Quality assurance is an essential aspect for ensuring the success of corporations worldwide. Consistent quality requirements across organizations of similar types ensure that these requirements can be accurately and easily evaluated. Shaping the Future Through Standardization is an essential scholarly book that examines quality and standardization within diverse organizations globally with a special focus on future perspectives, including how standards and standardization may shape the future. Featuring a wide range of topics such as economics, pedagogy, and management, this book is ideal for academicians, researchers, decision makers, policymakers, managers, corporate professionals, and students.

Mit grossem didaktischen Geschick gelingt es den Autoren, Begeisterung fur die Welt der geheimen Botschaften zu wecken. So gelingt der Einstieg in die Kryptologie ganz leicht. Viele Beispiele und Aufgaben regen dazu an, sich selbstandig mit diesem faszinierenden Gebiet zu beschaftigen und helfen dabei, den erlernten Stoff weiter zu vertiefen.

RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack maps that aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues. This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.

This book constitutes the refereed post-conference proceedings of two conferences: The 8th EAI International Conference on ArtsIT, Interactivity and Game Creation (ArtsIT 2019), and the 4th EAI International Conference on Design, Learning, and Innovation (DLI 2019). Both conferences were hosed in Aalborg, Denmark, and took place November 6-8, 2019. The 61 revised full papers presented were carefully selected from 98 submissions. The papers represent a forum for the dissemination of cutting-edge research results in the area of arts, design and technology, including open related topics like interactivity and game creation.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book will introduce you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You'll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You'll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you'll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You'll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language.

This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

The advancement of technology in today's world has led to the progression of several professional fields. This includes the classroom, as teachers have begun using new technological strategies to increase student involvement and motivation. ICT innovation including virtual reality and blended learning methods has changed the scope of classroom environments across the globe; however, significant research is lacking in this area. ICTs and Innovation for Didactics of Social Sciences is a fundamental reference focused on didactics of social sciences and ICTs including issues related to innovation, resources, and strategies for teachers that can link to the transformation of social sciences teaching and learning as well as societal transformation. While highlighting topics such as blended learning, augmented reality, and virtual classrooms, this book is ideally designed for researchers, administrators, educators, practitioners, and students interested in understanding current relevant ICT resources and innovative strategies for the didactic of social sciences and didactic possibilities in relation to concrete conceptual contents, resolution of problems, planning, decision making, development of social skills, attention, and motivation promoting a necessary technological literacy.

Binary detection is a ubiquitous problem in virtually all branches of science and technology. In many cases, binary detection must be carried out in a setting wherein the presence of an adversary aiming at inducing a wrong decision cannot be ruled out. Applications include network monitoring, spam filtering, multimedia forensics, video surveillance and biometric authentication to name but a few. In these cases, the attack is carried out at the time of testing. With the advent of widespread machine learning tools, the attacker can act during the learning phase, making it harder to detect. The main idea behind adversarial detection theory is to cast the detection problem into a game-theoretic framework. This allows the goals and the actions available to the two contenders to be rigorously defined. In this monograph, the authors address several variants of a general adversarial binary detection problem, depending on the knowledge available to the Defender and the Attacker of the statistical characterization of a system. They lead the reader through the considerations and solutions under two hypotheses, using a framework that can be adopted in many applications. This monograph, aimed at students, researchers and practitioners working in the application areas who want an accessible introduction to the theory behind Adversarial Binary Detection and the possible solutions to their particular problem.

In the recent past, many components of modern infrastructure such as transportation systems, power systems, climate and environment monitoring systems, education systems and even government are being increasingly interconnected through information networks. Central to the functioning of current-day information networks are strategies that facilitate distributed network information processing objectives. In this monograph, the authors address the overarching challenge of designing efficient information processing strategies from a fundamental network information theory viewpoint. The authors address several network communication problems which can be considered as building blocks of networks. They consider these problems from both the data transmission and the data storage perspectives. They devise structured coding schemes for the finite alphabet cases of these problems and for each problem provide at least one example where they prove that the structured coding scheme is optimal, whereas the unstructured coding scheme is strictly suboptimal. Toward studying the information-theoretic performance limits in each of these communication scenarios, they consider two key concepts: common information and code structure. They uncover a new fundamental connection between them, and develop the key elements of a unified coding framework.This monograph is aimed at students, researchers and practitioners in information theory and communications. It provides an in-depth discussion of the theory and techniques resulting in a framework that the reader can apply to further their own work.

In recent years, many developing regions across the globe have made rigorous efforts to become integrated into the global information society. The development and implementation of information communication technology (ICT) devices and policies within various fields of service have significantly aided in the infrastructural progression of these countries. Despite these considerable advancements, there remains a lack of research and awareness on this imperative subject. Developing Countries and Technology Inclusion in the 21st Century Information Society is an essential reference source that discusses the adoption and impact of ICT tools in developing areas of the world as well as specific challenges and sustainable uses within various professional fields. Featuring research on topics such as policy development, gender differences, and international business, this book is ideally designed for educators, policymakers, researchers, librarians, practitioners, scientists, government officials, and students seeking coverage on modern applications of ICT services in developing countries.

The authors of this monograph survey a suite of techniques based on the theory of polynomials, collectively referred to as polynomial methods. These techniques provide useful tools not only for the design of highly practical algorithms with provable optimality, but also for establishing the fundamental limits of inference problems through moment matching. The authors demonstrate the effectiveness of the polynomial method using concrete problems such as entropy and support size estimation, distinct elements problem, and learning Gaussian mixture models. This monograph provides a comprehensive, yet concise, overview of the theory covering topics such as polynomial approximation, polynomial interpolation and majorization, moment space and positive polynomials, orthogonal polynomials and Gaussian quadrature. The authors proceed to show the applications of the theory in statistical inference. Polynomial Methods in Statistical Inference provides students, and researchers with an accessible and complete treatment of a subject that has recently been used to solve many challenging problems in statistical inference.

Recent years have witnessed a rapid growth of large-scale machine learning and big data analytics, facilitating the developments of data intensive applications like voice/image recognition, real-time mapping services, autonomous driving, social networks, and augmented/virtual reality. These applications are supported by cloud infrastructures composed of large datacenters.The large scale distributed machine learning/data analytics systems provide the necessary processing power to handle these applications, but suffer three major performance bottlenecks; namely, communication, straggler and security. In this ground-breaking monograph, the authors introduce the novel concept of Coded Computing. Coded Computing exploits coding theory to optimally inject and leverage data/task redundancy in distributed computing systems, creating coding opportunities to overcome the bottlenecks. After introducing the reader to the core of the problem, the authors describe in detail each of the bottlenecks that can be overcome using Coded Computing. The monograph provides an accessible introduction into how this new technique can be used in developing large-scale computing systems.

The first half of the book provides an introduction to general topology, with ample space given to exercises and carefully selected applications. The second half of the text includes topics in asymmetric topology, a field motivated by applications in computer science. Recurring themes include the interactions of topology with order theory and mathematics designed to model loss-of-resolution situations.

Wahrend die Kryptologie Konzepte und Methoden aus der Komplexitatstheorie verwendet, ist die Forschung in der Komplexitatstheorie wiederum oft durch Fragen aus der Kryptologie motiviert. Der Band hebt die enge Verflechtung dieser beiden Gebiete hervor und fuhrt auf verstandlicher Weise in das faszinierende Gebiet der Kryptokomplexitat" ein. Das Buch enthalt zahlreiche Abbildungen und Ubungsaufgaben sowie ein ausfuhrliches Stichwort- und Literaturverzeichnis. Es eignet sich fur Studierende der Informatik, Mathematik oder Ingenieurswissenschaften."