This book introduces a novel transcoding algorithm for real time video applications, designed to overcome inter-operability problems between MPEG-2 to H.264/AVC. The new algorithm achieves 92.8% reduction in the transcoding run time at a price of an acceptable Peak Signal-to-Noise Ratio (PSNR) degradation, enabling readers to use it for real time video applications. The algorithm described is evaluated through simulation and experimental results. In addition, the authors present a hardware implementation of the new algorithm using Field Programmable Gate Array (FPGA) and Application-specific standard products (ASIC). • Describes a novel transcoding algorithm for real time video applications, designed to overcome inter-operability problems between H.264/AVC to MPEG-2; • Implements algorithm presented using Field Programmable Gate Array (FPGA) and Application-specific Integrated Circuit (ASIC); • Demonstrates the solution to real problems, with verification through simulation and experimental results.

The book discusses intelligent system design using soft computing and similar systems and their interdisciplinary applications. It also focuses on the recent trends to use soft computing as a versatile tool for designing a host of decision support systems.

Teaching and learning paradigms have attracted increased attention especially in the last decade. Immense developments of different ICT technologies and services have paved the way for alternative but effective approaches in educational processes. Many concepts of the agent technology, such as intelligence, autonomy and cooperation, have had a direct positive impact on many of the requests imposed on modern e-learning systems and educational processes. This book presents the state-of-the-art of e-learning and tutoring systems and discusses their capabilities and benefits that stem from integrating software agents. We hope that the presented work will be of a great use to our colleagues and researchers interested in the e-learning and agent technology.    

This book presents an exhaustive and timely review of key research work on fuzzy XML data management, and provides readers with a comprehensive resource on the state-of-the art tools and theories in this fast growing area.  Topics covered in the book include: representation of fuzzy XML, query of fuzzy XML, fuzzy database models, extraction of fuzzy XML from fuzzy database models, reengineering of fuzzy XML into fuzzy database models, and reasoning of fuzzy XML. The book is intended as a reference guide for researchers, practitioners and graduate students working and/or studying in the field of Web Intelligence, as well as for data and knowledge engineering professionals seeking new approaches to replace traditional methods, which may be unnecessarily complex or even unproductive.

This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean–variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuing advanced research and/or engaged in practical issues in the rapidly evolving field of portfolio optimization.  

Modern information and communication technologies, together with a cultural upheaval within the research community, have profoundly changed research in nearly every aspect. Ranging from sharing and discussing ideas in social networks for scientists to new collaborative environments and novel publication formats, knowledge creation and dissemination as we know it is experiencing a vigorous shift towards increased transparency, collaboration and accessibility. Many assume that research workflows will change more in the next 20 years than they have in the last 200. This book provides researchers, decision makers, and other scientific stakeholders with a snapshot of the basics, the tools, and the underlying visions that drive the current scientific (r)evolution, often called ‘Open Science.’

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.

This book presents the latest scientific research related to the field of Robotics. It involves different topics such as biomedicine, energy efficiency and home automation and robotics. The book is written by technical experts and researchers from academia and industry working on robotics applications. The book could be used as supplementary material for courses related to Robotics and Domotics.

This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor. skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.

The book compiles technologies for enhancing and provisioning security, privacy and trust in cloud systems based on Quality of Service requirements. It is a timely contribution to a field that is gaining considerable research interest, momentum, and provides a comprehensive coverage of technologies related to cloud security, privacy and trust. In particular, the book includes - Cloud security fundamentals and related technologies to-date, with a comprehensive coverage of evolution, current landscape, and future roadmap. - A smooth organization with introductory, advanced and specialist content, i.e. from basics of security, privacy and trust in cloud systems, to advanced cartographic techniques, case studies covering both social and technological aspects, and advanced platforms. - Case studies written by professionals and/or industrial researchers. - Inclusion of a section on Cloud security and eGovernance tutorial that can be used for knowledge transfer and teaching purpose. - Identification of open research issues to help practitioners and researchers. The book is a timely topic for readers, including practicing engineers and academics, in the domains related to the engineering, science, and art of building networks and networked applications. Specifically, upon reading this book, audiences will perceive the following benefits: 1. Learn the state-of-the-art in research and development on cloud security, privacy and trust. 2. Obtain a future roadmap by learning open research issues. 3. Gather the background knowledge to tackle key problems, whose solutions will enhance the evolution of next-generation secure cloud systems.

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

Cyberspace is everywhere in today’s world and has significant implications not only for global economic activity, but also for international politics and transnational social relations. This compilation addresses for the first time the “cyberization� of international relations - the growing dependence of actors in IR on the infrastructure and instruments of the internet, and the penetration of cyberspace into all fields of their activities. The volume approaches this topical issue in a comprehensive and interdisciplinary fashion, bringing together scholars from disciplines such as IR, security studies, ICT studies and philosophy as well as experts from everyday cyber-practice. In the first part, concepts and theories are presented to shed light on the relationship between cyberspace and international relations, discussing implications for the discipline and presenting fresh and innovative theoretical approaches. Contributions in the second part focus on specific empirical fields of activity (security, economy, diplomacy, cultural activity, transnational communication, critical infrastructure, cyber espionage, social media, and more) and address emerging challenges and prospects for international politics and relations.

The goal of this volume is to summarize the state-of-the-art in the utilization of computer vision techniques in the diagnosis of skin cancer. Malignant melanoma is one of the most rapidly increasing cancers in the world. Early diagnosis is particularly important since melanoma can be cured with a simple excision if detected early. In recent years, dermoscopy has proved valuable in visualizing the morphological structures in pigmented lesions. However, it has also been shown that dermoscopy is difficult to learn and subjective. Newer technologies such as infrared imaging, multispectral imaging, and confocal microscopy, have recently come to the forefront in providing greater diagnostic accuracy. These imaging technologies presented in this book can serve as an adjunct to physicians and  provide automated skin cancer screening. Although computerized techniques cannot as yet provide a definitive diagnosis, they can be used to improve biopsy decision-making as well as early melanoma detection, especially for patients with multiple atypical nevi.

This volume gathers the peer reviewed papers which were presented at the third edition of the International Workshop “Service Orientation in Holonic and Multi-agent Manufacturing and Robotics – SOHOMA’13� organized on June 20-22, 2013 by the Centre of Research in Computer Integrated Manufacturing and Robotics – CIMR Bucharest, and hosted by the University of Valenciennes, France. The book is structured in five parts, each one covering a specific research domain which represents a trend for modern manufacturing control: Distributed Intelligence for Sustainable Manufacturing, Holonic and Multi-Agent Technologies for Manufacturing Planning and Control; Service Orientation in Manufacturing Management and Control, Intelligent Products and Product-driven Automation and Robotics for Manufacturing and Services. These five evolution lines have in common concepts related to service orientation in a distributed planning and control agent-based industrial environment; today it is generally recognized that the Service Oriented Enterprise Architecture paradigm has been looked upon as a suitable and effective approach for industrial automation and management of manufacturing enterprises.

This book carries forward recent work on visual patterns and structures in digital images and introduces a near set-based a topology of digital images. Visual patterns arise naturally in digital images viewed as sets of non-abstract points endowed with some form of proximity (nearness) relation. Proximity relations make it possible to construct uniform topologies on the sets of points that constitute a digital image. In keeping with an interest in gaining an understanding of digital images themselves as a rich source of patterns, this book introduces the basics of digital images from a computer vision perspective. In parallel with a computer vision perspective on digital images, this book also introduces the basics of proximity spaces. Not only the traditional view of spatial proximity relations but also the more recent descriptive proximity relations are considered. The beauty of the descriptive proximity approach is that it is possible to discover visual set patterns among sets that are non-overlapping and non-adjacent spatially. By combining the spatial proximity and descriptive proximity approaches, the search for salient visual patterns in digital images is enriched, deepened and broadened. A generous provision of Matlab and Mathematica scripts are used in this book to lay bare the fabric and essential features of digital images for those who are interested in finding visual patterns in images. The combination of computer vision techniques and topological methods lead to a deep understanding of images.

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

The book presents state-of-the-art works in computational engineering. Focus is on mathematical modeling, numerical simulation, experimental validation and visualization in engineering sciences. In particular, the following topics are presented: constitutive models and their implementation into finite element codes, numerical models in nonlinear elasto-dynamics including seismic excitations, multiphase models in structural engineering and multiscale models of materials systems, sensitivity and reliability analysis of engineering structures, the application of scientific computing in urban water management and hydraulic engineering, and the application of genetic algorithms for the registration of laser scanner point clouds.

Nature-inspired algorithms such as cuckoo search and firefly algorithm have become popular and widely used in recent years in many applications. These algorithms are flexible, efficient and easy to implement. New progress has been made in the last few years, and it is timely to summarize the latest developments of cuckoo search and firefly algorithm and their diverse applications. This book will review both theoretical studies and applications with detailed algorithm analysis, implementation and case studies so that readers can benefit most from this book. Application topics are contributed by many leading experts in the field. Topics include cuckoo search, firefly algorithm, algorithm analysis, feature selection, image processing, travelling salesman problem, neural network, GPU optimization, scheduling, queuing, multi-objective manufacturing optimization, semantic web service, shape optimization, and others. This book can serve as an ideal reference for both graduates and researchers in computer science, evolutionary computing, machine learning, computational intelligence, and optimization, as well as engineers in business intelligence, knowledge management and information technology.  

This book summarizes research carried out in workshops of the SAGA project, an Initial Training Network exploring the interplay of Shapes, Algebra, Geometry and Algorithms. Written by a combination of young and experienced researchers, the book introduces new ideas in an established context. Among the central topics are approximate and sparse implicitization and surface parametrization; algebraic tools for geometric computing; algebraic geometry for computer aided design applications and problems with industrial applications. Readers will encounter new methods for the (approximate) transition between the implicit and parametric representation; new algebraic tools for geometric computing; new applications of isogeometric analysis and will gain insight into the emerging research field situated between algebraic geometry and computer aided geometric design.

This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.

This book charts the take-up of IT in Britain, as seen through the eyes of one company. It examines how the dawn of the digital computer age in Britain took place for different applications, from early government-sponsored work on secret defence projects, to the growth of the market for Elliott computers for civil applications. Features: charts the establishment of Elliott's Borehamwood Research Laboratories, and the roles played by John Coales and Leon Bagrit; examines early Elliott digital computers designed for classified military applications and for GCHQ; describes the analogue computers developed by Elliott-Automation; reviews the development of the first commercial Elliot computers and the growth of applications in industrial automation; includes a history of airborne computers by a former director of Elliott Flight Automation; discusses the computer architectures and systems software for Elliott computers; investigates the mergers, takeovers and eventual closure of the Borehamwood laboratories.

This book constitutes the thoroughly refereed proceedings of the second International Workshop on ICTs for Improving Patients Rehabilitation Research Techniques, REHAB 2014, held in Oldenburg, Germany, in May 2014. The 26 revised full papers presented were carefully reviewed and selected from 69 submissions. The papers focus on virtual and enhanced environments, covering topics such as motor rehabilitation; neuroimaging in rehabilitation; virtual rehabilitation; clinical assessment; cognitive rehabilitation; communication and language; ambisonics and audio environments; haptic devices; sensory impairment; medical systems; input devices; sensors and actuators; multi-user systems for user interaction; computer access; virtual humans; balance, posture and mobility; communications aids; tools for architectural/CAD design; product design, testing and prototyping; training tools for rehabilitation; augmented reality applications; human factors; rehabilitation robotics.

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Key Features: · Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. · Explains machine learning concepts as they arise in real-world case studies. · Shows how to diagnose, understand and address problems with machine learning systems. · Full source code available, allowing models and results to be reproduced and explored. · Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

The Internet and mobile telephones have made everyone more aware than ever of the computer revolution and its effects on the economy and society. 'As Time Goes By' puts this revolution in the perspective of previous waves of technical change: steam-powered mechanization, electrification, and motorization. It argues for a theory of reasoned economic history which assigns a central place to these successive technological revolutions.