Invited Speakers



University of Reading, England

Kevin Warwick is Professor of Cybernetics at the University of Reading, England, where he carries out research in artificial intelligence, control, robotics and cyborgs. Kevin was born in Coventry, UK and left school to join British Telecom, at the age of 16. At 22 he took his first degree at Aston University, followed by a PhD and research post at Imperial College, London. He subsequently held positions at Oxford, Newcastle and Warwick Universities before being offered the Chair at Reading, at the age of 33. As well as publishing over 500 research papers, Kevin's experiments into implant technology led to him being featured as the cover story on the US magazine,'Wired'. Kevin has been awarded higher doctorates (DSc) both by Imperial College and the Czech Academy of Sciences, Prague, and received Honorary Doctorates from Aston University, Coventry University and Bradford University. He was presented with The Future of Health Technology Award in MIT, was made an Honorary Member of the Academy of Sciences, St. Petersburg, and has received The IEE Senior Achievement Medal, the Mountbatten Medal and the Ellison-Cliffe Medal.

In 2000 Kevin presented the Royal Institution Christmas Lectures, entitled "The Rise of the Robots". Kevin's research involves the invention of an intelligent deep brain stimulator to counteract the effects of Parkinson Disease tremors. The tremors are predicted and a current signal is applied to stop the tremors before they start this is to be trialled in human subjects. Another project involves the use of cultured/biological neural networks to drive robots around the brain of each robot is made of neural tissue. Perhaps Kevin is though best known for his pioneering experiments involving a neuro-surgical implantation into the median nerves of his left arm to link his nervous system directly to a computer to assess the latest technology for use with the disabled. He was successful with the first extra-sensory (ultrasonic) input for a human and with the first purely electronic telegraphic communication experiment between the nervous systems of two humans.

Lecture Topic:Experiments into Biology-Technology Interaction

In this presentation a practical look is taken at how the use of implant and electrode technology can be employed to create biological brains for robots, to enable human enhancement and to diminish the effects of certain neural illnesses. In all cases the end result is to increase the range of abilities of the recipients. An indication is given of a number of areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking a biological brain directly with computer technology. The emphasis is clearly placed on experimental scientific studies that have been and are being undertaken and reported on. The area of focus is notably the need for a biological/technological connection, where a link is made directly with the cerebral cortex and/or nervous system. The presentation will consider the future in which robots have biological, or part-biological, brains and in which neural implants link the human nervous system bi-directionally with technology and the internet.


Laszlo Horvath

Professor of virtual engineering systems, Obuda University

Laszlo Horvath is university professor of virtual engineering systems at the Obuda University, John von Neumann Faculty of Informatics. He received the M.Sc. degree in programmable controlled manufacturing from the Budapest University of Technology and Economics in 1971. He received the Ph.D. degree from the Hungarian Academy of Sciences in 1973 and from the Budapest University of Technology and Economics in 1994 in modeling and computer aided development of industrial processes. During the past three decades, he filled several research and higher education positions in various areas of computer assisted engineering. He joined the predecessor of the Obuda University in 1992. Presently, he also serves as Vice President of Council of the Applied Informatics Doctoral School, Obuda University. He is senior member of IEEE and chair of the IEEE SMCS Hungary Section Chapter. His current research interests are intelligent modeling of products, human-computer interaction in engineering processes, and virtual spaces for engineers. He authored and coauthored near three hundred journal and conference papers in these areas.

Lecture Topic:Engineer Contribution to Product in Virtual Space

Constantly developing capabilities of product modeling resulted complex solutions for product design, innovation, and application at various industries integrating sophisticated model representations of product elements and structures, industry practices, and knowledge. At the meantime, product model has been developed into a new, time and space unrestricted communication medium for humans, sensor networks and other real world information capture sources, and increasingly intelligent controlled equipment. This talk concentrates on issues of human communication and the related knowledge representation in current and future product modeling. It discusses key ideas and practices in current virtual engineering including development towards virtual spaces, engineer friendly active knowledge representations, human and physical world communications at product model generation, virtual prototyping, solutions for industrial areas, and lifecycle management of product information. Talk also introduces some achievements of the University of Obuda in Hungary, including representation of background content of intended human influence based decisions and active and passive product behavior based product object definition. With the communication in centre, several control issues such as contextual consistency, coordinated intended influences, and context defined engineering objects are explained.


Vaclav Snasel

Dean Faculty of Electrical Engineering and Computer Science, VSB-Technical University, Ostrava

Vaclav Snasel is Professor of Computer Science. He works as researcher and university teacher. He is Dean Faculty of Electrical Engineering and Computer Science, head of research program IT for Knowledge Management Centre of Excellence, IT for Innovations.

Vaclav Snasel's research and development experience includes over 30 years in the Industry and Academia. He works in a multi-disciplinary environment involving artificial intelligence, social network, conceptual lattice, information retrieval, semantic web, knowledge management, data compression, machine intelligence, neural network, web intelligence, nature and Bio-inspired computing, data mining, and applied to various real world problems. He has given more than 15 plenary lectures and conference tutorials in these areas. He has authored/co-authored several refereed journal/conference papers, books and book chapters. He has published more than 400 papers (178 papers are recorded at Web of Science).

Abstract: Social Networks

The analysis of social networks is concentrated mainly on uncovering hidden relations and properties of network nodes (vertices). Most of the current approaches are focused mainly on different network types and network coefficients. On one hand, the analysis can be relatively simple and on the other hand more complex approaches to network dynamics can be used. In this lecture we introduce a novel social network analysis based on the so-called Forgetting Curve and Ant Colony Optimization (ACO) algorithm. We analyze a co-authorship network and identify two types of ties among its nodes. The Forgetting Curve and ACO are used to model the dynamics of such a network.

One of the most relevant features of social networks is the community structure. Since these networks are typically very complex, it is great interest to reduce these networks to much simpler. Clustering and low dimensional representation of high dimensional data are important problems in many diverse fields. In recent years various spectral methods to perform these tasks, based on the eigenvectors of adjacency matrices of graphs on the data have been developed. One of the successful models is based on theory of diffusion equation. It is closely related to Schrodinger's Equation for a free particle. The diffusion equation is used for measure of diffusion distance. We apply diffusion distance for social network partitioning.


English is the official language of the conference ( Paper and Presentation)