Invited Lectures:

Professor Mounia Lalmas,
University of Glasgow Department of Computing Science
http://www.dcs.gla.ac.uk/~mounia/

Professor Mounia Lalmas holds a Microsoft Research/RAEng Research Chair at the Department of Computing Science, University of Glasgow. Before that, she was Professor of Information Retrieval, at the department of Computer Science at Queen Mary, University of London, which she joined in 1999 as a lecturer. She is a Chartered IT Professional (CITP) and a Fellow of the British Computer Society (FBCS). She was also the (elected) vice chair, and before this the Information Director of ACM SIGIR. She is an editorial board member for ACM TOIS, IR (Springer) and IP&M (Elsevier). Her research focuses on the development and evaluation of intelligent access to interactive heterogeneous and complex information repositories, and covering a wide range of domains such as HTML, XML, and MPEG-7. From 2002 until 2007, she co-led with Norbert Fuhr the Evaluation Initiative for XML Retrieval (INEX), a large-scale project with over 80 participating organizations worldwide, which was responsible for defining the nature of XML retrieval, and how it should be evaluated. She is now working on technologies for aggregated search and bridging the digital divide. She is also currently getting back into theoretical information retrieval where she is looking at the use of quantum theory to model interactive information retrieval. She is/was the workshop co-chair at SIGIR 2004 and 2006, mentoring chair at SIGIR 2009, PR (co-) chair at CIKM 2008 and WI/IAT 2009, workshop chair at CIKM 2010, PC chair at ECIR 2006 (European Conference on Information Retrieval Research), vice co-chair for the XML and Web Data track at WWW 2009, and general co-chair of IIiX 2008 (Information Interaction in Context) and ECDL 2010 (European Conference on Digital Libraries).

Abstract
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, and voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies are providing search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology. In this talk, I will present the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that help users in their information seeking tasks. I will also discuss how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.

Dr. Dimitar P. Filev is a Senior Technical Leader, Intelligent Control & Information Systems, with Ford Research & Advanced Engineering. He has published 4 books, and over 180 articles in refereed journals and conference proceedings and holds numerous US and foreign patents. Dr. Filev is a recipient of the 2008 Norbert Wiener Award of the IEEE Society of Systems, Man, & Cybernetics and the 2006 Technical Excellence Award of IFSA. He is a co-editor-in-chief of the Journal on Evolving Systems, and is on the boards of the Journal of Automation, Mobile Robotics and Intelligent Systems, Int. J. of General Systems, Int. J. of Approximate Reasoning, and Int. J. of Applied Mathematics and Computer Science. Dr. Filev is a Fellow of IEEE and a VP of the IEEE Systems, Man, & Cybernetics Society. He was a President of the North American Fuzzy Information Processing Society (NAFIPS) 2006-2008. Dr. Filev received his PhD. degree in Electrical Engineering from the Czech Technical University in Prague in 1979.

Soft Comouting technologies in Intelligent Vehicle Control & Information Systems
This presentation discusses some of the research aspects and trends in designing driver aware intelligent automotive vehicle systems. The focus is on the progress of soft computing technologies and applications as a major enabler for introducing intelligent features and behaviors in vehicle control systems, improving the interaction between the driver and the vehicle, and vehicle personalization. The paper summarizes the long term theoretical research and practical experience of the speaker in the area of soft computing technologies and their industrial applications. In the following we review some of the research directions and original results related to designing intelligent vehicle systems.

Real Time Evolving Modeling. The evolving paradigm is based on the concept of evolving (expanding or shrinking) model structure which is capable of adjusting to the changes in the objects that cannot solely be represented by parameter adaptation. The concept of evolving systems is applied when a complex activity, e.g. driver's, are to be decomposed, learned, and analytically described by a set of simpler prototypical behaviors. These behaviors are further used for prediction of driver's actions and intentions, and decision making between different alternatives. Another area of application relates to the problem of real time learning of nonlinear mappings characterizing complex relationships between measured variables, e.g., fuel consumption prediction under variable conditions, by their decomposition, and simpler model approximation around the current operating point.

Granular Markov Models for On-Board Prediction & Optimization . The generalized Markov chain – a probabilistic model that synergistically combines the idea of transition probabilities with the information granulation paradigm – is introduced as tool for on-board stochastic modeling. We consider generalized Markov chains based on two different types of information granules – intervals and fuzzy subsets – and the algorithms for their learning from data. This approach is motivated by and intended for in-vehicle modeling traffic and road, long term and short term characterization of driver's preferences, recursive estimation of frequent stop locations and destinations, etc.

Real Time Intelligent Control Algorithms for Automotive Applications. Several algorithms from the family of intelligent control techniques (combination of adaptive control, real-time time possibilistic / probabilistic decision making, and reinforcement learning) addressing the problem of fuel economy and performance in modern vehicles are reviewed.

Alexander Gegov
University of Portsmouth, United Kingdom, alexander.gegov@port.ac.uk

Alexander Gegov is Reader in Computational Intelligence at the School of Computing, University of Portsmouth, UK. He holds a PhD in Control Systems and a DSc in Intelligent Systems – both from the Bulgarian Academy of Sciences. He has been Humboldt Guest Researcher at the University of Duisburg in Germany and EU Visiting Researcher at the Delft University of Technology in the Netherlands. Alexander Gegov’s research interests are in the theory of computational intelligence and complex systems as well as their application for modelling and control. He has published more than 20 journal articles and 40 conference papers. He is sole author of two research monographs published by Springer. He has recently introduced and started the development of the novel theory of fuzzy networks. Alexander Gegov has been reviewing papers for a number of scientific journals including IEEE Transactions on Fuzzy Systems and IEEE Transactions on Neural Networks. He has recently presented tutorials at the IEEE Conference on Fuzzy Systems, the IEEE Conference on Intelligent Systems and the IFSA World Congress. He is Member of several scientific organisations including IFAC and EUSFLAT.

Rule Based Networks: Theory and Applications
This presentation introduces the novel concept of a rule based network in ten sections. The first section discusses complexity as a systemic feature and the ability of rule based systems to handle different attributes of complexity. Section 2 reviews several types of rule based systems in the context of systemic complexity, including systems with single, multiple and networked rule bases. Section 3 introduces formal models for rule based networks such as Boolean matrices, binary relations, block schemes and topological expressions. Section 4 presents basic operations on nodes in rule based networks, including merging and splitting in horizontal, vertical and output context. Section 5 discusses some structural properties of basic operations such as associativity of merging and variability of splitting in horizontal, vertical and output context. Section 6 describes advanced operations on nodes in rule based networks, including node transformation for input augmentation, output permutation and feedback equivalence, as well as node identification in horizontal, vertical and output merging. Section 7 shows the application of the theoretical results from Sections 3-6 in feedforward rule based networks with single or multiple levels and layers. Section 8 illustrates the application of the theoretical results from Sections 3-6 in feedback rule based networks with single or multiple local and global feedback. Section 9 evaluates rule based networks by means of assessment of structural complexity, composition of hierarchical rule based systems, decomposition of standard rule based systems, indicators of model performance and applications for case studies. The last section highlights the theoretical significance, the application areas and the methodological impact of rule based networks. The presentation also introduces briefly the Matlab programs from the Rule Based Networks Toolbox developed by Nedyalko Petrov – a PhD student from the University of Portsmouth, UK.