Building the Agile Enterprise with Capabilities, Collaborations and Values, Second Edition covers advances that make technology more powerful and pervasive while, at the same time, improving alignment of technology with business. Using numerous examples, illustrations, and case studies, Fred Cummins, an industry expert, author and former fellow with EDS and Hewlett Packard, updates his first edition incorporating the following industry developments: The ubiquitous use of the Internet along with intelligent, mobile devices, which have enabled everyone and everything to be connected anytime, anywhere The emergence of a "business architecture" discipline that has driven improvements in business design and transformation practices The development of CMMN (Case Management Model and Notation) that will provide automation to support the collaboration of knowledge workers and managers The development of VDML (Value Delivery Modeling Language) that supports modeling of business design from a management perspective The importance of "big data" management and analysis as a new source of insight into evolution of the business and the ecosystem How the architecture of the agile enterprise and business modeling change enterprise governance, management and innovation Building the Agile Enterprise with Capabilities, Collaborations and Values, Second Edition is a must have reference for business leaders, CTOs; business architects, information systems architects and business process modeling professionals who wish to close the gap between strategic planning and business operations as well as the gap between business and IT and enhance the creation and delivery of business value.

The book covers computational statistics, its methodologies and applications for IoT device. It includes the details in the areas of computational arithmetic and its influence on computational statistics, numerical algorithms in statistical application software, basics of computer systems, statistical techniques, linear algebra and its role in optimization techniques, evolution of optimization techniques, optimal utilization of computer resources, and statistical graphics role in data analysis. It also explores computational inferencing and computer model's role in design of experiments, Bayesian analysis, survival analysis and data mining in computational statistics.

This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a "Python corner," which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.

ENGINEERING APPLICATIONS A comprehensive text on the fundamental principles of mechanical engineering Engineering Applications presents the fundamental principles and applications of the statics and mechanics of materials in complex mechanical systems design. Using MATLAB to help solve problems with numerical and analytical calculations, authors and noted experts on the topic Mihai Dupac and Dan B. Marghitu offer an understanding of the static behaviour of engineering structures and components while considering the mechanics of materials knowledge as the most important part of their design. The authors explore the concepts, derivations, and interpretations of general principles and discuss the creation of mathematical models and the formulation of mathematical equations. This practical text also highlights the solutions of problems solved analytically and numerically using MATLAB. The figures generated with MATLAB reinforce visual learning for students and professionals as they study the programs. This important text: Shows how mechanical principles are applied to engineering design Covers basic material with both mathematical and physical insight Provides an understanding of classical mechanical principles Offers problem solutions using MATLAB Reinforces learning using visual and computational techniques Written for students and professional mechanical engineers, Engineering Applications helpshone reasoning skills in order to interpret data and generate mathematical equations, offering different methods of solving them for evaluating and designing engineering systems.

Enterprise Level Security 2: Advanced Topics in an Uncertain World follows on from the authors' first book on Enterprise Level Security (ELS), which covered the basic concepts of ELS and the discoveries made during the first eight years of its development. This book follows on from this to give a discussion of advanced topics and solutions, derived from 16 years of research, pilots, and operational trials in putting an enterprise system together. The chapters cover specific advanced topics derived from painful mistakes and numerous revisions of processes. This book covers many of the topics omitted from the first book including multi-factor authentication, cloud key management, enterprise change management, entity veracity, homomorphic computing, device management, mobile ad hoc, big data, mediation, and several other topics. The ELS model of enterprise security is endorsed by the Secretary of the Air Force for Air Force computing systems and is a candidate for DoD systems under the Joint Information Environment Program. The book is intended for enterprise IT architecture developers, application developers, and IT security professionals. This is a unique approach to end-to-end security and fills a niche in the market.

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results. The demand for and volume of data from surveys, registers or other sources containing sensible information on persons or enterprises have increased significantly over the last several years. At the same time, privacy protection principles and regulations have imposed restrictions on the access and use of individual data. Proper and secure microdata dissemination calls for the application of statistical disclosure control methods to the da ta before release. This book is intended for practitioners at statistical agencies and other national and international organizations that deal with confidential data. It will also be interesting for researchers working in statistical disclosure control and the health sciences.

Matrix Algorithms in MATLAB focuses on the MATLAB code implementations of matrix algorithms. The MATLAB codes presented in the book are tested with thousands of runs of MATLAB randomly generated matrices, and the notation in the book follows the MATLAB style to ensure a smooth transition from formulation to the code, with MATLAB codes discussed in this book kept to within 100 lines for the sake of clarity. The book provides an overview and classification of the interrelations of various algorithms, as well as numerous examples to demonstrate code usage and the properties of the presented algorithms. Despite the wide availability of computer programs for matrix computations, it continues to be an active area of research and development. New applications, new algorithms, and improvements to old algorithms are constantly emerging.

Since 1984, Geophysical Data Analysis has filled the need for a short, concise reference on inverse theory for individuals who have an intermediate background in science and mathematics. The new edition maintains the accessible and succinct manner for which it is known, with the addition of: MATLAB examples and problem sets Advanced color graphics Coverage of new topics, including Adjoint Methods; Inversion by Steepest Descent, Monte Carlo and Simulated Annealing methods; and Bootstrap algorithm for determining empirical confidence intervals

The basics of computer algebra and the language of Mathematica are described. This title will lead toward an understanding of Mathematica that allows the reader to solve problems in physics, mathematics, and chemistry. Mathematica is the most widely used system for doing mathematical calculations by computer, including symbolic and numeric calculations and graphics. It is used in physics and other branches of science, in mathematics, education and many other areas. Many important results in physics would never be obtained without a wide use of computer algebra.

This book covers the whole range of numerical mathematics--from linear equations to ordinary differential equations--and details the calculus of errors and partial differential equations. In attempting to give a unified approach of theory, algorithms, applications, and use of software, the book contains many helpful examples and applications. Topics include linear optimization, numerical integration, initial value problems, and nonlinear equations. The book is appearing simultaneously with the problem-solving environment PAN, a system that contains an enlarged hypertext version of the text together with all of the programs described in the book, help systems, and utility tools. (PAN is licensed public domain software.) The text is ideally suited as an introduction to numerical methods and programming for undergraduates in computer science, engineering, and mathematics. It will also be useful to software engineers using NAG libraries and numerical algorithms.

This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021. Papers are contributed from established and emerging scholars, covering cutting-edge and contemporary innovative techniques in statistics and data science. Major areas of contribution include Bayesian statistics; computational statistics; data science; semi-parametric regression; and stochastic methods in biology, crop science, ecology and engineering. It will be a valuable edited collection for graduate students, researchers, and practitioners in a wide array of applied statistical and data science methods.

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows. New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book's CRC Press web page.

With recent advances in IT in areas such as AI and IoT, collaboration systems such as business chat, cloud services, conferencing systems, and unified communications are rapidly becoming widely used as new IT applications in global corporations' strategic activities. Through in-depth longitudinal studies of global corporations, the book presents a new theoretical framework and implications for IT-enabled dynamic capabilities using collaboration systems from the perspective of micro strategy theory and organization theory. The content of the book is based on longitudinal analyses that employ various qualitative research methods including ethnography, participant observation, action research and in-depth case studies of global corporations in Europe, the United States and Asia that actively use collaboration systems. It presents a new concept of micro dynamism whereby dynamic "IT-enabled knowledge communities" such as "IT-enabled communities of practice" and "IT-enabled strategic communities" create "IT-enabled dynamic capabilities" through the integration of four research streams - an information systems view, micro strategy view, micro organization view and knowledge-based view. The book demonstrates that collaboration systems create, maintain and develop "IT-enabled knowledge communities" within companies and are strategic IT applications for enhancing the competitiveness of companies in the ongoing creation of new innovation and the realization of sustainable growth in a 21st century knowledge-based society. This book is primarily written for academics, researchers and graduate students, but will also offer practical implications for business leaders and managers. Its use is anticipated not only in business and management schools, graduate schools and university education environments around the world but also in the broad business environment including management and leadership development training.

An accessible introduction to the theoretical and computational aspects of linear algebra using MapleTM

Many topics in linear algebra can be computationally intensive, and software programs often serve as important tools for understanding challenging concepts and visualizing the geometric aspects of the subject. Principles of Linear Algebra with Maple uniquely addresses the quickly growing intersection between subject theory and numerical computation, providing all of the commands required to solve complex and computationally challenging linear algebra problems using Maple. The authors supply an informal, accessible, and easy-to-follow treatment of key topics often found in a first course in linear algebra.

Requiring no prior knowledge of the software, the book begins with an introduction to the commands and programming guidelines for working with Maple. Next, the book explores linear systems of equations and matrices, applications of linear systems and matrices, determinants, inverses, and Cramer's rule. Basic linear algebra topics such as vectors, dot product, cross product, and vector projection are explained, as well as the more advanced topics of rotations in space, rolling a circle along a curve, and the TNB Frame. Subsequent chapters feature coverage of linear transformations from Rn to Rm, the geometry of linear and affine transformations, least squares fits and pseudoinverses, and eigenvalues and eigenvectors.

The authors explore several topics that are not often found in introductory linear algebra books, including sensitivity to error and the effects of linear and affine maps on the geometry of objects. The Maple software highlights the topic's visual nature, as the book is complete with numerous graphics in two and three dimensions, animations, symbolic manipulations, numerical computations, and programming. In addition, a related Web site features supplemental material, including Maple code for each chapter's problems, solutions, and color versions of the book's figures.

Extensively class-tested to ensure an accessible presentation, Principles of Linear Algebra with Maple is an excellent book for courses on linear algebra at the undergraduate level. It is also an ideal reference for students and professionals who would like to gain a further understanding of the use of Maple to solve linear algebra problems.

Computational Finance Using C and C#: Derivatives and Valuation, Second Edition provides derivatives pricing information for equity derivatives, interest rate derivatives, foreign exchange derivatives, and credit derivatives. By providing free access to code from a variety of computer languages, such as Visual Basic/Excel, C++, C, and C#, it gives readers stand-alone examples that they can explore before delving into creating their own applications. It is written for readers with backgrounds in basic calculus, linear algebra, and probability. Strong on mathematical theory, this second edition helps empower readers to solve their own problems. *Features new programming problems, examples, and exercises for each chapter. *Includes freely-accessible source code in languages such as C, C++, VBA, C#, and Excel.. *Includes a new chapter on the history of finance which also covers the 2008 credit crisis and the use of mortgage backed securities, CDSs and CDOs. *Emphasizes mathematical theory.

Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale "microanalysis" of single texts to large scale "macroanalysis" of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book's focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.

The ability to preserve electronic evidence is critical to presenting a solid case for civil litigation, as well as in criminal and regulatory investigations. Preserving Electronic Evidence for Trial provides everyone connected with digital forensics investigation and litigation with a clear and practical hands-on guide to the best practices in preserving electronic evidence. Corporate management personnel (legal & IT) and outside counsel need reliable processes for the litigation hold - identifying, locating, and preserving electronic evidence. Preserving Electronic Evidence for Trial provides the road map, showing you how to organize the digital evidence team before the crisis, not in the middle of litigation. This practice handbook by an internationally known digital forensics expert and an experienced litigator focuses on what corporate and litigation counsel as well as IT managers and forensic consultants need to know to communicate effectively about electronic evidence. You will find tips on how all your team members can get up to speed on each other's areas of specialization before a crisis arises. The result is a plan to effectively identify and pre-train the critical electronic-evidence team members. You will be ready to lead the team to success when a triggering event indicates that litigation is likely, by knowing what to ask in coordinating effectively with litigation counsel and forensic consultants throughout the litigation progress. Your team can also be ready for action in various business strategies, such as merger evaluation and non-litigation conflict resolution.

This book illustrates the potential for computer simulation in the study of modern slavery and worker abuse, and by extension in all social issues. It lays out a philosophy of how agent-based modelling can be used in the social sciences. In addressing modern slavery, Chesney considers precarious work that is vulnerable to abuse, like sweat-shop labour and prostitution, and shows how agent modelling can be used to study, understand and fight abuse in these areas. He explores the philosophy, application and practice of agent modelling through the popular and free software NetLogo. This topical book is grounded in the technology needed to address the messy, chaotic, real world problems that humanity faces-in this case the serious problem of abuse at work-but equally in the social sciences which are needed to avoid the unintended consequences inherent to human responses. It includes a short but extensive NetLogo guide which readers can use to quickly learn this software and go on to develop complex models. This is an important book for students and researchers of computational social science and others interested in agent-based modelling.

With this practical guide, you'll learn how to understand the needs of external customers without requirements elicitation or sign-offs, the difference between customer and business value, and why you need to create both. You'll discover how to respond to changes in the market and the actions of competitors. You'll understand how to develop new products, launch them into the market, and how to deliver business outcomes through the maturity and eventual retirement of your product.

Applied Computing in Medicine and Health is a comprehensive presentation of on-going investigations into current applied computing challenges and advances, with a focus on a particular class of applications, primarily artificial intelligence methods and techniques in medicine and health. Applied computing is the use of practical computer science knowledge to enable use of the latest technology and techniques in a variety of different fields ranging from business to scientific research. One of the most important and relevant areas in applied computing is the use of artificial intelligence (AI) in health and medicine. Artificial intelligence in health and medicine (AIHM) is assuming the challenge of creating and distributing tools that can support medical doctors and specialists in new endeavors. The material included covers a wide variety of interdisciplinary perspectives concerning the theory and practice of applied computing in medicine, human biology, and health care. Particular attention is given to AI-based clinical decision-making, medical knowledge engineering, knowledge-based systems in medical education and research, intelligent medical information systems, intelligent databases, intelligent devices and instruments, medical AI tools, reasoning and metareasoning in medicine, and methodological, philosophical, ethical, and intelligent medical data analysis.

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package.

Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Universite Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis.

This is the new, fully-revised edition to the book Bayesian Core: A Practical Approach to Computational Bayesian Statistics.

Jean-Michel Marin is Professor of Statistics at Universite Montpellier 2, France, and Head of the Mathematics and Modelling research unit. He has written over 40 papers on Bayesian methodology and computing, as well as worked closely with population geneticists over the past ten years.

Christian Robert is Professor of Statistics at Universite Paris-Dauphine, France. He has written over 150 papers on Bayesian Statistics and computational methods and is the author or co-author of seven books on those topics, including The Bayesian Choice (Springer, 2001), winner of the ISBA DeGroot Prize in 2004. He is a Fellow of the Institute of Mathematical Statistics, the Royal Statistical Society and the American Statistical Society. He has been co-editor of the Journal of the Royal Statistical Society, Series B, and in the editorial boards of the Journal of the American Statistical Society, the Annals of Statistics, Statistical Science, and Bayesian Analysis. He is also a recipient of an Erskine Fellowship from the University of Canterbury (NZ) in 2006 and a senior member of the Institut Universitaire de France (2010-2015)."