This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in R, making the book as self-contained as possible. It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory. Afterwards, we introduce all assumptions and formalize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. From that, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.

This book provides a tutorial in the use of Altair Compose and Altair Activate, software packages that provide system modeling and simulation facilities. Advanced system modeling software provide multiple ways of creating models: models can be programmed in specialized languages, graphically constructed as block-diagrams and state machines, or expressed mathematically in equation-based languages. Compose and Activate are introduced in this text in two parts. The first part introduces the multi-language environment of Compose and its use for modeling, simulation and optimization. The second describes the graphical system modeling and optimization with Activate, an open-system environment providing signal-based modeling as well as physical system component-based modeling. Throughout both parts are applied examples from mechanical, biological, and electrical systems, as well as control and signal processing systems. This book will be an invaluable addition with many examples both for those just interested in OML and those doing industrial scale modeling, simulation, and design. All examples are worked using the free basic editions of Activate and Compose that are available.

Rising concerns about the security of our data have made quantum cryptography a very active research field in recent years. Quantum cryptographic protocols promise everlasting security by exploiting distinctive quantum properties of nature. The most extensively implemented protocol is quantum key distribution (QKD), which enables secure communication between two users. The aim of this book is to introduce the reader to state-of-the-art QKD and illustrate its recent multi-user generalization: quantum conference key agreement. With its pedagogical approach that doesn't disdain going into details, the book enables the reader to join in cutting-edge research on quantum cryptography.

This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints. After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective, constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important. Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the - sometimes very low - convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control of the probability distributions of the search variates (mutation random variables).

In this thesis, "Human behavior on the Internet," the human anxiety is conceptualized. The following questions have guided the writing of the thesis: How humans behave with the Internet technology? What goes in their mind? What kinds of behaviors are shown while using the Internet? What is the role of the content on the Internet and especially what are the types of anxiety behavior on the Internet? By conceptualization this thesis aims to provide a model for studying whether humans show signs of less or exacerbated anxiety while using the Internet.

With complex systems and complex requirements being a challenge that designers must face to reach quality results, multi-formalism modeling offers tools and methods that allow modelers to exploit the benefits of different techniques in a general framework intended to address these challenges. Theory and Application of Multi-Formalism Modeling boldly explores the importance of this topic by gathering experiences, theories, applications, and solutions from diverse perspectives of those involved with multi-formalism modeling. Professionals, researchers, academics, and students in this field will be able to critically evaluate the latest developments and future directions of multi-formalism research.

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

With the diversification of Internet services and the increase in mobile users, efficient management of network resources has become an extremely important issue in the field of wireless communication networks (WCNs). Adaptive resource management is an effective tool for improving the economic efficiency of WCN systems as well as network design and construction, especially in view of the surge in mobile device demands. This book presents modelling methods based on queueing theory and Markov processes for a wide variety of WCN systems, as well as precise and approximate analytical solution methods for the numerical evaluation of the system performance. This is the first book to provide an overview of the numerical analyses that can be gleaned by applying queueing theory, traffic theory and other analytical methods to various WCN systems. It also discusses the recent advances in the resource management of WCNs, such as broadband wireless access networks, cognitive radio networks, and green cloud computing. It assumes a basic understanding of computer networks and queueing theory, and familiarity with stochastic processes is also recommended. The analysis methods presented in this book are useful for first-year-graduate or senior computer science and communication engineering students. Providing information on network design and management, performance evaluation, queueing theory, game theory, intelligent optimization, and operations research for researchers and engineers, the book is also a valuable reference resource for students, analysts, managers and anyone in the industry interested in WCN system modelling, performance analysis and numerical evaluation.

The ability to communicate with anyone at anytime, anywhere, necessitates a need to provide reliable, resilient, and sustainable applications to support the evolving uses of mobile technologies. Movement-Aware Applications for Sustainable Mobility: Technologies and Approaches focuses on the use of location sensing technology and its wider applicability in supporting sustainable mobility, and presents current research on developing innovative approaches for gathering, representing, storing and analyzing movement data sets being generated from location sensing technology. Offering a unique opportunity for learning about how different techniques are being used to develop new movement-aware applications, this reference work gives students, researchers and practitioners the opportunity to acquire new concepts in the production, awareness and use of sensors, data, and information products for supporting sustainable mobility.

It seems that only a short time ago, numerous academics and practitioners in the field were somewhat blinded by the successes of the dot-com developments in the private sector, and some of them enthusiastically claimed that public administration was to be revolutionized. But that did not happen, and also the dot-com soap bubble burst. This suggests that there is much yet to be learned about innovation in public administration, especially about innovations at the cornerstones of technological and institutional transformations. New and more fully developed formulations of theory into practice are needed. The goal of the editors of this book is to contribute to some aspects of the understanding of e-government. In order to understand electronic government, one has to scrutinize the various environments and contexts in which e-government is developed and implemented. As such, it builds upon the biological and environmental lines of reasoning that have been suggested by authors like Bonnie Nardi and Vicky O'Day, and Thomas Davenport and Laurence Prusak.

This book collects high-quality research papers presented at the International Conference on Computing Applications in Electrical & Electronics Engineering, held at Rajkiya Engineering College, Sonbhadra, India, on August 30-31, 2019. It provides novel contributions in computational intelligence, together with valuable reference material for future research. The topics covered include: big data analytics, IoT and smart infrastructures, machine learning, artificial intelligence and deep learning, crowd sourcing and social intelligence, natural language processing, business intelligence, high-performance computing, wireless, mobile and green communications, ad-hoc, sensor and mesh networks, SDN and network virtualization, cognitive systems, swarm intelligence, human-computer interaction, network and information security, intelligent control, soft computing, networked control systems, renewable energy sources and technologies, biomedical signal processing, pattern recognition and object tracking, and sensor devices and applications.

Today's enterprises are increasingly going global by becoming more distributed, leveraging resource bases in all parts of the world. Whether through offshore relationships, global support or application development programs, or linking disparate parts of organizations, enterprises are reducing costs by managing applications centrally, boosting security, and assuring network uptime. The Handbook of Research on Global Information Technology Management in the Digital Economy provides comprehensive coverage and definitions of the most important issues, concepts, trends and technologies in the field of the emerging sub-discipline of global information technology management, covering topics such as the technical platform for global IS applications, information systems projects spanning cultures, managing information technology in multidomestic/ international/global/transnational corporations, and global information technology systems and socioeconomic development in developing countries. With 25 authoratative contributions from 34 of the world's leading experts, this publication is a must-have for academic and research libraries.

This book presents a collection of papers on recent advances in problems concerning dynamics, optimal control and optimization. In many chapters, computational techniques play a central role. Set-oriented techniques feature prominently throughout the book, yielding state-of-the-art algorithms for computing general invariant sets, constructing globally optimal controllers and solving multi-objective optimization problems.

Decision-aiding software, the underpinning of computer-aided judicial analysis, can facilitate the prediction of how cases are likely to be decided, prescribe decisions that should be reached in such cases, and help administrate more efficiently the court process. It can do so, says Nagel, by listing past cases on each row of a spreadsheet matrix, by listing predictive criteria in the columns, and in general by showing for each factual element the estimated probability of winning a case. The software aggregates the information available and deduces likely outcomes. But it can also prescribe judicial decisions by listing alternatives in the rows, the goals to be achieved in the columns, and by showing relations between alternatives in the cells. By similar means decision-aiding software can also help perform administrative tasks, such as rationally assigning judges or other personnel to cases, and by sequencing cases to reduce the time consumed by each case.

In Part I, Nagel provides an overview of computer-aided analysis and the role of decision-aiding software in the legal process. In the second part he deals with judicial prediction from prior cases and from present facts; and in the third part he emphasizes the prescribing role of judges, particularly in deciding the rules that ought to be applied in civil and criminal procedures. Nagel also covers computer-aided mediation and provides a new perspective on judicial decisions. Then, in Part IV, he treats at length the process of judicial administration and how to improve its efficiency. Of particular interest to court personnel will be the benefits to be derived from reducing delays and in the docketing and sequencing of cases.