Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.

Academicians, diplomats, technicians and engineers working in the chemical, biological, radiological, nuclear, explosive and cyber fields will find this book valuable as a reference. Students studying in these related fields will also find this book useful as a reference.

The book was written for a broad audience, including technology managers and students in higher education. It is also intended as a guide for people who grew up with a background in business administration or engineering or a related area but who, in the course of their career paths, have reached a point where IT-related decisions have become their daily business, e.g., in digital transformation. The book describes the most important Web technologies and related business applications, and especially focuses on the business implications of these technologies. As such, it offers a solid technology- and business-focused view on the impact of the Web, and balances rules and approaches for strategy development and decision making with a certain technical understanding of what goes on “behind the scenes.”

Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.