Advanced Information and Knowledge Processing – serie

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

Ontologies are increasingly recognized as essential tools in information science. Although the concepts are well understood theoretically , the practical implementation of ontologies remains challenging. In this book, researchers in computer science, information systems, ontology engineering, urban planning and design, civil and building engineering, and architecture present an interdisciplinary study of ontology engineering and its application in urban development projects. The first part of the book introduces the general notion of ontology, describing variations in abstraction level, coverage, and formality. It also discusses the use of ontologies to achieve interoperability, and to represent multiple points of view and multilingualism. This is illustrated with examples from the urban domain. The second part is specific to urban development. It covers spatial and geographical knowledge representation, the creation of urban ontologies from various knowledge sources, the interconnection of urban models and the interaction between standards and domain models. The third part presents case studies of the development of ontologies for urban mobility, urban morphological processes, road systems, and cultural heritage. Other cases report on the use of ontologies to solve urban development problems, in construction business models, building regulations and urban regeneration. It concludes with a discussion of key challenges for the future deployment of ontologies in this domain. This book bridges the gap between urban practitioners and computer scientists. As the essence of most urban projects lies in making connections between worldviews, ontology development has an important role to play, in promoting interoperability between data sources, both formal (urban databases, Building Integrated Models, Geographical Information Systems etc.) and less formal (thesauri, text records, web sources etc.). This volume offers a comprehensive introduction to ontology engineeringfor urban development. It is essential reading for practitioners and ontology designers working in urban development.

Militarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict.Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power.Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au­ thors who have developed a local pattern analysis, a new strategy for dis­ covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv­ ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe­ culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter­ esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis­ tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining.

A new economy is emerging. An economy that is transforming the fundamental rules of business. An economy based on exploiting knowledge and innovation. An economy where knowledge is the main source of wealth for regions, nations, enter­ prises and people. This new economy is based on economic values far removed from those of the industrial economy. Value has shifted towards intangibles and in particular towards increasing value by incorporating knowledge into services and products. The advent of this new economy is rapidly changing the role and structure of global business. Winning enterprises are those best able to harness the benefits and opportunities of information and communication technology, capitalize on their knowledge base and move at the speed of the market. Knowledge management lies at the heart of the European Community's competi­ tiveness strategy. The European Commission facilitates and supports applied research in knowledge management through its Information Society Technologies (1ST) programme, a major theme of research and technological development within the European Union's Research and Technology Development Framework Programme. Specifically, the New Methods of Work and Electronic Commerce action of the 1ST programme supports long-term applied research in areas combin­ ing technological innovation with new work practices and advanced business and work models.

The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques.

The current rapid development in both computing power and the ability to present and mine complex data sets in useful ways provides the backdrop to Intelligence Management: Knowledge Driven Frameworks for Combating Terrorism and Organized Crime.  The chapters address the linkage between: law enforcement; developments in information and communication technologies and key ideas about the management of data, information, knowledge and intelligence. The work is conducted by a number of international academic and industrial research groups, law enforcement agencies, and end users.   Section 1 presents four chapters that address the details, outcomes, user needs and background theoretical ideas behind a large-scale research aand development project in this domain (The Odyssey Project).  This project explored the challenges of establishing a Pan-European ballistics and crime information intelligence network.  It represents an example of the type of system that is likely to become commonly used by Law Enforcement Agencies in the near future. Many of the challenges are not technical but organisational, legal, economic, social and political.  Sections 2 and 3 therefore present wider commentaries.  Section 2 explores other research and development projects that attempt to exploit the power of contemporary ICT systems to support Law Enforcement Agencies in many aspects of their work including investigations, data analysis and presentation, identification, training and crime prevention.  Section 3 takes a look at the social and organisational issues around aspects of crime prevention, crime detection and policing – with a view to the role of information and communication technologies in thesecontexts.

Service-Oriented Computing (SOC) allows software development time to be shortened by the composition of existing services across the Internet. Further exploitation of this revolutionary trend is feasible through automation, thanks to the use of software agents and techniques from distributed artificial intelligence.This book provides an overview of the related technologies and insight into state-of-the art research results in the field. The topics discussed cover the various stages in the life cycle of service-oriented software development using agent technologies to automate the development process and to manage services in a dynamic environment. The book presents both academic research results and the latest developments from industry.Researchers from academia and industry, as well as postgraduates, will find this cutting-edge volume indispensable in order to gain understanding of the issues associated with agent-based service-oriented computing along with recent, and likely future technology trends.

Information and communication technologies can provide new paradigms in healthcare provision. In particular, new Pervasive Healthcare technologies can revolutionise the dynamics of healthcare, enabling people to remain at home for longer, at lower costs to health and welfare organisations.This book reveals how pervasive healthcare technologies can be designed in conjunction with users and carers, as well as exploring the application of novel methods and technologies.

Web-based Support Systems (WSS) are an emerging multidisciplinary research area in which one studies the support of human activities with the Web as the common platform,mediumandinterface.TheInternetaffectseveryaspectofourmodernlife. Moving support systems to online is an increasing trend in many research domains. One of the goals of WSS research is to extend the human physical limitation of information processing in the information age. Research on WSS is motivated by the challenges and opportunities arising from the Internet. The availability, accessibility and ?exibility of information as well as the tools to access this information lead to a vast amount of opportunities. H- ever, there are also many challenges we face. For instance, we have to deal with more complex tasks, as there are increasing demands for quality and productivity. WSS research is a natural evolution of the studies on various computerized support systems such as Decision Support Systems (DSS), Computer Aided Design (CAD), and Computer Aided Software Engineering (CASE). The recent advancement of computer and Web technologies make the implementation of more feasible WSS. Nowadays, it is rare to see a system without some type of Web interaction. The research of WSS is classi?ed into four groups. • WSS for speci?c domains.

Richard Chbeir, Youakim Badr, Ajith Abraham, and Aboul-Ella Hassanien Abstract As the Web continues to grow and evolve, more and more data are becoming available. Particularly, multimedia and XML-based data are produced regularly and in increasing way in our daily digital activities, and their retrieval and access must be explored and studied in this emergent web-based era. This book provides reviews of the cutting-edge technologies and insights of various topics related to XML-based and multimedia information access and retrieval under the umbrella of Web Intelligence and reporting how organizations can gain compe- tive advantages by applying new different emergent techniques in the real-world scenarios. The primary target audience for the book includes researchers, scholars, postgraduate students and developers who are interested in advanced information retrieval on the web research and related issues. 1 Introduction Since the last two decades, Internet has changed our daily life by rede?ning the meanings and processes of business, commerce, marketing, ?nance, publishing, R. Chbeir Universite ' de Bourgogne, LE2I-UMR CNRS 5158, Fac. de Sciences Mirande, 21078 Dijon Cedex, France e-mail: richard.chbeir@u-bourgogne. fr Y. Badr INSA de Lyon, Universite ' de Lyon, Depart ' ement Informatique, 7 avenue Jean Capelle, 69621 Villeurbanne CX, France e-mail: youakim. badr@insa-lyon. fr A. Abraham Norwegian University of Science & Technology, Center for Quanti?able Quality of Service in Communication Systems, O. S. Bragstads plass 2E, 7491 Trondheim, Norway e-mail: ajith. abraham@ieee. org A. -E. Hassanien Kuwait University, College of Business & Administration, Dept.

Dynamic Business Process Formation fuses practical needs with theoretical input to present important research innovations in supporting Instant Virtual Enterprises (IVEs). This new organization type brings a combination of business dynamism and explicit business process structure to domains where on-the-fly formation of well-organized business networks is required to deal with the complexity of new products or services under high time pressure.This book contains the main results of the IST CrossWork project, and, importantly, looks beyond the boundaries of this project and sources input from related projects and general trends in collaborative enterprises and the automotive industry. Both the business and technical aspects of Virtual Enterprise coordination are covered within the modular structure of the book, which enables readers from different backgrounds to benefit from the book according to their interests.

The success of the World Wide Web depends on the ability of users to store, p- cess and retrieve digital information regardless of distance boundaries, languages and domains of knowledge. The universality and ?exibility of the World Wide Web have also enabled the rapid growth of a variety of new services and applications based on human–machine interaction. The semantics of exchanged information and services should be useful not only for human to human communications, but also in that machines would be able to understand and automatically process web content. Semanticsgive well-de?nedmeaningto web content and enable computersand p- ple to work in cooperation. Today, the crucial challenge becomes the development of languages to express information in a machine processable format. Now more than ever, new advanced techniques and intelligent approaches are required to tra- form the Web into a universal reasoning and computing machine. Web intelligence attempts to deal with this challenge by exploiting information technologies and - ti?cial intelligence approaches to design the next generation of web-empowered systems and services.

Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the efficiency of a multi-database mining application could be improved by processing more patterns in the application. A faster algorithm could also play an important role in developing a better application. Thus the efficiency of a multi-database mining application could be enhanced by choosing an appropriate multi-database mining model, an appropriate pattern synthesizing technique, a better pattern representation technique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.

As depicted in David Lodge’s celebrated novel Small World, the perceived size of our world experienced a progressive decrease as jet airplanes became affordable to ever greater shares of the earth’s population. Yet, the really dramatic shrinking had to wait until the mid-1990s, when Internet became widespread and the information age stopped being an empty buzzword. But small is not necessarily beautiful. We now live in a global village and, alas, some (often very powerful) voices state that we ought not expect any more privacy in it. Should this be true, we would have created our own nightmare: a global village combining the worst of conventional villages, where a lot of information on an individual is known by the other villagers, and conventional big cities, where the invidual feels lost in a grim and potentially dangerous place. Whereas security is essential for organizations to survive, individuals and so- times even companies also need some privacy to develop comfortably and lead a free life. This is the reason why individual privacy is mentioned in the Univ- sal Declaration of Human Rights (1948) and data privacy is protected by law in most Western countries. Indeed, without privacy, the rest of fundamental rights, like freedom of speech and democracy, are impaired. The outstanding challenge is to create technology that implements those legal guarantees in a way compatible with functionality and security. This book edited by Dr. Javier Herranz and Dr.

Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included.Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book.

Robot intelligence has become a major focus of intelligent robotics. Recent innovation in computational intelligence including fuzzy learning, neural networks, evolutionary computation and classical Artificial Intelligence provides sufficient theoretical and experimental foundations for enabling robots to undertake a variety of tasks with reasonable performance. This book reflects the recent advances in the field from an advanced knowledge processing perspective; there have been attempts to solve knowledge based information explosion constraints by integrating computational intelligence in the robotics context.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

The current rapid development in both computing power and the ability to present and mine complex data sets in useful ways provides the backdrop to Intelligence Management: Knowledge Driven Frameworks for Combating Terrorism and Organized Crime.  The chapters address the linkage between: law enforcement; developments in information and communication technologies and key ideas about the management of data, information, knowledge and intelligence. The work is conducted by a number of international academic and industrial research groups, law enforcement agencies, and end users.   Section 1 presents four chapters that address the details, outcomes, user needs and background theoretical ideas behind a large-scale research aand development project in this domain (The Odyssey Project).  This project explored the challenges of establishing a Pan-European ballistics and crime information intelligence network.  It represents an example of the type of system that is likely to become commonly used by Law Enforcement Agencies in the near future. Many of the challenges are not technical but organisational, legal, economic, social and political.  Sections 2 and 3 therefore present wider commentaries.  Section 2 explores other research and development projects that attempt to exploit the power of contemporary ICT systems to support Law Enforcement Agencies in many aspects of their work including investigations, data analysis and presentation, identification, training and crime prevention.  Section 3 takes a look at the social and organisational issues around aspects of crime prevention, crime detection and policing – with a view to the role of information and communication technologies in thesecontexts.

Ontologies are increasingly recognized as essential tools in information science. Although the concepts are well understood theoretically , the practical implementation of ontologies remains challenging. In this book, researchers in computer science, information systems, ontology engineering, urban planning and design, civil and building engineering, and architecture present an interdisciplinary study of ontology engineering and its application in urban development projects. The first part of the book introduces the general notion of ontology, describing variations in abstraction level, coverage, and formality. It also discusses the use of ontologies to achieve interoperability, and to represent multiple points of view and multilingualism. This is illustrated with examples from the urban domain. The second part is specific to urban development. It covers spatial and geographical knowledge representation, the creation of urban ontologies from various knowledge sources, the interconnection of urban models and the interaction between standards and domain models. The third part presents case studies of the development of ontologies for urban mobility, urban morphological processes, road systems, and cultural heritage. Other cases report on the use of ontologies to solve urban development problems, in construction business models, building regulations and urban regeneration. It concludes with a discussion of key challenges for the future deployment of ontologies in this domain. This book bridges the gap between urban practitioners and computer scientists. As the essence of most urban projects lies in making connections between worldviews, ontology development has an important role to play, in promoting interoperability between data sources, both formal (urban databases, Building Integrated Models, Geographical Information Systems etc.) and less formal (thesauri, text records, web sources etc.). This volume offers a comprehensive introduction to ontology engineeringfor urban development. It is essential reading for practitioners and ontology designers working in urban development.

Militarized Conflict Modeling Using Computational Intelligence examines the application of computational intelligence methods to model conflict. Traditionally, conflict has been modeled using game theory. The inherent limitation of game theory when dealing with more than three players in a game is the main motivation for the application of computational intelligence in modeling conflict.Militarized interstate disputes (MIDs) are defined as a set of interactions between, or among, states that can result in the display, threat or actual use of military force in an explicit way. These interactions can result in either peace or conflict. This book models the relationship between key variables and the risk of conflict between two countries. The variables include Allies which measures the presence or absence of military alliance, Contiguity which measures whether the countries share a common boundary or not and Major Power which measures whether either or both states are a major power.Militarized Conflict Modeling Using Computational Intelligence implements various multi-layer perception neural networks, Bayesian networks, support vector machines, neuro-fuzzy models, rough sets models, neuro-rough sets models and optimized rough sets models to create models that estimate the risk of conflict given the variables. Secondly, these models are used to study the sensitivity of each variable to conflict. Furthermore, a framework on how these models can be used to control the possibility of peace is proposed. Finally, new and emerging topics on modelling conflict are identified and further work is proposed.

Rough Set Theory, introduced by Pawlak in the early 1980s, has become an important part of soft computing within the last 25 years. However, much of the focus has been on the theoretical understanding of Rough Sets, with a survey of Rough Sets and their applications within business and industry much desired. Rough Sets: Selected Methods and Applications in Management and Engineering provides context to Rough Set theory, with each chapter exploring a real-world application of Rough Sets.Rough Sets is relevant to managers striving to improve their businesses, industry researchers looking to improve the efficiency of their solutions, and university researchers wanting to apply Rough Sets to real-world problems.

Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques presents an overall analysis of meta-programming, focusing on insights of meta-programming techniques, heterogeneous meta-program development processes in the context of model-driven, feature-based and transformative approaches.The fundamental concepts of meta-programming are still not thoroughly understood, in this well organized book divided into three parts the authors help to address this. Chapters include: Taxonomy of fundamental concepts of meta-programming; Concept of structural heterogeneous meta-programming based on the original meta-language; Model-driven concept and feature-based modeling to the development process of meta-programs; Equivalent meta-program transformations and metrics to evaluate complexity of feature-based models and meta-programs; Variety of academic research case studies within different application domains to experimentally verify the soundness of the investigated approaches.Both authors are professors at Kaunas University of Technology with 15 years research and teaching experience in the field. Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques is aimed at post-graduates in computer science and software engineering and researchers and program system developers wishing to extend their knowledge in this rapidly evolving sector of science and technology.

During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates.Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others.Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application.

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena.The artificial intelligence techniques used to model economic data include:multi-layer perceptron neural networksradial basis functionssupport vector machinesrough setsgenetic algorithmparticle swarm optimizationsimulated annealingmulti-agent systemincremental learningfuzzy networksSignal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation.Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics,and is a valuable source of reference for graduate students, researchers and financial practitioners.

In recent years, cryptographic techniques for protecting and hiding secret information have been included in directions of research on intelligent information management. Data can be managed securely due to the use of algorithms for ensuring the confidentiality of data, information splitting techniques as well as protocols for sharing information and methods of its reconstruction.This is why techniques of advanced splitting and reconstruction of information form the primary subject of Secure Information Management Using Linguistic Threshold Approach, whose main purpose is to discuss the so-called linguistic threshold schemes for information sharing. An attempt is also made to describe the opportunities of using these techniques to create new models of managing strategic information shared within a commercial organisation or a state institution. Such information is specially protected, and its contents are used only if the authorised users are enabled to access it. This monograph attempts to define a model structure of information flow and for assigning information shares to particular groups of individuals concerned. The proposed information flow model can be integrated into practical solutions within any organisation or institution, improving the functionality of its legacy information systems.The use of strong mathematical cryptographic models to manage information constitutes a scientific innovation and a demonstration of the methods and opportunities of using advanced techniques for confidentially exchanging information in tasks supporting data flow within a commercial organisation.The interdisciplinary nature of the solutions proposed means that the subject of linguistic threshold schemes forming part of intelligent information management becomes a new challenge for the research and application work carried out.The authors of this monograph hope that it will guide readers on an interesting journey through the cutting edge solution in the field of secure information management.

How do we design a self-organizing system? Is it possible to validate and control non-deterministic dynamics? What is the right balance between the emergent patterns that bring robustness, adaptability and scalability, and the traditional need for verification and validation of the outcomes?The last several decades have seen much progress from original ideas of “emergent functionality” and “design for emergence”, to sophisticated mathematical formalisms of “guided self-organization”. And yet the main challenge remains, attracting the best scientific and engineering expertise to this elusive problem.This book presents state-of-the-practice of successfully engineered self-organizing systems, and examines ways to balance design and self-organization in the context of applications.As demonstrated in this second edition of Advances in Applied Self-Organizing Systems, finding this balance helps to deal with practical challenges as diverse as navigation of microscopic robots within blood vessels, self-monitoring aerospace vehicles, collective and modular robotics adapted for autonomous reconnaissance and surveillance, self-managing grids and multiprocessor scheduling, data visualization and self-modifying digital and analog circuitry, intrusion detection in computer networks, reconstruction of hydro-physical fields, traffic management, immunocomputing and nature-inspired computation.Many algorithms proposed and discussed in this volume are biologically inspired, and the reader will also gain an insight into cellular automata, genetic algorithms, artificial immune systems, snake-like locomotion, ant foraging, birds flocking, neuromorphic circuits, amongst others. Demonstrating the practical relevance and applicability of self-organization, Advances in Applied Self-OrganizingSystems will be an invaluable tool for advanced students and researchers in a wide range of fields.

Remote web-based experimentation, enabling students and researchers to access the laboratory anytime via the Internet, is becoming an increasingly attractive way to complement or even replace traditional laboratory sessions. Placing a video camera & microphone before the equipment and apparatus to capture what is actually happening in the laboratory allows the images and audio data to be streamed to the client side. Researchers in different countries can share equipment and conduct research cooperatively and remotely. The authors summarise their research and discuss the development of the 5 web-based laboratories launched from the National University of Singapore. The principles, structure, and technologies required for the creation of Internet remote experimentation systems are discussed with particular emphasis on the integration of hardware and software systems. Also highlighted is the design and development of interfaces and components for use in typical web-based laboratories or similar web-control applications.

This work summarizes the theoretical and algorithmic basis of optimized pr- abilistic advising. It developed from a series of targeted research projects s- ported both by the European Commission and Czech grant bodies. The source text has served as a common basis of communication for the research team. When accumulating and re?ning the material we found that the text could also serve as • a grand example of the strength of dynamic Bayesian decision making, • a practical demonstration that computational aspects do matter, • a reference to ready particular solutions in learning and optimization of decision-making strategies, • a source of open and challenging problems for postgraduate students, young as well as experienced researchers, • a departure point for a further systematic development of advanced op- mized advisory systems, for instance, in multiple participant setting. These observations have inspired us to prepare this book. Prague, Czech Republic Miroslav K´ arn´ y October 2004 Josef B¨ ohm Tatiana V. Guy Ladislav Jirsa Ivan Nagy Petr Nedoma Ludv´ ?k Tesa? r Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 2 State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 2. 1 Operator supports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 2. 2 Mainstream multivariate techniques . . . . . . . . . . . . . . . . . 4 1. 2. 3 Probabilistic dynamic optimized decision-making . . . . . . 6 1. 3 Developed advising and its role in computer support . . . . . . . . . 6 1. 4 Presentation style, readership andlayout . . . . . . . . . . . . . . . . . . . 7 1. 5 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Underlying theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 1 General conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2. 2 Basic notions and notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .