serving as an associate editor
for the IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. Gary
served as Vice President for the
Technical Activities, IEEE Computational Intelligence Society in
2004-2005 and
is the founding editor-in-chief of the IEEE Computational
Intelligence
Magazine, 2006-2009. He was the
President
of the IEEE Computational Intelligence Society in 2010-2011 and is
elected as a
Distinguished Lecturer for the term 2012-2014. He
received Regents Distinguished Research
Award from OSU in 2009, 2011 Andrew P Sage Best Transactions Paper
award from
IEEE Systems, Man and Cybernetics Society, 2013 Meritorious Service
award from
IEEE Computational Intelligence Society and 2014 Lockheed Martin
Aeronautics
Excellence Teaching award. He is a Fellow
of IEEE and IET.
|
|
|
Wednesday March 12, 2014
|
Time
|
13:30-15:00 (EDT)
|
Location
|
Building M-50, NRC Auditorium, 1200 Montreal Road
(admission and parking are free)
|
Title
|
What ROC Curves Can't Do (and Cost Curves Can)
|
Speaker
|
|
| |
Research Officer
|
| |
Information and Communication Technologies
|
| |
National Research Council
|
Abstract
In this talk, our focus
is on the visualization of a classifier's performance. This is
one of the attractive features of ROC analysis - the tradeoff between
false positive rate and true positive rate can be seen directly. A good
visualization of classifier performance would allow an experimenter to
immediately see how well a classifier performs and to compare two
classifiers - to see when, and by how much, one classifier outperforms
others. We restrict the discussion to classification problems in which
there are only two classes. The main point of this talk is to
show that, even in this restricted case, ROC curves are not a good
visualization of classifier performance. In particular, they do
not allow any of the following importantexperimental questions to be
answered visually:
- What is classifier C's performance (expected cost)
given specific misclassification costs and class probabilities?
- For what misclassification costs and class
probabilities does classifier C outperform the trivial classifiers?
- For what misclassification costs and class
probabilities does classifier C1 outperform classifier C2?
- What is the difference in performance between
classifier C1 and classifier C2?
- What is the average of performance results from
several independent evaluations of classifier C (e.g. the results of
5-fold cross-validation)?
- What is the 90% confidence interval for classifier
C's performance?
- What is the significance (if any) of the difference
between the performance of classifier C1 and the performance of
classifier C2?
|
| |
Speaker Bio
Chris Drummond is a Research Officer
within the ICT Portfolio at the National Research Council (NRC). He
holds a B.Tech. in Applied Physics and an M.A.Sc. in Electrical
Engineering. In 1999 he completed his Ph.D. in Computer Science at the
University of Ottawa. There he spent a further three years as
postdoctoral researcher in its School of Information Technology and
Engineering until taking up his current position at the National
Research Council. His research interests center on machine learning and
cover such areas as data mining, learning agents, hybrid systems and
cost sensitive learning. Chris has published numerous scientific papers
on these topics.
|
|
|
Monday November 18, 2013
|
Time
|
13:30-15:00 (EDT)
|
Location
|
Building
M-50, NRC Auditorium, 1200 Montreal Road (admission and
parking are free)
|
Title
|
Data-Driven Ensemble Learning with Deep
Search and Class Rebalancing
|
Speaker
|
|
| |
Professor
|
| |
School of Electrical Engineering and Computer
Science
|
| |
University of Ottawa
|
Abstract
Data-driven ensemble learning, such as
Data-driven Error Correcting Output Coding (DECOC) is a multi-class
learning approach that combines binary base learners using a coding
matrix. The data-driven design of the code atrix takes the complexity
of the classification problem into consideration and does so in an
unsupervised, and therefore, highly efficient way compared with
alternative matrix design methods. In this research, we propose a novel
complexity measure which we can use to perform an efficient deep search
in the matrix design space. In addition, we also rectify the class
imbalance problem that can be encountered by some binary base problems.
Both strategies where first consolidated with a study on artificial
sets, and then tested on real-world domains. Our results show that our
method is not only more efficient than related leading methods on
real-world sets, but that it also outperforms them in terms of
classification accuracy.
|
| |
Speaker Bio
Nathalie
Japkowicz is a Professor of Computer Science at the School of
Electrical Engineering and Computer Science and the Director of the
Laboratory for Research on Machine Learning for Defense and Security at
the University of Ottawa. Recently she has been elected the President
of the Canadian Artificial Intelligence Association.
Professor Japkowicz received her Ph.D. from Rutgers University in 1999.
After teaching at Ohio State University and Dalhousie University, she
accepted a position at the University of Ottawa in 2000 where she has
worked ever since, except for academic leaves at Monash University
(Australia), Tufts University (USA) and Northern Illinois University
(USA). Throughout her career, she has supervised or co-supervised over
thirty Master’s and Ph.D. students. She has received a number of grants
and contracts totaling over a million dollars in direct outside funding.
She is the author or co-author of over 100 book chapters, articles and
papers and she co-authored the book entitled Evaluating Learning
Algorithms: A Classification Perspective (Cambridge University Press,
2011). In the past five years, she and her students have received two
best paper awards at conferences.
Recent applications of her research include: the monitoring of threats
to public safety by detecting Gamma-emitting hazardous materials, the
detection of underwater mines or improvised explosive devices with
multiple autonomous unmanned vehicle operations, the monitoring of
international conformation to the Comprehensive Nuclear Test Ban
Treaty, and the detection of serious threats to computer networks.
|
|
|
Tuesday April 30, 2013
|
Time
|
10:00-11:30 (EDT)
|
Location
|
Building
M-50, NRC Auditorium, 1200 Montreal Road (admission and
parking are free)
|
Title
|
Analysis of Opinions and
Emotions in Texts
|
Speaker
|
|
| |
Professor
|
| |
School of Electrical Engineering and Computer
Science
|
| |
University of Ottawa
|
Abstract
The automatic detection of opinions and emotions
in texts is important for applications such as market analysis,
affective computing, natural language interfaces, and intelligent
tutoring systems. Texts have been classified by topic, genre, sentiment
orientation (positive or negative opinions), or even by the gender of
the authors. This talk will focus on classifying texts by the opinion
and by the emotion expressed by the authors. Results on several
datasets will be presented. A global dataset is then used for training,
in order to obtain a more general classifier. Results of a hierarchical
classification approach will also be discussed.
|
| |
Speaker Bio
Diana
Inkpen is a Professor at the University of Ottawa, in the School
of Electrical Engineering and Computer Science. She obtained her
doctorate in 2003 from the University of Toronto, Department of
Computer Science. She has a Masters in Computer Science and Engineering
from the Technical University of Cluj-Napoca, Romania. Her research
interests are in the areas of Computational Linguistics and Artificial
Intelligence, more specifically: Natural Language Understanding,
Natural Language Generation, Lexical Semantics, Information Retrieval,
Speech Technology, and Intelligent Agents for the Semantic Web.
|
Date
|
Friday December 14, 2012
|
Time
|
10:00-12:00 (EDT)
|
Location
|
CBY A-605, University of Ottawa
|
Title
|
The Wealth of People: Framing
the Future of Knowledge and Work in the Digital Environment - From
Management to Collaboration and Knowledge Governance
|
Speaker
|
John Verdon
|
| |
Office of the Research Scientist
|
| |
Department of National Defence, Canada
|
Abstract
The presentation examines the implications of
hyper-connectivity associated with the digital environment and social
media. The thesis is summarized in a ‘McLuhanism” - If the Digital
Environment is the MEDIUM…then Social Computing
is the MESSAGE Entailing that Modes of Production
must become Programmable.
I argue that the purpose of traditional
organizational architecture aimed at minimizing ‘transaction costs’
must be re-evaluated. The digital environment has effected an
unprecedented collapse of traditional costs associated with any large
collective and collaborative efforts of people and organizations. The
emerging digital environment and social media capabilities represent
new modes of production that proliferate architectures of
participation, enable social computing and entail the development of
‘programmable organizations’.
A Medium is anything that extended the mind,
body or senses. Thus a Medium can be a new technology, process, idea or
original creative work. When a new Medium is created, its message
becomes clear in the resultant differences in human interactions and
activities. The nature of these differences is embodied in changes of
scale, pace, scope or pattern that a medium causes in us as individuals
or as a society or culture. These changes (Message) are distinct from
the content of the Medium.
Social computing then is the capacity for a
large network or ‘swarm’ of people to explore in parallel, a problem
space and produce a range of effective solutions, and/or produce a good
or service. Social computing increases the capability to search a
larger solution space, enable knowledge to flow where and when it is
needed and increases human and social capital and network trust.
What is meant by programmable modes of
production – is the rapid and agile generation, assemblage and
harnessing of knowledge networks, as and when needed, in a way that
doesn’t require an organization to re-configure, retool, or
re-architect its coordination structures and processes. A programmable
mode of production is reliant on responsible autonomy (based on trusted
personnel, agent-forum accountability, and context/competence-based
leadership) and networked individualism – as a social operating system.
To harness related capabilities organizational
architectures will soon require new sets of rules – institutional, and
governance frameworks. Institutional innovation is needed in order to
harness the increased capabilities as well as the cost savings made
possible by new modes of production.
|
| |
Speaker Bio
Mr.
John Verdon has a rich and
broad background in theoretical and applied social science research
which includes formal education in psychology, anthropology, sociology
and philosophy. His expertise is lies in foresight and strategic HR
research, especially as it relates to social media & the
digital environment, complexity sciences, knowledge management and
organizational architectures. His research also explores emerging
cognitive, biological and nano-technologies and their potential impact.
The aim of his research work is on the development of a
better theory and philosophy related to harnessing human capital in the
21 st Century.
|
Date
|
Tuesday October 30, 2012
|
Time
|
11:00-12:30 (EDT)
|
Location
|
Online Webinar (see registration below)
|
Title
|
On the Temporal Granularity on
Fuzzy Cognitive Maps
|
Speaker
|
Giovanni Acampora, Eindhoven University of
Technology
|
| |
Vincenzo Loia, University of Salerno
|
| |
Autilia Vitiello, University of Salerno
|
Webinar Details
https://attendee.gotowebinar.com/register/7266876103196019456
Please, connect 15 minutes ahead of time
|
| |
Abstract
The theory of fuzzy
cognitive maps (FCMs) is a powerful approach to modeling human
knowledge that is based on causal reasoning. Taking advantage of fuzzy
logic and cognitive map theories, FCMs enable system designers to model
complex frameworks by defining degrees of causality between causal
objects. They can be used to model and represent the behavior of simple
and complex systems by capturing and emulating the human being to
describe and present systems in terms of tolerance, imprecision, and
granulation of information. However, FCMs lack the temporal concept
that is crucial in many real-world applications, and they do not offer
formal mechanisms to verify the behavior of systems being represented,
which limit
conventional FCMs in knowledge representation. In this webinar, we
present a temporal extension to FCMs by exploiting a theory from formal
languages, namely, the timed automata, which bridges the aforementioned
inadequacies. Indeed, the theory of timed automata enables FCMs to
effectively deal with a double-layered temporal granularity, extending
the standard idea of B-time that characterizes the iterative nature of
a cognitive inference engine and offering model checking techniques to
test the cognitive and dynamic comportment of the framework being
designed. As shown through experiments, where the proposed approach has
been evaluated by simulating a complex municipality garbage collection
system, TAFCMs improve conventional FCMs by yielding better performance
in terms of representation of dynamic systems behavior.
|
| |
Speaker Bio
Dr.
Giovanni Acampora received the Laurea (cum laude) and Ph.D. degrees in
Computer Science from the University of Salerno, Salerno, Italy, in
2003 and 2007, respectively. Since July 2012, he is an Assistant
Professor at the School of Industrial Engineering, Information Systems,
Eindhoven University of Technology, the Netherlands. From March 2007 to
March 2012, he has been a Research Associate in the Department of
Mathematics and Computer Science, University of Salerno. He was also a
Member of the Multi-Agent Laboratory at the University of Salerno and
scientific co-responsible of the CORISA Research Centre. From September
2003 to June 2007, he was also in CRDC-ICT Domotic project, where he
was engaged in the research on multi-agent systems and artificial
intelligence applied to ambient intelligence environments. In this
context, he designed and developed the Fuzzy Markup Language, an
XML-based environment for modeling transparent fuzzy systems.
Currently, FML is under consideration by IEEE Standard Association to
become the first standard in the field of computational intelligence.
His current research interests include novel algorithms design
approaches inspired by natural systems as swarm intelligence,
evolutionary, and memetic strategies, investigating the designing of
novel human–computer interaction systems based on integration among
haptic hardware, virtual reality and augmented reality technologies,
formal methods from language theory area, and on the study of temporal
effects on the behavior of fuzzy systems modeled through fuzzy
controllers and fuzzy cognitive maps. He has written some seminal
papers on ambient intelligence and, in particular, his work about fuzzy
computation in smart environments is one of the most cited paper of
IEEE Transactions on Industrial Informatics.
Dr. Acampora serves as reviewer and associate and guest editor for
several international journals and conferences. Dr. Acampora is the
chair of the IEEE Computational Intelligence Society Standards
Committee. In this context, he also served as Chair of Task Force on
Taxonomy and Terminology and Vice-Chair of Task Force on New Standard
Proposal. From 2010, he serves as Secretary and Treasurer of Italian
Chapter of IEEE Computational Intelligence Society. Currently he is
chairing the IEEE Standard Association P1855 Workgroup related to the
FML standardization process.
Dr.
Vincenzo Loia (SM’08) received the Bachelor’s degree in computer
science from the University of Salerno, Fisciano, Italy, in 1984 and
the Ph.D. degree in computer science from the University of Paris VI,
Paris, France, in 1989. Since 1989, he has been a Faculty member with
the University of Salerno, where he teaches Operating Systems, Semantic
Web, and Multi-Agents Systems. He is currently a Full Professor of
computer science with the Department of Mathematics and Computer
Science. He is the author of more than 190 original research papers in
international journals, e-book chapters, and international conference
proceedings. His current research interests include merging soft
computing and agent technology to design technologically complex
environments, with particular interest in web intelligence
applications. Dr. Loia is the Co-Editor-in-Chief of Soft Computing and
the Editor-in-Chief of Ambient Intelligence and Humanized Computing. He
serves as an Editor for 14 other international journals. He has been
the Chair of the Emergent Technologies Technical Committee of the IEEE
Computational Intelligence Society, where he is currently the Chair the
of Task Force Intelligent Agents.
Autilia
Vitiello received the Laurea degree in Computer Science (cum laude)
from the University of Salerno (Italy) in 2009, discussing the thesis
"Time Sensitive Fuzzy Agents: formal model and implementation" (advisor
Prof. Vincenzo Loia). She is currently a PhD student at Department of
Computer Science of the University of Salerno under the supervision of
Prof. Vincenzo Loia and Dr. Giovanni Acampora. Her research interests
concern computational intelligence, and in particular, fuzzy logic and
knowledge representation theories. In last years, she is working on
evolutionary algorithms, above all, like means to solve the ontology
alignment problem.
|
Date
|
Friday September 28, 2012
|
Time
|
11:00-12:30 (EDT)
|
Location
|
Online Webinar (see registration below)
|
Title
|
Visual Data Mining with
Nonlinear Space Transformations and Virtual Reality
|
Speaker
|
|
| |
Research Officer
|
| |
Institute for Information Technology
|
| |
National Research Center (NRC)
|
Webinar Details
Event number: 596 122 422
Event password: This
event does not require a password.
Host key: 363761 (Use
this to reclaim host privileges.)
Event address: https://ieeemeetings.webex.com/ieeemeetings/onstage/g.php?d=596122422&t=a
|
| |
Abstract
The talk introduces a
visual data mining data exploration approach on heterogeneous
information systems of general kind. The technique facilitates the
process of understanding the underlying structure of single or compound
information systems, understood as collection or arbitrary entities
described in terms of a collection of properties (possibly
heterogeneous, imprecise and incomplete). The objects may be
characterized by relations defined on them as well. The approach is
based on nonlinear mappings between heterogeneous spaces with extended
information systems and a lower dimension space. A particular case is
that of spaces with 2,3 dimensions which could be visualized using
virtual reality techniques. The spaces can be also constructed for
unions of information systems (e.g. heterogeneous and incomplete data
sets together with knowledge bases composed by decision rules),
simplifying the process of discovery of interesting patterns and
relationships between the original data and the symbolic expressions
representing the knowledge. This approach has been applied successfully
to a wide variety of real-world domains (medicine, astronomy,
environment, etc.) and examples are presented.
|
| |
Speaker Bio
Dr.
Julio Valdes is a Senior Research Officer at the national research
council (NRC), Ottawa, Canada. He obtained his PHD from has a PhD in
mathematics (1987) from the Institute of Mathematics, Academy of
Sciences of Czechoslovakia. He is a senior member of IEEE Computational
Intelligence Society, and in 2005 he Co-chaired the Task Force
Computational Intelligence in Earth and Environmental Sciences. Also,
in International Neural Networks Society, he Co-chaired the SIG
Computational Intelligence in Earth and Environmental Sciences.
In Canada, he is an adjunct Professor in the School of Information
Technology and Engineering, University of Ottawa, Canada since 2008.
Also, since 2008 he is an adjunct Professor with the Department t of
Engineering and Computer Science, University du Québec en Outaouais,
Canada. He worked as a Professor in the Doctorate Program on Artificial
Intelligence, Department of Languages and Information Systems,
Polytechnic University of Catalonia, Barcelona, Spain in 2005.
His areas of interest are: artificial
intelligence (mathematical foundations of uncertainty processing and
inexact reasoning, knowledge engineering, expert systems and machine
learning), digital image and signal processing, pattern recognition,
virtual reality, soft computing (fuzzy logic, neural networks,
evolutionary algorithms, probabilistic reasoning, rough sets), data
mining, data analysis in general and hybrid systems. He also graduated
in geophysics (1977), oriented to geo-mathematics, mathematical
modeling of natural processes, computer elaboration and data
analysis-mining of earth science and environmental data, remote
sensing, physics and chemistry of external geodynamic processes and
geophysical-geochemical prospecting.
Dr. Valdes has published 206 papers: 45 in
refereed journals, 112 in Refereed Conference Proceedings, 24 in
Non-refereed Journals or Conferences, 13 technical reports, 13 books
and chapters, and more than 146 technical talks.
|
Date
|
Wednesday July 11, 2012
|
Time
|
8:30-10:00 (EDT)
|
Location
|
Online Webinar (see details below)
|
Title
|
Particle Swarm: From Cornfield
Vectors to Cognitive Radio
|
Speaker
|
Russell C. Eberhart
|
| |
CTO
|
| |
|
| |
|
Webinar Details
This webinar is organized
by the IEEE Ottawa CIS & SMC Joint Chapter and the IEEE Ottawa
CS Chapter..
Meeting information
Topic: CIS Live Webinar Links / Dial-in Information - July 11, 2012
Date: Wednesday, July 11, 2012
Time: 8:30 am, Eastern Daylight Time (New York, GMT-04:00)
Meeting Number: 590 856 057
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|
| |
Abstract
Particle swarm
optimization has evolved from modeling social systems to applications
for security and defense. Engineering applications for estimating
battery state of charge and optimizing container port yard planning
were among early successes. Applications to the fields of extended
analog computing and biomedical engineering are ongoing. A recent focus
has emerged in the fields of security and defense applications.
Included are developments in unmanned vehicle mission planning
optimization, intelligent traffic barrier networks, and resource
allocation optimization for cognitive radio.
|
| |
Speaker Bio
Russell
C. Eberhart is the CTO of Phoenix Data Corporation, Indianapolis,
Indiana. He is also Professor Emeritus of Electrical and Computer
Engineering at the Purdue School of Engineering and Technology, Indiana
University Purdue University Indianapolis (IUPUI). He was formerly Vice
President and CTO of Computelligence, LLC. He received his Ph.D. from
Kansas State University in electrical engineering. He is co-editor of a
book on neural networks (1991), and co-author of Computational
Intelligence PC Tools, published in 1996 by Academic Press. He is
co-author of a book with Jim Kennedy and Yuhui Shi entitled Swarm
Intelligence, published by Morgan Kaufmann in 2001. He is the
co-author, with Yuhui Shi, of a book entitled Computational
Intelligence: Concepts to Implementations, published in August 2007 by
Morgan Kaufmann/Elsevier. He was awarded the IEEE Third Millenium
Medal. In January 2001, he became a Fellow of the IEEE. He was elected
a Fellow of the American Institute for Medical and Biological
Engineering in 2002. He has been awarded four U. S. Patents, and is
co-inventor for another pending patent. He has done ground-breaking
work in applying swarm intelligence to human tremor analysis, sleep
disorders medicine, evolutionary analog computing, logistics, spectrum
warfare, and optimization of resource allocation.
|
Date
|
Wednesday June 27, 2012
|
Time
|
9:00-10:00 (EDT)
|
Location
|
Online Webinar (see registration below)
|
Title
|
Decentralized Coordination in
Smart Grids by Self Organizing Dynamic Fuzzy Agents
|
Speaker
|
Alfredo Vaccaro
|
| |
Assistant Professor
|
| |
|
| |
|
Webinar Details
This webinar is organized
by the IEEE Ottawa CIS & SMC Joint Chapter.
Registration is free but
it is required..
After registering you will receive a
confirmation email containing information about joining the Webinar.
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than an hour. However, time is allowed for Questions and Answers.
For all other locations, please, check the actual time in your time
zone. If you are not sure, you can use this Time Zone Converter
Please, login at least 15 minutes earlier to check your connection and
make sure that you are ready to attend the talk when it begins.
|
| |
Registration
Space is limited.
Reserve your Webinar seat now at:
https://www2.gotomeeting.com/register/446316930
|
| |
Abstract
Optimal power system
asset control is one of the main issues to address in a Smart Grids
context. In this domain, the application of traditional hierarchical
control paradigms has some disadvantages that could hinder their
application in Smart Grids where the constant growth of grid complexity
and the need for massive pervasion of
Distribution Generation Systems (DGS) require more scalable, more
flexible control and regulation paradigms. To try and overcome these
challenges, in this webinar a decentralized non-hierarchal control
architecture based on intelligent and cooperative smart entities is
described. The proposed solution intends to bring two main
contributions to the existing literature. The first is the definition
of a decentralized architecture aimed at computing the actual value of
the cost function and its gradient without the need of a central fusion
center acquiring and processing all the sensor acquisitions. The second
is the proposal of a distributed and cooperative optimization strategy
aimed at identifying the optimal asset of the grid controllers.
|
| |
Speaker Bio
Alfredo
Vaccaro got the MSc. degree cum laude and commendation in Electronic
Engineering from the University of Salerno in 1998. Since March 2002 he
is Research Scientist and Ass. Professor of Electric Power Systems at
the Department of Engineering of University of Sannio. His special
fields of interest include electric power system analysis with
particular emphasis to innovative architectures and new paradigms for
smart electricity systems, cooperative smart sensor networks for
protection and diagnostic of complex electricity systems, integration
of distributed generation systems on electrical networks, soft
computing and interval based methodologies in power systems analysis,
power system communication for Wide Area Monitoring Systems.
|
Date
|
Wednesday November 23, 2011
|
Time
|
9:30-11:30 (EDT)
|
Location
|
Building M-50, NRC Auditorium, 1200 Montreal
Road
|
Title
|
Anomaly Detection in Wireless
Sensor Networks: Visual Assessment and Clustering in Environmental
Monitoring Systems
|
Speaker
|
|
| |
Retired
|
| |
Milton, FL, USA
|
| |
|
Abstract
A. General information
about wireless sensor networks (WSNs). There are four categories of
network anomalies: isolated and epoch anomalies are aberrant behavior
internal to a single node; second order anomalies are atypical behavior
of an entire node; and higher order anomalies are one or more subtrees
of nodes in the network that exhibit anomalous behavior. We discuss two
types of models to detect anomalies; DCAD models that use data capture
by level sets of elliptical summaries; and ESAD models that rely on
visual assessment of elliptical summaries, with detection based on
single linkage clustering.
B. We define and
illustrate three (DCAD) models that use data capture by level sets of
ellipsoids having effective radii chosen with differing assumptions
(viz., % of points captured, % of points within k standard deviations
from the mean, and % of points captured based on the chi-squared
distribution. Examples are given using real WSN data from the Intel
Berkeley Research Lab (IBRL).
C. The ESAD models use
visual assessment of elliptical summaries for anomaly detection. These
models begin with four measures of similarity on sets of ellipsoids,
namely compound normalized, transformation energy, Bhattacharya
distance and focal dissimilarity. We define the four measures and
compare them with some simple two-dimensional examples that reveal some
surprising differences between human and mathematical assessment of
elliptical similarities.
D. The similarities in C
easily become dissimilarities, so we can apply visual assessment
techniques (the recursive iVAT method of talk R1.C) to images of the
(dis)similarity data. These images enable us to assess cluster tendency
amongst the set of ellipsoids, and estimate the number of clusters (of
elliptical summaries) in the data.
E. We show that these images are capable of detecting each of the
anomalous behaviors defined in A with numerical examples using both
real WSN and artificial data. The real data include the IBRL network,
the Great Barrier Reef Ocean Observation System, and the Grand St.
Bernard network for wind monitoring in a mountain pass on the border
between France and Switzerland. Our model reliable detects first and
second order anomalies in each of the three real data sets that are
caused by Cyclone Hamish and node drift. These examples illustrate the
real effectiveness of the ESAD model for detecting unusual events in
environmental monitoring networks.
|
| |
Speaker Bio
Jim
received the PhD in Applied Mathematics from Cornell University in
1973. Jim is past president of NAFIPS (North American Fuzzy Information
Processing Society), IFSA (International Fuzzy Systems Association) and
the IEEE CIS (Computational Intelligence Society): founding editor the
Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy
Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE
3rd Millennium, IEEE CIS Fuzzy Systems Pioneer, and IEEE technical
field award Rosenblatt medals. Jim's interests: woodworking,
optimization, motorcycles, pattern recognition, cigars, clustering in
very large data, fishing, co-clustering, blues music, wireless sensor
networks, poker and visual clustering. Jim retired in 2007, and will be
coming to a university near you soon.
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Date
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Tuesday September 27, 2011
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Time
|
14:00-15:30 (EDT)
|
Location
|
Room 379, Building M-50, NRC, 1200 Montreal Road
|
Title
|
Multi-Sensor Fusion Performance
Assessment
|
Speaker
|
|
| |
Fusion Research Engineer
|
| |
US Air Force Research Laboratory (AFRL)
|
| |
Currently an AFRL Exchange Scientist to
DRDC/Decision Support Group (Valcartier, QC)
|
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|
Abstract
Contemporary research
thrusts of information fusion (IF) deal with the integration of
multiple sensors. The output from individual sensors and exploitation
results in performance modeling, assessment and evaluation. Information
fusion over the sensor products requires an integrated performance
evaluation. For example, the use of imaging sensors from a variety of
platforms requires a performance analysis over operating conditions of
sensors, targets, and the environment (e.g. congested and urban
terrains). The talk will focus on (1) tracking and sensor fusion
performance evaluation, (2) methods of evaluation including common data
sets, comparative analysis, and metrics, and (3) explorative examples
of multisensor results to aid decision support.
Examples presented that demonstrate IF
performance evaluation include: synthetic aperture radar (SAR), radar
tracking, feature analysis and recognition, unattended ground sensors,
and, wide area surveillance from Electro-optical sensors to detect,
locate, follow targets. Additionally, metrics of Measures of
Performance from low-level information fusion tracking and
identification will be explored as enabling Measures of Effectiveness
for high-level information fusion situation awareness.
|
| |
Speaker Bio
Erik
Blasch is currently an AFRL exchange scientist to Defence R&D
Canada at Valcartier in the Future C2 Concepts and Structures Group of
the C2 Decision Support Systems Section. Prior to the sabbatical, Dr.
Blasch was the Information Fusion Evaluation Tech Lead for the Air
Force Research Laboratory - COMprehensive Performance Assessment of
Sensor Exploitation (COMPASE) Center (AFRL/RYAA), Adjunct EE and BME
Professor in at Wright State University (WSU) and Air Force Institute
of Technology (AFIT), and a reserve officer with the Air Force Office
of Scientific Research (AFRL/AFOSR). He was a founding member of the
International Society of Information Fusion (ISIF) in 1998 and the 2007
ISIF President. He is currently on the Board of Governors of the IEEE
Aerospace and Electronics Systems Society. Dr. Blasch has focused on
Automatic Target Recognition, Targeting Tracking, and Information
Fusion research compiling 350+ scientific papers and book chapters. He
is active in ISIF, IEEE (AES and SMC), and SPIE. Dr. Blasch received
his B.S. in Mechanical Engineering from the Massachusetts Institute of
Technology in 1992 and Master’s Degrees in Mechanical (‘94), Health
Science (‘95), and Industrial Engineering (‘95) (Human Factors) from
Georgia Tech and attended University of Wisconsin for an MD/PHD in
Mech. Eng/Neurosciences (‘95-97) until being called to Active Duty in
the United States Air Force. He completed an MBA(‘98), MSEE(‘98), MS
Econ(‘99), MS/PhD Psychology (ABD) (‘01), and a PhD in Electrical
Engineering (‘99) from Wright State University and is a graduate of Air
War College (‘09). He is a Fellow of SPIE.
|
Date
|
Tuesday June 14, 2011
|
Time
|
11:00-12:30 (EDT)
|
Location
|
Online Webinar (see registration below)
|
Title
|
Semantic knowledge-based
framework to improve the situation awareness of autonomous underwater
vehicles
|
Speaker
|
Emilio Miguelanez Martin
|
| |
Senior Development Engineer
|
| |
|
| |
|
Webinar Details
This webinar is organized
by the IEEE Toronto Signals & Computational Intelligence
Chapter, the IEEE Ottawa CIS Chapter and the IEEE UKRI CIS Chapter.
Registration is free but
it is required..
After registering you will receive a
confirmation email containing information about joining the Webinar.
System Requirements:
PC-based attendees
Required: Windows© 2000, XP Home, XP Pro, 2003 Server, Vista, 7
Macintosh©-based attendees
Required: Mac OS© X 10.4 (Tiger©) or newer
Note: the webinar is expected to last no longer
than an hour. However, time is allowed for Questions and Answers.
For all other locations, please, check the actual time in your time
zone. If you are not sure, you can use this Time Zone Converter
Please, login at least 15 minutes earlier to check your connection and
make sure that you are ready to attend the talk when it begins.
|
| |
Registration
Space is limited.
Reserve your Webinar seat now at:
h
https://www2.gotomeeting.com/register/546211042
|
| |
Abstract
This work proposes a
semantic world model framework for hierarchical distributed
representation of knowledge in autonomous underwater systems. This
framework aims to provide a more capable and holistic system, involving
semantic interoperability among all involved information sources. This
will enhance interoperability, independence of operation, and situation
awareness of the embedded service-oriented agents for autonomous
platforms. The results obtained specifically impact on mission
flexibility, robustness and autonomy. The presented framework makes use
of the idea that heterogeneous real-world data of very different type
must be processed by (and run through) several different layers, to be
finally available in a suited format and at the right place to be
accessible by high-level decision making agents. In this sense, the
presented approach shows how to abstract away from the raw real-world
data step by step by means of semantic technologies. The work concludes
by demonstrating the benefits of the framework in a real scenario. A
hardware fault is simulated in a REMUS 100 AUV while performing a
mission. This triggers a knowledge exchange between the status
monitoring agent and the adaptive mission planner embedded agent. By
using the proposed framework, both services can interchange information
while remaining domain independent during their interaction with the
platform. These results are readily applicable to land and air robotics.
|
| |
Speaker Bio
Emilio
Miguela ́n ̃ez (M’01) received the MPhys degree in Physics from the
University of Manchester in 2000. Then, he pursued a MSc degree in
Information Technology (Systems) and his PhD degree on the application
of evolutionary computation approaches to fault diagnosis in the domain
of semiconductor at Heriot-Watt University. He started his professional
career working as research engineer at Test Advantage Ltd developing
intelligent systems and knowledge mining solutions for the
semiconductor manufacturing environment.
|
Date
|
Wednesday May 18, 2011
|
Time
|
13:00-14:30 (EDT)
|
Location
|
Online Webinar (see registration below)
|
Title
|
Signal Fusion Using Novel
Weighted Averages
|
Speaker
|
|
| |
|
| |
|
| |
|
Webinar Details
This webinar is organized
by the IEEE Toronto Signals & Computational Intelligence
Chapter, the IEEE Ottawa CIS Chapter and the Coastal Los Angeles
Section.
Registration is free but
it is required..
After registering you will receive a
confirmation email containing information about joining the Webinar.
System Requirements:
PC-based attendees
Required: Windows© 2000, XP Home, XP Pro, 2003 Server, Vista, 7
Macintosh©-based attendees
Required: Mac OS© X 10.4 (Tiger©) or newer
Note: the webinar is expected to last no longer
than an hour. However, time is allowed for Questions and Answers.
For all other locations, please, check the actual time in your time
zone. If you are not sure, you can use this Time Zone Converter
Please, login at least 15 minutes earlier to check your connection and
make sure that you are ready to attend the talk when it begins.
|
| |
Registration
Space is limited.
Reserve your Webinar seat now at:
https://www2.gotomeeting.com/register/188381851
|
| |
Abstract
The weighted average is
arguably the earliest and most widely used form of signal fusion, but,
traditionally, the weighted average is limited to numerical values for
signals and weights. Using precise numerical values is often
problematic, and suggests that more versatile—novel—weighted averages
(NWAs) are needed, ones that are not limited to numbers. This webinar
describes the following hierarchy of NWAs: the interval weighted
average in which intervals are used for signals and/or weights; the
fuzzy weighted average in which type-1 fuzzy sets are used for signals
and/or weights; and, the linguistic weighted average in which words
modeled by interval type-2 fuzzy sets are used for signals and/or
weights. The webinar describes how these NWAs can be computed, and how
NWAs can be used across a broad spectrum of decision making. Two
hierarchical decision making applications illustrate the use of NWAs.
|
| |
Speaker Bio
Jerry
M. Mendel received the Ph.D. degree in electrical engineering from the
Polytechnic Institute of Brooklyn, Brooklyn, NY. Currently he is
Professor of Electrical Engineering and Systems Architecting
Engineering at the University of Southern California in Los Angeles,
where he has been since 1974. He has published over 500 technical
papers and is author and/or editor of nine books, including Uncertain
Rule-based Fuzzy Logic Systems: Introduction and New Directions
(Prentice-Hall, 2001) and Perceptual Computing: Aiding People in Making
Subjective Judgments (Wiley & IEEE Press, 2010). His present
research interests include: type-2 fuzzy logic systems and their
applications to a wide range of problems, including smart oil field
technology and computing with words. He is a Life Fellow of the IEEE, a
Distinguished Member of the IEEE Control Systems Society, and a Fellow
of the International Fuzzy Systems Association. He was President of the
IEEE Control Systems Society in 1986.
|
Date
|
Tuesday April 26, 2011
|
Time
|
10:00-11:30 (EDT)
|
Location
|
Online Webinar (see registration below)
|
Title
|
Type-2 Fuzzy Logic Controllers:
A way Forward for Fuzzy Systems in Real World Environments
|
Speaker
|
|
| |
|
| |
|
| |
|
Webinar Details
This webinar is organized
by the IEEE Toronto Signals & Computational Intelligence
Chapter, theIEEE Ottawa CIS Chapter, the IEEE Computational
Intelligence Society, the IEEE Region 7 (IEEE Canada) and the IEEE UK
and Republic of Ireland CIS Chapter.
Registration is free but
it is required..
After registering you will receive a
confirmation email containing information about joining the Webinar.
System Requirements:
PC-based attendees
Required: Windows? 2000, XP Home, XP Pro, 2003 Server, Vista
Macintosh?-based attendees
Required: Mac OS? X 10.4 (Tiger?) or newer
Note: the webinar is expected to last no longer
than an hour. However, time is allowed for Questions and Answers.
For all other locations, please, check the actual time in your time
zone. If you are not sure, you can use this Time Zone Converter
Please, login at least 15 minutes earlier to check your connection and
make sure that you are ready to attend the talk when it begins.
|
| |
Registration
Space is limited. Reserve your Webinar seat now
at:
https://www2.gotomeeting.com/register/792957898
|
| |
Abstract
Type-1 Fuzzy Logic
Controllers (FLCs) have been applied to date with great success to many
different applications. However, for many real-world applications,
there is a need to cope with large amounts of uncertainties. The
traditional type-1 FLC using crisp type-1 fuzzy sets cannot directly
handle such uncertainties.
A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to
produce a better performance. Hence, type-2 FLCs will have the
potential to overcome the limitations of type-1 FLCs and produce a new
generation of fuzzy controllers with improved performance for many
applications, which require handling high levels of uncertainty.
Through the review of the various type-2 FLC applications, it has been
shown that as the level of imprecision and uncertainty increases, the
type-2 FLC will provide a powerful paradigm to handle the high level of
uncertainties present in real-world environments. It has been also
shown in various applications that the type-2 FLCs have given very good
and smooth responses that have always outperformed their type-1
counterparts. Thus, using a type-2 FLC in real-world applications can
be a better choice since the amount of uncertainty in real systems most
of the time is difficult to estimate. It is envisaged to see a wide
spread of type-2 FLCs in many real-world application in the next
decade.
This talk will introduce the interval type-2 FLCs and how they present
a way forward for fuzzy systems in real world environments and
applications that face high levels of uncertainties. The talk will
present different ways to design interval type-2 FLCs. The talk will
also present the successful application of type-2 FLCs to many real
world settings including industrial environments, mobile robots,
ambient intelligent environments and intelligent decision support
systems. The talk will conclude with an overview on the future
directions of type-2 FLCs.
|
| |
Speaker Bio
Prof.
Hani Hagras is a Professor in the School of Computer Science and
Electronic Engineering, Director of the Computational Intelligence
Centre and the Head of the Fuzzy Systems Research Group in the
University of Essex, UK. His major research interests are in
computational intelligence, notably type-2 fuzzy systems, fuzzy logic,
neural networks, genetic algorithms, and evolutionary computation. His
research interests also include ambient intelligence, pervasive
computing and intelligent buildings. He is also interested in embedded
agents, robotics and intelligent control. He has authored more than 200
papers in international journals, conferences and books. He is a Fellow
of the Institution of Engineering and Technology (IET (IEE)) and a
Senior Member of the Institute of Electrical and Electronics Engineers
(IEEE). He was the Chair of IEEE Computational Intelligence Society
(CIS) Senior Members Sub-Committee. He is also the Vice- Chair of the
IEE CIS Emergent Technologies Technical Committee. His research has won
numerous prestigious international awards where most recently he was
awarded by the IEEE Computational Intelligence Society (CIS), the 2004
Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems. He
is an Associate Editor of the IEEE Transactions on Fuzzy Systems. He is
also an Associate Editor of the International Journal of Robotics and
Automation, the Journal of Cognitive Computation, the Journal of
Applied Computational Intelligence and Soft Computing and the Journal
of Ambient Computing and Intelligence. He is a member of the IEEE
Computational Intelligence Society (CIS) Fuzzy Systems Technical
Committee. Prof. Hagras chaired several international conferences where
most recently he served as the General Co-Chair of the 2007 IEEE
International Conference on Fuzzy systems London, July 2007 and he
served as the Programme Chair of 2009 IEEE Symposium on Intelligent
Agents, Nashville, USA, April 2009. He also served as the Programme
Chair of the 2010 International Conference on Intelligent Systems and
Design (ISDA 2010) and he is also the General Co-Chair of the 2011 IEEE
Symposium on Intelligent Agents and 2011 IEEE Symposium on Advances to
Type-2 Fuzzy Logic Systems.
|
Date
|
Friday December 17, 2010
|
Time
|
13:00-14:00
|
Location
|
Room 5077, SITE Building, University of Ottawa
|
Title
|
Nature-Inspired Optimization in
Fault-Reactive Wireless Sensor and Robot Networks
|
Speaker
|
Rafael Falcon
|
| |
Ph.D. Candidate, SITE
|
| |
University of Ottawa
|
| |
|
|
The talk will be followed by the annual meeting
of the chapter
|
Abstract
Small teams of mobile robots provide nowadays
the ability to assist wireless sensor networks in many threatening
scenarios that unexpectedly arise during their operational lifetime.
The perceived risk or vulnerability that the network is exposed to
triggers an immediate, corporate action from the robotic agents
(actuators). We focus on a sort of actuators which are able to carry
static sensors and deploy them all over the field. By doing so, they
can collaboratively react to hardware/software faults that stem in the
sensor nodes, thus preserving the network coverage. Determining the
true ensemble of faulty nodes and the ensuing replacement trajectory
are NP-hard problems which call for the application of metaheuristic
optimization algorithms. In this talk, we will illustrate how the
social interaction mechanisms present in natural systems like ant
colonies, bird flocks, firefly swarms and groups of chromosomes can be
exploited to solve challenging combinatorial optimization problems in
the context of a fault-reactive wireless sensor and robot network.
|
| |
Speaker Bio
Rafael
Falcon received his Bachelor and Master degrees in Computer Science
from Universidad Central de Las Villas (Cuba) in 2003 and 2006,
respectively, before joining the School of Information Technology and
Engineering ( University of Ottawa ) in 2008 as a PhD candidate. He has
co-edited two Springer volumes on fuzzy and rough set theories and is a
member of the International Rough Set Society, IEEE and IEEE
Computational Intelligence Society. His current research interests
embrace wireless sensor and robot networks, evolutionary optimization,
fuzzy logic and fault-tolerant systems. Besides having served as
reviewer for prestigious IEEE CIS journals and conferences, the speaker
is collaborating nowadays with an industry partner in the Ottawa area
to embed the core protocols of his PhD thesis into real-world,
sensory-operated environments.
|
Date
|
Wednesday May 12, 2010
|
Time
|
19:00-20:00
|
Location
|
Room 5077, SITE Building, University of Ottawa
|
Title
|
The Use of General Neuron
Functions within Neural Networks
|
Speaker
|
Alan J. Barton
|
| |
NRC-CNRC Institute for Information Technology
|
| |
|
Abstract
Classical
Artificial Neural Networks (ANNs) may have their neurons organized in
many ways. For example, Feedforward Neural Networks are aggregations of
weights multiplied by inputs and controlled via activation functions;
with the potential addition of bias nodes.
This presentation will demonstrate the use of a methodology for
construction of ANNs that do not require the use of weights nor the
apriori specification of activation functions when learning the
functions associated to the neurons within the ANN as is performed
within the classical case. A brief discussion of related work and
examples will be shown from various applications of the approach,
including:
- Biological Data, such as Magnetic Resonance
Spectra from Brain Cancer samples and clinical data from Breast Cancer
samples,
- Geophysical Prospecting Data, such as from
Insunza measurements for learning about the presence of an underground
cave, and
- Hydrochemical Data, such as from Werenskiold
glacial water samples for learning about global climate changes.
In
addition, if time permits, a preliminary analysis of the parameters
controlling the construction of the ANNs will be stated (e.g. the
Parameter Space) along with a discussion of one possible way to analyse
the set of constructed ANNs (e.g. the Mathematical Expression Space;
based on a recently published similarity measure).
|
| |
Speaker Bio
Alan J.
Barton's current research interests lie within Computational
Intelligence (e.g. Neural Networks, Evolutionary Computation, etc.) and
High Performance Computing (e.g. Distributed and Parallel Computation,
etc.). He holds a Master of Computer Science (M.C.S.) from
Carleton University in Ottawa, Ontario, Canada (2009) and has a
combined Computer Science and Mathematics degree (B.Sc.) from the
University of Victoria in British Columbia, Canada (2000). He has also
completed the required course and laboratory work (Proteomics, Genomics
and Bioinformatics) and obtained a certificate in BioInformatics (2006).
|
Date
|
Thursday March 25, 2010
|
Time
|
18:00-21:00
|
Location
|
Algonquin College, T-Building, Room T327
|
Title
|
DRDC Biometrics Activities and
IEEE CIS Biometrics Mission
|
Speaker
|
Qinghan Xiao, Defence Scientist
|
| |
Defence Research and Development Canada
|
| |
|
Abstract
In recent years, biometric
technologies have emerged as solutions to security-related applications
such as access control, identity verification, forensic investigation,
and terrorism suspect identification. Various biometric technologies
are available for identifying or verifying an individual by measuring
his/her fingerprint, hand, face, signature, voice, or a combination of
the traits. Since a biometric trait cannot be captured in precisely the
same way twice, biometric matching is always a “fuzzy comparison”. This
feature makes computational intelligence (CI), which is primarily based
on artificial intelligence, neural networks, fuzzy logic, evolutionary
computing, etc., an ideal solution for solving different biometric
problems.
This seminar will address the following issues based on the speaker’s
expertise in biometric technologies and experience in organizing IEEE
biometric activities:
- Biometrics and biometric applications
- Various biometric technologies along with
their advantages and limitations
- Generic biometric system configuration
- Examples of biometric implementations
- DRDC Ottawa biometric R&D
- Facial presence monitoring system for
information security
- Multi-biometric fusion
- Non-ideal fingerprint image processing
- Fingerprint spoofing and anti-spoofing
- IEEE CIS biometrics mission and activities
- Task Force on Biometrics of Intelligent
Systems Applications (ISA) TC
- Special sessions and workshops
- “Biometrics, theory, methods, and
applications”, IEEE Press Series on Computational Intelligence
|
| |
Speaker Bio
Qinghan Xiao, IEEE Senior Member,
is a Defence Scientist at the Defence R&D Canada. He has served
as the Chair of Task Force on Biometrics of Intelligent Systems
Applications Technical Committee since 2008, and has been recently
appointed as the CIS representative at IEEE Biometrics Council. His
current research interests include biometric, smart card and RFID
technologies. Dr. Xiao is a Canadian delegate of the ISO/IEC JTC1 SC37
standards committee on biometrics, and leads “Red Team/Blue Team” study
for the Canadian Operational Support Command. He has been invited as
speaker and chaired biometric special sessions/workshops for several
national and international events. Dr. Xiao holds a Ph.D. in Computer
Science from the University of Regina.
|
Date
|
Wednesday March 17, 2010
|
Time
|
14:00-16:00
|
Location
|
Room 5084, SITE Building, University of Ottawa
|
Title
|
Development of a Robotic
Platform for Human Robot Interaction
|
Speaker
|
Genci Capi , Associate Professor
|
| |
University of Toyoma, Japan
|
| |
Department of Electrical and Electronic Systems
Engineering
|
| |
|
Abstract
Robots are, or soon will be, used
in such critical domains as search and rescue, military battle, mine
and bomb detection, scientific exploration, law enforcement, and
hospital care. Such robots must coordinate their behaviors with the
requirements and expectations of human team members; they are more than
mere tools but rather quasi-team members whose tasks have to be
integrated with those of humans. In the Intelligent Robotics Lab,
University of Toyama we are working on Human-Robot Interaction and in
this talk I will present the research work on:
1. Gesture recognition for human robot interaction.
2. Human robot interaction using natural language.
3. Intelligent robot navigation using the visual information.
4. Robot Map Building.
|
| |
Speaker Bio
Genci Capi is an Associate
Professor of Electrical and Electronic Engineering at the University of
Toyama, and the Director of the Intelligent Robotics Laboratory. His
current research interests are in the areas of intelligent and
autonomous robots, brain-machine interface and multi-robots system. He
is the recipient of several awards such as the Highly Commended Award
from the Literati Awards for Excellence. Dr. Capi is member of the
editorial board of several journals and organizer of many conferences
in the field of intelligent robots. He has authored more than 100
articles in high-impact journals and conferences proceedings.
|
Date
|
Tuesday February 23, 2010
|
Time
|
11:00-12:00
|
Location
|
Room 5077, SITE Building, University of Ottawa
|
Title
|
Risk-Based Multiobjective
Optimization for a Vehicle Fleet Mix Problem
|
Speaker
|
Slawo Wesolkowski, Scientist
|
| |
DRDC-Ottawa
|
| |
Centre for Operational Research and Analysis
(CORA)
|
| |
|
| |
|
Abstract
Organizations
transporting people and cargo are concerned about determining how many
vehicles they need to accomplish required transportation tasks. Those
approaches usually involve using Discrete Event Simulation (DES).
However, integrating DES in a framework to determine an optimal fleet
is impossible due to the high computational cost of DES and the very
large combinatorial space of possible fleets. Therefore, a surrogate or
approximate model for DES needs to be devised. In this talk, the
Stochastic Fleet Estimation (SaFE) model is presented, a very simple
Monte Carlo-based model, which generates a vehicle fleet based on the
average set of required tasks that the fleet is supposed to accomplish
(the average fleet). This model is then used within a multiobjective
optimization framework (using NSGA II as the optimizer) in order to
determine optimal fleets with respect to different objectives. The
optimization searches for Pareto-optimal combinations of valid
platform-assignments for a list of tasks, which can be applied to
entire scenarios output by SaFE. The following three objectives are
used: performance, cost and risk. Variance information associated with
the average platform numbers generated by SaFE is used to compute
different risk-based objectives. Various optimal solution fleets are
discussed.
|
| |
Speaker Bio
Slawo
Wesolkowski is a Scientist at DRDC CORA. He has previously worked for
Vantage Point International (now C-CORE), NCR Canada Ltd., Nortel,
Moteurs Leroy-Somer (France), the University of Waterloo, and the
National Research Council of Canada. He holds five US patents, and one
Canadian/EU patent. He obtained BASc, MASc and PhD degrees in Systems
Design Engineering from the University of Waterloo, Canada. He is
currently Vice President Members Activities of the IEEE Computational
Intelligence Society. He was the General Chair of the 2009 IEEE
Symposium on Computational Intelligence for Security and Defense
Applications (IEEE CISDA).
|
Date
|
Friday December 04, 2009
|
Time
|
12:30-13:30
|
Location
|
Room 5084, SITE Building, University of Ottawa
|
Title
|
Much To Do About Bidirectional
Associative Memory
|
Speaker
|
Sylvain Chartier, Assistant Professor
|
| |
University of Ottawa
|
| |
School of Psychology
|
| |
|
| |
|
|
The talk will be followed by the annual meeting
of the chapter
|
Abstract
Associative
memory is at the core of human learning. This type of learning is best
captured using Bidirectional Associative Memory (BAM). BAM has been
extensively studied since its introduction by Kosko (1988). Over the
years, several variants have been proposed to overcome the original
model’s limited storage capacities and improve its noise sensitivity,
and most of today’s BAM models can store and recall all the patterns in
a learning set. Over the last few years, we proposed a BAM that is
able; to learn online any type of correlated patterns (binary and real
values); to perform multi-step pattern recognition as well as
one-to-many association; to control the attractors (switch from
fixed-points to dynamic orbits, momentarily disable desired
attractors); to perform nonlinear separable task and perform autonomous
perceptual feature creation; to learn in noisy environments; to create
and reorganize its cluster-based categories in a flexible way and to
encompasses competitive and topological model properties. All those
behaviors are obtained using the same learning rule (based on
covariance only), the same output function and the same general
architecture. Therefore, the proposed BAM is one step closer to unified
various classes of models within a general architecture as well as
being a good candidate to explain human learning behaviors.
|
| |
Speaker Bio
Sylvain
Chartier received the B.A. degree from
the University of Ottawa, in 1993 and the B.Sc. and Ph.D. degrees from
the Université du Québec à Montréal, in 1996 and 2004, respectively,
all in psychology. His doctoral thesis was on the development of an
artificial neural network for autonomous categorization. From 2004 to
2007, he was a post-doctoral fellow at the Centre de recherche de
l’Institut Philippe-Pinel de Montréal where he conducted research on
eye-movement analysis and classification. Since 2007, He is an
Assistant Professor at the University of Ottawa. His research interests
are in the development of unsupervised and supervised recurrent
associative memories. He
is also interested in nonlinear time series analysis as well as
cognition, perception, and statistics.
|
Date
|
Monday November 02, 2009
|
Time
|
10:30-11:30
|
Location
|
Building M-50, NRC Auditorium, 1200 Montreal
Road
|
Title
|
Swarm Intelligence: Where We've
Been and Where We're Going
|
Speaker
|
Russel Eberhart , University Professor
|
| |
Indiana University Purdue University
Indianapolis
|
| |
Purdue School of Engineering and Technology
|
| |
|
| |
|
Abstract
The
definition of, and basic principles of, swarm intelligence are first
discussed. Application areas are listed. Three
swarm intelligence paradigm examples are reviewed: cultural algorithms,
ant colony optimization, and particle swarm optimization.
Tracking and optimizing dynamic systems with swarms is
discussed. Recent applications are summarized: unmanned air
vehicle mission planning, putting a person in the swarm using an NK
landscape game, and optimizing resource allocation. Measuring
swarm population diversity, and using non-parametric statistics for
performance metrics are reviewed. Finally, recent
accomplishments of the swarm intelligence field, and challenges being
faced by the field, are outlined.
|
| |
Speaker Bio
Russell
C. Eberhart
is Professor of Electrical and Computer Engineering at the Purdue
School of Engineering and Technology, Indiana University Purdue
University Indianapolis (IUPUI). He is also Vice President
and Chief Technology Officer of Computelligence LLC, Indianapolis,
Indiana. He received his Ph.D. from Kansas State University
in electrical engineering. He is co-editor of a book on
neural networks, and co-author of Computational Intelligence
PC Tools, published in 1996 by Academic Press. He
is co-author of a book with Jim Kennedy and Yuhui Shi entitled Swarm
Intelligence, published by Morgan Kaufmann/Academic Press in
April 2001. He was awarded the IEEE Third Millenium
Medal. In 2001, he became a Fellow of the IEEE, and in 2002
he became a Fellow of the American Institute for Medical and Biological
Engineering. He is the co-author, with Yuhui Shi,
of a book entitled Computational Intelligence: Concepts to
Implementations, published by Morgan Kaufmann/Elsevier in
2007. His areas of research include swarm intelligence and
extended analog computing, and the analysis of sleepy and inattentive
driving.
|
Date
|
Friday July 10, 2009
|
Time
|
13:00-14:00
|
Location
|
Conference Room, Building T-86 5084, DRDC
Ottawa
|
Title
|
Cognitive Tracking Radar
|
Speaker
|
Simon Haykin, University Professor
|
| |
McMaster University
|
| |
Cognitive Systems Laboratory
|
| |
|
| |
|
Abstract
In
early 2006, I described the
idea of Cognitive Radar in an invited paper that was published in the
IEEE Signal Processing Magazine. This paper was followed by an invited
chapter on Cognitive Radar that appeared in a book edited by Fulvio
Gini. With these two contributions, the idea of Cognitive radar was
born.
In this
lecture I will expand
on the practical implementation of a Cognitive Tracking Radar. Most
importantly, experimental results will be presented tot
demonstrate the practical validity of this new and exciting
development,. The Cognitive Tracker builds on two
functional blocks:
(a) the
newly discovered
Cubature Kalman Filter for estimating the state of the radar
environment, and
(b)
approximate dynamic
programming for the optimum selection of transmitted radar waveform on
the basis of information passed onto the transmitter by the receiver.
The
experimental study is based
on data pertaining to an "object falling in space." I will close the
lecture by describing my vision as to how I see the impact of cognition
on such diverse areas of application as radar, wireless communications,
and the power grid.
|
| |
Speaker Bio
Simon
Haykin received his B.Sc.
(First-class Honours), Ph.D., and D.Sc., all in Electrical Engineering
from the University of Birmingham, England. He is a Fellow of the Royal
Society of Canada, and a Fellow of the Institute of Electrical
and Electronics Engineers. He is the recipient of the Henry Booker
Medal from 2002, the Honorary Degree of Doctor of Technical Sciences
from ETH Zentrum, Zurich,
Switzerland,
1999, and many other medals and prizes. He is a pioneer in adaptive
signal-processing with emphasis on applications in radar and
communications, an area of research which has occupied much of his
professional life.
|
| |
DRDC Seminar Series
This
presentation is part of
the Defence R&D Canada - Ottawa
Seminar
Series. Visitors are encouraged to contact the seminar committee (drdco.seminar@drdc-rddc.gc.ca)
before traveling to DRDC Ottawa. Further logistical information is
appended below. We invite
speakers from government,
industry and academia, in
all aspects of defense research and related civil applications.
Shirleys
Bay Campus staff
without access to Building 5A can present themselves at the building¹s
main entrance at 9:45; an escort to Conf. Room B will be available.
DRDC employees without access to Shirleys Bay Campus should explicitly
request special arrangements from the DRDC Ottawa seminar committee. In
addition, staff members are welcome to invite other visitors; however,
security arrangements become the sole responsibility of the staff
making this invitation.
|
Date
|
Thursday April 30, 2009
|
Time
|
10:30-11:30
|
Location
|
Room 5084, SITE Building, University of Ottawa
|
Title
|
Hand Gesture Recognition and
Applications in Human-Computer Interactions
|
Speaker
|
Qing Chen, Post-Doc
|
| |
University of Ottawa
|
| |
School of Information Technology and
Engineering
|
| |
|
| |
|
Abstract
In this talk, I will
introduce our work on vision-based hand gesture recognition and its
applications in human-computer interactions. To effectively detect
different hand gestures and analyze hand movements, we divide
the problem into two levels. The lower level of our approach focuses on
detecting and recognizing different hand postures with a set of
Haar-like features. The advantages and limitations of this approach
compared with other approaches such as color-based algorithms will be
discussed. The higher-level tries to analyze the trajectories of the
hand postures detected by the lower-level using a set of
predefined grammars. With the recognized gestures, we can
convert them in to a set of gesture commands for HCI applications such
as playing a car navigation game and interacting with a web-based
browser.
|
| |
Speaker Bio
Qing Chen is currently a
PostDoc at the DiscoverLab of University of Ottawa. He received his
Ph.D. in Electrical Engineering from University of Ottawa in 2008. His
Ph.D. research is focused on real-time vision-based hand tracking and
gesture recognition. His general research interests include
vision-based object detection, tracking and recognition,
statistical/syntactic pattern recognition, vision-based human-computer
interactions. He also received his M.A.Sc from University of Ottawa in
2004 and his Bachelor degree from Jianghan Petroleum Institute, Hubei,
China in 1994.
|
Date
|
Tuesday February 10, 2009
|
Time
|
12:00-13:30
|
Location
|
Room 5084, SITE Building, University of Ottawa
|
Title
|
Neural Networks for
High-Frequency Electronic Modeling and Design
|
Speaker
|
Q. J. Zhang, Professor (IEEE Fellow)
|
| |
Carleton University
|
| |
Department of Electronics
|
| |
|
| |
|
Abstract
Recent advances in the
application of Artificial Neural Networks (ANN) to radio-frequency (RF)
and microwave design created an exciting direction of computer-aided
modeling and design of high-frequency electronics. ANNs are trained to
learn the high-frequency behavior of electronic components, and trained
ANNs can be used as models for high-level electronic design. The ANN
models are much faster than detailed electromagnetic/physics based
models of electronic components, and more accurate than conventional
empirical/equivalent circuit models. It leads to substantial increase
in modeling accuracy, speed, and flexibility. Applications are being
made in modeling and design of passive and active RF/microwave
electronic components and circuits, high-speed VLSI interconnects,
printed antennas, LTCC circuits, semiconductor devices, measurement
standards, filters, amplifiers, mixers and so on. Automated model
generation algorithms integrating data generation and ANN training are
being developed. Knowledge based neural networks exploiting prior
knowledge such as empirical/semi-analytical models are being introduced
in microwave computer-aided design (CAD). This leads to new level of
CAD methodologies combining equivalent circuit/empirical models,
electromagnetic/physics simulation and behavioral modeling with ANN and
optimization algorithms for fast and accurate design of high-frequency
circuits and systems. This talk presents a review of the state of the
art in these emerging directions. The presentations highlight
implementable methodologies for automated modeling and design of
high-frequency electronic components, circuits and systems. The
presentation covers fundamental concepts and methodologies, industrial
applications, and future trends in R&D.
|
| |
Speaker Bio
Q.J. Zhang received the
B.Eng. degree from the Nanjing University of Science and Technology,
Nanjing, China in 1982, and the Ph.D. Degree in Electrical Engineering
from McMaster University, Hamilton, Canada, in 1987. He joined the
Department of Electronics, Carleton University, Ottawa, Canada in 1990
where he is presently a Professor.
His research interests are modeling, optimization and neural networks
for high-speed/high-frequency electronic design, and has over 200
publications in the area. He is an author of the book Neural Networks
for RF and Microwave Design (Boston: Artech House, 2000), and a
coeditor of Modeling and Simulation of High-Speed VLSI Interconnects
(Boston: Kluwer, 1994). He is a contributor to Encyclopedia of RF and
Microwave Engineering, (New York: Wiley, 2005), Fundamentals of
Nonlinear Behavioral Modeling for RF and Microwave Designs, (Boston:
Artech House, 2005), Tutorials on Emerging Methodologies and
Applications in Operations Research, (New York: Springer, 2005), and
Analog Methods for Computer-Aided Circuit Analysis and Diagnosis, (New
York: Marcel Dekker, 1988). He was a Guest co-Editor for the Special
Issue on High-Speed VLSI Interconnects for the International Journal of
Analog Integrated Circuits and Signal Processing (Boston: Kluwer,
1994), and twice a Guest Editor for the Special Issues on Applications
of ANN to RF and Microwave Design for the International Journal of RF
and Microwave CAE (New York: Wiley, 1999, 2002).
Dr. Zhang is on the editorial board of the IEEE Transactions on
Microwave Theory and Techniques, the International Journal of RF and
Microwave CAE, and the International Journal of Numerical Modeling. He
is an Associate Editor for the Journal of Circuits, Systems and
Computers. He is a member of the Technical Committee on CAD of the IEEE
MTT Society. He is a Fellow of the IEEE, and a Fellow of the
Electromagnetics Academy..
|
Date
|
Thursday December 18, 2008
|
Time
|
10:30-12:00
|
Location
|
Building M-50, NRC Auditorium, 1200 Montreal
Road
|
Title
|
Soft Computing for Sensor and
Algorithm Fusion
|
Speaker
|
James M. Keller , Professor
|
| |
University of Missouri-Columbia
|
| |
Electrical and Computer Engineering Department
|
| |
|
| |
|
The talk will be followed
by the annual meeting of the chapter
|
Abstract
Sensor and algorithm
fusion is playing an increasing role in many application domains. As
detection and recognition problems become more complex and costly (for
example, landmine detection and automatic activity monitoring), it is
apparent that no single source of information can provide the ultimate
solution. However, complementary information can be derived from
multiple sources. Given a set of outputs from constituent sources,
there are many frameworks within which to combine the pieces into a
more definitive answer. This tutorial will focus on the fusion of
multiple partial confidence values within the framework of fuzzy set
theory.
So, the question then
becomes: what methodology do we use to combine partial decision
information? There are many choices, but I will focus on the use of
fuzzy set theoretic mechanisms to fuse confidence from multiple
sources. Two general approaches will be considered, fuzzy integrals and
fuzzy logic rule-based systems. Fuzzy integrals have a long history and
have been studied in the context of pattern recognition and information
fusion for several years being first introduced for this purpose by
Tahani and Keller in 1990. Fuzzy integrals combine the objective
evidence supplied by each information source with the expected worth of
each subset of information sources (via a fuzzy measure) to assign
confidence to hypotheses or to rank alternatives in decision making.
This is a nonlinear combination of information and the worth of the
information for the decision in question, dealing with the uncertainty
in both forms of data. Different fuzzy measures yield different
integration operations, including averaging, linear combinations of
order statistics, and many others. Measures can be found by heuristic
assignment or via training algorithms. New results with discriminative
training will be discussed. Next, a fusion system based on a linguistic
extension of the Choquet fuzzy integral will be shown. The uncertainty
in the data is now expressed as a linguistic vector, i.e., a vector of
fuzzy sets. The linguistic Choquet integral is used to fuse both
position and confidence uncertainty in the landmine detection scenario.
Fuzzy logic rule-based
systems provide another mechanism to fuse together the results of
different features, classification algorithms and sensors. Such a
system employs rules much like those that a human expert might derive.
Again, uncertainty in the component parts is modeled by linguistic
variables taking on fuzzy sets as values. I will describe the
application of fuzzy rule-based classifiers in image processing and
landmine detection.
|
| |
Speaker Bio
James M. Keller received
the Ph.D. in Mathematics in 1978. He holds the University of Missouri
Curators’ Professorship in the Electrical and Computer Engineering and
Computer Science Departments on the Columbia campus. He is also the R.
L. Tatum Professor in the College of Engineering. His research
interests center on computational intelligence: fuzzy set theory and
fuzzy logic, neural networks, and evolutionary computation with a focus
on problems in computer vision, pattern recognition, and information
fusion including bioinformatics, spatial reasoning in robotics,
geospatial intelligence, sensor and information analysis in technology
for eldercare, and landmine detection. His industrial and government
funding sources include the Electronics and Space Corporation, Union
Electric, Geo-Centers, National Science Foundation, the Administration
on Aging, The National Institutes of Health, NASA/JSC, the Air Force
Office of Scientific Research, the Army Research Office, the Office of
Naval Research, the National Geospatial Intelligence Agency, and the
Army Night Vision and Electronic Sensors Directorate. Professor Keller
has coauthored over 300 technical publications.
Jim is a Fellow of the
Institute of Electrical and Electronics Engineers (IEEE) for whom he
has presented live and video tutorials on fuzzy logic in computer
vision, is an International Fuzzy Systems Association (IFSA) Fellow, is
a national lecturer for the Association for Computing Machinery (ACM),
is an IEEE Computational Intelligence Society Distinguished Lecturer,
and is a past President of the North American Fuzzy Information
Processing Society (NAFIPS). He received the 2007 Fuzzy Systems Pioneer
Award from the IEEE Computational Intelligence Society. He finished a
full six year term as Editor-in-Chief of the IEEE
Transactions on Fuzzy Systems, is an Associate Editor of the International
Journal of Approximate Reasoning,
and is on the editorial board of Pattern Analysis and
Applications, Fuzzy Sets and Systems,International
Journal of Fuzzy Systems, and the Journal of
Intelligent and Fuzzy Systems. He is currently the Vice
President for Publications of the IEEE Computational Intelligence
Society. He was the conference chair of the 1991 NAFIPS Workshop,
program co-chair of the 1996 NAFIPS meeting, program co-chair of the
1997 IEEE International Conference on Neural Networks, and the program
chair of the 1998 IEEE International Conference on Fuzzy Systems. He
was the general chair for the 2003 IEEE International Conference on
Fuzzy Systems.
|
Date
|
Thursday July 3, 2008
|
Time
|
11:00-12:00
|
Location
|
Room 379, Building M-50, NRC Auditorium, 1200
Montreal Road
|
Title
|
Applying Relational Learning to
Structural Molecular Biology Problems
|
Speaker
|
Marcel Turcotte, Professor
|
| |
University of Ottawa
|
| |
School of Information, Technology and
Engineering
|
| |
|
| |
|
Abstract
I will
present our work on applying relational learning to discover rules
characterizing protein folds. Inductive Logic Programming (ILP) has
been chosen for its ability to 1) discover relations, 2) represent
background information, as well as 3) its expressiveness. Several
representations for the background set have been explored, and the
results have been interpreted in their biological context.
The rules that were automatically found are often similar to the
descriptions found in SCOP (a database of protein folds) or the
published scientific literature. Finally, I will conclude presenting
some future applications, in particular, for the determination of
protein function from structure information.
This is a joint work with M.J.E. Sternberg and S. Muggleton from
Imperial College London/UK.
|
| |
Speaker Bio
I (Marcel
Turcotte) completed a Ph.D. at the University of Montreal, Canada,
under the supervision of Guy Lapalme and Robert Cedergren. I was then a
postoctoral fellow at the University of Florida, USA, where I worked
with Steven Benner on evolutionary-based approaches to protein
secondary structure prediction. I then moved to the United Kingdom to
work in the Biomolecular Modelling Laboratory (Mike Sternberg, head) of
the Imperial Cancer Research Fund. In 2000, I returned to Canada where
I joined the School of Information Technology and Engineering at the
University of Ottawa.
|
Date
|
Tuesday March 18, 2008
|
Time
|
10:00-11:00
|
Location
|
Room 5084, SITE Building, University of Ottawa
|
Title
|
Neural Networks for Environment Recognition and
Local Navigation of a Mobile Robot
|
Speaker
|
Moufid Harb, Ph.D., Research Scientist
|
| |
Larus Technologies Corp.
|
| |
|
| |
University of Ottawa
|
| |
School of Information, Technology and
Engineering
|
| |
|
| |
|
Abstract
This
presentation will focus on a computer based design and test of three
neural controllers for local navigation, and another two neural
networks for environmental recognition, fed off-line by a simulated
model of a laser range-finder. These neural networks are the major
components of a control system that performs a global neural navigation
of a mobile robot, which could be used to perform industrial missions
within industrial environments. This control system can guide a mobile
robot to track its predefined path to arrive to its final goal through
a set of sub-goals, or autonomously plan its path to arrive to the
desired final goal, and to avoid obstacles that are found along the
way. The presentation will include simulation results and live
demonstrations.
|
| |
Speaker Bio
Moufid
Harb (M'07) is a Research Collaborator with the School of Information
Technology and
Engineering at the University
of Ottawa,
and a Research Scientist with Larus Technologies. He received his
Bachelor of Eng. in Electrical Engineering in 1983 from Damascus University,
his M.Sc. in
Instrument Design and Application in 1994 from Manchester
University, Institute
of Science
and Technology (UMIST), England-UK and his Ph.D. in Electrical
Engineering in 2001 from Damascus University/Syria in collaboration
with Ruher University of Bochum/Germany. His research interests include
autonomous robotic navigation, sensor modeling and simulation, and
intelligent systems. Dr. Harb is a member of the IEEE Ottawa Section.
He is a member of the IEEE Instrumentation and Measurement Society, and
the IEEE Computational Intelligence Society. He is currently a
vice-chair of the IEEE Computational Intelligence Society - Ottawa
Chapter.
|
Date
|
Wednesday December 19, 2007
|
Time
|
10:30-11:30
|
Location
|
Room 5084, SITE Building, University of Ottawa
|
Title
|
Does pain hurt in both French and English?
|
Speaker
|
Oana Frunza, Ph.D. Candidate
|
| |
University of Ottawa
|
| |
Text Analysis and MAchine LEarning (TAMALE)
Group
|
| |
|
| |
|
|
The talk will be followed by the annual meeting
of the chapter
|
Abstract
Cognates are pair of
words in different languages similar in spelling and meaning. They can
help a second-language learner on the tasks of vocabulary expansion and
reading comprehension. False friends are pairs of words that have
similar spelling but different meanings. Partial cognates are pairs of
words in two languages that have the same meaning in some, but not all
contexts. Detecting the actual meaning of a partial cognate in context
can be useful for Machine Translation tools and for Computer-Assisted
Language Learning tools.
In this talk I will
present research that I have done for cognates and false-friends
identification and partial cognate disambiguation tasks. I will
describe the method that we propose to automatically classify a pair of
words as cognates or false friends, and also a supervised and a
semi-supervised method to disambiguate partial cognates between two
languages. We applied all our methods to French and English, but they
can be applied to other pairs of languages as well.
I will also present a
tool that I built to annotate French texts with equivalent English
cognates or false friends, in order to help a second-language learner.
|
| |
Speaker Bio
Oana Frunza is currently
a Ph.D. Candidate at University of Ottawa, Canada. She is doing
research in Natural Language Processing and Machine Learning with Dr.
Diana Inkpen. She has a Computer Science background form “Babes-Bolyai”
University, Romania and a M.S.C. Diploma from University of Ottawa,
Canada in Natural Language Processing and Machine Learning. Her main
research is focused on automatic text classification, semantic
representation and machine learning techniques applied to various text
processing tasks. She is an author or co-author of papers that were
published in prestigious international conferences.
|
Date
|
Friday November 09, 2007
|
Time
|
10:30-12:00
|
Location
|
Building M-50, NRC Auditorium, 1200 Montreal
Road
|
Title
|
Data Mining, Neural Networks and Rule
Extraction
|
Speaker
|
Jacek M. Zurada, Distinguished University
Scholar (IEEE Fellow)
|
| |
University of Louisville
|
| |
Computational Intelligence Laboratory
|
| |
|
| |
|
Abstract
This
lecture discusses paradigms of
neurocomputing in context of effective data mining tasks such as
data-driven modeling, feature extraction, dimensionality reduction,
visualization, knowledge extraction and logic rule discovery. These
tasks often involve handling of heterogenous, subjective, imprecise and
noisy data. Of special importance here is the concept of dimensionality
reduction of input data vectors. An approach is presented that leads to
reduced models achieved through evaluation of sensitivity matrices of
perceptron networks. When developing reduced models it is also useful
to eliminate underutilized internal weights and, possibly, also neurons
via pruning techniques. The concluding part of the talk reviews the
potential of perceptron networks for producing understandable IF-THEN
rules.
|
| |
Speaker Bio
Dr. Jacek M. Zurada
serves as the Distinguished University Scholar and Professor of
Electrical and Computer Engineering at the University of Louisville,
Louisville, Kentucky, USA. He is the author of several books such
Introduction to Artificial Neural Systems, and co-editor of
Computational Intelligence: Imitating Life, and of Knowledge Based
Neurocomputing. He is also the author or co-author of more than 300
journal and conference papers in the area of neural networks and
computational intelligence. In 1998-2003, Dr. Zurada was the
Editor-in-Chief of IEEE Transactions on Neural Networks. In 2004-05 he
served as the President of IEEE Computational Intelligence Society. He
is an IEEE Fellow.
|
|
|
Date
|
Tuesday July 17, 2007
|
Time
|
13:30-14:30
|
Location
|
Room 379, Building M-50, NRC Auditorium, 1200
Montreal Road
|
Title
|
|
Speaker
|
Stan Matwin, Professor
|
| |
University of Ottawa
|
| |
School of Information, Technology and
Engineering
|
| |
|
| |
|
Abstract
In this
talk we will discuss two
current applications of Machine Learning, being developed at the Text
Analysis and Machine Learning (TAMALE) research group at the University
of Ottawa. The first application (joint work with Dr. D. Inkpen), in
cooperation with TrialStat, targets screening of medical abstracts for
a Systematic Review System. Systematic reviews are the basic tool of
Evidence-based-medicine. We will describe the task, and outline the
requirements and challenges a Machine Learning solution must meet. The
second application is in the area of Digital games Based Learning. In a
joint effort with Distil Interactive, we are using Machine Learning in
acquiring profiles of different classes of people using digital games
for skill certification. Here as well we will outline some of the
requirements and challenges the task presents from the perspective of
Machine Learning. Interestingly, some of the challenges are shared by
both tasks and are among the challenges before the entire field.
|
| |
Speaker Bio
Stan Matwin is a
professor at the School of Information Technology and Engineering,
University of Ottawa, where he directs the Text Analysis and Machine
Learning (TAMALE) lab. His research is in machine learning, data
mining, and their applications, as well as in technological aspects of
Electronic Commerce. Author and co-author of 150 research papers, he
has worked at universities in Canada, the U.S., Europe and Latin
America, where in 1997 he held the UNESCO Distinguished Chair in
Science and Sustainable Development. Former president of the Canadian
Society for the Computational Studies of Intelligence (CSCSI) and of
the IFIP Working Group 12.2 (Machine Learning). Founding Director of
the Graduate Certificate in Electronic Commerce at University of
Ottawa. Founding Director of the Information Technology Cluster of the
Ontario Research Centre for Electronic Commerce. Chair of the NSERC
Grant Selection Committee for Computer Science and member of the Board
of Directors of Communications and Information Technology Ontario
(CITO). Recipient of a CITO Champion of Innovation Award. Programme
Committee Chair and Area Chair for a number of international
conferences in AI and Machine Learning. Member of the Editorial Boards
of the Machine Learning Journal, Computational Intelligence Journal,
and the Intelligent Data Analysis Journal.
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Date
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Monday February 07, 2007
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Time
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17:30-19:00
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Location
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Room 5084, SITE Building, University of Ottawa
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Title
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Information Technologies Applied to Medicine
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Speaker
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Monique Frize, Professor
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Carleton University
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Department of Systems and Computer Engineering
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University of Ottawa
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School of Information, Technology and
Engineering
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Abstract
The talk will present
current research and development in biomedical engineering. Using
machine learning and data mining techniques such as artificial neural
networks and case-based reasoning, we design and attempt to improve the
performance of these tools to predict premature births (before the 23rd
week of gestation); we also predict complications for infants in
intensive care; we are developing an automated technique to assess pain
levels in babies and adults using an infra-red camera and imaging
techniques.
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Speaker Bio
Dr. Frize joins Carleton
University, as a Professor in the Department of Systems and Computer
Engineering, and the University of Ottawa, as a Professor in the School
of Information Technology and Engineering, in July 1997.
Dr. Frize graduated with a Bachelor of Applied Science (Electrical
Engineering), received an Athlone Fellowship and completed a Master's
in Philosophy in Electrical Engineering (Engineering in Medicine) at
Imperial College of Science and Technology in London (UK), a Master's
of Business Administration at the Université de Moncton (New
Brunswick), and a doctorate from Erasmus Universiteit in Rotterdam, The
Netherlands.
Monique Frize worked as a clinical engineer for 18 years, initially at
Hopital Notre-Dame in Montreal (1971-79), and then was appointed as
Director of the Regional Clinical Engineering Service in Moncton, New
Brunswick, providing services for seven hospitals in the South-Eastern
region. Dr. Frize was also Research Associate in the Faculty of Science
and Engineering at UniversitJ de Moncton and was the first Chair of the
Division of Clinical Engineering for the International Federation of
Medical and Biological Engineering (IFMBE). In December, l989, she was
appointed the first holder of the Nortel-NSERC Women in Engineering
Chair at the University of New Brunswick (Fredericton) and Professor in
the Electrical Engineering department.
In 1992, Monique Frize received an Honorary Doctorate from the
University of Ottawa (DU); in June 1993, a Ryerson Fellowship; in 1994,
an Honourary Doctorate in Science (DSc) at York University; in 1995, an
Honourary Doctorate in Engineering at Lakehead (DEng). She was inducted
as a Fellow of the Canadian Academy of Engineering in 1992 and as
Officer of the Order of Canada in October 1993. In 1995, Dr. Frize
received the Second Historical Professional Achievement Award (jointly
with Dr. Michael Shaffer) from the American College of Clinical
Engineers, for her paper: "Clinical Engineering in today's hospital:
Perspectives of the Administrator and the Clinical Engineer". In
September 1996, Dr. Frize received the 6th
Annual Meritas-Tabaret Award for career achievement from the Alumni
Association of the University of Ottawa and the Advocacy Award
presented by WITT (Women in Trades and Technology) in May 1997. Born in
Montreal, Canada, Dr. Frize's mother tongue is French, and she is
fluently bilingual. She is married to Peter Frize and they have a son,
Patrick Nicholas.
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Date
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Monday December 04, 2006
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Time
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10:30-12:00
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Location
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Building M-50, NRC Auditorium, 1200 Montreal
Road
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Title
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Lying, Deception and Face Familiarity with
Visual Evoked Potentials
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Speaker
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Evangelia Micheli-Tzanakou, Professor
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Rutgers University
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Department of Biomedical Engineering
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Director of Computational Intelligence
Laboratories
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Abstract
The
recent urgency for counter-terrorism and the fight to protect ones’
homeland are of grave concern to nations throughout the world. Finding an efficient
process that could have screened passengers prior to boarding a flight
or even a train may have derailed many devastating events.
Scientific research has
continuously proved that there is an explicit marker of neuronal
activity that correlates with awareness, past experience, and
short-term interactions from the well-known P300 peak of an evoked
potential. This
study examines the effects of memory recognition to certain key stimuli
mixed with irrelevant variables in an effort to identify if a trained
terrorist, per se, could not only be identified from a group of
subjects, but also validate that the willingness to withhold
information is out of the control of the individual; it is simple, one
has no control in concealing their brain’s activity. Face familiarity
is also examined through a series of experiments.
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Speaker Bio
Dr. Tzanakou is
Professor, Director of Computational Intelligence Laboratories
(2000-present); Chair Biomedical Eng., Rutgers University (1990-00).
Has published 250+ scientific papers. Authored/co-authored 4
books/edited proceedings, graduated 37 M.S. and Ph.D.'s; Founding
Fellow, American Institute for Medical & Biological Eng., 1993;
Member of Sigma Xi and Eta Kappa Nu; Honorary Member: British Brain
Research Association; European Brain/Behavior Society.
Awards: NJ Women of Achievement, 1995; Featured "Notable
Twentieth Century Scientists," 1994; Achievement Award, Society of
Women Engineers, 1992; Outstanding IEEE Branch Advisor/Counselor, 1985;
Pioneer (IEEE Web page). Book Series Editor: Biomedical
Engineering, Plenum/Klewer (1999-present); Biomedical Systems, IES Book
series, CRC Press (1997-present); Editorial Board: IEEE Transactions
on Nano-BioScience (2002-present); Biomedical Engineering On
Line, (2001-present), IEEE Transactions on Biomedical
Information Technology (1997-01); Intern. J. Adv.
Computational Intelligence (1997-99); Advanced Computational
Intelligence and Intelligent Informatics, (2000-present); International
J. on Artificial Intelligence Tools, (2004-present). Associate Editor,
IEEE Transactions on Neural Networks
(2000-present), (1989-92).
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Date
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Friday November 17, 2006
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Time
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17:30-19:00
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Location
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Room 5084, SITE Building, University of Ottawa
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Title
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Speaker
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Diana Inkpen, Assistant Professor
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University of Ottawa
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School of Information Technology and Engineering
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Abstract
Browsing through large
volumes of spoken audio is known to be a challenging task for end
users. To alleviate this problem we can allow users to gist a spoken
audio document by glancing over a transcript generated through
Automatic Speech Recognition, or to implement information retrieval
systems over the text transcribed by the speech recognizer.
Unfortunately, such transcripts typically contain many recognition
errors which are highly distracting and make gisting more difficult. I
present an approach that detects recognition errors by identifying
words which are semantic outliers with respect to other words in the
transcript. I investigate a wide range of evaluation measures and show
that we can significantly reduce the number of errors in content words,
with the trade-off of losing some good content words.
Also described are information retrieval experiments from a collection
of spontaneous speech. I show comparative results for indexing the
automatic transcripts as opposed to indexing the manual summaries and
keywords available in the collection.
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Speaker Bio
Dr. Diana Inkpen is a
professor at the School of Information Technology and Engineering,
University of Ottawa since July 2003. She obtained her doctorate in
2003 from University of Toronto, Department of Computer
Science. She has a Masters in Computer Science and
Engineering from the Technical University of Cluj-Napoca, Romania. Her
research projects and publications are in the areas of Computational
Linguistics and Artificial Intelligence, more specifically: Information
Retrieval, Information Extraction, Natural Language Understanding,
Natural Language Generation, Speech Processing, and Intelligent Agents
for the Semantic Web.
Dr. Inkpen is involved in many collaborative research projects, with
University of Toronto, University of Waterloo, IBM Centre for Advanced
Studies, and the National Research Council (the Institute for
Information Technology and the Canada Institute for Scientific and
Technical Information). The team led by Dr. Diana Inkpen and composed
of two of her graduate students won the international competition in
Information Retrieval CLEF 2005 (Cross-Language Evaluation Forum), the
CL-SR task (Cross-Language Spoken Retrieval).
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Date
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Friday September 15, 2006
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Time
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10:30-12:00
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Location
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Room 379, Building M-50, NRC Auditorium, 1200
Montreal Road
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Title
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Graph Mining and its Applications
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Speaker
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Jian Pei, Assistant Professor
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Simon Fraser University
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Computing Science Department
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Abstract
Graph models are
popularly used in many applications, such as marketing analysis,
protein interactions, social networks, and web analysis. Mining
significant and interesting graph patterns from collections of graphs
as well as other types of data has become an important research
problem. In this talk, I shall discuss the problem of mining graph
databases and graph patterns in three aspects: how to model patterns in
graphs, how to mine large graphs and how to handle many graphs.
Particularly, I shall present several interesting approaches recently
developed by us. The quasi-clique mining method finds dense areas
across multiple large graphs. The ADI approach indexes databases with a
large number of (relatively small) graphs and mines frequent
sub-graphs. The frequent closed partial order mining approach derives
DAG models from large sequence databases. I shall also address the
potential extensions of the above methods.
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Speaker Bio
Jian Pei received a Ph.D.
degree in Computing Science from Simon Fraser University, in 2002. He
is currently an Assistant Professor of Computing Science at Simon
Fraser University. His research interests can be summarized as
developing effective and efficient data analysis techniques for novel
data intensive applications. Particularly, he is currently interested
in various techniques of data mining, data warehousing, online
analytical processing, and database systems, as well as their
applications in bioinformatics, privacy preservation, and education.
His current research is supported in part by the Natural Sciences and
Engineering Research Council of Canada (NSERC), the National Science
Foundation (NSF) of the United States, Hewlett-Packard Company (HP),
and the Canadian Imperial Bank of Commerce (CIBC). He has published
prolifically in refereed journals, conferences, and workshops, has
served extensively in the organization committees and the program
committees of many international conferences and workshops, and has
been a reviewer for the leading academic journals in his fields. He is
a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD.
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Date
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Thursday February 09, 2006
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Time
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15:00-16:30
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Location
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NRC Institute for Biological Sciences, 1200
Montreal Road Building M-54, Room 235
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Title
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A Unified Model of Spike-Time-Dependent
Plasticity and Chemotropic Gradients in Retinotopic Map Formation
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Speaker
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Jean-Philippe Thivierge , Psychology Ph.D.
candidate
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McGill University
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Laboratory for Natural and Simulated Cognition
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Abstract
Both activity-dependent
and independent processes play a role in
the development of
the vertebrate visual system. Molecular
guidance cues provide a rough topography of
early projections,
while refinement of termination zones (TZs) occurs later on through correlated
retinal activity. Experiments involving B2
subunit knock-out
mice have found a cumulative role of removing activity-dependent
and independent processes, thus arguing for their distinct
roles. A computational account of these results is proposed, based on
a unified model that combines chemotropic
gradients and
spike-time-dependent synaptic plasticity. The model is employed to
simulate recent empirical data, and proposes possible
interactions between activity-dependent and independent processes.
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Speaker Bio
Jean-Philippe
Thivierge is a
post-doctoral fellow at the Université de Montréal, where
his research interests include the application of
computational intelligence tools to cognitive modelling, structures and
algorithms in neural networks, bioinformatics, and developmental
computational neurobiology. He already has a wide range of
publications in these areas, including some very interesting
computational models for the development of the visual
system. JP has been very active as a young leader in his area
of research, in business ventures, and with his professional
associations. He was Local Arrangements Chair for the 2005
International Joint Conference on Neural Networks, for which he
also chaired a session of international leaders to celebrate
developments arising from Donald Hebb's work in Montreal 50
years ago. He also has been a guest editor for the "IEEE
International Journal of Neural Networks", "Journal of Machine
Learning", and he has been collaborating with the NRC.
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Date
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Thursday December 01, 2005
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Time
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15:00-16:30
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Location
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NRC Auditorium, 1200 Montreal Road Building M-50
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Title
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Computer Vision for Augmented Reality - the
ARTag system
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Speaker
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Mark Fiala, Research Officer
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NRC-CNRC Institute for Information Technology
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Computational Video Group
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Abstract
Augmented Reality (AR) is
the convergence of the real world and virtual computer generated
imagery, it is the fusion of real and virtual reality through
overlaying virtual objects over real images or video. A virtual object
can be made to look like it belongs in a real scene if it is rendered
from the right viewpoint, something done routinely in movie making but
still a research topic for real time systems where you can look at and
walk around virtual objects using a head-mounted display, PDA,
cellphone, or tablet PC. To do this, the graphics rendering system must
know the pose of the camera, this pose determination can be done
accurately and inexpensively using computer vision. One way is to use
markers like the ARTag marker system that will be described in the
talk. Designing markers to add to the environment for robust detection
in camera and video imagery is a computer vision application useful to
situations where a camera-object pose is desired such as AR, industrial
position tracking, photo-modeling and robot navigation. Examples of
augmented reality and the ARTag system developed at the NRC will be
shown.
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Speaker Bio
Dr. Mark Fiala is a
computer vision researcher at Canada's National Research Council (NRC),
where he works in the Computational Video Group centered in Ottawa,
Ontario. His work includes fiducial marker systems, panoramic vision,
and general computer vision topics such as image segmentation and
camera calibration. He graduated from his PhD in Electrical Engineering
in 2002 in the field of panoramic computer vision. He also holds an
Electrical Engineering BSc and has spent over 5 years in industry in
hardware design for imaging and telecom applications. His best known
recent work is the "ARTag" fiducial marker system.
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Date
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Thursday September 29, 2005
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Time
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17:00-18:30
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Speaker
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Adrian Chan, Assistant Professor
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Department of Systems and Computer Engineering
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Carleton University
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Abstract
For decades there has
been extensive research and development on myoelectrically controlled
powered prostheses. The basic premise of the device is that myoelectric
signals from residual muscles could be used as a control signal for
upper arm prostheses. The advantages of such a prosthesis are: 1) it
frees the user from straps and harnesses required of body powered and
mechanical switch control; 2) the myoelectric signal can be
noninvasively detected on the surface of the skin; 3) proportional
control can be implemented with relative ease and the amplitude of the
myoelectric signal varies in sympathy with the contractile force; and
4) the muscle activity required to provide a control signal is
relatively small and can resemble the effort of an intact limb. In
recent, years pattern recognition techniques have been explored to
provide users an interface that is more natural and intuitive, while
providing a higher degree of classification accuracy and
controllability. Work has been extended from this application towards
myoelectric speech recognition; using myoelectric signals from facial
muscles to perform speech recognition. Such a device would be useful
for persons with temporary or permanent speech impairments.
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Speaker Bio
Dr. Adrian D.C. Chan
graduated with his B.A.Sc. in Computer Engineering, University of
Waterloo (1997), M.A.Sc. in Electrical Engineering, University of
Toronto (1999), and Ph.D. in Electrical Engineering, University of New
Brunswick (2002). Currently, he is an Assistant Professor in the
Department of Systems and Computer Engineering, Carleton University.
His research is in biomedical engineering, focusing on biological
signal processing and noninvasive sensors. Dr. Chan has been recognized
as one of Macleans 25 Best and Brightest (2004) and Ottawa Life
Magazine's Top 50 People in the Capital (2005), and received the Ottawa
Life Sciences Council Dr. Michael Smith Promising Scientist Award
(2004).
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Date
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Thursday April 21, 2005
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Time
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17:00-18:30
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Speaker
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Wail Gueaieb, Assistant Professor
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School of Information Technology and Engineering
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University of Ottawa
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Abstract
A decentralized adaptive
fuzzy controller is proposed for addressing the problem of controlling
the positions and internal forces within multiple coordinated
manipulator systems in the face of parametric and modeling
uncertainties as well as external disturbances. The controller makes
use of a multi-input multi-output fuzzy logic engine and a systematic
online adaptation mechanism to fully approximate the overall system's
dynamics. Unlike conventional adaptive controllers, the proposed
controller does not require a perfect prior model of the system's
dynamics nor does it require a linear parameterization of the system's
uncertain physical parameters. Using a Lyapunov stability approach, the
controller is proven to be robust in the face of varying intensity
levels of the aforementioned uncertainties, and the position and the
internal forces are proven to asymptotically converge to zero under
such conditions. Through a computer simulation of two 3-DOF
manipulators, the performance of the controller is verified and
compared to that of one of the most efficient conventional adaptive
controllers proposed in the literature.
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Speaker Bio
Dr. Wail Gueaieb received
the Bachelor and Master’s degrees in Computer Engineering and
Information Science from Bilkent University, Turkey, in 1995 and 1997,
respectively, and the Ph.D. in Intelligent Mechatronics from the
University of Waterloo, Canada, in 2001. He then joined Intelligent
Mechatronic Systems Inc. in 2001 where he held the positions of a
senior systems design engineer in expert systems and a software
manager. During his career at Intelligent Mechatronic Systems Inc., he
worked on the design, implementation, and productization of a new
generation of smart advanced automotive safety systems. He is also the
author/co-author of three patents. In July 2004, he joined the School
of Information Technology and Information Science (SITE). His areas of
expertise span the fields of intelligent systems design using tools of
computational intelligence with application to a wide range of
industries.
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Date
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Wednesday February 23, 2005
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Time
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18:00-19:30
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Speaker
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B. John Oommen, FIEEE, Professor
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School of Computer Science
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Carleton University
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Abstract
All of the research that
has been done in learning, has involved learning from a Teacher
who is either deterministic or stochastic. In this talk, we shall
present the first known results of how a learning mechanism can learn
while interacting with either a stochastic teacher
or a stochastic compulsive liar. In the first
instance, the teacher intends to teach the learning mechanism. In the
second, the compulsive liar intends to consciously mislead the learning
mechanism. We shall present a formal strategy for the mechanism to
perform ε-optimal learning without it knowing whether it is interacting
with a teacher or a compulsive liar. Believe It Or Not - IT
WORKS !
A joint work with Dr.
Govindachari (presently in Bangalore) and Dr. Kuipers (Texas).
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Speaker Bio
Dr. John Oommen was born
in Coonoor, India on September 9, 1953. He obtained his B.Tech. degree
from the Indian Institute of Technology, Madras, India in 1975. He
obtained his M.E. from the Indian Institute of Science in Bangalore,
India in 1977. He then went on for his M.S. and Ph. D. which he
obtained from Purdue University, in West Lafayettte, Indiana in 1979
and 1982 respectively. He joined the School of Computer Science at
Carleton University in Ottawa, Canada, in the 1981-82 academic year. He
is still at Carleton and holds the rank of a Full Professor. His
research interests include Automata Learning, Adaptive Data Structures,
Statistical and Syntactic Pattern Recognition, Stochastic Algorithms
and Partitioning Algorithms. He is the author of more than 215 refereed
journal and conference publications and is a Fellow of the IEEE. Dr.
Oommen is on the Editorial Board of the IEEE Transactions on
Systems, Man and Cybernetics, and Pattern
Recognition.
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Date
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Wednesday December 15, 2004
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Time
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16:00-17:30
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Speaker
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Dmitry O. Gorodnichy, Research Officer
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NRC-CNRC Institute for Information Technology
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Computational Video Group
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The talk will be followed by the annual meeting
of the chapter
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Abstract
The purpose of this talk
is two-fold. First, the audience will be introduced with the basics of
the attractor-based associative neural networks. These networks are
known as a mathematical tool for building recognition systems which
work in a fashion similar to that of the human brain. Second, the
audience will be presented with a new framework for recognizing faces
in video. While the problem of recognizing faces in video has received
a lot of attention recently, in particular, because of such a highly
demanded application of it as security surveillance, this problem is
often erroneously treated as an extension of the problem of recognizing
faces in photographs. Photographs, which are usually taken under very
constrained conditions, provide hard biometrics data, as do, for
instance, the fingerprints. Video footage, on the other hand, such as
the one taken by a surveillance camera, will very unlikely contain the
facial data of high quality and is therefore the source of "softer"
biometrics. As we will show, however, the soft biometrics provided by
video is still very informative and can be efficiently used to memorize
and recognize faces. In the demonstrations to be shown, the developed
mini brain model allows one to discriminate guests of a talk show in a
prerecorded low-resolution video.
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Speaker Bio
Dr. Dmitry Gorodnichy is
a research officer with the Computational Video Group of the Institute
for Information Technology of the National Research Council of Canada.
He has two Ph.D. degrees: one - in Computing Science (2000) from the
University of Alberta, Edmonton, Canada, for his work on Vision-based
World Model Learning, and the other (1997) - in Mathematics from the
Glushkov Cybernetics Center of Ukrainian Ac.Sc., Kiev, Ukraine, for his
work on Mathematical models of human memory. His MSc (with honours) in
Information Technology (1994) is from the Moscow Institute of Physics
and Technology, Moscow, Russia. He is the author of two patents and
over thirty conference and journal papers, including an IJCNN Best
Presentation Award paper, a recipient of several scientific awards,
including the Young Investigator Award from the Canadian Image
Processing and Pattern Recognition Society and the NRC-CNRC Outstanding
Scientific Achievement Award. He is the principle investigator of
Nouse™ (Nose as Mouse) and Blink Detection perceptual vision
technologies featured in the 2002 and 2003 NRC-CNRC Annual Reports, and
is listed as one of 2003 Leaders of Tomorrow by the Partnership Group
for Science and Engineering of Canada. He was the Program Chair for the
International Conference on Vision Interface, the organizer and the
program chair of the First IEEE Workshop on Face Processing in Video
and is now the Exhibits Chair for the INNS-IEEE International Joint
Conference on Neural Networks to be held in Montreal next year. He is a
reviewer for many scientific conferences, journals and organizations,
including NSERC, and is also presently the Chair for IEEE Computational
Intelligence Society, Ottawa Chapter.
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Date
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Thursday November 25, 2004
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Time
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16:00-17:00
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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The heterogeneous neuron model and its use in
hybrid neural networks within computational intelligence compound
systems
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Speaker
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Julio J. Valdés, Senior Research Officer
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NRC-CNRC Institute for Information Technology
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Integrated Reasoning Group
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Abstract
A framework is presented
for processing heterogeneous information based on the construction of
general observational domains, and similarity-based function calculi
suitable for data mining and other tasks in domains which can be
described by the corresponding observational models. These calculi are
intuitive, simple, and sufficiently general for classification and
pattern recognition tasks. Functions in these calculi are represented
by a particular kind of neuron models and their behavior is illustrated
with examples from real-world domains showing their capabilities in
processing heterogeneous, incomplete and fuzzy information, possibly
with time dependencies.
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Speaker Bio
Dr. Julio Valdés has a
PhD in mathematics (1987). His areas of interest are: artificial
intelligence (mathematical foundations of uncertainty processing and
inexact reasoning, knowledge engineering, expert systems and machine
learning), digital image and signal processing, pattern recognition,
virtual reality, soft computing (fuzzy logic, neural networks,
evolutionary algorithms, probabilistic reasoning, rough sets), data
mining, data analysis in general and hybrid systems. He also graduated
in geophysics (1977), oriented to geomathematics, mathematical modeling
of natural processes, computer elaboration and data analysis-mining of
earth science and environmental data, remote sensing, physics and
chemistry of external geodynamic processes and geophysical-geochemical
prospecting.
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Date
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Wednesday May 05, 2004
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Time
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17:00-18:30
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Neural Network Modeling of 3D Objects for
Virtualized Reality Applications
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Speaker
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Ana-Maria Cretu, Ph.D. Candidate
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Sensing and Modeling Research Laboratory
(SMRLab)
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School of Information Technology and Engineering
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University of Ottawa
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Abstract
This talk presents a
critical comparison between three neural architectures for 3D object
representation in terms of purpose, computational cost, complexity,
conformance and convenience, ease of manipulation and potential uses in
the context of virtualized reality. Starting from a pointcloud that
embeds the shape of the object to be modeled, a volumetric
representation is obtained using a multilayered feedforward neural
network or a surface representation using either the self-organizing
map or the neural gas network. The representation provided by the
neural networks is simple, compact and accurate. The models can be
easily transformed in size, position (affine transformations) and shape
(deformation). Some potential uses of the presented architectures in
the context of virtualized reality are for the modeling of set
operations and object morphing, for the detection of objects collision
and for object recognition, object motion estimation and segmentation.
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Speaker Bio
Ana-Maria Cretu obtained
her Master degree from the School of Information Technology and
Engineering at the University of Ottawa, Canada, where she is now a PhD
student. Ms. Cretu's research interests include neural networks, 3D
object modeling, tactile sensing and multi-sensor data fusion. She is a
Student Member of IEEE.
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Wednesday February 25, 2004
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Time
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16:00-17:00
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Speaker
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Peter Turney, Senior Research Officer
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Information Analysis and Retrieval Group
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NRC Institute for Information Technology
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Abstract
This talk will present an
algorithm for learning from unlabeled text, based on the Vector Space
Model (VSM) of information retrieval, that can solve verbal analogy
questions of the kind found in the Scholastic Aptitude Test (SAT). A
verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D";
for example, mason:stone::carpenter:wood. SAT analogy questions provide
a word pair, A:B, and the problem is to select the most analogous word
pair, C:D, from a set of five choices. The VSM algorithm correctly
answers 47% of a collection of 374 college-level analogy questions
(random guessing would yield 20% correct). This research is motivated
by relating it to work in cognitive science and linguistics, and by
applying it to a difficult problem in natural language processing,
determining semantic relations in noun-modifier pairs. The problem is
to classify a noun-modifier pair, such as "laser printer", according to
the semantic relation between the noun (printer) and the modifier
(laser). The approach is to use a supervised nearest-neighbour
algorithm that assigns a class to a given noun-modifier pair by finding
the most analogous noun-modifier pair in the training data. With 30
classes of semantic relations, on a collection of 600 labeled
noun-modifier pairs, the learning algorithm attains an F value of 26.5%
(random guessing: 3.3%). With 5 classes of semantic relations, the F
value is 43.2% (random: 20%). The performance is state-of-the-art for
these challenging problems.
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Speaker Bio
Dr. Peter Turney is a
Senior Research Officer in the Interactive Information Group of the
National Research Council. In 1988, he obtained his PhD from the
University of Toronto, where he then accepted a Postdoctoral
Fellowship. He joined the NRC in 1989, and he has since worked on a
variety of projects, all involving applications of machine learning
technology. His recent work focuses on machine learning applied to
natural language. He is the author or co-author of more than sixty
publications, a past editor of Canadian Artificial Intelligence
magazine, and a member of the Advisory Board of the Journal of
Artificial Intelligence Research.
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Wednesday December 03, 2003
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Time
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16:00-17:30
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Location
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Room 5084 of SITE Building at the University of
Ottawa
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Title
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Hardware Neural Network Architectures Using
Random Data Representation
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Speaker
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Emil M. Petriu, Dr. Eng., P.Eng., Professor
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Sensing and Modeling Research Laboratory
(SMRLab)
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School of Information Technology and Engineering
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University of Ottawa
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Abstract
The idea of using
computational techniques that mimic the processing behaviour of the
biological nervous systems was advanced by von Neuman in 1965. The
resulting random-pulse machine concept deals with analog variables
represented by the mean rate of random-pulse streams using simple
digital circuits to perform arithmetic and logic operations. This
concept presents a good trade-off between the electronic circuit
complexity and the computational accuracy.
The talk presents the
random-pulse data representation and discusses how it can be used to
the design of modular random-pulse neural networks. A generalization of
the random-pulse machine concept, namely the multi-bit random-data
machine, is then discussed. The advantage of using multi-bit data
instead of pulses (which are 1-bit data) is a considerable reduction in
the time needed to get an acceptable accuracy for the statistical
averages of the data streams carrying the information. As in the case
of the random-pulse machine, the arithmetic operations are performed by
relatively simple logic circuits. The resulting architectures have high
functional packing density making them suitable for the VLSI
implementation of hardware parallel neural networks.
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| |
Speaker Bio
Emil M. Petriu is a
professor in the School of Information Technology and Engineering at
the University of Ottawa, Canada, where he has been since 1985. Dr.
Petriu's research interests include intelligent sensors, robot sensing
and perception, neural networks, and fuzzy control. During his career
he has published more than 180 technical papers, authored two books,
edited other two books, and received two patents. He is a Fellow of
IEEE, Fellow of the Canadian Academy of Engineering, and Fellow of the
Engineering Institute of Canada. For more info, see www.smrlab.uottawa.ca
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Friday April 25, 2003
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Time
|
16:00-17:00
|
Location
|
Room 5084 of SITE Building at the University of
Ottawa
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Title
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Collaborative Virtual Environments
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Speaker
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Nicolas D. Georganas, FIEEE, Professor
|
| |
Distributed and Collaborative Virtual
Environments Research Laboratory (DISCOVER)
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| |
School of Information Technology and Engineering
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University of Ottawa
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Abstract
One of the hottest topics
in Virtual Reality research is that of "Distributed" Virtual
Environments (DVE). The idea behind DVE is very simple; a simulated
world runs not on one computer system, but on several, using a series
of client server applications. The computers are connected over a
network and people using those computers are able to interact in real
time, sharing the same virtual world.
Collaborative Virtual
Environments (CVE) add new dimensions to human-factors, networking, and
database issues. For example, human-factors research in VR has
traditionally focused on the development of natural interfaces for
manipulating virtual objects and traversing virtual landscapes.
Collaborative manipulation, on the other hand, requires the
consideration of how participants should interact with each other in a
shared space, in addition to how co-manipulated objects should behave.
Other issues include: how participants should be represented in the
collaborative environment; how to effectively transmit non-verbal cues
that real-world collaborators so casually and effectively use; how to
best transmit video and audio via a channel that allows both public
addressing as well as private conversations to occur; how to filter
relevant information to reduce processing (increase performance) at
each client for large worlds; and how to sustain a virtual environment
even when all its participants have left.
This talk will expose
basic notions in CVE and describe several applications.
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Speaker Bio
Nicolas D. Georganas, is
Distinguished University Professor and Canada Research Chair in
Information Technology, School of Information Technology and
Engineering, University of Ottawa, Canada. He is a Fellow of IEEE,
Fellow of the Canadian Academy of Engineering, Fellow of the
Engineering Institute of Canada, and Fellow of the Royal Society of
Canada. In 2002, he received the Killam Prize for Engineering, Canada's
highest award for career achievements in research. His research
interests are in Multimedia Communications, Pervasive Computing,
Intelligent Sensors, Tele-Haptics and Collaborative Virtual
Environments. For more info, see www.discover.uottawa.ca
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