This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.

This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

This volume provides the audience with an updated, in-depth and highly coherent material on the conceptually appealing and practically sound information technology of Computational Intelligence applied to the analysis, synthesis and evaluation of social networks. The volume involves studies devoted to key issues of social networks including community structure detection in networks, online social networks, knowledge growth and evaluation, and diversity of collaboration mechanisms. The book engages a wealth of methods of Computational Intelligence along with well-known techniques of linear programming, Formal Concept Analysis, machine learning, and agent modeling. Human-centricity is of paramount relevance and this facet manifests in many ways including personalized semantics, trust metric, and personal knowledge management; just to highlight a few of these aspects. The contributors to this volume report on various essential applications including cyber attacks detection, building enterprise social networks, business intelligence and forming collaboration schemes.

Given the subject area, this book is aimed at a broad audience of researchers and practitioners. Owing to the nature of the material being covered and a way it is organized, the volume will appeal to the well-established communities including those active in various disciplines in which social networks, their analysis and optimization are of genuine relevance. Those involved in operations research, management, various branches of engineering, and economics will benefit from the exposure to the subject matter.

This survey of portfolio theory, from its modern origins through more sophisticated, "postmodern" incarnations, evaluates portfolio risk according to the first four moments of any statistical distribution: mean, variance, skewness, and excess kurtosis. In pursuit of financial models that more accurately describe abnormal markets and investor psychology, this book bifurcates beta on either side of mean returns. It then evaluates this traditional risk measure according to its relative volatility and correlation components. After specifying a four-moment capital asset pricing model, this book devotes special attention to measures of market risk in global banking regulation. Despite the deficiencies of modern portfolio theory, contemporary finance continues to rest on mean-variance optimization and the two-moment capital asset pricing model. The term postmodern portfolio theory captures many of the advances in financial learning since the original articulation of modern portfolio theory. A comprehensive approach to financial risk management must address all aspects of portfolio theory, from the beautiful symmetries of modern portfolio theory to the disturbing behavioral insights and the vastly expanded mathematical arsenal of the postmodern critique. Mastery of postmodern portfolio theory's quantitative tools and behavioral insights holds the key to the efficient frontier of risk management.

This book investigates the mineralogy and shock effects of Yanzhuang chondrite, using modern micro-mineralogical experimental techniques, including SEM, TEM, EPMA, Raman microprobe spectroscopy, instrumental neutron activation analysis, X-ray micro-diffraction analysis, micro-PIXE analysis and laser ablation ICP-MS. The micro-structural and micro-morphological characteristics as well as chemical composition of minerals were studied in details. Based on the studies in the shock effects of rocks and minerals, and the detailed study in the shock-produced melt, the book concludes that Yanzhuang chondrite is the most heavily shocked ordinary H group chondrite ever found and that it contains the most abundant shock induced melt among all known shock-melt-bearing chondritic meteorites.

This monograph takes an interdisciplinary and cross-cultural approach to 20th and 21st -century Canadian Daoist poetry, fiction and criticism in comparative, innovative and engaging ways. Of particular interest are the authors' refreshing insights into such holistic and topical issues as the globalization of concepts of the Dao, the Yin/Yang, the Heaven-Earth-Humanity triad, the Four Greats, Five Phases, Non-action and so on, as expressed in Canadian literature and criticism - which produces Canadian-constructed Daoist poetics, ethics and aesthetics. Readers will come to understand and appreciate the social and ecological significance of, formal innovations, moral sensitivity, aesthetic principles and ideological complexity in Canadian-Daoist works.

The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry and government organizations. Data sets such as customer transactions for a mega-retailer, weathermonitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data.

This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible."

This monograph is the first academic work to apply a neo-Marxist approach to 20th-century Canadian social realist novels, pursuing a refreshingly (neo-)Marxist approach to such issues as Bakhtinian notions of the novelistic form and dialogism as applied to Canadian socio-political novels influenced by various socialisms, socialist-feminist concerns, economic and sexual politics, and the genre of social realism. In so doing, it demonstrates that Marxist socialism is as relevant today as it was in the 1930s, just as social realist novels continue to thrive as a critique of capitalism. Readers will find valuable insights into the social significance, formal innovations, moral sensitivity, aesthetic enrichment, and ideological complexity of Canadian social realist novels.

This book introduces readers to the use of formic acid for efficient organic synthesis. It describes the N-methylation of aromatic and aliphatic amines with formic acid using a boron-based catalyst [B(C6F5)3] in combination with silanes and without the need for an expensive transition metal catalyst. It also shows that formic acid interacts with alkynes and allyl alcohols through a carbonylation process that generates carbon monoxide in situ from nickel and palladium catalysis, respectively, doing away with the need to use high-pressure CO gas and offering a user-friendly and practical method for preparing functionalized , - and , -unsaturated carboxylic acids. The findings presented not only enrich the field of 'C1 chemistry,' but also support the advancement of green and sustainable chemistry.

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques.

A spherical actuator is a novel electric device that can achieve 2/3-DOF rotational motions in a single joint with electric power input. It has advantages such as compact structure, low mass/moment of inertia, fast response and non-singularities within the workspace. It has promising applications in robotics, automobile, manufacturing, medicine and aerospace industry. This is the first monograph that introduces the research on spherical actuators systematically. It broadens the scope of actuators from conventional single-axis to multi-axis, which will help both beginners and researchers to enhance their knowledge on electromagnetic actuators. Generic analytic modeling methods for magnetic field and torque output are developed, which can be applied to the development of other electromagnetic actuators. A parametric design methodology that allows fast analysis and design of spherical actuators for various applications is proposed. A novel non-contact high-precision 3-DOF spherical motion sensing methodology is developed and evaluated with experiments, which shows that it can achieve one order of magnitude higher precision than conventional methods. The technologies of nondimensionalization and normalization are introduced into magnetic field analysis the first time, and a benchmark database is established for the reference of other researches on spherical actuators.

Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies. This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.

This thesis introduces novel and significant results regarding the analysis and synthesis of positive systems, especially under l1 and L1 performance. It describes stability analysis, controller synthesis, and bounding positivity-preserving observer and filtering design for a variety of both discrete and continuous positive systems. It subsequently derives computationally efficient solutions based on linear programming in terms of matrix inequalities, as well as a number of analytical solutions obtained for special cases. The thesis applies a range of novel approaches and fundamental techniques to the further study of positive systems, thus contributing significantly to the theory of positive systems, a "hot topic" in the field of control.

This volume is devoted to interactive and iterative processes of decision-making- I2 Fuzzy Decision Making, in brief. Decision-making is inherently interactive. Fuzzy sets help realize human-machine communication in an efficient way by facilitating a two-way interaction in a friendly and transparent manner. Human-centric interaction is of paramount relevance as a leading guiding design principle of decision support systems. The volume provides the reader with an updated and in-depth material on the conceptually appealing and practically sound methodology and practice of I2 Fuzzy Decision Making. The book engages a wealth of methods of fuzzy sets and Granular Computing, brings new concepts, architectures and practice of fuzzy decision-making providing the reader with various application studies. The book is aimed at a broad audience of researchers and practitioners in numerous disciplines in which decision-making processes play a pivotal role and serve as a vehicle to produce solutions to existing problems. Those involved in operations research, management, various branches of engineering, social sciences, logistics, and economics will benefit from the exposure to the subject matter. The book may serve as a useful and timely reference material for graduate students and senior undergraduate students in courses on decision-making, Computational Intelligence, operations research, pattern recognition, risk management, and knowledge-based systems.

The book provides a timely coverage of the paradigm of knowledge distillation—an efficient way of model compression. Knowledge distillation is positioned in a general setting of transfer learning, which effectively learns a lightweight student model from a large teacher model. The book covers a variety of training schemes, teacher–student architectures, and distillation algorithms. The book covers a wealth of topics including recent developments in vision and language learning, relational architectures, multi-task learning, and representative applications to image processing, computer vision, edge intelligence, and autonomous systems. The book is of relevance to a broad audience including researchers and practitioners active in the area of machine learning and pursuing fundamental and applied research in the area of advanced learning paradigms.

This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology -oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students.

Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

This book provides an overview of the history of integrative bioinformatics and the actual situation and the relevant tools. Subjects cover the essential topics, basic introductions, and latest developments; biological data integration and manipulation; modeling and simulation of networks; as well as a number of applications of integrative bioinformatics. It aims to provide basic introduction of biological information systems and guidance for the computational analysis of systems biology. This book covers a range of issues and methods that unveil a multitude of omics data integration and relevance that integrative bioinformatics has today. It contains a unique compilation of invited and selected articles from the Journal of Integrative Bioinformatics (JIB) and annual meetings of the International Symposium on Integrative Bioinformatics.

This book explains how investor behavior, from mental accounting to the combustible interplay of hope and fear, affects financial economics. The transformation of portfolio theory begins with the identification of anomalies. Gaps in perception and behavioral departures from rationality spur momentum, irrational exuberance, and speculative bubbles. Behavioral accounting undermines the rational premises of mathematical finance. Assets and portfolios are imbued with "affect." Positive and negative emotions warp investment decisions. Whether hedging against intertemporal changes in their ability to bear risk or climbing a psychological hierarchy of needs, investors arrange their portfolios and financial affairs according to emotions and perceptions. Risk aversion and life-cycle theories of consumption provide possible solutions to the equity premium puzzle, an iconic financial mystery. Prospect theory has questioned the cogency of the efficient capital markets hypothesis. Behavioral portfolio theory arises from a psychological account of security, potential, and aspiration.

Dunhuang studies refer to a discipline focusing on Dunhuang Manuscripts, Dunhuang grotto art, the theory of Dunhuang studies, and Dunhuang history and geography. It is a broad subject of studying, excavating, sorting, and protecting the cultural relics and documents in the Dunhuang area of China. The General Theory of Dunhuang Studies explores the basic concept of Dunhuang studies. It presents a more comprehensive and systematic study of six aspects of Dunhuang, covering the background of Dunhuang studies in orientalism, the history of Dunhuang, Dunhuang grotto art, the scattering of Dunhuang cultural relics, Dunhuang manuscripts, and the history of Dunhuang studies, and discussing and summarizing the relevant national and international research. The General Theory of Dunhuang Studies has extensively absorbed the research achievements of domestic and foreign academic circles and the author's decades of academic research experience. As a comprehensive and systematic academic monograph with both academic depth and extensive readability, the book provides descriptions, theory and objective comments written in a clear and straightforward style; the book is intended for professional scholars, graduates and general readers. It is an excellent teaching and learning resource for those interested in understanding and learning about Dunhuang studies. However, it is also a helpful reference book for readers interested in Dunhuang culture.

The book is a collection of high-quality research papers presented at 7th Euro-China Conference on Intelligent Data Analysis and Applications, hosted by Communication University of Zhejiang, China and technically co-sponsored by Shandong University of Science and Technology, China; Zhejiang Lab, China; and Fujian University of Technology, China. The book covers areas like intelligent data analysis, computational intelligences, signal processing, and all associated applications of artificial intelligence.

This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others. The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.