Tutorials and Workshops offered on October 9, 2011:

• For enrollment in any of the Tutorials/Workshops listed below, IEEE SMC 2011 charges non-IEEE student attendees a small CDROM processing fee of $20 USD (additional to the conference registration fee). Paid attendees will receive a CDROM copy of presentation materials.
• IEEE Student Members may attend a Tutorial/Workshop free of charge with paid conference registration.

IEEE SMC 2011 Tutorials/Workshops

For information regarding the Workshop on Brain-Machine Interfaces to be held October 10-11, 2011, click here.

(1)         Intelligence Technology for Cyber-Physical Robot System

This tutorial consists of four parts: the basic concepts of Cyber-Physical Robot System (CPRS) and intelligence technology, the degree of consideration-based mechanism of thought (DoC-MoT), multi-objective quantum-inspired evolutionary algorithm (MQEA) with preference-based solution selection algorithm, and the real world implementations and applications, which have been proposed and carried out in the Robot Intelligence Technology Lab., KAIST during the last several years. Human beings will be living in a ubiquitous world in which all IT devices are fully networked so that they can offer us desired services at any place and anytime. This shift has hastened the ubiquitous revolution, which has further manifested itself in the new multidisciplinary research area, ubiquitous robotics. It initiates the third generation of robotics following the first generation of the industrial robot and the second generation of the personal robot. A fairy tale introduced Genie, which, upon springing from a lamp, served Aladdin. The ubiquitous era brings us to the threshold of the realization of this dream, through ubiquitous robotics. Moreover, the robots shall have their own genome in which a specific personality is encoded. This concept leads to the research on genetic robotics. Cyber-physical robot system (CPRS) combines these new concepts of next generation robots for the convergence of computational and physical systems. This tutorial introduces the recent progress and development of ubiquitous robot, genetic robot and CPRS along with the new classification of robot intelligence, such as cognitive intelligence, social intelligence, behavioral intelligence, ambient intelligence, swarm intelligence and genetic intelligence. A ubiquitous robot is composed of three forms of robots: software robot, embedded robot and mobile robot to represent an amalgamation of the tripartite personification of entities of perception, thinking and action. The genetic robot has its own genetic codes to represent a specific personality. CPRS conjoins and coordinates the software agents and physical robots including SW and HW resources. This tutorial introduces a cognitive architecture to implement intelligence technology for the CPRS. The intelligence technology shall provide us with seamless, calm, and context-aware services in a networked environment.

Professor Jong-Hwan Kim received his B.S., M.S. and Ph.D. degrees in Electronics Engineering from Seoul National University, Korea, in 1981, 1983 and 1987, respectively. Since 1988, he has been with the Department of Electrical Engineering at KAIST and is currently Professor. He was Head of Robotics Program, KAIST for 2004-2006. He is Adjunct Professor of Griffith University, Australia and Honorary Professor of De La Salle University, the Philippines. Dr. Kim is Director for both of the National Robotics Research Center for Robot Intelligence Technology and the National Research Lab for Cognitive Humanoid Robots. His research interests include computational intelligence and ubiquitous and genetic robotics. Dr. Kim has authored 5 books and 3 edited books, 2 journal special issues and around 300 refereed papers in technical journals and conference proceedings. He currently serves as an Associate Editor of the IEEE Trans. on Evolutionary Computation and the IEEE Computational Intelligence Magazine. Dr. Kim was one of the co-founders of the Int'l Conf. on Simulated Evolution and Learning in 1996. He was General Chair for the IEEE Congress on Evolutionary Computation, Korea, 2001 and General Chair for the IEEE Int'l Symp. on Computational Intelligence in Robotics and Automation, Korea, 2009. He has been on the program committees and advisory boards of more than 100 international conferences. Dr. Kim has delivered over 150 invited talks, keynote speeches and tutorials on computational intelligence and robotics in over 20 countries. His name was included in the Barons 500 Leaders for the New Century in 2000 as the Father of Robot Football. He is the Founder of FIRA and IROC and is currently serving them as President. Dr. Kim was the recipient of the science and technology award from the President of Republic of Korea in 1997 and has been elevated to 2009 IEEE Fellow.

(2)         Model-Based Systems Engineering

Model-Based Systems Engineering (MBSE) is emerging as a pivotal paradigm, placing modeling as the underlying activity of systems' and products' lifecycle management. SysML is the OMG (Object Management Group) standard for MBSE, while Object-Process Methodology (OPM) is in the process of becoming an ISO standard and the basis for model-based standards authoring. OPM and SysML complement each other, as each is especially suited for different lifecycle stages: OPM, which is simple and holistic, is most fit for the early conceptual stages, whereas SysML, which provides for expressing details, is good for later design stages. The tutorial introduces the principles, syntax, and semantics of OPM and SysML, highlighting their commonalities and differences, and showing how to use both in ways that promote synergy and improve critical system features, notably clarity, consistency, and completeness. The resulting model is an indispensible blueprint that puts all the stakeholders on the same page across the system lifecycle span.

Professor Dov Dori is Information and Systems Engineering Professor and Head of the Enterprise System Modeling Laboratory at the Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, and Research Affiliate at the Engineering Systems Division, Massachusetts Institute of Technology. His research interests include conceptual modeling of complex systems, systems architecture and design, and software and systems engineering. Prof. Dori who invented and developed Object-Process Methodology (OPM), presented in his 2002 book. He has authored over 200 journal and conference publications and book chapters, and is Fellow of the International Association for Pattern Recognition, Fellow of INCOSE, and Senior Member of IEEE and ACM.

(3)         Robust Machine Learning Techniques for Human Activity Recognition using Body-Worn Sensors

Human activity recognition can be used to devise assistants that provide proactive support by exploiting the knowledge of the user's context, determined from sensors located on-body. By using on body sensors, typically integrated into clothing or found in mobile gadgets, the system can operate at any time, regardless of the user's location. Activity recognition is an enabling technology that can lead to great societal benefits. There are a wide range of domains which can benefit from it, as evidenced in the wearable, mobile and pervasive computing communities: health care (in particular elderly assistance), industrial worker assistance, sports, entertainment, human-computer interfaces, human-robot interaction, etc. The design and development of activity recognition systems pose important challenges to the machine learning community. They typically involve high-dimensional, multimodal streams of data characterized by a large variability, where data portions may be missing or labels can be unreliable. Notwithstanding the large amount of research endeavors aimed at tackling these issues, the comparison of different approaches is often not possible due to the lack of common benchmarking tools and datasets that allow for replicable and fair testing procedures across several research groups. The aim of this workshop is to discuss and compare different methods for robust activity recognition, as well as putting forward the need for common resources for such comparison. To promote such comparison, the workshop will present the outcome of a machine learning challenge where contributed methods will be evaluated on a public benchmark database of daily activities recorded using a multimodal network of on-body sensors.

The benchmarking dataset contains sensor data of subjects performing early morning activities at home. The sensors include, among others, 19 accelerometers and inertial measurement units worn on the body. The dataset is labeled and comprises activities ranging from simple motion primitives and gestures to composite activities. Thus, this dataset is geared at (semi-) supervised machine learning techniques and offers a rich playground to assess methods such as feature selection/extraction, classifier calibration and adaptation, data fusion, automatic segmentation, besides comparing various classification approaches. This dataset captures the challenges common to many other activity recognition scenarios. Thus, methods proved to be robust on this dataset can likely be successfully translated to other challenging activity recognition problems. The activity recognition challenge will be launched and advertised in the SMC and applied machine learning communities, as well as in the wearable, mobile and pervasive computing communities via web-presence, mailing lists (e.g., connectionists, euron, ubicomp mailing lists), direct contacts to parties expressing interest, and contacts to research projects (e.g., at the EU or NSF level). (See more details at http://www.opportunity-project.eu/challenge).

Workshop Organizers:
Dr. Ricardo Chavarriaga, Defitech Foundation Chair on Non-invasive Brain-Machine Interface, EPFL, Lausanne, Switzerland, is a senior researcher at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a Ph.D. in Computational Neuroscience from the EPFL in 2005, and worked for three years at the Idiap Research Institute in Martigny, Switzerland. His research is focused on Non-Invasive Brain-Computer Interfaces (BCI) and multimodal Human-machine interaction, in particular the study of brain activity that may be used to convey information about the user's cognitive processes (e.g., error recognition, anticipation of future events, and decision making) and its potential use for controlling complex devices and neuroprosthesis. Moreover, he is also interested in the development of machine learning methods for robust activity recognition using heterogeneous sensor networks.

Dr. Daniel Roggen, Institut f. Elektronik, Wearable Computing Group ETHZ, Zürich, Switzerland, received his M.S. in microengineering in 2000 from the EPFL (Swiss Federal Institute of Technology) in Lausanne, Switzerland. He then worked for the company VisioWave (now belonging to General Electric) in the optimization of wavelet-based video compression algorithms for video surveillance on Intel Itanium and IA-32 architectures, as well as various other video processing algorithms. He carried out his Ph.D. research at the Laboratory of Intelligent Systems of EPFL, from which he received his Ph.D. degree in 2005. In his Ph.D. he developed bio-inspired electronic circuits with fault-tolerance, learning, and developmental capabilities that were applied to the control of autonomous mobile robots and to signal processing. This work was carried out in context of the EU FP5 FET project POEtic. Since 2005 he is Senior Research Fellow in the Wearable Computing Lab at ETH Zürich. His activities include context recognition algorithms, embedded wearable systems, sensor fusion, and learning and adaptivity in wearable systems.

Professor Dr. Alois Ferscha, Institut für Pervasive Computing, Johannes Kepler Universität Linz, Austria, received the Mag. degree in 1984, and a Ph.D. in business informatics in 1990, both from the University of Vienna, Austria. From 1986 through 2000 he was with the Department of Applied Computer Science at the University of Vienna at the levels of assistant and associate professor. In 2000 he joined the University of Linz as full professor where he is now head of the department for Pervasive Computing and the speaker of the JKU Pervasive Computing Initiative. Prof. Ferscha has published on topics related to parallel and distributed computing, e.g., Computer Aided Parallel Software Engineering, Performance Oriented Distributed/Parallel Program Development, Parallel and Distributed Discrete Event Simulation, Performance Modeling/Analysis of Parallel Systems and Parallel Visual Programming. Currently he is focussed on Pervasive Computing, Embedded Software Systems, Wireless Communication, Multiuser Cooperation, Distributed Interaction and Distributed Interactive Simulation. He has been the project leader of several national and international research projects.

Workshop Presenters:
Prof. Nobuo Kawaguchi received the B.E, M.E and Ph.D. in Computer Science from Nagoya University, Japan, in 1990, 1992, and 1997 respectively. From 1995 he was with Department of Electrical and Electronic Engineering and Information Engineering, School of Engineering, Nagoya University at the levels of assistant and associate professor. Since 2009, he has been a Professor of Department of Computational Science and Engineering, Graduate School of Engineering, Nagoya University. During 1999-2004, he was working with CIAIR (Center for Integrated Acoustic Information Research), and made a major contribution on its large scale in-car speech database which includes speech dialog with car-driving information for more than 500 subjects in the real-world driving environment. He also developed a location based timetable information system named "TimeTable.Locky" as a iPhone Application. The series of the system was downloaded more than a million times. Prof. Nobuo Kawaguchi has his research interest in the areas of Human Activity Recognition, Smart Environmental System and Ubiquitous Communication Systems. He is now serving a chair person of the Human Activity Sensing Consortium (HASC), Japan.

Prof. Dr. Paul Lukowicz has a Ph.D. in Computer Science from the University of Karlsruhe in Germany. After his Ph.D., he went to ETH Zurich and then to a Professorship in Computer Engineering at the University of Medical Informatics and Technology in Hall in Tirol, Austria (UMIT) where his group worked on health related applications of pervasive and wearable computing and context recognition. Paul Lukowicz is Associate Editor in Chief, IEEE Pervasive Magazine and member of the Editorial Board Member Hindawi Advances in Human Computer Interaction. Between 2008 and 2010 he has been in charge of the Wearable Computing Department of the IEEE Pervasive Magazine. His research is devoted to adaptive, intelligent systems seamlessly integrated in the environment. This includes wearable computing, sensors and sensor networks, activity and context recognition, software tools, system models, and a wide range of pervasive computing applications. With particular interest in large-scale systems that self organize to cooperate in dynamic, opportunistic configurations. In the application area his group has a strong emphasis on health and wellness related systems.

Dr. Kristof Van Laerhoven leads the Embedded Sensing Systems (ESS) group at Technische Universität Darmstädt. The ESS group investigates the topic of Long-term Activity Recognition with Wearable Sensors. In this project they develop both hardware and algorithms for small-scale activity sensors that recognize the type of activities that the user wearing the sensor performs. They collaborate closely with psychiatrists from King's College's Institute of Psychiatry to test their work in the field of bipolar patient monitoring, and have several long-term platform trials planned over the next five years. After several works on robotics and neural networks at Luc Steels' VUB AI lab, Dr. Van Laerhoven got a Ph.D. at the Embedded Interactive Systems group at the University of Lancaster in the United Kingdom with Hans Gellersen. Then he moved to the TU Darmstädt for a postdoc position with Bernt Schiele's Multimodal Interactive Systems group. After this, he was granted an Emmy Noether Research Group by the German Research Foundation (DFG), on the theme of Long-Term Activity Recognition with Wearable Sensors, which started in 2010.

Dr. Thomas Ploetz currently holds a research associate position within the EPSRC SiDE research hub (connected home & community project) working there in the field of activity recognition. The focus of his work is on assistive technology, mainly on the development of pervasive / ubiquitous computing methods to support people with special needs in their private home. His general scientific interests are on any kind of applied statistical pattern recognition and machine learning. Especially the analysis of (multimodal) sequential data and related stochastic modeling when only little annotated training data is available. He studied computer science in Germany and received the Diploma (equivalent to MSc.) in 2001 from Bielefeld University. He then worked towards his Ph.D. (received 2005) within the Applied Computer Science group of Gerhard Sagerer (University of Bielefeld, Germany) until 2006. Following this he joined the Intelligent Systems group of Gernot A. Fink at the Robotics Research Institute of TU Dortmund University (as a senior researcher). He currently is a visiting researcher at Georgia Institute of Technology Atlanta, Georgia, United States.

(4)         An Introduction to Enterprise Engineering from an Ontological Perspective

Within the Enterprise Information Systems domain, especially in the context of Cyber-Physical and Socio-Technical Systems, Enterprise Engineering is an evolving research field receiving significant attention from different disciplines. In this full-day tutorial the focus will be on the different aspects involved in Enterprise Engineering, with special attention to the role of the Zachman Framework as guidance in Enterprise Architecture. It aims to provide an overview of the history and meaning of different terms, including Ontologies, models, frameworks and meta-models, and how all these concepts could be applied within the context of Cyber-Physical and Enterprise Information Systems today.

Mr. John Zachman is the originator of the "Framework for Enterprise Architecture," also known as the Zachman® Framework, which has received broad acceptance around the world as an integrative framework, or "periodic table" of descriptive representations for Enterprises. Mr. Zachman is not only known for this work on Enterprise Architecture, but is also known for his early contributions to IBM's Information Strategy methodology (Business Systems Planning) as well as to their Executive team planning techniques (Intensive Planning). Mr. Zachman retired from IBM in 1990, having served them for 26 years. He is Chief Executive Officer of his own education and consulting business, Zachman International®. Mr. Zachman serves on the Executive Council for Information Management and Technology (ECIMT) of the United States Government Accountability Office (GAO) and on the Advisory Board of the Data Administration Management Association International (DAMA-I) from whom he was awarded the 2002 Lifetime Achievement Award. He was awarded the 2009 Enterprise Architecture Professional Lifetime Achievement Award from the Center for Advancement of the Enterprise Architecture Profession as well as the 2004 Oakland University, Applied Technology in Business (ATIB), Award for IS Excellence and Innovation.

Mr. Zachman has been focusing on Enterprise Architecture since 1970 and has written extensively on the subject. He is the author of the book, The Zachman® Framework for Enterprise Architecture: A Primer on Enterprise Engineering and Manufacturing. He has facilitated innumerable executive team planning sessions. He travels nationally and internationally, teaching and consulting, and is a popular conference speaker, known for his motivating messages on Enterprise Architecture issues. He has spoken to many thousands of enterprise managers and information professionals on every continent. In addition to his professional activities, Mr. Zachman serves on the Elder Council of the Church on the Way (First Foursquare Church of Van Nuys, California), the Board of Directors of Living Way Ministries, a radio and television ministry of the Church on the Way, the President's Cabinet of the King's University, the Board of Directors of the Los Angeles Citywide Children's Christian Choir and on the Board of Directors of Native Hope International, a Los Angeles-based ministry to the Native American people.

Prior to joining IBM, Mr. Zachman served as a line officer in the United States Navy and is a retired Commander in the U. S. Naval Reserve. He chaired a panel on "Planning, Development and Maintenance Tools and Methods Integration" for the U.S. National Institute of Standards and Technology. He holds a degree in Chemistry from Northwestern University, has taught at Tufts University, has served on the Board of Councilors for the School of Library and Information Management at the University of Southern California, as a Special Advisor to the School of Library and Information Management at Emporia State University, on the Advisory Council to the School of Library and Information Management at Dominican University and on the Advisory Board for the Data Resource Management Program at the University of Washington. He has been a Fellow for the College of Business Administration of the University of North Texas and currently is listed in Cambridge Who's Who.

Professor Alta van der Merwe is currently a principal researcher at the Meraka Institute of the CSIR, where her duties include research, human capital development, being involved as an academic member in collaboration with universities, and playing a role in professional and subject societies. Prof van der Merwe is currently appointed as professor extraordinary at Unisa within the School of Computing where she is involved in the supervision of several MSc and PhD students. She serves on several international research working groups, steering committees, advisory boards and editorial boards. She is the chair of the South African IEEE SMC Society Chapter, the editor of the SAIEE (Software Engineering track), co-editor of the newly established IBIMA journal and served on the organizing Committee of the Advance Enterprise Repository Workshop (AER 2009). She was instrumental in establishing a research group spanning academia, research institutions and industry, the Enterprise Architecture Research Forum (AERF), which is a leader in the field of development of the domain of Enterprise Architecture. She was also involved in the establishment of the Open Group Enterprise Architecture Academic Forum, which focuses on the enhancement of Enterprise Architecture (EA) as an academic field. Currently her own research interest is the use of models within the field of Enterprise Engineering and Enterprise Architecture.

Professor Aurona Gerber completed an electronic engineering degree at the University of Pretoria in 1987. Aurona have been employed in different milieus', including a research institution (CSIR), an academic institution (University of South Africa) and industry (Didata). During her employment in these different environments, she was involved in the development, research and academic side of technology applications. Aurona Gerber completed her Ph.D. in 2007 in Semantic Web technologies, and is currently employed at the CSIR as senior researcher in knowledge representation and reasoning. Aurona is involved in supervision of Master and Ph.D. students and was appointed as professor extraordinary at the North-west University in 2009. Aurona Gerber serves on different program committees for conferences, review panels for journals and is currently the vice-chair of IEEE SMC Society Chapter South Africa. Her research interest is within the domain of Ontology Engineering, with a special interest in the use of models and meta-models to do knowledge representation.

(5)         Smart Grid - An Intelligent Cyber-Physical Power System

This tutorial will focus on the following major topics, starting with introduction to smart grid, and the field of computational intelligence, and its applications in system identification, control and optimization with emphasis on the smart grid environment.

• Cyber-physical systems
• Smart Grid - What is a Smart Grid? Is it an intelligent cyber-physical power system? Vehicle-to-grid technology; SmartParks; Wind integration; Wide area monitoring and control; Generator maintenance scheduling; voltage predictions; reactive power and voltage control; microgrids
• Computational Intelligence - Neural Networks; Neuro-Fuzzy; Evolutionary Neuro Systems; Swarm Intelligence; Artificial Immune Systems (AIS).
• Nonlinear System identification - Using neural networks and particle swarm optimization.
• Intelligent Control - Adaptive and Optimal using neural networks, fuzzy control and AIS.
• Dynamic Optimization using learning methods

The primary aim of this tutorial is to provide power, control and system engineers/researchers from industry/academia, new to smart grid challenges, the field of computational and learning methods with the fundamentals required to benefit from and contribute to the rapidly growing field of intelligent systems applications in the smart grid environment. In particular, a clear understanding of the different strategies for designing intelligent identifiers and controllers will be developed by means of examples for nonlinear systems.

Professor G. K. Venayagamoorthy received his Ph.D. degree in electrical engineering from the University of Natal, South Africa. He is a Professor of Electrical and Computer Engineering, and the founder and Director of the Real-Time Power and Intelligent Systems (RTPIS) Laboratory at Missouri University of Science and Technology (Missouri S&T). He was a Visiting Researcher with ABB Corporate Research, Sweden, in 2007. His research interests are in the development and applications of advanced computational algorithms for real-world applications, including power systems stability and control, smart grid applications and sensor networks. He has published over 375 articles in refereed journals and conference proceedings. He has been involved in approximately US $7 million of competitive research funding in the last seven years. Dr. Venayagamoorthy is a recipient of several awards including a 2007 US Office of Naval Research Young Investigator Program Award, a 2004 NSF CAREER Award, the 2010 Innovation Award from St. Louis Academy of Science, the 2010 IEEE Region 5 Outstanding Member Award, the 2006 IEEE Power and Energy Society Walter Fee Outstanding Young Engineer Award, a 2007 Missouri S&T Teaching Commendation Award, a 2006 Missouri S&T School of Engineering Teaching Excellence Award, a 2008, 2007 and 2005 Missouri S&T Faculty Excellence Award and a 2009 Missouri S&T Faculty Research Award. Dr. Venayagamoorthy is the Chair of the IEEE CIS Task Force on Smart Grid. He has been involved in the leadership and organization of many conferences including the Chair of the 2011 IEEE Symposium of Computational Intelligence Applications in Smart Grid (CIASG). Dr. Venayagamoorthy is a Fellow of the Institution of Engineering and Technology (IET), UK, and the South African Institute of Electrical Engineers. He is a Senior Member of the IEEE and the International Neural Network Society (INNS), and a Member of the American Society for Engineering Education. He is member of Board of Governors of INNS.

Professor Jianda Han is the deputy director of the State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences. His research interests include nonlinear estimation and control, autonomy of mobile robots, and robotic system developments and integrations. His group has proposed a series of control techniques for the autonomy of mobile robots, most of which have been implemented on the home-developed prototypes and application systems, such as the polar robot that is the first one in China successfully tested in Antarctica in 2007, and the 40kg and 120kg (take-off weight) flying robots that are now tested for rescue, power cable construction, and precision agriculture applications. He has published over 120 journal and conference papers and book chapters.

(6)         Practical Feature Selection

Dimensionality reduction (DR) is a vital step in many machine learning and pattern recognition tasks. It is capable of improving model interpretability, recognition performance, accuracy, as well as the cost effectiveness of the designed systems. In the most general DR form - feature extraction (FE) - a set of new features is generated using a suitable transformation from all the original measurements. While FE may capture discriminatory information, which is not transparent from the original data, in its constrained form, known as feature (or variable or attribute) selection (FS), the original measurement meaning is preserved. In FS the measurement weighting is binary. Discarding irrelevant and redundant information can reduce measurement acquisition costs as well as speed up the learning process. FS is widely applied in various fields; in supervised and unsupervised learning, remote sensing, document processing, image retrieval, bio-informatics, medical diagnostics, as well as in robotics, etc. An extensive framework of FS methods has been developed to date, with different approaches customized for different DR scenarios. The purpose of the tutorial is to give a concise overview of the existing feature selection approaches together with a discussion of various aspects of their applicability to real world recognition tasks. The tutorial will introduce the dimensionality reduction terminology, develop the underlying mathematical models and provide practical guidelines for the practitioner. The topics discussed will include the challenges of the curse of dimensionality, computational complexity in high-dimensional feature spaces, feature over-selection, choice of feature subset size, stability issues, etc. An important part of the tutorial will be a practical session demonstrating the process of feature selection, using a specialized open source software library known as "Feature Selection Toolbox." The tutorial participants will receive a copy of the presentation, supplementary materials including recommended list of references, as well as a CD containing the presented Feature Selection Toolbox software with documentation and applied code examples.

Professor Josef Kittler heads the Centre for Vision, Speech and Signal Processing at the Faculty of Engineering and Physical Sciences, University of Surrey. He received his BA, Ph.D. and D.Sc. degrees from the University of Cambridge. He teaches and conducts research in the subject area of Signal Processing and Machine Intelligence, with a focus on Biometrics, Video and Image Database retrieval, and Cognitive Vision. He published a Prentice Hall textbook on Pattern Recognition: A Statistical Approach and several edited volumes, as well as more than 700 scientific papers, including in excess of 170 journal papers. He serves on the Editorial Board of Springer Lecture Notes on Computer Science. He served as President of the International Association for Pattern Recognition 1994-1996. He is Fellow of the Royal Academy of Engineering and a recipient of the KS Fu Prize from the International Association for Pattern Recognition. In 2008 he was awarded the IET Faraday Medal and in 2009 he became EURASIP Fellow. His citation h-index exceeds 50.

Professor Pavel Pudil is with Prague University of Economics and Institute of Information Theory and Automation of the Czech Academy of Sciences. Pavel has been active in the area of statistical pattern recognition and feature selection in particular for more than 20 years. In 2000 he was elected IAPR Fellow. His contributions to the field include some of the most widely applied tools, in particular the floating search feature selection method (cited more than 1200 times). His citation h-index is 13.

Dr. Petr Somol is now with Prague University of Economics, Petr has contributed to the field of feature selection by about 70 papers throughout the last 14 years of his activity within the Institute of Information Theory and Automation of the Czech Academy of Sciences. His contributions include feature selection algorithms (Fast Branch & Bound), theoretical results (feature selection stability measures) as well as initiation of the Feature Selection Toolbox development. His work has found roughly 500 citations. His citation h-index is 11.

(7)         Adaptive Biometric Systems that Can Improve with Use

Performances of biometric recognition systems can degrade quickly when the input biometric traits exhibit substantial variations compared to training examples (called "templates") collected during the enrollment stage of system's users. These variations can be temporary (e.g., illumination changes) or due to the natural change of biometrics over the time ("aging" effect), and it is nearly impossible to represent them well with a few templates collected during the enrollment ("training") phase. On the other hand, a lot of new unlabelled biometric data, which could be exploited to adapt the system to input data variations, are made available during the system operation over the time. This tutorial deals with adaptive biometric systems that can improve with use by exploiting unlabelled data. We introduce and motivate adaptive biometric systems, critically review the state of the art, and place previous works into a taxonomy that we propose. The outline of the tutorial is below:

• Introduction to biometric recognition systems. Motivations for adaptive biometric systems.
• Template update in adaptive biometric systems. Other adaptation techniques.
• Key concepts. Supervised and semi-supervised methods. Online and offline methods.
• Template representation.
• A taxonomy of template update methods.
• A critical review of the state of the art. Placing previous works in the proposed taxonomy.
• Open issues for future research.

Professor Fabio Roli received his M.S. degree, with honors, and Ph.D. degree in Electronic Engineering from the University of Genoa, Italy. He was adjunct professor at the University of Trento, Italy, in 1993 and 1994. In 1995, he joined the Dept. of Electrical and Electronic Engineering of the University of Cagliari, Italy, where he is now professor of computer engineering and head of the research group on pattern recognition and applications (http://prag.diee.unica.it). Dr. Roli has always done research on pattern recognition, machine learning, and image analysis, in the context of real applications including biometric recognition, video surveillance, and computer security. He did seminal work on the fusion of multiple classifiers and its applications to computer security. Dr. Roli established the popular workshop series on multiple classifier systems and co-chaired its ten editions (http://www.diee.unica.it/mcs). On these topics, he has published more than one hundred papers at conferences and in journals. Recently, he focused his activity on pattern recognition in adversarial environments. He gave four invited lectures on this topic, and a plenary talk on adversarial classification at the international conference ICMLC 2009. Dr. Roli is a Senior Member of the IEEE, Board of Governors member-at-large of the IEEE SMC Society, and Fellow of the International Association for Pattern Recognition. He was the chairman of the IAPR Technical Committee on Statistical Techniques in Pattern Recognition from 2004 to 2008. Dr. Roli is a member of the governing board of the International Association for Pattern Recognition.