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  • HP Linear Barcode Scanner with
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  • Information Technology Security and Risk Management : Inductive Cases for Information Security
    Information Technology Security and Risk Management : Inductive Cases for Information Security

    Information Technology Security and Risk Management: Inductive Cases for Information Security is a compilation of cases that examine recent developments and issues that are relevant to IT security managers, risk assessment and management, and the broader topic of IT security in the 21st century.As the title indicates, the cases are written and analyzed inductively, which is to say that the authors allowed the cases to speak for themselves, and lead where they would, rather than approach the cases with presuppositions or assumptions regarding what the case should be "about".In other words, the authors were given broad discretion to interpret a case in the most interesting and relevant manner possible; any given case may be "about" many things, depending on the perspective adopted by the reader, and many different lessons may be learned.The inductive approach of these cases reflects the design philosophy of the advanced IT Security and Risk Management course we teach on the topic here at the University of Canterbury, where all discussions begin with the analysis of a specific case of interest and follow the most interesting and salient aspects of the case in evidence.In our course, the presentation, analysis, and discussion of a case are followed by a brief lecture to address the conceptual, theoretical, and scholarly dimensions arising from the case.The inductive approach to teaching and learning also comes with a huge advantage – the students seem to love it, and often express their appreciation for a fresh and engaging approach to learning the sometimes-highly-technical content of an IT security course.As instructors, we are also grateful for the break in the typical scripted "chalk-and-talk" of a university lecture afforded by the spontaneity of the inductive approach. We were motivated to prepare this text because there seems to be no other book of cases dedicated to the topic of IT security and risk management, and because of our own success and satisfaction with inductive teaching and learning.We believe this book would be useful either for an inductive, case-based course like our own or as a body of cases to be discussed in a more traditional course with a deductive approach.There are abstracts and keywords for each case, which would help instructors select cases for discussions on specific topics, and PowerPoint slides are available as a guide for discussion about a given case.

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  • Modern Cryptography : Applied Mathematics for Encryption and Information Security
    Modern Cryptography : Applied Mathematics for Encryption and Information Security

    This expanded textbook, now in its second edition, is a practical yet in depth guide to cryptography and its principles and practices.Now featuring a new section on quantum resistant cryptography in addition to expanded and revised content throughout, the book continues to place cryptography in real-world security situations using the hands-on information contained throughout the chapters.Prolific author Dr. Chuck Easttom lays out essential math skills and fully explains how to implement cryptographic algorithms in today's data protection landscape.Readers learn and test out how to use ciphers and hashes, generate random keys, handle VPN and Wi-Fi security, and encrypt VoIP, Email, and Web communications.The book also covers cryptanalysis, steganography, and cryptographic backdoors and includes a description of quantum computing and its impact on cryptography.This book is meant for those without a strong mathematics background with only just enough math to understand the algorithms given.The book contains a slide presentation, questions and answers, and exercises throughout. Presents new and updated coverage of cryptography including new content on quantum resistant cryptography;Covers the basic math needed for cryptography - number theory, discrete math, and algebra (abstract and linear);Includes a full suite of classroom materials including exercises, Q&A, and examples.

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  • Linear Algebra and Learning from Data
    Linear Algebra and Learning from Data


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  • Is it linear or non-linear?

    The relationship between the variables is non-linear.

  • What is the linear function when certain information is given?

    The linear function can be determined when certain information is given by using the formula y = mx + b, where m is the slope of the line and b is the y-intercept. The slope can be found using the formula m = (y2 - y1) / (x2 - x1) when two points (x1, y1) and (x2, y2) on the line are given. The y-intercept can be found by identifying the value of y when x = 0. Once the slope and y-intercept are determined, the linear function can be written in the form y = mx + b.

  • Can you provide information about the turntable Audio Linear 4001?

    The Audio Linear 4001 is a high-end turntable manufactured by the German company Audio Linear. It is known for its precision engineering and high-quality components, resulting in exceptional sound quality. The turntable features a heavy platter, a low-torque motor, and a high-quality tonearm, all contributing to its excellent performance. The Audio Linear 4001 is a popular choice among audiophiles looking for a top-of-the-line turntable.

  • What is the difference between a linear term, a linear equation, and a linear function?

    A linear term is a single variable or constant raised to the power of 1, such as 3x or 5. A linear equation is an equation that can be written in the form y = mx + b, where x is the independent variable, y is the dependent variable, m is the slope, and b is the y-intercept. A linear function is a mathematical relationship between two variables that can be represented by a straight line on a graph, and it can be expressed in the form f(x) = mx + b, where f(x) represents the dependent variable, x is the independent variable, m is the slope, and b is the y-intercept.

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  • Management and Information Technology after Digital Transformation
    Management and Information Technology after Digital Transformation

    With the widespread transformation of information into digital form throughout society – firms and organisations are embracing this development to adopt multiple types of IT to increase internal efficiency and to achieve external visibility and effectiveness – we have now reached a position where there is data in abundance and the challenge is to manage and make use of it fully.This book addresses this new managerial situation, the post-digitalisation era, and offers novel perspectives on managing the digital landscape. The topics span how the post-digitalisation era has the potential to renew organisations, markets and society.The chapters of the book are structured in three topical sections but can also be read individually.The chapters are structured to offer insights into the developments that take place at the intersection of the management, information systems and computer science disciplines.It features more than 70 researchers and managers as collaborating authors in 23 thought-provoking chapters. Written for scholars, researchers, students and managers from the management, information systems and computer science disciplines, the book presents a comprehensive and thought-provoking contribution on the challenges of managing organisations and engaging in global markets when tools, systems and data are abundant.

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  • Linear Algebra With Machine Learning and Data
    Linear Algebra With Machine Learning and Data

    This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining.The book offers a case study approach where each case will be grounded in a real-world application.This text is meant to be used for a second course in applications of Linear Algebra to Data Analytics, with a supplemental chapter on Decision Trees and their applications in regression analysis.The text can be considered in two different but overlapping general data analytics categories: clustering and interpolation.Knowledge of mathematical techniques related to data analytics and exposure to interpretation of results within a data analytics context are particularly valuable for students studying undergraduate mathematics.Each chapter of this text takes the reader through several relevant case studies using real-world data.All data sets, as well as Python and R syntax, are provided to the reader through links to Github documentation.Following each chapter is a short exercise set in which students are encouraged to use technology to apply their expanding knowledge of linear algebra as it is applied to data analytics.A basic knowledge of the concepts in a first Linear Algebra course is assumed; however, an overview of key concepts is presented in the Introduction and as needed throughout the text.

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  • Information Technology : An Introduction for Today’s Digital World
    Information Technology : An Introduction for Today’s Digital World

    This revised edition has more breadth and depth of coverage than the first edition.Information Technology: An Introduction for Today’s Digital World introduces undergraduate students to a wide variety of concepts that they will encounter throughout their IT studies and careers. The features of this edition include: Introductory system administration coverage of Windows 10 and Linux (Red Hat 7), both as general concepts and with specific hands-on instruction Coverage of programming and shell scripting, demonstrated through example code in several popular languages Updated information on modern IT careers Computer networks, including more content on cloud computing Improved coverage of computer security Ancillary material that includes a lab manual for hands-on exercisesSuitable for any introductory IT course, this classroom-tested text presents many of the topics recommended by the ACM Special Interest Group on IT Education (SIGITE).It offers a far more detailed examination of the computer and IT fields than computer literacy texts, focusing on concepts essential to all IT professionals – from system administration to scripting to computer organization.Four chapters are dedicated to the Windows and Linux operating systems so that students can gain hands-on experience with operating systems that they will deal with in the real world.

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  • RC Micro Linear Servo - 2PCS Upgrade 9mm 1.5g Digital Linear Coreless Servo for Mini Aircraft
    RC Micro Linear Servo - 2PCS Upgrade 9mm 1.5g Digital Linear Coreless Servo for Mini Aircraft

    RC Micro Linear Servo - 2PCS Upgrade 9mm 1.5g Digital Linear Coreless Servo for Mini Aircraft

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  • What is the linear span of a linear space?

    The linear span of a linear space is the set of all possible linear combinations of the vectors in that space. In other words, it is the collection of all possible vectors that can be obtained by scaling and adding the vectors in the space. The linear span forms a subspace of the original space and is often used to describe the "space" that is covered by the original set of vectors. It is a fundamental concept in linear algebra and is used to understand the properties and structure of vector spaces.

  • Is my proof on linear independence in linear algebra correct?

    To provide accurate feedback on the correctness of your proof on linear independence in linear algebra, I would need to review the specific details and steps of your proof. Please share your proof or key arguments so that I can assess its validity and provide you with constructive feedback.

  • Is my proof about linear independence in linear algebra correct?

    To provide an accurate assessment of your proof, I would need to review the specific details and steps of your proof. Linear independence in linear algebra is a fundamental concept, and it is important to ensure that the proof is logically sound and follows the definitions and properties of linear independence. If you can provide the details of your proof, I would be happy to review it and provide feedback on its correctness.

  • What is the difference between linear equations and linear functions?

    Linear equations and linear functions are closely related, but there is a key difference between the two. A linear equation is a mathematical statement that two expressions are equal, typically in the form y = mx + b. On the other hand, a linear function is a mathematical relationship between an independent variable and a dependent variable, typically in the form f(x) = mx + b. In other words, a linear equation is a specific instance of a linear function, where the equation represents the relationship between the variables.

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