Readers seeking to gain a handle on the internet's global expansion will find this book rich in scholarly foundations combined with cutting-edge discussion of emerging ICTs and services and the complex societal contexts in which they are embedded. To explore possibilities to the fullest extent, a sociotechnical systems approach is employed, focusing on the interplay of technical, social, cultural, political, and economic dynamics to explore alternative futures (ones that are not part of the dominant discourse about the internet). These shared perspectives are not well addressed elsewhere in current discussions. Awareness of these dynamics, and the fluidity of the future, is important, as humankind moves forward into the uncertain future. Due to the sociotechnical complexity of the Internet, policymakers, businesspeople, and academics worldwide have struggled to keep abreast of developments. This volume's approach is intended to stimulate dialogue between academics and practitioners on a topic that will affect most aspects of human life in the near-term future.

This document outlines the recommendations/considerations for the GOARN Strategy 2022-2026. Strategy recommendations were developed to shape and define the future directions of the Network through an extensive strategy development process involving interviews with key stakeholders, a survey to GOARN partners, scenario-building workshops, a strategic workshop at the GOARN Global Meeting of Partners, regional consultations with regional focal points and partners, and meetings/workshops with Steering Committee Members to refine and finalize. The Strategy document will be implemented by the GOARN Steering Committee and GOARN partners.

The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€"exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€"these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R.

Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection. It will be useful as a reference text for graduate students in different areas including electrical engineering, computer science engineering, civil engineering, and electronics and communications engineering. Features: Technologies and algorithms associated with the application of big data for smart cities Discussions on big data theory modeling and simulation for smart cities Applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS) Discussions on concepts including smart education, smart culture, and smart transformation management for social and societal changes

This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India, during 4–5 September 2020. The book addresses the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like data-driven industrial IoT, bioinformatics, network and security, autonomous computing and various other aligned areas. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber-physical system and smart agriculture. All papers have been carefully reviewed. The book is of interest to computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.

A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.

Organizations today understand that superior talent can create competitive business advantage. Executives are working with human resource managers and talent professionals to significantly improve their organization's ability to attract, develop, deploy, and retain the talent needed to achieve the organization's strategies. Effective CEOs and senior leaders are realizing that strong talent resources are as critical to business success as financial resources. This book in the SIOP Professional Practice Series provides an up-to-date review and summary of current and leading-edge talent management practices in organizations. A comprehensive book, Strategy-Driven Talent Management brings together an outstanding group of leading practitioners who present state-of-the-art ideas, best practices, and guidance on how to recruit, select, assimilate, develop, and retain exceptional talent and integrate talent management efforts with organizational strategy. Written for human resource professionals, industrial-organizational psychologists, and corporate executives, this key resource is a clear must-read guide to the emerging field of strategic talent management. Strategy-Driven Talent Management shows how to build competitive advantage through an integrated and strategic talent management program summarizes what it takes to attract, develop, deploy, and retain the best talent for the strategic needs of an organization reviews critical issues such as managing talent in global organizations and measuring the effectiveness of talent management programs includes case examples and CEO interviews from leading-edge companies such as PepsiCo, Microsoft, Home Depot, Cargill, and Allstate, which reveal how each of these organizations drives talent management with their business strategies This essential must-have HR resource offers insight into the future of strategic talent management, an extensive annotated bibliography and suggestions for preparing the next generation of organizational leaders.