2014 International Conference on

2014 International Conference on Adaptive and Intelligent Systems - ICAIS'14

September 8-10, 2014, Bournemouth - UK     



Prof. Ludmila I Kuncheva Prof. Joao Gama Prof. Kurt Geihs

Prof. Ludmila I Kuncheva

Bangor University, UK

Prof. João Gama

University of Porto, PT

Prof. Kurt Geihs

University of Kassel, DE

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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Sponsoring Organizations

International Neural Network Society

Important Dates

Workshop/Special Session proposal:
April 13, 2014
Full paper submission:
June 10, 2014
June 22, 2014

Acceptance notification:
July 01, 2014
July 06, 2014
Final camera ready:
July 11, 2014
July 16, 2014