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A unique examination of the civic use, regulation, and politics of communication and data technologies City life has been reconfigured by our use--and our expectations--of communication, data, and sensing technologies. This book examines the civic use, regulation, and politics of these technologies, looking at how governments, planners, citizens, and activists expect them to enhance life in the city. Alison Powell argues that the de facto forms of citizenship that emerge in relation to these technologies represent sites of contention over how governance and civic power should operate. These become more significant in an increasingly urbanized and polarized world facing new struggles over local participation and engagement. The author moves past the usual discussion of top-down versus bottom-up civic action and instead explains how citizenship shifts in response to technological change and particularly in response to issues related to pervasive sensing, big data, and surveillance in "smart cities."
Scientific evidence from different countries around the globe shows that those with low or inadequate health-related knowledge and skills include all ages, social, and economic backgrounds. The consequences of this inadequacy simultaneously affect individuals, healthcare systems, and society in many ways, such as healthcare quality and cost. Research on health literacy can provide insight on how to improve the communication of health issues, raise awareness, and promote the lifelong learning of patients and healthcare professionals. Optimizing Health Literacy for Improved Clinical Practices examines the latest advances in providing and helping patients and medical professionals to understand basic health information and the services that are most appropriate. Featuring coverage on a broad range of topics such as patient engagement, mobile health, and health communication, this book is geared towards medical professionals, hospital adminstrators, healthcare providers, academicians, and researchers in the field.
A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
This book presents the state-of-the-art in the emerging field of data science and includes models for layered security with applications in the protection of sites—such as large gathering places—through high-stake decision-making tasks. Such tasks include cancer diagnostics, self-driving cars, and others where wrong decisions can possibly have catastrophic consequences. Additionally, this book provides readers with automated methods to analyze patterns and models for various types of data, with applications ranging from scientific discovery to business intelligence and analytics. The book primarily includes exploratory data analysis, pattern mining, clustering, and classification supported by real life case studies. The statistical section of this book explores the impact of data mining and modeling on the predictability assessment of time series. Further new notions of mean values based on ideas of multi-criteria optimization are compared with their conventional definitions, leading to new algorithmic approaches to the calculation of the suggested new means. The style of the written chapters and the provision of a broad yet in-depth overview of data mining, integrating novel concepts from machine learning and statistics, make the book accessible to upper level undergraduate and graduate students in data mining courses. Students and professionals specializing in computer and management science, data mining for high-dimensional data, complex graphs and networks will benefit from the cutting-edge ideas and practically motivated case studies in this book.
A comprehensive introduction to Bayesian optimization that starts from scratch and carefully develops all the key ideas along the way.
The aim of the book is to present contributions in theory, policy and practice to the science and policy of sustainable intensification by means of technological and institutional innovations in agriculture. The research insights re from Sub-Saharan Africa and South Asia. The purpose of this book is to be a reference for students, scholars and practitioners inthe field of science and policy for understanding and identifying agricultural productivity growth potentials in marginalized areas.
The three-volume set LNCS 11857, 11858, and 11859 constitutes the refereed proceedings of the Second Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2019, held in Xi’an, China, in November 2019. The 165 revised full papers presented were carefully reviewed and selected from 412 submissions. The papers have been organized in the following topical sections: Part I: Object Detection, Tracking and Recognition, Part II: Image/Video Processing and Analysis, Part III: Data Analysis and Optimization.
This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field. The editors and contributors to this book are pioneers in the area of active sensing and sensor management, and represent the diverse communities that are targeted.
This book constitutes the refereed proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 held in East Lansing, MI, USA, in March 2019. The 59 revised full papers were carefully reviewed and selected from 76 submissions. The papers are divided into 8 categories, each representing a key area of current interest in the EMO field today. They include theoretical developments, algorithmic developments, issues in many-objective optimization, performance metrics, knowledge extraction and surrogate-based EMO, multi-objective combinatorial problem solving, MCDM and interactive EMO methods, and applications.
The medical profession requires extensive training and preparation in order to ensure the success and competency of future doctors and healthcare professionals. With an emphasis on professional development and medical education, current professionals in this field acknowledge the importance of residency programs and training in the professional development of future doctors. Optimizing Medicine Residency Training Programs presents a comprehensive overview of chapters ranging from the history of medicine to opportunities and research for further exploration geared toward the professional development and medical training for the next generation of doctors and healthcare professionals. This publication is an essential reference source for academicians, practitioners, and professionals interested in the education and training of modern medical professionals.