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Summary: "A timely new edition of the classic journalism guide, now featuring updated material on the importance of reporting in the age of media mistrust and fake news--and how journalists can use technology while also navigating its challenges. More than two decades ago, the Committee of Concerned Journalists gathered some of America's most influential newspeople to ask the question "What is journalism for?" Through exhaustive research, surveys, interviews, and public forums, they identified the essential elements that define journalism and its role in our society. The result is one of the most important books on the media ever written, winner of the Goldsmith Book Prize from Harvard, the Society of Professional Journalists Award, and the Bart Richards Award from Penn State University. Updated with new material covering the ways journalists can deftly leverage technology to their advantage, especially given the shifting revenue architecture--with "clickbait" fading away and consumers paying a higher premium for quality reporting--this fourth edition of The Elements of Journalism is an essential read for journalists, students, and anybody hoping to stay informed in contentious times"-- Provided by publisher.

Summary: "In Digital Finance: Security Tokens and Unlocking the Real Potential Blockchain, readers will realize why security tokens are creating huge excitement in the financial industry. By combining the latest in technology and the potential for automated world-class investor protections, regulatory compliance and customer service, security tokens offer a plethora of benefits that will drive cost reductions, enhance flexibility and open up opportunities for new business models and revenue streams. Digital Finance thoroughly examines how this powerful technology can overhaul our current financial infrastructure in a way that will increase efficiency, transparency, and security"-- Provided by publisher.

Summary: "This book discusses the relationship between media, conflict and democratisation in Africa from the perspective of media ethics. Despite the commonly held view that conflict is a destructive political force that can destabilize democracies, the argument in this book is that while many conflicts can become violent and destructive, they can also be managed in a way that can render them productive and communicative to democracy. Drawing on theoretical insights from the fields of journalism studies, political studies and cultural studies, the book discusses the ethics of conflict coverage and proposes a normative model for covering conflict and democratisation. The book argues for an 'ethics of listening' which would enable the media to de-escalate violent conflict and contribute to the deepening of an agonistic democratic culture in contexts of high inequality, ethnic and racial polarisation and uneven access to media. This argument is illustrated by examples drawn from recent events in African democracies such as student protests, community activism, struggles for resources and social media conflicts. The book also scrutinizes the media's ethical roles and responsibilities in African societies by considering questions regarding journalistic professionalism, ethical codes and regulation in the context of rising misinformation. Provides a critical African perspective on global debates about media, politics, and democracy and the media's ethical commitments in contexts of conflict"-- Provided by publisher.

Summary: Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn: Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel This book is for anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended. Hong Zhou, PhD is a professor of computer science and mathematics and has been teaching c ourses in computer science, data science, mathematics, and informatics at the University of Saint Joseph for more than 15 years. His research interests include bioinformatics, data mining, software agents, and blockchain. Prior to his current position, he was as a Java developer in Silicon Valley. Dr. Zhou believes that learners can develop a better foundation of data mining models when they visually experience them step-by-step, which is what Excel offers. He has employed Excel in teaching data mining and finds it an effective approach for both data mining learners and educators.

Summary: "This book is a collection of research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field"-- Provided by publisher.

Summary: "Investigative Journalism, Democracy and the Digital Age explores watchdog reporting in the digital age. Mapping new forms of global collaborative investigative journalism, the book debunks the myth that traditional newsrooms and investigative journalism are dying, and shows how journalists are adapting and experimenting. The book brings together expert analysis and lively examples of investigative journalism in developed economies, with a close focus on the United States, United Kingdom and Australia. Combining decades of investigative journalism analyses with expert interviews with watchdog reporters from across the globe, the author shows us how investigative journalism thrives despite significant challenges, and explores why investigative journalism is integral to democratic accountability and public trust in the news in a fake news era"-- Provided by publisher.

Summary: "The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book"-- Provided by publisher.

Summary: This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19. .

Summary: "The Routledge Handbook on Historic Urban Landscapes in Asia-Pacific sheds light onto the balancing act of urban heritage management, focusing specifically on the Asia-Pacific regions in which this challenge is imminent and in need of effective solutions. Urban heritage, while being threatened amidst myriad forces of global and ecological change, provides a vital social, cultural, and economic asset for regeneration and sustenance of liveability of inhabited urban areas worldwide. This six-part volume takes a critical look at the concept of Historic Urban Landscapes, the approach that UNESCO promotes to achieve holistic management of urban heritage, through the lens of issues, prospects, and experiences of urban regeneration of the selected geo-cultural context. It further discusses the difficult task that heritage managers encounter in conceptualizing, mapping, curating, and sustaining the plurality, poetics, and politics of urban heritage of the regions in question. The connective thesis that weaves the chapters in this volume together reinforces for readers that the management of urban heritage considers cities as dynamic entities, palimpsests of historical memories, collages of social diversity, territories of contested identities, and sites for sustainable liveability. Throughout this edited collection, chapters argue for recognizing the totality of the eco-cultural urban fabric, embracing change, building social cohesion, and initiating strategic socio-economic progress in the conservation of historic urban landscapes. Containing thirty-six contributions written by leading regional experts, and illustrated with over 200 black and white images and tables, this volume provides a much-needed resource on historic urban landscapes for students, scholars and researchers"-- Provided by publisher.

Summary: "This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data"-- Provided by publisher.

Summary: "Where Ideas Go to Die explores the troubled relationship of US journalism and intellect. A defender of common sense, the press is irked at intellect yet often dependent on its critical autonomy. A postwar observation from Richard Hofstadter applies to contemporary journalists: "Men do not rise in the morning, grin at themselves in their mirrors, and say: 'Ah, today I shall torment an intellectual and strangle an idea!'" The book nevertheless documents the prowess of news media in the policing of intellect. Control extends beyond suppression of ideas and ways of thinking to the aggressive rendering of dissent into deviance. The social control of intellect by journalism is accompanied by social control of journalism in newsrooms and in classrooms where norms are cultivated. Anti-intellectualism consequently operates like dark matter in media, a presence inferred by its effects rather than directly observed or acknowledged. When journalists anticipate a punitive public, the reified resentment is no more real than the fiction of omnipotent citizens in democratic theory, yet the audience imagined compels how intellect is rendered in the news as nuisance, deviance, or object of ridicule. Journalism's contribution to the social control of ideas is poignantly democratic: audiences are cast in consequential roles that affirm their wisdom in a closed, self-referential system. The book concludes with a discussion about what intellectual journalism would look like. Interviews with 25 "dangerous professors" demonstrate how alliances in the academic-media nexus can seed intellect in newswork"-- Provided by publisher.

Summary: Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn: Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and Keras Deploy and train reinforcement learning–based solutions via cloud resources Apply practical applications of reinforcement learning.

Summary: "Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you'll find the information, insight, and tools you need to flourish in today's data-driven economy. You'll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling. Understand how use ML tools in real world business problems, where causation matters more that correlation. data science programs by scripting in the R programming language Today's business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It's about the exciting things being done around Big Data to run a flourishing business. It's about the precepts, principals, and best practices that you need know for best-in-class business data science"-- Provided by publisher.

Summary: The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks, cloud-based systems (e.g., Salesforce), databases (e.g., Oracle), AWS machine learning (e.g., Amazon Lex), and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our history. AI will be used from the C-suite to the distribution warehouse floor. Replete with case studies, this book provides a working knowledge of AI's current and future capabilities and the impact it will have on every business. It covers everything from healthcare to warehousing, banking, finance and education. It is essential reading for anyone involved in industry.

Summary: Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling. As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields.

Summary: Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you. The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment. The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn to predict conditions for your bot and develop a conversational agent as a web application. Finally you will deploy your chatbot on your own server with AWS. You will: Gain the basics of natural language processing using Python Collect data and train your data for the chatbot Build your chatbot from scratch as a web app Integrate your chatbots with Facebook, Slack, and Telegram Deploy chatbots on your own server.

Summary: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You'll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You'll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.

Summary: Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic in the hobbyist world of computing understandable and approachable. Machine learning, also commonly referred to as deep learning (DL), is currently being integrated into a multitude of commercial products as well as widely being used in industrial, medical, and military applications. It is hard to find any modern human activity, which has not been "touched" by artificial intelligence (AI) applications. Building on the concepts first presented in Beginning Artificial Intelligence with the Raspberry Pi, you’ll go beyond simply understanding the concepts of AI into working with real machine learning experiments and applying practical deep learning concepts to experiments with the Pi board and computer vision. What you learn with Machine Learning with the Raspberry Pi can then be moved on to other platforms to go even further in the world of AI and ML to better your hobbyist or commercial projects.

Summary: "Amid the torrent of information we receive every day, aggregation of previously reported stories allows readers to streamline, summarize, and process the news. While aggregation in one form or another has been part of journalism since the nineteenth century, it has taken on a new prominence and influence in today's media ecosystem While aggregation practices are increasingly being adopted by new digital entities and leading news organizations via news apps, e-mail newsletters, and other formats, they are denigrated by journalists as not "real" journalism and inferior to reporting. More recently, aggregation has been viewed with suspicion as a practice that allows for the news to be repackaged in ways that reflect political bias. Despite the tendency of some aggregators to distort the news, whether for the purpose of clicks or politics, aggregation, Coddington argues, serve an important purpose in the contemporary news environment. Given that aggregation is likely here to stay, journalists and readers need to develop practices to better understand and improve its implementation and influence. Coddington's work is based on his fieldwork and interviews with aggregators at five different news organizations, including both startups and legacy media organizations that use aggregation. The book focuses on how aggregators make decisions on what to publish, how they create narrative, how they understand their audiences, and how they view their own work"-- Provided by publisher.

Summary: "Always Get the Name of the Dog is a guide to journalistic interviewing written by a journalist for journalists. It features advice from some of the best writers and reporters in the business and takes a comprehensive view of media interviewing across multiple platforms, while emphasizing active learning to give readers actionable steps to become great media interviewers. Through real scenarios and examples, this text takes future journalists through the steps of the interview, from research to source identification to question development and beyond. Whether you are a journalism student or an experienced reporter looking to sharpen your skills, this text can help make sure you get all you need from every interview you conduct"-- Provided by publisher.

Summary: "Now in its fourth edition, Media Effects again features essays from some of the finest scholars in the field and serves as a comprehensive reference volume for scholars, teachers, and students. This edition contains both new and updated content that reflects our media-saturated environments, including chapters on social media, video games, mobile communication, and virtual technologies. In recognition of the multitude of research trajectories within media effects, this edition also includes new chapters on narratives, positive media, the self and identity, media selection, and cross-cultural media effects. As scholarship in media effects continues to evolve and expand, Media Effects serves as a benchmark of theory and research for the current and future generations of scholars. Ideal for scholars and for undergraduate and graduate courses in media effects, media psychology, media theory, psychology, sociology, political science, and related disciplines"-- Provided by publisher.

Summary: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points ? Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP ? Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP ? Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.

Summary: This book focuses on mediated intercultural communication in the context of globalization. Analyzing social and traditional media using qualitative, interpretive, and critical and cultural perspectives, contributors engage with diverse topics - ranging from hybrid identities in different communities, to journalistic collaborations in the global media landscape. In addition, the authors also examine the placeless and borderless communities of diaspora members, their transnational identities, and the social media stories that shape and are shaped by them.

Summary: "Meet the biggest animal on land--the African elephant! Everything about this title is big, from the full-bleed photographs to the content." -- Amazon.com.

Summary: This volume seeks to analyse the emerging wave of data journalism in the Global South. It does so by examining trends, developments and opportunities for data journalism in the aforementioned contexts. Whilst studies in this specific form of journalism are increasing in numbers and significance, there remains a dearth of literature on data journalism in less developed regions of the world. By demonstrating an interest in data journalism across countries including Chile, Argentina, the Philippines, South Africa and Iran, among others, this volume contributes to multifaceted transnational debates on journalism, and is a crucial reference text for anyone interested in data journalism in the 'developing' world. Drawing on a range of voices from different fields and nations, sharing empirical and theoretical experiences, the volume aims to initiate a global dialogue among journalism practitioners, researchers and students.

Summary: Build smart applications using cutting-edge technologies such as rules engines, code automation frameworks, and natural language processing (NLP). This book provides step-by-step instructions on how to port nine rules engines (CLIPS, JRuleEngine, DTRules, Zilonis, TermWare, Roolie, OpenRules, JxBRE, and JEOPS) to the Android platform. You'll learn how to use each rules engine to build a smart application with sample code snippets so that you can get started with programming smart applications immediately. Build Android-Based Smart Applicationsalso describes porting issues with other popular rules engines (Drools, JLisa, Take, and Jess). This book is a step-by-step guide on how to generate a working smart application from requirement specifications. It concludes by showing you how to generate a smart application from unstructured knowledge using the Stanford POS (Part of Speech) tagger NLP framework. You will: Evaluate the available rules engines to see which rules engine is best to use for building smart applications Build smart applications using rules engines Create a smart application using NLP Automatically generate smart application from requirement specifications.

Summary: Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. You will: Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms .

Summary: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! You will: Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.

Summary: Use Microsoft Azure to optimally design your data solutions and save time and money. Scenarios are presented covering analysis, design, integration, monitoring, and derivatives. This book is about data and provides you with a wide range of possibilities to implement a data solution on Azure, from hybrid cloud to PaaS services. Migration from existing solutions is presented in detail. Alternatives and their scope are discussed. Five of six chapters explore PaaS, while one focuses on SQL Server features for cloud and relates to hybrid cloud and IaaS functionalities. What You'll Learn: Know the Azure services useful to implement a data solution Match the products/services used to your specific needs Fit relational databases efficiently into data design Understand how to work with any type of data using Azure Hybrid and Public cloud features Use non-relational alternatives to solve even complex requirements Orchestrate data movement using Azure services Approach analysis and manipulation according to the data life cycle.

Summary: This collection of essays presents new formulations of ideas and practices within documentary media that respond critically to the multifaceted challenges of our age. As social media, augmented reality, and interactive technologies play an increasing role in the documentary landscape, new theorizations are needed to account for how such media both represents recent political, socio-historical, environmental, and representational shifts, and challenges the predominant approaches by promoting new critical sensibilities. The contributions to this volume approach the idea of "critical distance" in a documentary context and in subjects as diverse as documentary exhibitions, night photography, drone imagery, installation art, mobile media, nonhuman creative practices, sound art and interactive technologies. It is essential reading for scholars, practitioners and students working in fields such as documentary studies, film studies, cultural studies, contemporary art history and digital media studies.

Summary: Citizen Journalism as Conceptual Practice provides a conceptualization of citizen journalism as a political practice developed through analyses of an historical and postcolonial case. Arguing that citizen journalism is first and foremost situated, embodied and political rather than networked and technology-based, the book offers a grounded analysis of the colonial newspaper, The Herald, published in St. Croix (Virgin Islands) 1915-25 by a descendant of enslaved people and independently of the colonial ruler, Denmark. The analysis is informed by Deleuze and Guattari’s approach to knowledge production and formulates a critical reading of citizens’ and subjects’ mediated political engagements then as well as now. The book discusses current approaches to citizen journalism before turning to The Herald, which is then read against the grain in an attempt to show the embodied politics of colonial history and cultural forms of citizen engagement as these politics evolve in this particular case of journalism

Summary: Regional Parallelism and Corruption Scandals in Nigeria is a ground-breaking book that offers fresh perspectives on the character and role of the African media in covering corruption scandals. It explores whether reports regarding corruption stem from the efforts of journalists who employ investigative journalism, or if it is a mere coincidence promoted by the activities of anti-corruption agencies. To that end, the book develops a media systems theory for Africa based on the coverage of corruption scandals in the Nigerian press. This new theoretical paradigm, defined as regional parallelism, argues that African countries are nations within nations, and that therefore any comparative study of the media system should recognize this understanding. The book combines both interviews with journalists as well as qualitative content analysis of newspapers to determine the patterns and issues that influence the reporting of corruption. It also looks at corruption within the media itself, taking into account factors such as regionalism and ethnicity within the practice of journalism.

Summary: This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.

Summary: This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking. To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions. Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.

Summary: Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning,

Subject: This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.

Summary: Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.

Summary: "Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at a cocktail party rather than a legitimate target of scientific inquiry. Scientists were allowed to posit only that the probability that one thing was associated with another. This all changed with Judea Pearl, whose work on causality was not just a victory for common sense, but a revolution in the study of the world"-- Provided by publisher.

Summary: Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations. You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy—others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers. You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. You will: Compare competing technologies and see why TensorFlow is more popular Generate text, image, or sound with GANs Predict the rating or preference a user will give to an item Sequence data with recurrent neural networks.

Summary: This book confronts the question of why our culture is so fascinated by the apocalypse. It ultimately argues that while many see the post-apocalyptic genre as reflective of contemporary fears, it has actually co-evolved with the transformations in our mediascape to become a perfect vehicle for transmedia storytelling. The post-apocalyptic offers audiences a portal to a fantasy world that is at once strange and familiar, offers a high degree of internal consistency and completeness, and allows for a diversity of stories by different creative teams in the same story world. With case studies of franchises such as The Walking Dead and The Terminator, Transmedia Storytelling and the Apocalypse offers analyses of how shifts in media industries and reception cultures have promoted a new kind of open, world-building narrative across film, television, video games, and printing For transmedia scholars and fans of the genre, this book shows how the end of the world is really just the beginning....

Summary: Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. You will: Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code.