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Principles of Computer Security: Comp TIA Security and Beyond
by Gregory White Chuck Cothren Roger Davis Dwayne Williams Wm. ConklinLearn IT security fundamentals while getting complete coverage of the objectives for the latest release of CompTIA Security+ certification exam SY0-501. This thoroughly revised, full-color textbook discusses communication, infrastructure, operational security, attack prevention, disaster recovery, computer forensics, and much more. Written by a pair of highly respected security educators, Principles of Computer Security: CompTIA Security+® and Beyond, Fifth Edition (Exam SY0-501) will help you pass the exam and become a CompTIA certified computer security expert.
Principles Of Computer Security Lab Manual
by Vincent Nestler Keith Harrison Matthew Hirsch Wm. Conklin Corey SchouPractice the Computer Security Skills You Need to Succeed! <p><p> 40+ lab exercises challenge you to solve problems based on realistic case studies <p> Step-by-step scenarios require you to think critically <p> Lab analysis tests measure your understanding of lab results <p> Key term quizzes help build your vocabulary <p> Labs can be performed on a Windows, Linux, or Mac platform with the use of virtual machines
Principles of Computer Systems and Network Management
by Dinesh Chandra VermaSystems Management is emerging as the predominant area for computer science in the enterprise, with studies showing that the bulk (up to 80%) of an enterprise IT budget is spent on management/operational issues and is the largest piece of the expenditure. This textbook provides an overview of the field of computer systems and network management. Systems management courses are being taught in different graduate and undergraduate computer science programs, but there are no good books with a comprehensive overview of the subject. This text book will provide content appropriate for either an undergraduate course (junior or senior year) or a graduate course in systems management.
Principles of Cyber-Physical Systems
by Rajeev AlurA foundational text that offers a rigorous introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems.A cyber-physical system consists of a collection of computing devices communicating with one another and interacting with the physical world via sensors and actuators in a feedback loop. Increasingly, such systems are everywhere, from smart buildings to medical devices to automobiles. This textbook offers a rigorous and comprehensive introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems. The book draws on a diverse set of subdisciplines, including model-based design, concurrency theory, distributed algorithms, formal methods of specification and verification, control theory, real-time systems, and hybrid systems, explaining the core ideas from each that are relevant to system design and analysis.The book explains how formal models provide mathematical abstractions to manage the complexity of a system design. It covers both synchronous and asynchronous models for concurrent computation, continuous-time models for dynamical systems, and hybrid systems for integrating discrete and continuous evolution. The role of correctness requirements in the design of reliable systems is illustrated with a range of specification formalisms and the associated techniques for formal verification. The topics include safety and liveness requirements, temporal logic, model checking, deductive verification, stability analysis of linear systems, and real-time scheduling algorithms. Principles of modeling, specification, and analysis are illustrated by constructing solutions to representative design problems from distributed algorithms, network protocols, control design, and robotics.This book provides the rapidly expanding field of cyber-physical systems with a long-needed foundational text by an established authority. It is suitable for classroom use or as a reference for professionals.
Principles of Cybercrime
by Jonathan CloughWe live in a digital age. The proliferation of digital technology, and the convergence of computing and communication devices, has transformed the way in which we socialise and do business. While overwhelmingly positive, there has also been a dark side to these developments. Proving the maxim that crime follows opportunity, virtually every advance has been accompanied by a corresponding niche to be exploited for criminal purposes; so-called 'cybercrimes'. Whether it be fraud, child pornography, stalking, criminal copyright infringement or attacks on computers themselves, criminals will find ways to exploit new technology. The challenge for all countries is to ensure their criminal laws keep pace. The challenge is a global one, and much can be learned from the experience of other jurisdictions. Focusing on Australia, Canada, the UK and the US, this book provides a comprehensive analysis of the legal principles that apply to the prosecution of cybercrimes.
Principles of Data Fabric: Become a data-driven organization by implementing Data Fabric solutions efficiently
by Sonia MezzettaApply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn to design Data Fabric architecture effectively with your choice of toolBuild and use a Data Fabric solution using DataOps and Data Mesh frameworksFind out how to build Data Integration, Data Governance, and Self-Service analytics architectureBook DescriptionData can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered.The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You'll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you'll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles.By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution.What you will learnUnderstand the core components of Data Fabric solutionsCombine Data Fabric with Data Mesh and DataOps frameworksImplement distributed data management and regulatory compliance using Data FabricManage and enforce Data Governance with active metadata using Data FabricExplore industry best practices for effectively implementing a Data Fabric solutionWho this book is forIf you are a data engineer, data architect, or business analyst who wants to learn all about implementing Data Fabric architecture, then this is the book for you. This book will also benefit senior data professionals such as chief data officers looking to integrate Data Fabric architecture into the broader ecosystem.
Principles of Data Mining
by Max BramerThis book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.
Principles of Data Mining (Undergraduate Topics in Computer Science)
by Max BramerThis book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification.
Principles of Data Mining (Undergraduate Topics in Computer Science)
by Max BramerData Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail.This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data.Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field.Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
Principles of Data Mining
by Max BramerData Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
Principles of Data Mining
by David J. Hand Heikki Mannila Padhraic SmythThe growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Principles of Data Science (Transactions on Computational Science and Computational Intelligence)
by Hamid R. Arabnia Robert Stahlbock Kevin Daimi Cristina Soviany Leonard Heilig Kai BrüssauThis book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science.Introduces various techniques, methods, and algorithms adopted by Data Science expertsProvides a detailed explanation of data science perceptions, reinforced by practical examplesPresents a road map of future trends suitable for innovative data science research and practice
Principles of Data Science: A beginner's guide to essential math and coding skills for data fluency and machine learning
by null Sinan OzdemirTransform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey FeaturesLearn practical data science combined with data theory to gain maximum insights from dataDiscover methods for deploying actionable machine learning pipelines while mitigating biases in data and modelsExplore actionable case studies to put your new skills to use immediatelyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPrinciples of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What you will learnMaster the fundamentals steps of data science through practical examplesBridge the gap between math and programming using advanced statistics and MLHarness probability, calculus, and models for effective data controlExplore transformative modern ML with large language modelsEvaluate ML success with impactful metrics and MLOpsCreate compelling visuals that convey actionable insightsQuantify and mitigate biases in data and ML modelsWho this book is forIf you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you’ll find this book useful. Familiarity with Python programming will further enhance your learning experience.
Principles of Data Science
by Sinan OzdemirLearn the techniques and math you need to start making sense of your data About This Book • Enhance your knowledge of coding with data science theory for practical insight into data science and analysis • More than just a math class, learn how to perform real-world data science tasks with R and Python • Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn • Get to know the five most important steps of data science • Use your data intelligently and learn how to handle it with care • Bridge the gap between mathematics and programming • Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results • Build and evaluate baseline machine learning models • Explore the most effective metrics to determine the success of your machine learning models • Create data visualizations that communicate actionable insights • Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
Principles of Data Wrangling: Practical Techniques for Data Preparation
by Tye Rattenbury Joseph M. Hellerstein Jeffrey Heer Sean Kandel Connor CarrerasA key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?"Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations.Appreciate the importance—and the satisfaction—of wrangling data the right way.Understand what kind of data is availableChoose which data to use and at what level of detailMeaningfully combine multiple sources of dataDecide how to distill the results to a size and shape that can drive downstream analysis
Principles of Database and Solid-State Drive Co-Design (Synthesis Lectures on Data Management)
by Alberto Lerner Philippe BonnetThis book offers a comprehensive resource on Solid-State Drives (SSD) as the field undergoes a radical evolution characterized by the incredible variety of SSD forms and their rapid diversification. It proposes a new classification system to help readers navigate the SSD landscape. For years, the evolution of SSDs was obscured by the unchanging abstractions of block devices and POSIX I/O, but it is apparent that these abstractions have become a problematic hinderance to performance and also fail to reduce software complexity. The book explores how such a state of affairs impacts the database community in at least two ways. First, it considers how using SSDs through legacy interfaces that hide internal mechanisms invariably results in erratic performance. While the blame often goes to the notoriously expensive garbage collection of SSDs, the authors argue that in truth, several other complex processes result in nonlinear effects on latency and bandwidth. The book describes these processes and how they are implemented in modern devices, knowledge that will help system designers better choose SSDs and shape database workloads to match their performance characteristics. Second, the book explores how the inadequacy of the traditional I/O abstractions opens up an entire research field focused on the co-design of database management systems and SSD. Such research aims at devising mechanisms and policies coupling the storage manager of database and SSD internals, e.g., placing an SSD FTL under the control of database, changing SSD sub-systems in response to the workload, or executing logic within an SSD on a database’s behalf. The book introduces these principles of DBMS/SSD co-design and argues that a more seamless integration of databases and storage solutions as well as the study of SSD variations adapted to database computations are central to the development of the next generation of database systems.
Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data
by Wilfried Lemahieu Seppe Vanden Broucke Bart BaesensThis comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL; MongoDB; Neo4j Cypher; and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.
Principles Of Digital Communication: A Top-Down Approach
by Bixio RimoldiThis comprehensive and accessible text teaches the fundamentals of digital communication via a top-down-reversed approach, specifically formulated for a one-semester course. The unique approach focuses on the transmission problem and develops knowledge of receivers before transmitters. In doing so it cuts straight to the heart of the digital communication problem, enabling students to learn quickly, intuitively, and with minimal background knowledge. Beginning with the decision problem faced by a decoder and going on to cover receiver designs for different channels, hardware constraints, design trade-offs, convolutional coding, Viterbi decoding, and passband communication, detail is given on system-level design as well as practical applications in engineering. All of this is supported by numerous worked examples, homework problems, and MATLAB simulation exercises to aid self-study, providing a solid basis for students to specialize in the field of digital communication and making it suitable for both traditional and flipped classroom teaching.
Principles of Digital Communication and Coding (Dover Books on Electrical Engineering)
by Jim K. Omura Andrew J. ViterbiWritten by two distinguished experts in the field of digital communications, this classic text remains a vital resource three decades after its initial publication. Its treatment is geared toward advanced students of communications theory and to designers of channels, links, terminals, modems, or networks used to transmit and receive digital messages.The three-part approach begins with the fundamentals of digital communication and block coding, including an analysis of block code ensemble performance. The second part introduces convolutional coding, exploring ensemble performance and sequential decoding. The final section addresses source coding and rate distortion theory, examining fundamental concepts for memoryless sources as well as precepts related to memory, Gaussian sources, and universal coding. Appendixes of useful information appear throughout the text, and each chapter concludes with a set of problems, the solutions to which are available online.
Principles of Game Audio and Sound Design: Sound Design and Audio Implementation for Interactive and Immersive Media
by Jean-Luc SinclairPrinciples of Game Audio and Sound Design is a comprehensive introduction to the art of sound for games and interactive media using Unity. This accessible guide encompasses both the conceptual challenges of the artform as well as the technical and creative aspects, such as sound design, spatial audio, scripting, implementation and mixing. Beginning with basic techniques, including linear and interactive sound design, before moving on to advanced techniques, such as procedural audio, Principles of Game Audio and Sound Design is supplemented by a host of digital resources, including a library of ready-to-use, adaptable scripts. This thorough introduction provides the reader with the skills and tools to combat the potential challenges of game audio independently. Principles of Game Audio and Sound Design is the perfect primer for beginner- to intermediate-level readers with a basic understanding of audio production and Unity who want to learn how to gain a foothold in the exciting world of game and interactive audio.
Principles of High-Performance Processor Design: For High Performance Computing, Deep Neural Networks and Data Science
by Junichiro MakinoThis book describes how we can design and make efficient processors for high-performance computing, AI, and data science. Although there are many textbooks on the design of processors we do not have a widely accepted definition of the efficiency of a general-purpose computer architecture. Without a definition of the efficiency, it is difficult to make scientific approach to the processor design. In this book, a clear definition of efficiency is given and thus a scientific approach for processor design is made possible. In chapter 2, the history of the development of high-performance processor is overviewed, to discuss what quantity we can use to measure the efficiency of these processors. The proposed quantity is the ratio between the minimum possible energy consumption and the actual energy consumption for a given application using a given semiconductor technology. In chapter 3, whether or not this quantity can be used in practice is discussed, for many real-world applications. In chapter 4, general-purpose processors in the past and present are discussed from this viewpoint. In chapter 5, how we can actually design processors with near-optimal efficiencies is described, and in chapter 6 how we can program such processors. This book gives a new way to look at the field of the design of high-performance processors.
Principles of Image Printing Technology
by Yuri V. KuznetsovPrinciples of Image Printing Technology is a unique review of technology use in the printing industry since the time of the medieval engravers and busy newsroom typesetters. It provides a historical review of the advancement of technology and describes in-depth both technical fundamentals and industrial procedures. Intended primarily for students in graphic communications programs, this book includes all the necessary background for understanding printing technology. In addition, by providing findings from basic research studies and industrial processes that have been omitted elsewhere in published volumes, it offers a useful guide to researchers and professionals in the printing industry.
Principles of Imprecise-Information Processing
by Shiyou LianThe book showcases cutting-edge concepts and methods, and presents the principle of imprecise-information processing. It also proposes a new theory and technology for imprecise-information processing that differs from fuzzy technology, thus providing a platform for related applications and laying the theoretical basis for further research. Imprecise-information processing - a type of processing based on flexible linguistic values and quantifiable rigid linguistic values - is an important component of intelligence science and technology. This book offers an easy-to-understand overview of the basic principles and methods of imprecise-information processing, allowing readers to develop related applications or pursue further research.
Principles of Imprecise-Information Processing: A New Theoretical and Technological System on Flexible Linguistic Information
by Shiyou LianThis book reveals the origin of imprecise information and establishes the mathematical models of flexible words and related mathematical and logical theories. It presents a set of models and algorithms of reasoning and computation, thus building a platform for related applications and laying a foundation for further research. Applying the theories and technologies in the book builds flexible AI systems to solve corresponding clustering, classification, judging, recognition, control, prediction, regression, etc., problems, and to enhance the Agent’s and Robot’s abilities to perceive environment and control behavior. Combine large language models (LLMs) with flexible linguistic information technologies in the book to enhance and extend the capability of natural language processing, and to realize understanding and generation of flexible words. This book differentiates imprecision from the uncertainty of information and explicitly treats imprecise-information processing as an independent subject. Imprecise-information processing is an indispensable research topic and branch field of artificial intelligence. With a new perspective and idea, the book starts from the origin of imprecise information, comprehensively and systematically expounds the principles, methods, and applications of imprecise-information processing, and establishes a new theoretical and technological system different from fuzzy technology on flexible linguistic information. The audience of this book includes scholars, engineers, teachers, Ph.D. candidates, postgraduates, and advanced undergraduates in the fields of artificial intelligence, natural language processing, Agent, Robot, automation, information, computer, logics, mathematics, language, as well as brain and cognitive science, etc.
Principles Of Incident Response And Disaster Recovery
by Michael Whitman Herbert Mattord Andrew GreenPrinciples Of Incident Response And Disaster Recovery, 2nd Edition presents methods to identify vulnerabilities within computer networks and the countermeasures that mitigate risks and damage. From market-leading content on contingency planning, to effective techniques that minimize downtime in an emergency, to curbing losses after a breach, this text is the resource needed in case of a network intrusion.