With the explosion of video and image data available on the Internet, desktops and mobile devices, multimedia search has gained immense importance. Moreover, mining semantics and other useful information from large-scale multimedia data to facilitate online and local multimedia content analysis, search, and other related applications has also gained an increasing attention from the academia and industry. The rapid increase of multimedia data has brought new challenges to multimedia content analysis and multimedia retrieval, especially in terms of scalability. While on the other hand, large-scale multimedia data has also provided new opportunities to address these challenges and other conventional problems in multimedia analysis. The massive associated metadata, context and social information available on the Internet, desktops and mobile devices, and the large number of grassroots users, are a valuable resource that could be leveraged to solve the these difficulties. This is the first reference book on the subject of internet multimedia search and mining and it will be extremely useful for graduates, researchers and working professionals in the field of information technology and multimedia content analysis.

This book dives into the heart of how to design distributed control architectures for heterogeneous teams of humans, robots, and automated systems, enabling them to achieve greater cooperation and autonomy through the use of network technologies. It provides a wide range of practical, proven strategies for pervasive communication and collaborative problem solving abilities of humans, robots, and their environments. Each chapter consists of a presentation of findings from the latest research in networked robots and ambient intelligence. The chapters also detail how to allow robots to achieve universal access to the extended functionality of the environment that brings various cost effective services to those in need. Readers can envision a realistic view of what can be expected from a networked human robot cooperative environment in the next decade.

This brief textbook explains the principles of social network analysis. The book goes beyond theoretical concepts and gives the reader complete knowledge about how to apply analytical techniques using Pajek to perform a large-scale network analysis. The book covers the topic in 2 sections – the first detailing fundamentals of research design and the next one about methods and applications. Readers can then apply the techniques in this book to other online communities, such as Facebook and Twitter. The book is intended for networking students and general readers who want to learn the basics without going deep into mathematical methods. It is also useful for researchers and professionals from other fields seeking to understand the basics of large-scale social network analysis.

Agent technology has generated lots of excitement in the past decade. Currently, multi-agent systems (MAS) composed of autonomous agents representing individuals or organizations and capable of reaching mutually beneficial agreements through negotiation and argumentation are becoming increasingly important and pervasive. Research on both automated negotiation and argumentation in MAS has a vigorous, exciting tradition. However, efforts to integrate both areas have received only selective attention in the academia and the practitioner literature. A symbiotic relationship could significantly strengthen each area’s progress and trigger new R&D challenges and prospects toward the advancement of automated negotiators and argumentation tools. Negotiation and Argumentation in Multi-Agent Systems presents the current state-of-the-art on the theory and practice of automated negotiation and argumentation in MAS. The eBook encourages the interaction between these two areas in data modelling and attempts to converge them toward mutual enhancement and synergism. Equally, the monograph brings together researchers and industry practitioners specialized in these areas to share R&D results and discuss existing and emerging theoretical and applied problems. This book is intended as a textbook for graduate courses and a reference book for researchers, advanced-level students in Computers Science, and IT practitioners.

Introduction to Computer Data Representation introduces readers to the representation of data within computers. Starting from basic principles of number representation in computers, the book covers the representation of both integer and floating point numbers, and characters or text. It comprehensively explains the main techniques of computer arithmetic and logical manipulation. The book also features chapters covering the less usual topics of basic checksums and ‘universal’ or variable length representations for integers, with additional coverage of Gray Codes, BCD codes and logarithmic representations. The description of character coding includes information on both MIME and Unicode formats. Introduction to Computer Data Representation also includes historical aspects of data representation, explaining some of the steps that developers took (and the mistakes they made) that led to the present, well-defined and accepted standards of data representation techniques. The book serves as a primer for advanced computer science graduates and a handy reference for anyone wanting to learn about numbers and data representation in computers.

Model Driven development (MDD) is a software and systems development model that involves the application of visual modeling principles and best practices.

This book provides a thorough investigation of the application of chaos theory and fractal analysis to computer vision. The field of chaos theory has been studied in dynamical physical systems, and has been very successful in providing computational models for very complex problems ranging from weather systems to neural pathway signal propagation. Computer vision researchers have derived motivation for their algorithms from biology and physics for many years as witnessed by the optical flow algorithm, the oscillator model underlying graphical cuts and of course neural networks. These algorithms are very helpful for a broad range of computer vision problems like motion segmentation, texture analysis and change detection. The contents of this book include chapters in biological vision systems, foundations of chaos and fractals, behavior of images and image sequences in phase space, mathematical measures for analyzing phase space, applications to pre-attentive vision and applications to post-attentive vision. This book is intended for graduate students, upper division undergraduates, researchers and practitioners in image processing and computer vision. The readers will develop a solid understanding of the concepts of chaos theory and their application to computer vision. Readers will be introduced to a new way of thinking about computer vision problems from the perspective of complex dynamical systems. This new approach will provide them a deeper understanding of the various phenomena present in complex image scenes.

Enterprise Architecture (EA) has evolved to become a prominent presence in today’s information systems and technology landscape. The EA discipline is rich in frameworks, methodologies, and the like. However, the question of ‘value’ for business ;professionals remains largely unanswered – that is, how best can Enterprise Architecture and Enterprise Architects deliver value to the enterprise? Enterprise Architecture for Business Success answers this question. Enterprise Architecture for Business Success is primarily intended for IT professionals working in the area of Enterprise Architecture. The eBook gives practical insights into what constitutes EA and how it might be practiced in a typical resource constrained business environment. The contents of the eBook include a brief guideline about EA systems and terminology, followed by notes on how to design enterprise systems in line with business strategies. The eBook also presents case studies which help to demonstrate the distance between theory and reality when it comes to optimizing IT infrastructure for successfully achieving business goals. Lengthy theoretical discussions are avoided in favor of focusing more on the practice and tools of EA. Readers will find value in this eBook, whether they are an IT consultant or a manager, an EA team lead or member, or just someone keen to learn about real-world EA.

Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.

An intelligent system is one which exhibits characteristics including, but not limited to, learning, adaptation, and problem-solving. Artificial Neural Network (ANN) Systems are intelligent systems designed on the basis of statistical models of learning that mimic biological systems such as the human central nervous system. Such ANN systems represent the theme of this book. This book also describes concepts related to evolutionary methods, clustering algorithms, and other networks which are complementary to ANN systems. The book is divided into two parts. The first part explains basic concepts derived from the natural biological neuron and introduces purely scientific frameworks used to develop a viable ANN model. The second part expands over to the design, analysis, performance assessment, and testing of ANN models. Concepts such as Bayesian networks, multi-classifiers, and neuromorphic ANN systems are explained, among others. Artificial Neural Systems: Principles and Practice takes a developmental perspective on the subject of ANN systems, making it a beneficial resource for students undertaking graduate courses and research projects, and working professionals (engineers, software developers) in the field of intelligent systems design.