In the past five years, the field of electrostatic discharge (ESD) control has under gone some notable changes. Industry standards have multiplied, though not all of these, in our view, are realistic and meaningful. Increasing importance has been ascribed to the Charged Device Model (CDM) versus the Human Body Model (HBM) as a cause of device damage and, presumably, premature (latent) failure. Packaging materials have significantly evolved. Air ionization techniques have improved, and usage has grown. Finally, and importantly, the government has ceased imposing MIL-STD-1686 on all new contracts, leaving companies on their own to formulate an ESD-control policy and write implementing documents. All these changes are dealt with in five new chapters and ten new reprinted papers added to this revised edition of ESD from A to Z. Also, the original chapters have been augmented with new material such as more troubleshooting examples in Chapter 8 and a 20-question multiple-choice test for certifying operators in Chapter 9. More than ever, the book seeks to provide advice, guidance, and practical ex amples, not just a jumble of facts and generalizations. For instance, the added tailored versions of the model specifications for ESD-safe handling and packaging are actually in use at medium-sized corporations and could serve as patterns for many readers."

The term Web Intelligence is de?ned as a new line of scienti?c research and development, whichisusedtoexplorethefundamentalrolesandpractical- pactofArti?cialIntelligence togetherwithadvancedInformationTechnology and its e?ect on the future generations of Web-empowered products. These include systems, services, amongst other activities, all of which are carried out by the Web Intelligence Consortium (http: //wi-consortium.org/). Web Intelligence was ?rst coined in the late 1999's. From that time, many new algorithms, methods and techniques were developedand used extracting both knowledge and wisdom from the data originating from the Web. A number of initiatives have been adopted by the world communities in this areaofstudy.Theseincludebooks, conferenceseries, andjournals.Thislatest book encomposes a variaty of up to date state of the art approaches in Web Intelligence. Furthermore, it hightlights successful applications in this area of research within a practical context. The present book aims to introduce a selection of research applications in the area of Web Intelligence. We have selected a number of researchers around the world, all of which are experts in their respective research areas. Each chapter focuses on a speci?c topic in the ?eld of Web Intelligence. Furthermorethe bookconsistsofa numberofinnovativeproposalswhichwill contribute to the development of web science and technology for the lo- term future, rendering this collective work a valuable piece of knowledge. It was a great honour to have collaborated with this team of very talented experts. We also wish to express our grattitude to those who reviewed this book o?ering their constructive feedbacks.

In System-on-Chip Architectures and Implementations for Private-Key Data Encryption, new generic silicon architectures for the DES and Rijndael symmetric key encryption algorithms are presented. The generic architectures can be utilised to rapidly and effortlessly generate system-on-chip cores, which support numerous application requirements, most importantly, different modes of operation and encryption and decryption capabilities. In addition, efficient silicon SHA-1, SHA-2 and HMAC hash algorithm architectures are described. A single-chip Internet Protocol Security (IPSec) architecture is also presented that comprises a generic Rijndael design and a highly efficient HMAC-SHA-1 implementation.

In the opinion of the authors, highly efficient hardware implementations of cryptographic algorithms are provided in this book. However, these are not hard-fast solutions. The aim of the book is to provide an excellent guide to the design and development process involved in the translation from encryption algorithm to silicon chip implementation.

Digital forensics deals with the acquisition, preservation, examination, analysis and presentation of electronic evidence. Computer networks, cloud computing, smartphones, embedded devices and the Internet of Things have expanded the role of digital forensics beyond traditional computer crime investigations. Practically every crime now involves some aspect of digital evidence; digital forensics provides the techniques and tools to articulate this evidence in legal proceedings. Digital forensics also has myriad intelligence applications; furthermore, it has a vital role in cyber security -- investigations of security breaches yield valuable information that can be used to design more secure and resilient systems. Advances in Digital Forensics XVI describes original research results and innovative applications in the discipline of digital forensics. In addition, it highlights some of the major technical and legal issues related to digital evidence and electronic crime investigations. The areas of coverage include: themes and issues, forensic techniques, filesystem forensics, cloud forensics, social media forensics, multimedia forensics, and novel applications. This book is the sixteenth volume in the annual series produced by the International Federation for Information Processing (IFIP) Working Group 11.9 on Digital Forensics, an international community of scientists, engineers and practitioners dedicated to advancing the state of the art of research and practice in digital forensics. The book contains a selection of sixteen edited papers from the Sixteenth Annual IFIP WG 11.9 International Conference on Digital Forensics, held in New Delhi, India, in the winter of 2020. Advances in Digital Forensics XVI is an important resource for researchers, faculty members and graduate students, as well as for practitioners and individuals engaged in research and development efforts for the law enforcement and intelligence communities.

Based on the results of the study carried out in 1996 to investigate the state of the art of workflow and process technology, MCC initiated the Collaboration Management Infrastructure (CMI) research project to develop innovative agent-based process technology that can support the process requirements of dynamically changing organizations and the requirements of nomadic computing. With a research focus on the flow of interaction among people and software agents representing people, the project deliverables will include a scalable, heterogeneous, ubiquitous and nomadic infrastructure for business processes. The resulting technology is being tested in applications that stress an intensive mobile collaboration among people as part of large, evolving business processes. Workflow and Process Automation: Concepts and Technology provides an overview of the problems and issues related to process and workflow technology, and in particular to definition and analysis of processes and workflows, and execution of their instances. The need for a transactional workflow model is discussed and a spectrum of related transaction models is covered in detail. A plethora of influential projects in workflow and process automation is summarized. The projects are drawn from both academia and industry. The monograph also provides a short overview of the most popular workflow management products, and the state of the workflow industry in general. Workflow and Process Automation: Concepts and Technology offers a road map through the shortcomings of existing solutions of process improvement by people with daily first-hand experience, and is suitable as a secondary text for graduate-level courses on workflow and process automation, and as a reference for practitioners in industry.

This book addresses the privacy issue of On-Line Analytic Processing (OLAP) systems. OLAP systems usually need to meet two conflicting goals. First, the sensitive data stored in underlying data warehouses must be kept secret. Second, analytical queries about the data must be allowed for decision support purposes. The main challenge is that sensitive data can be inferred from answers to seemingly innocent aggregations of the data. This volume reviews a series of methods that can precisely answer data cube-style OLAP, regarding sensitive data while provably preventing adversaries from inferring data.

The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model. Authors pay attention to the four main findings of the book : -Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features. - Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis. - The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis. - Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

Web Intelligence is a new direction for scientific research and development that explores the fundamental roles as well as practical impacts of artificial intelligence and advanced information technology for the next generation of Web-empowered systems, services, and environments. Web Intelligence is regarded as the key research field for the development of the Wisdom Web (including the Semantic Web). As the first book devoted to Web Intelligence, this coherently written multi-author monograph provides a thorough introduction and a systematic overview of this new field. It presents both the current state of research and development as well as application aspects. The book will be a valuable and lasting source of reference for researchers and developers interested in Web Intelligence. Students and developers will additionally appreciate the numerous illustrations and examples.

This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches. This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state of the art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily. Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

Collaborative Networks for a Sustainable World Aiming to reach a sustainable world calls for a wider collaboration among multiple stakeholders from different origins, as the changes needed for sustainability exceed the capacity and capability of any individual actor. In recent years there has been a growing awareness both in the political sphere and in civil society including the bu- ness sectors, on the importance of sustainability. Therefore, this is an important and timely research issue, not only in terms of systems design but also as an effort to b- row and integrate contributions from different disciplines when designing and/or g- erning those systems. The discipline of collaborative networks especially, which has already emerged in many application sectors, shall play a key role in the implemen- tion of effective sustainability strategies. PRO-VE 2010 focused on sharing knowledge and experiences as well as identi- ing directions for further research and development in this area. The conference - dressed models, infrastructures, support tools, and governance principles developed for collaborative networks, as important resources to support multi-stakeholder s- tainable developments. Furthermore, the challenges of this theme open new research directions for CNs. PRO-VE 2010 held in St.

This book focuses on three interdependent challenges related to managing transitions toward sustainable development, namely (a) mapping sustainability for global knowledge e-networking, (b) extending the value chain of knowledge and e-networking, and (c) engaging in explorations of new methods and venues for further developing knowledge and e-networking. While each of these challenges constitutes fundamentally different types of endeavors, they are highly interconnected. Jointly, they contribute to our expansion of knowledge and its applications in support of transitions toward sustainable development.

The central theme of this book revolves around ways of transcending barriers that impede the use of knowledge and knowledge networking in transitions toward sustainability. In order to transcend these barriers, we examine the potential contributions of innovations in information technologies as well as computation and representation of attendant complexities. A related theme addresses new ways of managing information and systematic observation for the purpose of enhancing the value of knowledge. Finally, this book shows applications of new methodologies and related findings that would contribute to our understanding of sustainablity issues that have not yet been explored. In many ways, this is a book of theory and of practice; and it is one of methods as well as policy and performance.

This 2nd edition has been completely revised and updated, with additional new chapters. It presents state-of-the-art research in this area and focuses on key topics such as: visualization of semantic and structural information and metadata in the context of the emerging Semantic Web; Ontology-based Information Visualization and the use of graphically represented ontologies; Semantic Visualizations using Topic Maps and graph techniques; Recommender systems for filtering and recommending on the Semantic Web; SVG and X3D as new XML-based languages for 2D and 3D visualisations; methods used to construct and visualize high quality metadata and ontologies; and navigating and exploring XML documents using interactive multimedia interfaces. The design of visual interfaces for e-commerce and information retrieval is currently a challenging area of practical web development.

This book reports on the development and validation of a generic defeasible logic programming framework for carrying out argumentative reasoning in Semantic Web applications (GF@SWA). The proposed methodology is unique in providing a solution for representing incomplete and/or contradictory information coming from different sources, and reasoning with it. GF@SWA is able to represent this type of information, perform argumentation-driven hybrid reasoning to resolve conflicts, and generate graphical representations of the integrated information, thus assisting decision makers in decision making processes. GF@SWA represents the first argumentative reasoning engine for carrying out automated reasoning in the Semantic Web context and is expected to have a significant impact on future business applications. The book provides the readers with a detailed and clear exposition of different argumentation-based reasoning techniques, and of their importance and use in Semantic Web applications. It addresses both academics and professionals, and will be of primary interest to researchers, students and practitioners in the area of Web-based intelligent decision support systems and their application in various domains.

There is a broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-the-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference work for those who are conducting research into feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.

This doctoral thesis reports on an innovative data repository offering adaptive metadata management to maximise information sharing and comprehension in multidisciplinary and geographically distributed collaborations. It approaches metadata as a fluid, loosely structured and dynamical process rather than a fixed product, and describes the development of a novel data management platform based on a schemaless JSON data model, which represents the first fully JSON-based metadata repository designed for the biomedical sciences. Results obtained in various application scenarios (e.g. integrated biobanking, functional genomics and computational neuroscience) and corresponding performance tests are reported on in detail. Last but not least, the book offers a systematic overview of data platforms commonly used in the biomedical sciences, together with a fresh perspective on the role of and tools for data sharing and heterogeneous data integration in contemporary biomedical research.

Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.

Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area. Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Computer access is the only way to retrieve up-to-date sequences and this book shows researchers puzzled by the maze of URLs, sites, and searches how to use internet technology to find and analyze genetic data. The book describes the different types of databases, how to use a specific database to find a sequence that you need, and how to analyze the data to compare it with your own work.
The content also covers sequence phenotype, mutation, and genetic linkage databases; simple repetitive DNA sequences; gene feature identification; and prediction of structure and function of proteins from sequence information. This book will be invaluable to those starting a career in life sciences research as well as to established researchers wishing to make full use of available resources.
Key Features
* Describes a wide range of databases: DNA, RNA, protein, pathways, and gene expression
* Enables readers to access the information they need from databases on the web
* Includes a directory of URLs for easy reference
* Invaluable for those starting a career in life sciences research and also for established researchers wishing to make full use of available resources.

This book constitutes the refereed proceedings of the 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, held in Costa de Caparica, Portugal, in July 2020. The 20 full papers and 24 short papers presented were carefully reviewed and selected from 91 submissions. The papers present selected results produced in engineering doctoral programs and focus on technological innovation for industry and service systems. Research results and ongoing work are presented, illustrated and discussed in the following areas: collaborative networks; decisions systems; analysis and synthesis algorithms; communication systems; optimization systems; digital twins and smart manufacturing; power systems; energy control; power transportation; biomedical analysis and diagnosis; and instrumentation in health.

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Advice involves recommendations on what to think; through thought, on what to choose; and via choices, on how to act. Advice is information that moves by communication, from advisors to the recipient of advice. Ivan Jureta offers a general way to analyze advice. The analysis applies regardless of what the advice is about and from whom it comes or to whom it needs to be given, and it concentrates on the production and consumption of advice independent of the field of application. It is made up of two intertwined parts, a conceptual analysis and an analysis of the rationale of advice. He premises that giving advice is a design problem and he treats advice as an artifact designed and used to influence decisions. What is unusual is the theoretical backdrop against which the author's discussions are set: ontology engineering, conceptual analysis, and artificial intelligence. While classical decision theory would be expected to play a key role, this is not the case here for one principal reason: the difficulty of having relevant numerical, quantitative estimates of probability and utility in most practical situations. Instead conceptual models and mathematical logic are the author's tools of choice. The book is primarily intended for graduate students and researchers of management science. They are offered a general method of analysis that applies to giving and receiving advice when the decision problems are not well structured, and when there is imprecise, unclear, incomplete, or conflicting qualitative information.

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful." Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data--now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you'll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head--an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

This book contributes to an improved understanding of knowledge-intensive business services and knowledge management issues. It offers a complex overview of literature devoted to these topics and introduces the concept of 'knowledge flows', which constitutes a missing link in the previous knowledge management theories. The book provides a detailed analysis of knowledge flows, with their types, relations and factors influencing them. It offers a novel approach to understand the aspects of knowledge and its management not only inside the organization, but also outside, in its environment.