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PLENARY TALKS
Bio-data: Prof. Ludmila I Kuncheva
Ludmila I Kuncheva
is professor at the School of Computer Science at the Bangor
University, UK. More information on Prof. Kuncheva can be found on the
web site:
http://pages.bangor.ac.uk/~mas00a/
Talk: Feature Extraction for Change Detection
An online classifier is only accurate if the
distribution of the incoming data is the same as the distribution of
the data the classifier was trained upon. Therefore, detecting a
change and retraining the classifier accordingly is an important task.
This talk will challenge the concept of change and change
detectability from unsupervised streaming data. What constitutes a
change? The answer is uniquely determined by the context. But in order
to have change detection methods that work across problems, the
relevant features must be exposed. The talk will be focused on feature
extraction for the purposes of change detection.
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Bio-data: Prof. João Gama
João Gama
is professor at the Laboratory of Artificial Intelligence and Decision
Support, and Faculty of Economics, University of Porto, Portugal. More
information on Prof. Gama can be found on the web site:
http://www.liaad.up.pt/area/jgama/.
Talk: Distributed Data Stream Mining
The phenomenal growth of mobile and
embedded devices coupled with their ever-increasing computational and
communications capacity presents an exciting new opportunity for
real-time, distributed intelligent data analysis in ubiquitous
environments. In these contexts centralized approaches have limitations
due to communication constraints, power consumption (e.g. in sensor
networks), and privacy concerns. Distributed online algorithms are
highly needed to address the above concerns. The focus of this talk is
on distributed stream mining algorithms that are highly scalable,
computationally e cient and resource-aware. These features enable the
continued operation of data stream mining algorithms in highly dynamic
mobile and ubiquitous environments.
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Bio-data: Prof.
Kurt Geihs
Kurt Geihs is a full
professor in the EECS Department at the University of Kassel (Germany) where he
leads the Distributed Systems Group. His current research focuses on
self-adaptive systems, collaborative autonomous mobile robots, and development
methods for socially-aware computing systems. He has published more than 150
refereed articles and is author / coauthor / editor of several books. Before
joining the University of Kassel he was professor at TU Berlin and University of
Frankfurt, and researcher at the IBM European Networking Center. He was a guest
scientist at Sintef and NTNU in Trondheim (Norway), University of Pretoria
(South Africa), Microsoft Research in Cambridge (UK) and IBM Research in
Hawthorne (USA). From 2007–2013 he was a member of the panel Computer Science of
the European Research Council. He holds a PhD from RWTH Aachen, a M.Sc. from UC
Los Angeles (USA), and a Diplom Degree from TU Darmstadt, all in Computer
Science. More
information on Prof. Geihs can be found on the web site:
http://www.uni-kassel.de/eecs/fachgebiete/vs/team/person/578-Kurt-Geihs.html.
Talk:
Music Lessons and Other Exercises
For more than a decade researchers have explored software systems that dynamically adapt their behavior at run-time in response to changes in their operational environments, user preferences, and underlying computing infrastructure. Our particular focus has been on context-aware, self-adaptive applications in ubiquitous computing scenarios. In my presentation I will discuss lessons learned from three projects in the realm of self-adaptive systems. Some of these insights relate to purely technical concerns. Others touch on socio-technical concerns that substantially influence the users’ acceptance of self-adaptive applications. Our experiments have shown that both kinds of concerns are important. We claim that an interdisciplinary software development methodology is needed in order to produce socially aware applications that are both acceptable and accepted.
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