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Summary: An unbiased approach to the latest digital marketing models, offering students and practitioners a range of tools to implement in their digital marketing planning and strategy. Covering all aspects of digital marketing planning, and the latest digital marketing models, the book aims to provide a roadmap for a digital marketing journey. As such, its structure maps against the development of a digital marketing plan and concludes with a ready-made digital marketing plan template to download and adapt. In addition to this, the content is supported by case examples from real-world organizations, and a number of features throughout the chapters: Smartphone sixty seconds features in every chapter, to evaluate influencers in relation to the topic covered; digital tool boxes introduce professional tools (for example how Google shares its aggregate data to inform marketers about shopping insights, trends and benchmarks; ethical insights provide a reflective and challenging look at social issues and the negative sides to marketing. The book is complemented by online resources for both instructors and students, these include PowerPoint slides, an instructor 2019's guide, exercises and activities relating to each chapter, digital marketing planning documents, digital marketing model templates, quizzes, annotated recommended video links, links to free online tools and SAGE journal article recommendations. Suitable for digital and e-marketing courses on marketing and advertising degrees as well as professional courses for anyone interested in gaining a holistic understanding of digital marketing.

An unbiased approach to the latest digital marketing models, offering students and practitioners a range of tools to implement in their digital marketing planning and strategy. Covering all aspects of digital marketing planning, and the latest digital marketing models, the book aims to provide a roadmap for a digital marketing journey. As such, its structure maps against the development of a digital marketing plan and concludes with a ready-made digital marketing plan template to download and adapt. In addition to this, the content is supported by case examples from real-world organizations, and a number of features throughout the chapters: smartphone sixty seconds features in every chapter, to evaluate influencers in relation to the topic covered. Digital tool boxes introduce professional tools (for example how Google shares its aggregate data to inform marketers about shopping insights, trends and benchmarks). Ethical insights provide a reflective and challenging look at social issues and the negative sides to marketing. The book is complemented by online resources for both instructors and students, these include PowerPoint slides, an instructor's guide, exercises and activities relating to each chapter, digital marketing planning documents, digital marketing model templates, quizzes, annotated recommended video links, links to free online tools and SAGE journal article recommendations. Suitable for digital and e-marketing courses on marketing and advertising degrees as well as professional courses for anyone interested in gaining a holistic understanding of digital marketing.

Summary: "Digital Storytelling shows you how to create immersive, interactive narratives across a multitude of platforms, devices, and media. From age-old storytelling techniques to cutting-edge development processes, this book covers creating stories for all forms of New Media, including transmedia storytelling, video games, mobile apps, and more"-- Provided by publisher.

Summary: This book addresses computer scientists, IT specialists, mathematicians, knowledge engineers and programmers, who are engaged in research and practice of multicriteria decision making. Fuzzy measures, also known as capacities, allow one to combine degrees of preferences, support or fuzzy memberships into one representative value, taking into account interactions between the inputs. The notions of mutual reinforcement or redundancy are modeled explicitly through coefficients of fuzzy measures, and fuzzy integrals, such as the Choquet and Sugeno integrals combine the inputs. Building on previous monographs published by the authors and dealing with different aspects of aggregation, this book especially focuses on the Choquet and Sugeno integrals. It presents a number of new findings concerning computation of fuzzy measures, learning them from data and modeling interactions. The book does not require substantial mathematical background, as all the relevant notions are explained. It is intended as concise, timely and self-contained guide to the use of fuzzy measures in the field of multicriteria decision making.

Summary: Disrupting Journalism Ethics sets out to disrupt and change how we think about journalism and its ethics. The book contends that long-established ways of thinking, which have come down to us from the history of journalism, need radical conceptual reform, with alternate conceptions of the role of journalism and fresh principles to evaluate practice. Through a series of disruptions, the book undermines the traditional principles of journalistic neutrality and "just the facts" reporting. It proposes an alternate philosophy of journalism as engagement for democracy. The aim is a journalism ethic better suited to an age of digital and global media. As a philosophical pragmatist, Stephen J. A. Ward critiques traditional conceptions of accuracy, neutrality, detachment and patriotism, evaluating their capacity to respond to ethical dilemmas for journalists in the 21st century. The book proposes a holistic mindset for doing journalism ethics, a theory of journalism as advocacy for egalitarian democracy, and a global redefinition of basic journalistic norms. The book concludes by outlining the shape of a future journalism ethics, employing these alternative notions. Disrupting Journalism Ethics is an important intervention into the role of journalism today. It asks: what new role journalists should play in today’s digital media world? And what new mind-set, new aims, and new standards ought jounalists to embrace? The book aims to persuade―and provoke―ethicists, journalists, students, and members of the public to disrupt and invent.

Summary: "From the best-selling authors of Deep Learning: Engage the World Change the World, this book is a must-have practical guide to help you implement your ideas. Packed with tools, tips, and protocols, this resource shows you how to design deep learning, measure progress, and assess the conditions needed to mobilize and sustain innovation and deep learning. Dive Into Deep Learning: Tools for Engagement builds on the call to action in Deep Learning: Engage the World Change the World by providing a comprehensive approach for mobilizing deep learning in classrooms, schools, districts, and systems"-- Provided by publisher.

Summary: "This book covers the idea of artificial intelligence for document analysis. It discusses optical character recognition techniques emphasising on Bangla isolated handwritten characters, script identification from character level texts and signature data"-- Provided by publisher.

Summary: "This book addresses major issues and challenges in drone-based solutions proposed for IoT-enabled cellular/computer networks, routing/communication protocols, surveillances applications, secured data management, and positioning approaches. It focuses mainly on smart and context-aware implementations"-- Provided by publisher.

Summary: EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis introduces EEG-based machine learning solutions for diagnosis and assessment of treatment efficacy for a variety of conditions. With a unique combination of background and practical perspectives for the use of automated EEG methods for mental illness, it details for readers how to design a successful experiment, providing experiment designs for both clinical and behavioral applications. This book details the EEG-based functional connectivity correlates for several conditions, including depression, anxiety, and epilepsy, along with pathophysiology of depression, underlying neural circuits and detailed options for diagnosis. It is a necessary read for those interested in developing EEG methods for addressing challenges for mental illness and researchers exploring automated methods for diagnosis and objective treatment assessment.

Summary: Evaluating Research in Academic Journals is a guide for students who are learning how to evaluate reports of empirical research published in academic journals. It breaks down the process of evaluating a journal article into easy-to-understand steps, and emphasizes the practical aspects of evaluating research – not just how to apply a list of technical terms from textbooks. The book avoids oversimplification in the evaluation process by describing the nuances that may make an article publishable even when it has serious methodological flaws. Students learn when and why certain types of flaws may be tolerated, and why evaluation should not be performed mechanically. Each chapter is organized around evaluation questions. For each question, there is a concise explanation of how to apply it in the evaluation of research reports. Numerous examples from journals in the social and behavioral sciences illustrate the application of the evaluation questions, and demonstrate actual examples of strong and weak features of published reports. Common-sense models for evaluation combined with a lack of jargon make it possible for students to start evaluating research articles the first week of class.

Summary: Dr Birks eloquently guides the reader through a complex field of mediated post-truth politics in an engaging and accessible critique of fact checking journalism. In this book we are treated to a highly sophisticated analysis of the dynamics and epistemologies of fact checking journalism, with detailed examples from a UK perspective, where Birks demonstrates the importance of understanding fact-checking in relation to the quality of political debate. The book is essential reading for anyone wanting to understand the contested nature of fact and truth claims in journalism today.? ?Einar Thorsen, Associate Professor of Journalism and Communication, Bournemouth University, UK ?In an era of post-factual democracy, fact-checkers should act as an antidote to politician?s ?bullshit?, helping citizens to make informed choices. Birks? important research is a must read for anyone seeking to understand fact checkers or create a fact check system that plays a role in monitoring the health of democratic debate.? ?Darren G. Lilleker, Associate Professor in Political Communication, Bournemouth University, UK This timely book examines the role of fact-checking journalism within political policy debates, and its potential contribution to public engagement. Understanding facts not to operate in a political vacuum, the book argues for a wide remit for fact-checking journalism beyond empirically-checkable facts, to include the causal relationships and predictions that form part of wider political arguments and are central to electoral pledges. Whilst these statements cannot be proven or disproven, fact-checking can, and sometimes does, ask pertinent critical questions about the premises of those claims and arguments. The analysis centres on the three dedicated national British fact-checkers during the UK?s 2017 snap general election, including their activity and engagement on Twitter. The book also makes a close political discourse and argumentation analysis of three key issue debates in flagship reporting from Channel 4 News and the BBC.

Summary: This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the exemplar code of the algorithms." Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filtering, SURF, PCA-SIFT, moving object detection and tracking, development of symmetry operators, LBP texture analysis, Adaboost, and a new appendix on color models. Coverage of distance measures, feature detectors, wavelets, level sets and texture tutorials has been extended.

Summary: This extraordinary book, written by leading players in a burgeoning technology revolution, is about the merger of finance and technology (fintech), and covers its various aspects and how they impact each discipline within the financial services industry. It is an honest and direct analysis of where each segment of financial services will stand. Fintech: The New DNA of Financial Services provides an in-depth introduction to understanding the various areas of fintech and terminology such as AI, big data, robo-advisory, blockchain, cryptocurrency, InsurTech, cloud computing, crowdfunding and many more. Contributions from fintech innovators discuss banking, insurance and investment management applications, as well as the legal and human resource implications of fintech in the future

Summary: "Foundations of Deep Reinforcement Learning is the fastest, most accessible introduction to deep reinforcement learning (DRL). Deep reinforcement learning (DRL) systems are achieving breakthrough performance in many applications. DRL machine learning resembles human learning in multiple ways: It's exceptionally flexible, can learn and discover strategies without supervision, and can efficiently balance exploration with exploitation. The authors begin with intuition, then carefully explain DRL theory and algorithms, and finish with implementations and practical tips. Throughout, the authors bridge theory and practice, helping you integrate components and tune parameters that must work together optimally for DRL algorithms to succeed. They illuminate important recent advances through hands-on examples written in Python and built with their advanced SLM Lab software library. This guide is intended for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python"-- Provided by publisher.

Summary: Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation introduces the fundamentals of hyperspectral or imaging spectroscopy data, including hyperspectral data processes, sensor systems, spectral libraries, and data mining and analysis, covering both the strengths and limitations of these topics. This book also presents and discusses hyperspectral narrowband data acquired in numerous unique spectral bands in the entire length of the spectrum from various ground-based, airborne, and spaceborne platforms. The concluding chapter provides readers with useful guidance on the highlights and essence of Volume I through the editors' perspective.

Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.

Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.

Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.

Summary: Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Summary: "Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare!"-- Publisher's description.

Summary: This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence."-

Summary: Leverage the power of Java and deep learning to build production-grade Computer Vision applications Key Features Build real-world Computer Vision applications using the power of neural networks Implement image classification, object detection, and face recognition Know best practices on effectively building and deploying deep learning models in Java Book Description Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The course is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy. What you will learn Discover neural Networks and their applications in Computer Vision Explore the popular Java frameworks and libraries for deep learning Build deep neural networks in Java Implement an end-to-end image classification application in Java Perform real-time video object detection using deep learning Enhance performance and deploy applications for production Who this book is for This book is for data scientists, machine learning developers and deep learning practitioners with Java knowledge who want to implement machine learning and deep neural networks in the computer vision domain. You will need to have a basic knowledge of Java programming.

Summary: The book examines the birth, development, and mode of operation of the Hebrew popular press that progressed in Ottoman Palestine between 1884 and the eruption of World War I in 1914. The inquiry yields a profile of the printers, editors, and journalists, and examines the editors' working patterns, the gathering of journalistic information, and distribution of the resulting product in the public sphere. Addressing the fact that nearly all of the Hebrew press in the eighteenth and nineteenth centuries appealed to an elitist intellectual and affluent readership, the book breaks new ground by showing that from the 1880s onward, a popular press came into being in Palestine for the first time in the history of the Hebrew press. The focus is on three popular newspapers that evolved in Jerusalem along the lines of the Western popular press. While profiling the readership of the popular Hebrew press the book also investigates reading practices. Analysing the contribution of the press to the modernization of the Hebrew language, this pioneering volume is a key resource for students and scholars of communication, media and Hebrew studies, and media and Jewish history.

Summary: "In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable. In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage. If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial. In a 2014 editorial co-authored with Stephen Hawking, Russell wrote, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last." Solving the problem of control over AI is not just possible; it is the key that unlocks a future of unlimited promise"-- Provided by publisher.

Summary: "A deep dive into the world of online and multimedia longform storytelling, this book charts the renaissance in deep reading, viewing and listening associated with the literary mind, and the resulting implications of its rise in popularity. David O. Dowling argues that although developments in media technology have enabled the ascendance of nonfictional storytelling to new heights through new forms, it has done so at the peril of these intensely persuasive designs becoming deployed for commercial and political purposes. He shows how traditional boundaries separating genres and dividing editorial from advertising content have fallen with the rise of media hybridity, drawing attention to how the principle of an independent press can be reformulated for the digital ecosystem. Immersive Longform Storytelling is a compelling examination of storytelling, covering multimedia features, on-demand documentary television, branded digital documentaries, interactive online documentaries, and podcasting. This book's focus on both form and effect make it a fascinating read for scholars and academics interested in storytelling and the rise of new media"-- Provided by publisher.

Summary: In the ever-changing information environment of the early twenty-first century, citizens and journalists alike are eagerly adapting to new technologies, and India is no different. The country’s communication revolution in the post-liberalization era has led to one of the largest media markets in the world. Further, changes in media ownerships and the blending of news with opinions have impacted established practices of reporting. Given the breadth and scope of India’s media, there is little meaningful literature available about journalism practised in the country today. Indian Journalism in a New Era brings together informative and critical contributions about contemporary Indian journalism from twenty-one Indian and global scholars and journalists. The book is divided into four different sections, each addressing one relevant aspect: history and evolving changes; social media and e-journalism; marginalization; and pedagogy, ethics, and public sphere. The contributors address issues like changes in journalism practices, socio-economic conditions of the Indian state, and minority politics. Holistically, the volume focuses on the ways to approach and analyse the enormity and scope in Indian journalism, media technology, and global relations.

Summary: This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets. Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships. The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data? Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems. The innovations presented are of primary importance for: a. The academic research community b. The ICT market c. Ph. D. students and early stage researchers d. Schools, hospitals, rehabilitation and assisted-living centers e. Representatives from multimedia industries and standardization bodies.

Summary: Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agricultureAddresses AI use in weed management, disease detection, yield prediction and crop productionUtilizes case studies to provide real-world insights and direction

Summary: This book discusses emerging technologies in the field of the Internet of Things and big data, an area that will be scaled in next two decades. Written by a team of leading experts, it is the only book focusing on the broad areas of both the Internet of things and big data. The thirteen chapters present real-time experimental methods and theoretical explanations, as well as the implementation of these technologies through various applications. Offering a blend of theory and hands-on practices, the book enables graduate, postgraduate and research students who are involved in real-time project scaling techniques to understand projects and their execution. It is also useful for senior computer students, researchers and industry workers who are involved in experimenting with the Internet of Things and big data technologies, helping them to solve the real-time problem. Moreover, the chapters covering cutting-edge technologies help multidisciplinary researchers who are bridging the gap of two different outset real-time problems.

Summary: This book provides an overview of the most recent developments in Internet of Things (IoT) security and data protection. It presents the results of several international research projects addressing this topic from complementary angles. It starts by analyzing the main privacy and security threats on IoT, as well as the evolution of data protection norms, such as the European General Data Protection Regulation (GDPR), and their impact on IoT. Through a comprehensive and systematic approach, the contributors present new perspectives on IoT and Cloud Computing security requirements. They discuss the most recent approach to support trusted IoT, including new models of privacy risk assessment, labeling and certification, and contractual tools (such as Privacy PACT). Practical implementations, such as in the European Large Scale Pilots on IoT for Smart Cities (Synchronicity), are presented, explaining how they address security, privacy and data protection. Finally, innovative models to secure IoT systems are presented for the network and end-nodes security, including network threats analysis. Shares results from several international research projects on IoT, cybersecurity, and privacy; Combines complementary expertise from top experts in IoT, security, and data protection; Provides a comprehensive view on how to protect IoT deployments and make them comply with the GDPR.

Summary: Journalism, Gender and Power revisits the key themes explored in the 1998 edited collection News, Gender and Power. It takes stock of progress made to date, and also breaks ground in advancing critical understandings of how and why gender matters for journalism and current democratic cultures. This new volume develops research insights into issues such as the influence of media ownership and control on sexism, women's employment, and "macho" news cultures, the gendering of objectivity and impartiality, tensions around the professional identities of journalists, news coverage of violence against women, the sexualization of women in the news, the everyday experience of normative hierarchies and biases in newswork, and the gendering of news audience expectations, amongst other issues. These issues prompt vital questions for feminist and gender-centred explorations concerned with reimagining journalism in the public interest. Contributors to this volume challenge familiar perspectives, and in so doing, extend current parameters of dialogue and debate in fresh directions relevant to the increasingly digitalized, interactive intersections of journalism with gender and power around the globe. Journalism, Gender and Power will inspire readers to rethink conventional assumptions around gender in news reporting--conceptual, professional, and strategic--with an eye to forging alternative, progressive ways forward.

Summary: "Journalism, Power, and Investigation presents a contemporary, trans-national analysis of investigative journalism. Beginning with a detailed introduction that examines the relationship between this form of public communication and normative conceptions of democracy, the book offers a selection of spirited contributions to current debates concerning the place, function and political impact of investigative work. The fourteen chapters, produced by practicing journalists, academics, and activists, cover a range of topics, with examples drawn from the global struggle to produce reliable, in-depth accounts of public events. The collection brings together a range of significant, present-day investigations from across the world. These include an assignment conducted in the dangerous sectarian environment of Iraq, a close engagement with Spain's Memory Movement and the work of radical charity Global Witness. Chapters also examine the troubled political legacy of Wikileaks, legal constraints on investigative journalism in the UK and the bold international agenda of the investigative collective the Ferret. This is alongside other analytical pieces on events in Bermuda, Brazil, and Egypt. Investigative journalism is a form of reportage that has long provided a benchmark for in-depth, critical interventions. Along with case studies, Journalism, Power and Investigation gives students and researchers an insight into the principles and methods that animate this global search for truth and justice"-- Provided by publisher.

Summary: Learning for the Age of Artificial Intelligence is a richly informed argument for curricular change to educate people towards achievement and success as intelligent machine systems proliferate. Describing eight key competences, this comprehensive volume prepares educational leaders, designers, researchers, and policymakers to effectively rethink the knowledge, skills, and environments that students need to thrive and avoid displacement in today's technology-enhanced culture and workforce. Essential insights into school operations, machine learning, complex training and assessment, and economic challenges round out this cogent, relatable discussion about the imminent evolution of the education sector.

Summary: This is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks--which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning--most notably, multi-task learning, transfer learning, and metalearning--because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.