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Computational Social Networks: Security and Privacy
by Ajith AbrahamThis book is the second of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, concentrating on topics relating to security and privacy (the other two volumes review Tools, Perspectives, and Applications, and Mining and Visualization in CSNs). Topics and features: presents the latest advances in security and privacy issues in CSNs, and illustrates how both organizations and individuals can be protected from real-world threats; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, and the representation and analysis of social networks, with a focus on issues of security, privacy, and anonymization; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in network technology.
Computational Social Networks: Mining and Visualization
by Ajith AbrahamThis book is the third of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, with a specific focus on knowledge discovery and visualization of complex networks (the other two volumes review Tools, Perspectives, and Applications, and Security and Privacy in CSNs). Topics and features: presents the latest advances in CSNs, and illustrates how organizations can gain a competitive advantage from a better understanding of complex social networks; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, and the representation and analysis of social networks, highlighting methods for the data mining of CSNs; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in network technology.
Computational Social Networks: Tools, Perspectives and Applications
by Ajith Abraham and Aboul-Ella HassanienThis book is the first of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, with a specific focus on practical tools, applications, and open avenues for further research (the other two volumes review issues of Security and Privacy, and Mining and Visualization in CSNs). Topics and features: presents the latest advances in CSNs, and illustrates how organizations can gain a competitive advantage by applying these ideas in real-world scenarios; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, the representation and analysis of social networks, and the use of semantic networks in knowledge discovery and visualization; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in network technology.
Computational Social Networks: 5th International Conference, CSoNet 2016, Ho Chi Minh City, Vietnam, August 2-4, 2016, Proceedings (Lecture Notes in Computer Science #9795)
by Hien T. Nguyen Vaclav SnaselThis book constitutes the refereed proceedings of the 5th International Conference on Computational Social Networks, CSoNet 2016, held in Ho Chi Minh City, Vietnam, in August 2016. The 30 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers cover topics on common principles, algorithms and tools that govern social network structures/topologies, functionalities, social interactions, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media.
Computational Social Networks: 4th International Conference, CSoNet 2015, Beijing, China, August 4-6, 2015, Proceedings (Lecture Notes in Computer Science #9197)
by My T. Thai Nam P. Nguyen Huawei ShenThis book constitutes the refereed proceedings of the 4th International Conference on Computational Social Networks, CSoNet 2015, held in Beijing, China, in August 2015.The 23 revised full papers and 3 short papers presented together with 2 extended abstracts were carefully reviewed and selected from 101 submissions and cover topics on social information diffusion; network clustering and community structure; social link prediction and recommendation; and social network structure analysis.
Computational Social Psychology (Frontiers of Social Psychology)
by Andrzej Nowak Robin R. Vallacher Stephen J. ReadComputational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology.
Computational Social Psychology (Frontiers of Social Psychology)
by Robin R. Vallacher; Stephen J. Read; Andrzej NowakComputational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology.
Computational Statics and Dynamics: An Introduction Based on the Finite Element Method
by Andreas ÖchsnerThis book introduces readers to modern computational mechanics based on the finite element method. It helps students succeed in mechanics courses by showing them how to apply the fundamental knowledge they gained in the first years of their engineering education to more advanced topics.In order to deepen readers’ understanding of the derived equations and theories, each chapter also includes supplementary problems. These problems start with fundamental knowledge questions on the theory presented in the chapter, followed by calculation problems. In total over 80 such calculation problems are provided, along with brief solutions for each.This book is especially designed to meet the needs of Australian students, reviewing the mathematics covered in their first two years at university. The 13-week course comprises three hours of lectures and two hours of tutorials per week.
Computational Statics and Dynamics: An Introduction Based on the Finite Element Method
by Andreas ÖchsnerThis book is the 2nd edition of an introduction to modern computational mechanics based on the finite element method. It includes more details on the theory, more exercises, and more consistent notation; in addition, all pictures have been revised. Featuring more than 100 pages of new material, the new edition will help students succeed in mechanics courses by showing them how to apply the fundamental knowledge they gained in the first years of their engineering education to more advanced topics. In order to deepen readers’ understanding of the equations and theories discussed, each chapter also includes supplementary problems. These problems start with fundamental knowledge questions on the theory presented in the respective chapter, followed by calculation problems. In total, over 80 such calculation problems are provided, along with brief solutions for each. This book is especially designed to meet the needs of Australian students, reviewing the mathematics covered in their first two years at university. The 13-week course comprises three hours of lectures and two hours of tutorials per week.
Computational Statistical Methodologies and Modeling for Artificial Intelligence (ISSN)
by Priyanka Harjule Azizur Rahman Basant Agarwal Vinita TiwariThis book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering.The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence
Computational Statistical Methodologies and Modeling for Artificial Intelligence (ISSN)
by Priyanka Harjule Azizur Rahman Basant Agarwal Vinita TiwariThis book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering.The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence
Computational Statistics: Volume 1: Proceedings of the 10th Symposium on Computational Statistics
by Yadolah Dodge Joe WhittakerThe Role of the Computer in Statistics David Cox Nuffield College, Oxford OXIINF, U.K. A classification of statistical problems via their computational demands hinges on four components (I) the amount and complexity of the data, (il) the specificity of the objectives of the analysis, (iii) the broad aspects of the approach to analysis, (ill) the conceptual, mathematical and numerical analytic complexity of the methods. Computational requi rements may be limiting in (I) and (ill), either through the need for special programming effort, or because of the difficulties of initial data management or because of the load of detailed analysis. The implications of modern computational developments for statistical work can be illustrated in the context of the study of specific probabilistic models, the development of general statistical theory, the design of investigations and the analysis of empirical data. While simulation is usually likely to be the most sensible way of investigating specific complex stochastic models, computerized algebra has an obvious role in the more analyti cal work. It seems likely that statistics and applied probability have made insufficient use of developments in numerical analysis associated more with classical applied mathematics, in particular in the solution of large systems of ordinary and partial differential equations, integral equations and integra-differential equations and for the ¢raction of "useful" in formation from integral transforms. Increasing emphasis on models incorporating specific subject-matter considerations is one route to bridging the gap between statistical ana.
Computational Statistics: Volume 2: Proceedings of the 10th Symposium on Computational Statistics, COMPSTAT, Neuchâtel, Switzerland, August 1992
by Yadolah Dodge Joe WhittakerThe papers assembled in this book were presented at the biannual symposium of Inter national Association for Statistical Computing in Neuchcitel, Switzerland, in August of 1992. This congress marked the tenth such meeting from its inception in 1974 at Vienna and maintained the tradition of providing a forum for the open discussion of progress made in computer oriented statistics and the dissemination of new ideas throughout the statistical community. It was gratifying to see how well the groups of theoretical statisti cians, software developers and applied research workers were represented, whose mixing is an event made uniquely possible by this symposium. While maintaining traditions certain new features have been introduced at this con ference: there were a larger number of invited speakers; there was more commercial sponsorship and exhibition space; and a larger body of proceedings have been published. The structure of the proceedings follows a standard format: the papers have been grouped together according to a rough subject matter classification, and within topic follow an approximate aphabetical order. The papers are published in two volumes ac cording to the emphasis of the topics: volume I gives a slight leaning towards statistics and modelling, while volume II is focussed more on computation; but this is certainly only a crude distinction and the volumes have to be thought of as the result of a single en terprise.
Computational Statistics (Statistics and Computing)
by James E. GentleComputational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods.
Computational Statistics and Data Intelligence: APCAMS 2023, Chongqing, China, June 24–26 (Springer Proceedings in Mathematics & Statistics #463)
by Hari M. Srivastava Wenfeng Wang Wanyang DaiThis book gathers selected papers presented at the Asia-Pacific Conference on Applied Mathematics and Statistics held on June 24–26, 2023, in Chongqing, China. It presents the most recent research and advances in various areas of applied mathematics and statistics, span from mathematical theory, calculation, modeling, simulation, to applications such as big data and image processing.
Computational Statistics and Mathematical Modeling Methods in Intelligent Systems: Proceedings of 3rd Computational Methods in Systems and Software 2019, Vol. 2 (Advances in Intelligent Systems and Computing #1047)
by Radek Silhavy Petr Silhavy Zdenka ProkopovaThis book presents real-world problems and exploratory research in computational statistics, mathematical modeling, artificial intelligence and software engineering in the context of the intelligent systems. This book constitutes the refereed proceedings of the 3rd Computational Methods in Systems and Software 2019 (CoMeSySo 2019), a groundbreaking online conference that provides an international forum for discussing the latest high-quality research results.
Computational Statistics Handbook with MATLAB (Chapman And Hall/crc Computer Science And Data Analysis Ser.)
by Wendy L. Martinez Angel R. MartinezA Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the i
Computational Statistics in Data Science
by Walter W. Piegorsch Richard A. Levine Hao Helen Zhang Thomas C. M. LeeAn essential roadmap to the application of computational statistics in contemporary data science In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find: A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learningPerfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.
Computational Statistics in Data Science
by Walter W. Piegorsch Richard F. Levine Hao Helen Zhang Thomas C. M. LeeAn essential roadmap to the application of computational statistics in contemporary data science In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find: A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learningPerfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.
Computational Stem Cell Biology: Methods and Protocols (Methods in Molecular Biology #1975)
by Patrick CahanThis volume details methods and protocols to further the study of stem cells within the computational stem cell biology (CSCB) field. Chapters are divided into four sections covering the theory and practice of modeling of stem cell behavior, analyzing single cell genome-scale measurements, reconstructing gene regulatory networks, and metabolomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.Authoritative and cutting-edge, Computational Stem Cell Biology: Methods and Protocols will be an invaluable guide to researchers as they explore stem cells from the perspective of computational biology.
Computational Stochastic Programming: Models, Algorithms, and Implementation (Springer Optimization and Its Applications #774)
by Lewis NtaimoThis book provides a foundation in stochastic, linear, and mixed-integer programming algorithms with a focus on practical computer algorithm implementation. The purpose of this book is to provide a foundational and thorough treatment of the subject with a focus on models and algorithms and their computer implementation. The book’s most important features include a focus on both risk-neutral and risk-averse models, a variety of real-life example applications of stochastic programming, decomposition algorithms, detailed illustrative numerical examples of the models and algorithms, and an emphasis on computational experimentation. With a focus on both theory and implementation of the models and algorithms for solving practical optimization problems, this monograph is suitable for readers with fundamental knowledge of linear programming, elementary analysis, probability and statistics, and some computer programming background. Several examples of stochastic programming applications areincluded, providing numerical examples to illustrate the models and algorithms for both stochastic linear and mixed-integer programming, and showing the reader how to implement the models and algorithms using computer software.
Computational Structural Analysis and Finite Element Methods
by A. KavehGraph theory gained initial prominence in science and engineering through its strong links with matrix algebra and computer science. Moreover, the structure of the mathematics is well suited to that of engineering problems in analysis and design. The methods of analysis in this book employ matrix algebra, graph theory and meta-heuristic algorithms, which are ideally suited for modern computational mechanics. Efficient methods are presented that lead to highly sparse and banded structural matrices. The main features of the book include: application of graph theory for efficient analysis; extension of the force method to finite element analysis; application of meta-heuristic algorithms to ordering and decomposition (sparse matrix technology); efficient use of symmetry and regularity in the force method; and simultaneous analysis and design of structures.
Computational Studies on Cultural Variation and Heredity (KAIST Research Series)
by Ji-Hyun LeeThis book explores the emerging concept of cultural DNA, considering its application across different fields and examining commonalities in approach. It approaches the subject from four different perspectives, in which the topics include theories, analysis and synthesis of cultural DNA artefacts. After an opening section which reviews theoretical work on cultural DNA research, the second section discusses analysis & synthesis of cultural DNA at the urban scale. Section three covers analysis & synthesis of cultural DNA artefacts, and the final section offers approaches to grammar-based cultural DNA research.The book places emphasis on two specific axes: one is the scale of the object under discussion, which ranges from the small (handheld artefacts) to the very large (cities); and the other is the methodology used from analysis to synthesis. This diverse approach with detailed information about grammar-based methodologies toward cultural DNA makes the book unique.This book will serve as a source of inspiration for designers and researchers trying to find the essence, archetype, and the building blocks of our environment for the incorporation of social and cultural factors into their designs.
Computational Surface and Roundness Metrology
by Balasubramanian Muralikrishnan Jayaraman Raja“Computational Surface and Roundness Metrology” provides an extraordinarily practical and hands-on approach towards understanding the diverse array of mathematical methods used in surface texture and roundness analysis. The book, in combination with a mathematical package or programming language interface, provides an invaluable tool for experimenting, learning, and discovering the many flavors of mathematics that are so routinely taken for granted in metrology. Whether the objective is to understand the origin of that ubiquitous transmission characteristics curve of a filter we see so often yet do not quite comprehend, or to delve into the intricate depths of a deceptively simple problem of fitting a line or a plane to a set of points, this book describes it all (in exhaustive detail). From the graduate student of metrology to the practicing engineer on the shop floor, this book is a must-have reference for all involved in metrology, instrumentation/optics, manufacturing, and electronics.
Computational Sustainability (Studies in Computational Intelligence #645)
by Jörg Lässig Kristian Kersting Katharina MorikThe book at hand gives an overview of the state of the art research in Computational Sustainability as well as case studies of different application scenarios. This covers topics such as renewable energy supply, energy storage and e-mobility, efficiency in data centers and networks, sustainable food and water supply, sustainable health, industrial production and quality, etc. The book describes computational methods and possible application scenarios.