"The first magnetic recording device was demonstrated and patented by the Danish inventor Valdemar Poulsen in 1898. Poulsen made a magnetic recording of his voice on a length of piano wire. MAGNETIC RECORDING traces the development of the watershed products and the technical breakthroughs in magnetic recording that took place during the century from Paulsen's experiment to today's ubiquitous audio, video, and data recording technologies including tape recorders, video cassette recorders, and computer hard drives.
An international author team brings a unique perspective, drawn from professional experience, to the history of magnetic recording applications. Their key insights shed light on how magnetic recording triumphed over all competing technologies and revolutionized the music, radio, television and computer industries. They also show how these developments offer opportunities for applications in the future.
MAGNETIC RECORDING features 116 illustrations, including 92 photographs of historic magnetic recording machines and their inventors."
Sponsored by:
IEEE Magnetics Society

Data and its technologies now play a large and growing role in humanities research and teaching. This book addresses the needs of humanities scholars who seek deeper expertise in the area of data modeling and representation. The authors, all experts in digital humanities, offer a clear explanation of key technical principles, a grounded discussion of case studies, and an exploration of important theoretical concerns. The book opens with an orientation, giving the reader a history of data modeling in the humanities and a grounding in the technical concepts necessary to understand and engage with the second part of the book. The second part of the book is a wide-ranging exploration of topics central for a deeper understanding of data modeling in digital humanities. Chapters cover data modeling standards and the role they play in shaping digital humanities practice, traditional forms of modeling in the humanities and how they have been transformed by digital approaches, ontologies which seek to anchor meaning in digital humanities resources, and how data models inhabit the other analytical tools used in digital humanities research. It concludes with a glossary chapter that explains specific terms and concepts for data modeling in the digital humanities context. This book is a unique and invaluable resource for teaching and practising data modeling in a digital humanities context.

Corpus Annotation gives an up-to-date picture of this fascinating new area of research, and will provide essential reading for newcomers to the field as well as those already involved in corpus annotation. Early chapters introduce the different levels and techniques of corpus annotation. Later chapters deal with software developments, applications, and the development of standards for the evaluation of corpus annotation. While the book takes detailed account of research world-wide, its focus is particularly on the work of the UCREL (University Centre for Computer Corpus Research on Language) team at Lancaster University, which has been at the forefront of developments in the field of corpus annotation since its beginnings in the 1970s.

A practical guide for multivariate statistical techniques-- now updated and revised
In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of interest.
Greatly revised and updated, this Second Edition provides helpful examples, graphical orientation, numerous illustrations, and an appendix detailing statistical software, including the S (or Splus) and SAS systems. It also offers
* An expanded chapter on cluster analysis that covers advances in pattern recognition
* New sections on inputs to clustering algorithms and aids for interpreting the results of cluster analysis
* An exploration of some new techniques of summarization and exposure
* New graphical methods for assessing the separations among the eigenvalues of a correlation matrix and for comparing sets of eigenvectors
* Knowledge gained from advances in robust estimation and distributional models that are slightly broader than the multivariate normal

This Second Edition is invaluable for graduate students, applied statisticians, engineers, and scientists wishing to use multivariate techniques in a variety of disciplines.

"Covers all areas of computer-based data acquisition--from basic concepts to the most recent technical developments--without the burden of long theoretical derivations and proofs. Offers practical, solution-oriented design examples and real-life case studies in each chapter and furnishes valuable selection guides for specific types of hardware."

In engineering work and other practical situations, methods of a non-stop character are often needed. The computer intensive methods outlined in this book should show how to pass many obstacles that could not previously be overcome. Much emphasis in this book is placed on applications in science, economics, reliability, meteorology, medicine and transportation. In principle every area where data deserve statistical analyses there is a relevant application of these new methods. This book is aimed at classically educated statisticians as well as the younger generation.

Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.

GENSTAT is a general purpose statistical computing system with a flexible command language operating on a variety of data structures. It may be used on a number of computer ranges, either interactively for exploratory data analysis, or in batch mode for standard data analysis.
The great flexibility of GENSTAT is demonstrated in this handbook by analysing the wide range of examples discussed in Applied Statistics - Principles and Examples (Cox and Snell, 1981). GENSTAT programs are listed for each of the examples. Most of the data sets are small but often it is these seemingly small problems which involve the most tricky statistical and computational procedures. This handbook is self-contained although for a full description of the analysis and interpretation it should be used in parallel with Applied Statistics - Principles and Examples.

Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own. This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool. You'll learn how to: Import, manipulate, visualize, and investigate data in Power BI Approach solutions for both self-service and enterprise BI Use Power BI in your organization's business intelligence strategy Produce effective reports and dashboards Create environments for sharing reports and managing data access with your team Determine the right solution for using Power BI offerings based on size, security, and computational needs

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies. Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyse the data, know what questions to ask of it and where and how the insights from the data can add the most value. Talent Intelligence is a practical guide that explains everything HR professionals need to know to achieve this. It outlines what Talent Intelligence (TI) is why it's important, how to use it to improve business results and includes guidance on how HR professionals can build the business case for it. This book also explains how and why talent intelligence is different from workforce planning, sourcing research and standard predictive HR analytics and shows how to assess where in the organization talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders. Most importantly, this book covers KPIs and metrics for success, short-term and long-term TI goals, an outline of what success looks like and the skills needed for effective Talent Intelligence. It also features case studies from organizations including Philips, Barclays and Kimberly-Clark.

Artificial intelligence is changing the world of work. How can HR professionals understand the variety of opportunities AI has created for the HR function and how best to implement these in their organization? This book provides the answers. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, artificial intelligence can add value throughout the work of HR professionals. Artificial Intelligence for HR demonstrates how to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice. Alongside coverage of employee engagement and recruitment, this second edition features new material on applications of AI for virtual work, reskilling and data integrity. Packed with practical advice, research and new and updated case studies from global organizations including Uber, IBM and Unilever, the second edition of Artificial Intelligence for HR will equip HR professionals with the knowledge they need to improve people operational efficiencies, and allow AI solutions to become enhancements for driving business success.

This volume deals with two complementary topics. On one hand the book deals with the problem of determining the the probability distribution of a positive compound random variable, a problem which appears in the banking and insurance industries, in many areas of operational research and in reliability problems in the engineering sciences. On the other hand, the methodology proposed to solve such problems, which is based on an application of the maximum entropy method to invert the Laplace transform of the distributions, can be applied to many other problems. The book contains applications to a large variety of problems, including the problem of dependence of the sample data used to estimate empirically the Laplace transform of the random variable. Contents Introduction Frequency models Individual severity models Some detailed examples Some traditional approaches to the aggregation problem Laplace transforms and fractional moment problems The standard maximum entropy method Extensions of the method of maximum entropy Superresolution in maxentropic Laplace transform inversion Sample data dependence Disentangling frequencies and decompounding losses Computations using the maxentropic density Review of statistical procedures

Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.

Artificial intelligence is changing the world of work. How can HR professionals understand the variety of opportunities AI has created for the HR function and how best to implement these in their organization? This book provides the answers. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, artificial intelligence can add value throughout the work of HR professionals. Artificial Intelligence for HR demonstrates how to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice. Alongside coverage of employee engagement and recruitment, this second edition features new material on applications of AI for virtual work, reskilling and data integrity. Packed with practical advice, research and new and updated case studies from global organizations including Uber, IBM and Unilever, the second edition of Artificial Intelligence for HR will equip HR professionals with the knowledge they need to improve people operational efficiencies, and allow AI solutions to become enhancements for driving business success.

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit. Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data. Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments.

The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems discusses the recent, rapid development of Internet of things (IoT) and its focus on research in smart cities, especially on surveillance tracking systems in which computing devices are widely distributed and huge amounts of dynamic real-time data are collected and processed. Efficient surveillance tracking systems in the Big Data era require the capability of quickly abstracting useful information from the increasing amounts of data. Real-time information fusion is imperative and part of the challenge to mission critical surveillance tasks for various applications. This book presents all of these concepts, with a goal of creating automated IT systems that are capable of resolving problems without demanding human aid.

This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.

This book examines how cloud-based services challenge the current application of antitrust and privacy laws in the EU and the US. The author looks at the elements of data centers, the way information is organized, and how antitrust, competition and privacy laws in the US and the EU regulate cloud-based services and their market practices. She discusses how platform interoperability can be a driver of incremental innovation and the consequences of not promoting radical innovation. She evaluates applications of predictive analysis based on big data as well as deriving privacy-invasive conduct. She looks at the way antitrust and privacy laws approach consumer protection and how lawmakers can reach more balanced outcomes by understanding the technical background of cloud-based services.

Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.).

Learn from Today's Most Successful Workforce Analytics Leaders Transforming the immense potential of workforce analytics into reality isn't easy. Pioneering practitioners have learned crucial lessons that can help you succeed. The Power of People shares their journeys-and their indispensable insights. Drawing on incisive case studies and vignettes, three experts help you bring purpose and clarity to any workforce analytics project, with robust research design and analysis to get reliable insights. They reveal where to start, where to find stakeholder support, and how to earn "quick wins" to build upon. You'll learn how to sustain success through best-practice data management, technology usage, partnering, and skill building. Finally, you'll discover how to earn even more value by establishing an analytical mindset throughout HR, and building two key skills: storytelling and visualization. The Power of People will be invaluable to HR executives establishing or leading analytics functions; HR professionals planning analytics projects; and any business executive who wants more value from HR.

Construct and implement a data warehousing plan.
In their efforts to collect information that will give them an edge, many companies have amassed vast amounts of data. Often this data becomes unwieldy and difficult to translate into anything useful. Data warehousing, storing all of this data in a system that allows for rapid retrieval of customized information, is the solution. This book, written by a data warehousing authority, is a step-by-step guide to creating and managing a data warehouse from start to finish, reviewing marketing, technology, and design issues.

The Critical Infrastructure Protection Survey recently released by Symantec found that 53% of interviewed IT security experts from international companies experienced at least ten cyber attacks in the last five years, and financial institutions were often subject to some of the most sophisticated and large-scale cyber attacks and frauds.

The book by Baldoni and Chockler analyzes the structure of software infrastructures found in the financial domain, their vulnerabilities to cyber attacks and the existing protection mechanisms. It then shows the advantages of sharing information among financial players in order to detect and quickly react to cyber attacks. Various aspects associated with information sharing are investigated from the organizational, cultural and legislative perspectives. The presentation is organized in two parts: Part I explores general issues associated with information sharing in the financial sector and is intended to set the stage for the vertical IT middleware solution proposed in Part II. Nonetheless, it is self-contained and details a survey of various types of critical infrastructure along with their vulnerability analysis, which has not yet appeared in a textbook-style publication elsewhere. Part II then presents the CoMiFin middleware for collaborative protection of the financial infrastructure.

The material is presented in an accessible style and does not require specific prerequisites. It appeals to both researchers in the areas of security, distributed systems, and event processing working on new protection mechanisms, and practitioners looking for a state-of-the-art middleware technology to enhance the security of their critical infrastructures in e.g. banking, military, and other highly sensitive applications. The latter group will especially appreciate the concrete usage scenarios included.

What if you could peer into the minds of an entire population? What if you could target the weakest with rumours that only they saw?

In 2016, an obscure British military contractor turned the world upside down. Funded by a billionaire on a crusade to start his own far-right insurgency, Cambridge Analytica combined psychological research with private Facebook data to make an invisible weapon with the power to change what voters perceived as real.

The firm was created to launch the then unknown Steve Bannon's ideological assault on America. But as it honed its dark arts in elections from Trinidad to Nigeria, 24-year-old research director Christopher Wylie began to see what he and his colleagues were unleashing.

He had heard the disturbing visions of the investors. He saw what CEO Alexander Nix did behind closed doors. When Britain shocked the world by voting to leave the EU, Wylie realised it was time to expose his old associates. The political crime of the century had just taken place - the weapon had been tested - and nobody knew.

This book is designed to enable and encourage health professionals and family support workers to include fathers in the process of their work. It focuses on the enormous potential value of accessing men at a time they are known to be particularly receptive - before and after the birth - within the context of providing solutions in the debate about problematic aspects of masculinity and fatherhood. It looks at how important the father's role is within the family environment and how fathers should be encouraged to take part in the upbringing of their children.