Algorithms and data structures Books

Book SynopsisTable of ContentsPart I: Rules of the Game 1. Introducing Network Algorithmics 2. Network Implementation Models 3. Fifteen Implementation Principles 4. Principles in Action Part II: Playing with Endnodes 5. Copying Data 6. Transferring Control 7. Maintaining Timers 8. Demultiplexing 9. Protocol Processing Part III: Playing with Routers 10. Exact-Match Lookups 11. Prefix-Match Lookups 12. Packet Classification 13. Switching 14. Scheduling Packets 15. Routers as Distributed Systems Part IV: Endgame 16. Measuring Network Traffic 17. Network Security 18. Conclusions Appendix: Detailed Models

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Book SynopsisThe conventional wisdom was that biology influenced mathematics and computer science. But a new approach has taken hold: that of transferring methods and tools from computer science to biology. The reverse trend is evident in Grammars and Automata for String Processing: From Mathematics and Computer Science to Biology and Back. The contributors address the structural (syntactical) view of the domain. Mathematical linguistics and computer science can offer various tools for modeling complex macromolecules and for analyzing and simulating biological issues. This collection is valuable for students and researchers in biology, computer science, and applied mathematics.Table of ContentsLogistics, Languages and Combinatorics. Models of Molecular Computing.

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Book SynopsisRandomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the ChernoffâHoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as ChernoffâHoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus makingTrade ReviewReview of the hardback: 'It is beautifully written, contains all the major concentration results, and is a must to have on your desk.' Richard LiptonReview of the hardback: 'Concentration bounds are at the core of probabilistic analysis of algorithms. This excellent text provides a comprehensive treatment of this important subject, ranging from the very basic to the more advanced tools, including some recent developments in this area. The presentation is clear and includes numerous examples, demonstrating applications of the bounds in analysis of algorithms. This book is a valuable resource for both researchers and students in the field.' Eli Upfal, Brown UniversityReview of the hardback: 'Concentration inequalities are an essential tool for the analysis of algorithms in any probabilistic setting. There have been many recent developments on this subject, and this excellent text brings them together in a highly accessible form.' Alan Frieze, Carnegie Mellon UniversityReview of the hardback: 'The book does a superb job of describing a collection of powerful methodologies in a unified manner; what is even more striking is that basic combinatorial and probabilistic language is used in bringing out the power of such approaches. To summarize, the book has done a great job of synthesizing diverse and important material in a very accessible manner. Any student, researcher, or practitioner of computer science, electrical engineering, mathematics, operations research, and related fields, could benefit from this wonderful book. The book would also make for fruitful classes at the undergraduate and graduate levels. I highly recommend it.' Aravind Srinivasan, SIGACT NewsReview of the hardback: '… the strength of this book is that it is appropriate for both the beginner as well as the experienced researcher in the field of randomized algorithms … The exposition style […] combines informal discussion with formal definitions and proofs, giving first the intuition and motivation for the probabalistic technique at hand. … I highly recommend this book both as an advanced as well as an introductory textbook, which can also serve the needs of an experienced researcher in algorithmics.' Yannis C. Stamatiou, Mathematical ReviewsReviews of the hardback: 'This timely book brings together in a comprehensive and accessible form a sophisticated toolkit of powerful techniques for the analysis of randomized algorithms, illustrating their use with a wide array of insightful examples. This book is an invaluable resource for people venturing into this exciting field of contemporary computer science research.' Prabhakar Ragahavan, Yahoo ResearchTable of Contents1. Chernoff–Hoeffding bounds; 2. Applying the CH-bounds; 3. CH-bounds with dependencies; 4. Interlude: probabilistic recurrences; 5. Martingales and the MOBD; 6. The MOBD in action; 7. Averaged bounded difference; 8. The method of bounded variances; 9. Interlude: the infamous upper tail; 10. Isoperimetric inequalities and concentration; 11. Talagrand inequality; 12. Transportation cost and concentration; 13. Transportation cost and Talagrand's inequality; 14. Log–Sobolev inequalities; Appendix A. Summary of the most useful bounds.

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Book SynopsisExplores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer ScienceA flexible, interactive teaching format enhanced by a large selection of examples and exercisesDeveloped from the author's own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science.After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes' theorem, and Chebyshev inequality. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications. The author then describes the powerful tool of generating functions, which is demonstrated in enumeTrade Review…helpful to any mathematics student who wishes to acquire a background in classical probability and analysis … This is a remarkably beautiful book that would be a pleasure for a student to read, or for a teacher to make into a year's course.—Harvey Cohn, Computing Reviews, May 2010Table of ContentsPreliminaries. Combinatorics. Probability. More about Probability. Recurrences or Difference Equations. Introduction to Generating Functions. Enumeration with Generating Functions. Further Enumeration Methods. Combinatorics of Strings. Introduction to Asymptotics. Asymptotics and Generating Functions. Review of Analytic Techniques. Appendices. Bibliography. Answers/Hints to Selected Problems. Index.

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Table of ContentsA table of contents is not available for this title.

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Book SynopsisEnergy-Aware Memory Management for Embedded Multimedia Systems: A Computer-Aided Design Approach presents recent computer-aided design (CAD) ideas that address memory management tasks, particularly the optimization of energy consumption in the memory subsystem. It explains how to efficiently implement CAD solutions, including theoretical methods and novel algorithms. The book covers various energy-aware design techniques, including data-dependence analysis techniques, memory size estimation methods, extensions of mapping approaches, and memory banking approaches. It shows how these techniques are used to evaluate the data storage of an application, reduce dynamic and static energy consumption, design energy-efficient address generation units, and much more.Providing an algebraic framework for memory management tasks, this book illustrates how to optimize energy consumption in memory subsystems using CAD solutions. The algorithmic style ofTable of ContentsComputer-Aided Design for the Energy Optimization in the Memory Architecture of Embedded Systems. The Power of Polyhedra. Computation of Data Storage Requirements for Affine Algorithmic Specifications. Polyhedral Techniques for Parametric Memory Requirement Estimation. Storage Allocation for Streaming-Based Register File. Optimization of the Dynamic Energy Consumption and Signal Mapping in Hierarchical Memory Organizations. Leakage Current Mechanisms and Estimation in Memories and Logic. Leakage Control in SoCs. Energy-Efficient Memory Port Assignment. Energy-Efficient Address-Generation Units and Their Design Methodology. Index.

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Book SynopsisProbability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreTrade Review"Bayesian Programming comprises a methodology, a programming language, and a set of tools for developing and applying … complex models. … The approach is described in great detail, with many worked examples backed up by an online code repository. Unlike other books that tend to focus almost entirely on mathematics, this one gives equal time to conceptual and methodological guidance for the model-builder. It grapples with the knotty problems that arise in practice, some of which do not yet have clear solutions."—From the Foreword by Stuart Russell, University of California, Berkeley"The book has many worked examples backed up by an online code repository. The book provides a contibution on conceptual and methodological guidelines for model-builders. The authors discuss the problem how to build a Bayesian computer. The book has an excellent bibliography."—Nirode C. Mohanty, in Zentralblatt MATH 1281 Table of ContentsIntroduction. Bayesian Programming Principles: Basic Concepts. Incompleteness and Uncertainty. Description = Specification + Identification. The Importance of Conditional Independence. Bayesian Program = Description + Question. Bayesian Programming Cookbook: Information Fusion. Bayesian Programming with Coherence Variables. Bayesian Programming Subroutines. Bayesian Programming Conditional Statement. Bayesian Programming Iteration. Bayesian Programming Formalism and Algorithms: Bayesian Programming Formalism. Bayesian Models Revisited. Bayesian Inference Algorithms Revisited. Bayesian Learning Revisited. Frequently Asked Questions and Frequently Argued Matter: Frequently Asked Question and Frequently Argued Matter. Glossary. Index.

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Book SynopsisSince the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers.Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks.The book discusses foraTrade Review"The book is valuable reading both for teaching inspiration as well as for research insights into optimization, modeling, mathematical biology, and robot programming."—Zentralblatt MATH 1327Table of ContentsIntroduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.

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Book SynopsisDESCRIPTION Any large application needs an efficient way to handle the constant messages passing between components in the system. Billed as "messaging that just works," the RabbitMQ message broker initially appeals to developers because it's lightweight, easy to set up, and low maintenance. They stick with it because it's powerful, fast, and up to nearly anything that can be thrown at it. This book takes readers beyond the basics and explores the challenges of clustering and distributing messages across enterprise-level data-centers using RabbitMQ. RabbitMQ in Depth is a practical guide to building and maintaining message-based systems. This book covers detailed architectural and operational use of RabbitMQ with an emphasis on not just how it works but why it works the way it does. It provides examples and detailed explanations of everything from low-level communication to integration with third-party systems. It also offers insights needed to make core architectural choices and develop procedures for effective operational management. KEY FEATURES Approachable detailed resource Explains the "how" and "why" of RabbitMQ Takes readers well beyond the basics AUDIENCE Written for programmers with a basic understanding of messaging oriented systems and RabbitMQ. ABOUT THE TECHNOLOGY RabbitMQ is an open-source message broker software that programs can use to exchange messages with each other to create scalable and reliable application architectures.

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Book SynopsisThe big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features · Selecting the right AI project to meet specific business goals · Economizing resources to deliver the best value for money · How to measure the success of your AI efforts in the business terms · Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

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Book Synopsis

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Book Synopsis

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Book SynopsisAlgorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems. This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java. The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.Trade Review"This is another mainstream textbook on algorithms and data structures, mainly intended for undergraduate students and professionals … . The two-layer index table is also detailed and helpful. I do enjoy reading the informative sections of historical notes and further findings at the end of each chapter. … This book is very well written, with the help of … clear figures and tables, as well as many interesting and inspiring examples." Zhizhang Shen, Zentralblatt MATH, Vol. 1146, 2008"... the book develops the basic fundamental principles underlying their design and analysis without sacrificing depth or rigor. The authors' insight, knowledge and active research on algorithms and data structures provide a very solid approach to the book. I particularly liked their "as informally as possible and as formally as necessary" writing style, and I enjoyed a lot their decision to not only discuss classical results, but to broaden the view to alternative implementations, memory hierarchies and libraries, which transmits novelty and increases interest...I think that this book will be a superb addition particularly useful for teachers of undergraduate courses, to graduate students in Computer Science, and to researchers that work, or intend to work, with algorithms." Jordi Petit, Computer Science Review 3, 2009 "Mehlhorn and Sanders write well, and the well-organized presentation reflects their experience and interest in the various topics... it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. [...]This text is intended for undergraduate computer science (CS) majors, and focuses on algorithm analysis. … it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. Finally, the book contains interesting tidbits that are not readily available elsewhere." M. G. Murphy, ACM Computing Reviews, October 2008"A 'Toolbox' should be portable, practical, and useful. This book is all these, covering a nice swath of the classic CS algorithms but addressing them in a way that is accessible to the student and practitioner. Furthermore, it manages to incorporate interesting examples as well as subtle examples of wit compressed into its 300 pages. Although it is not tied to any one language or library, it provides practical references to efficient open-source implementations of many of the algorithms and data structures; these should be the first refuge of the commercial developer. I can easily recommend this book as an intermediate undergraduate text, a refresher for those of us who only dimly remember our intermediate undergraduate courses, and as a reference for the professional development craftsman." Hal C. Elrod, SIGACT News Book Review Column 42(4) 2011Table of ContentsAppetizer: Integer Arithmetics.- Representing Sequences by Arrays and Linked Lists.- Hash Tables and Associative Arrays.- Sorting and Selection.- Priority Queues.- Sorted Sequences.- Graph Representation.- Graph Traversal.- Shortest Paths.- Minimum Spanning Trees.- Generic Approaches to Optimization.

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Book SynopsisThis book constitutes the proceedings of the Third International Conference on Abstract State Machines, B, VDM, and Z, which took place in Pisa, Italy, in June 2012. The 20 full papers presented together with 2 invited talks and 13 short papers were carefully reviewed and selected from 59 submissions. The ABZ conference series is dedicated to the cross-fertilization of five related state-based and machine-based formal methods: Abstract State Machines (ASM), Alloy, B, VDM, and Z. They share a common conceptual foundation and are widely used in both academia and industry for the design and analysis of hardware and software systems. The main goal of this conference series is to contribute to the integration of these formal methods, clarifying their commonalities and differences to better understand how to combine different approaches for accomplishing the various tasks in modeling, experimental validation and mathematical verification of reliable high-quality hardware/software systems.

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Book SynopsisThe design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen­ dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith­ mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa­ tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven­ tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.Table of ContentsI. Foundations.- 1. Machine Models: RAM and RASP.- 2. Randomized Computations.- 3. A High Level Programming Language.- 4. Structured Data Types.- 4.1 Queues and Stacks.- 4.2 Lists.- 4.3 Trees.- 5. Recursion.- 6. Order of Growth.- 7. Secondary Storage.- 8. Exercises.- 9. Bibliographic Notes.- II. Sorting.- 1. General Sorting Methods.- 1.1 Sorting by Selection, a First Attempt.- 1.2 Sorting by Selection: Heapsort.- 1.3 Sorting by Partitioning: Quicksort.- 1.4 Sorting by Merging.- 1.5 Comparing Different Algorithms.- 1.6 Lower Bounds.- 2. Sorting by Distribution.- 2.1 Sorting Words.- 2.2 Sorting Reals by Distribution.- 3. The Lower Bound on Sorting, Revisited.- 4. The Linear Median Algorithm.- 5. Exercises.- 6. Bibliographic Notes.- III. Sets.- 1. Digital Search Trees.- 1.1 Tries.- 1.2 Static Tries or Compressing Sparse Tables.- 2. Hashing.- 2.1 Hashing with Chaining.- 2.2 Hashing with Open Addressing.- 2.3 Perfect Hashing.- 2.4 Universal Hashing.- 2.5 Extendible Hashing.- 3. Searching Ordered Sets.- 3.1 Binary Search and Search Trees.- 3.2 Interpolation Search.- 4. Weighted Trees.- 4.1 Optimum Weighted Trees, Dynamic Programming, and Pattern Matching.- 4.2 Nearly Optimal Binary Search Trees.- 5. Balanced Trees.- 5.1 Weight-Balanced Trees.- 5.2 Height-Balanced Trees.- 5.3 AdvancedTopicson(a,b)-Trees.- 5.3.1 Mergable Priority Queues.- 5.3.2 Amortized Rebalancing Cost and Sorting Presorted Files.- 5.3.3 Finger Trees.- 5.3.4 Fringe Analysis.- 6. Dynamic Weighted Trees.- 6.1 Self-Organizing Data Structures and Their Amortized and Average Case Analysis.- 6.1.1 Self-Organizing Linear Lists.- 6.1.2 Splay Trees.- 6.2 D-trees.- 6.3 An Application to Multidimensional Searching.- 7. A Comparison of Search Structures.- 8. Subsets of a Small Universe.- 8.1 The Boolean Array (Bitvector).- 8.2 The O(log log N) Priority Queue.- 8.3 The Union-Find Problem.- 9. Exercises.- 10. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Table of ContentsVII. Multidimensional Data Structures.- 1. A Black Box Approach to Data Structures.- 1.1 Dynamization.- 1.2 Weighting and Weighted Dynamization.- 1.3 Order Decomposable Problems.- 2. Multi-dimensional Searching Problems.- 2.1 D-dimensional Trees and Polygon Trees.- 2.2 Range Trees and Multidimensional Divide and Conquer.- 2.3 Lower Bounds.- 2.3.1 Partial MatchRetrieval in Minimum Space.- 2.3.2 The Spanning Bound.- 3. Exercises.- 4. Bibliographic Notes.- VIII. Computational Geometry.- 1. Convex Polygons.- 2. Convex Hulls.- 3. Voronoi Diagrams and Searching Planar Subdivisions.- 3.1 Voronoi Diagrams.- 3.2 Searching Planar Subdivisions.- 3.2.1 Removal of Large Independent Sets.- 3.2.2 Path Decompositions.- 3.2.3 Searching Dynamic Planar Subdivisions.- 3.3 Applications.- 4. The Sweep Paradigm.- 4.1 Intersection of Line Segments and Other Intersection Problems in the Plane.- 4.2 Triangulation and its Applications.- 4.3 Space Sweep.- 5. The Realm of Orthogonal Objects.- 5.1 Plane Sweep for Iso-Oriented Objects.- 5.1.1 The Interval Tree and its Applications.- 5.1.2 The Priority Search Tree and its Applications.- 5.1.3 Segment Trees.- 5.1.4 Path Decomposition and Plane Sweep for Non-Iso-Oriented Objects.- 5.2 Divide and Conquer on Iso-Oriented Objects.- 5.2.1 The Line Segment Intersection Problem.- 5.2.2 The Measure and Contour Problems.- 5.3 Intersection Problems in Higher-Dimensional Space.- 6. Geometric Transforms.- 6.1 Duality.- 6.2 Inversion.- 7. Exercises.- 8. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Book SynopsisInformatik ist der Schlüssel, um unsere zunehmend digitalisierte Welt zu verstehen! In diesem Buch lesen Sie nicht nur, wie Navis den günstigsten Weg bestimmen, wie so viele Bilder auf eine kleine Speicherkarte passen oder welche Dinge ein Computer eben nicht ausrechnen kann. Mit Papier und Bleistift und den Bastelvorlagen können Sie die Antworten auf diese und viele weitere Fragen selbst buchstäblich begreifen. Ein Computer ist dafür gar nicht nötig! Genau genommen sind im Buch sogar mehrere Computer aus Pappe enthalten, anhand derer man besser versteht, wie die "echten" Geräte gestaltet sind und wie diese funktionieren. Als Neuerung gibt es ergänzende, aktive Webseiten, die Sie frei (und ohne Werbung) aus dem Internet abrufen können, um mit ihnen zu experimentieren. Das Buch ist für alle da, die schon immer mal hinter die Kulissen der Wissenschaft Informatik schauen wollten: Vom Schüler zum Lehrer, vom Studenten zum Professor, vom interessierten Laien zum IT-Experten, der zwar genau weiß, wie er bestimmte Dinge zu tun hat, aber vielleicht nicht, warum sie so funktionieren oder wie er den Kern seiner tägliche Arbeit seiner Familie verständlich machen kann. Die 5. Auflage enthält zusätzliche Kapitel mit neuem Material sowie die Erweiterung und Überarbeitung der vorhandenen Kapitel. Das bewährte Hands-on-Konzept mit Experimenten und Bastelbögen zum Ausschneiden ist der durchgängige rote Faden.Stimmen zu vorhergehenden Auflagen:„Wer mit einem Informatikstudium liebäugelt, erhält einen Vorgeschmack auf das, was ihn erwartet - alle anderen können das Buch einfach zum Vergnügen lesen.“ c't – Magazin für Computertechnik„Lassen Sie sich also ein auf das ‚Abenteuer Informatik’! Ich bin sicher, dass Sie Spaß daran haben“ LOG IN – Informatische Bildung und Computer in der Schule„Auch wenn es unglaublich klingt: Abenteuer Informatik ist ein Buch über wichtige Prinzipien der modernen informationsverarbeitenden Alltagswelt, das man beim Lesen nicht mehr aus der Hand legen will.“ BIOspektrum„Mit bester Empfehlung!" PM – Praxis der Mathematik„Bits zum Begreifen" Bild der WissenschaftProf. Dr. Jens Gallenbacher liegt am Herzen, die Fachwissenschaft Informatik lebendig und mit einem hohen Allgemeinbildungsgrad zu vermitteln. Er ist an der Johannes Gutenberg-Universität in Mainz für die Ausbildung neuer Informatiklehrerinnen und -lehrer verantwortlich. Um zu zeigen, dass Informatik mehr mit menschlicher Kreativität und konsequentem Denken zu tun hat als mit Computern, verzichtet er dabei weitgehend auf den Einsatz der Geräte. Seine Konzepte werden vom Kindergarten bis zur universitären Grundlagenausbildung eingesetzt.Trade Review“... Erklärungen werden wie in algorithmischen Schritten sehr genau analysiert und führen bis zu wesentlichen Grundlagen der Informatik. ... Weit verständlich, fesselnd, zum Nachdenken und Diskutieren. Mitunter notwendige Geduld wird reich belohnt. Als Vertiefung daneben gut "Algorithmen kapieren" ...” (Rolf Becker-Friedrich, in: ekz-Informationsdienst, Heft 49, 2021)Table of ContentsEinleitung.- 1 Sag' mir wohin ...- 2 Ordnung muss sein!- 3 Ich packe meinen Koffer und ...- 4 Der Trick mit dem Binären.- 5 100000000000 Jahre Informatik?- 6 Von Kamelen und dem Nadelöhr.- 7 Verluste gibt es doch immer!- 8 Erkennungsdienst.- 9 Paketpost.- 10 Alles im Fluss.- 11 Ordnung im Chaos.-12 Mit Sicherheit.- 13 Rechnen mit Strom.- 14 Besser rechnen mit Strom.- 15 Allmächtiger Computer!?.- 16 Spielchen gefällig?- 17 Schnelle Antworten.- 18 Computer auf der Schulbank.- Glossar.

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Book SynopsisThe conical approach provides a geometrical understanding of optimization and is a powerful research tool and useful problem-solving technique (for example, in decision support and real time control applications). Conical optimality conditions are first stated in a very general optimization framework, and then applied to linear programming. A complete theory along with primal and dual algorithms is given, and solutions and algorithms are also provided for vector and robust linear optimization. The advantages of parameter dependence of conical methods are fully discussed. In addition to numerical results, the book provides source codes and detailed documentation of a Modula-2 implementation for the main algorithms.Table of ContentsPart I: General Theory Part II: Further Advanced Results Part III: Implementations and Numerical Results

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Book Synopsis1 Integers.- 2 Congruences and Residue Class Rings.- 3 Encryption.- 4 Probability and Perfect Secrecy.- 5 DES.- 6 AES.- 7 Prime Number Generation.- 8 Public-Key Encryption.- 9 Factoring.- 10 Discrete Logarithms.- 11 Cryptographic Hash Functions.- 12 Digital Signatures.- 13 Other Systems.- 14 Identification.- 15 Secret Sharing.- 16 Public-Key Infrastructures.- Solutions of the exercises.- References.Trade ReviewFrom the reviews: Zentralblatt Math "[......] Of the three books under review, Buchmann's is by far the most sophisticated, complete and up-to-date. It was written for computer-science majors - German ones at that - and might be rough going for all but the best American undergraduates. It is amazing how much Buchmann is able to do in under 300 pages: self-contained explanations of the relevant mathematics (with proofs); a systematic introduction to symmetric cryptosystems, including a detailed description and discussion of DES; a good treatment of primality testing, integer factorization, and algorithms for discrete logarithms, clearly written sections describing most of the major types of cryptosystems, and explanations of basic concepts of practical cryptography such as hash functions, message authentication codes, signatures, passwords, certification authorities, and certificate chains. This book is an excellent reference, and I believe that it would also be a good textbook for a course for mathematics or computer science majors, provided that the instructor is prepared to supplement it with more leisurely treatments of some of the topics." N. Koblitz (Seattle, WA) - American Math. Society Monthly. J.A. Buchmann Introduction to Cryptography "It gives a clear and systematic introduction into the subject whose popularity is ever increasing, and can be recommended to all who would like to learn about cryptography. The book contains many exercises and examples. It can be used as a textbook and is likely to become popular among students. The necessary definitions and concepts from algebra, number theory and probability theory are formulated, illustrated by examples and applied to cryptography." —ZENTRALBLATT MATH "For those of use who wish to learn more about cryptography and/or to teach it, Johannes Buchmann has written this book. … The book is mathematically complete and a satisfying read. There are plenty of homework exercises … . This is a good book for upperclassmen, graduate students, and faculty. … This book makes a superior reference and a fine textbook." (Robert W. Vallin, MathDL, January, 2001) "Buchmann’s book is a text on cryptography intended to be used at the undergraduate level. … the intended audiences of this book are ‘readers who want to learn about modern cryptographic algorithms and their mathematical foundations … . I enjoy reading this book. … Readers will find a good exposition of the techniques used in developing and analyzing these algorithms. … These make Buchmann’s text an excellent choice for self study or as a text for students … in elementary number theory and algebra." (Andrew C. Lee, SIGACT News, Vol. 34 (4), 2003) From the reviews of the second edition: "This is the english translation of the second edition of the author’s prominent german textbook ‘Einführung in die Kryptographie’. The original text grew out of several courses on cryptography given by the author at the Technical University Darmstadt; it is aimed at readers who want to learn about modern cryptographic techniques and its mathematical foundations … . As compared with the first edition the number of exercises has almost been doubled and some material … has been added." (R. Steinbauer, Monatshefte für Mathematik, Vol. 150 (4), 2007)Table of ContentsIntegers.- Congruences and Residue Class Rings.- Encryption.- Probability and Perfect Secrecy.- DES.- AES.- Prime Number Generation.- Public-Key Encryption.- Factoring.- Discrete Logarithms.- Cryptographic Hash Functions.- Digital Signatures.- Other Systems.- Identification.- Public-Key Infrastructures.- Solutions of the Odd Exercises.- Subject Index.- Bibliography.

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Book SynopsisPresents an account of graph theory. Written with students of mathematics and computer science in mind, this book reflects the state of the subject and emphasizes connections with other branches of pure mathematics. It presents a survey of fresh topics and includes more than 600 exercises.Trade Review"...This book is likely to become a classic, and it deserves to be on the shelf of everyone working in graph theory or even remotely related areas, from graduate student to active researcher."--MATHEMATICAL REVIEWSTable of Contents1: Fundamentals. 2: Electrical Networks. 3: Flows, Connectivity and Matching. 4: Extremal Problems. 5: Colouring. 6: Ramsey Theory. 7: Random Graphs. 8: Graphs, Groups and Matrices. 9: Random Walks on Graphs. 10: The Tutte Polynomial.

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Book SynopsisProgrammers, scientists, and engineers are always in need of newer techniques and algorithms to manipulate and interpret images. Algorithms for Image Processing and Computer Vision is an accessible collection of algorithms for common image processing applications that simplifies complicated mathematical calculations.Table of ContentsPreface xxi Chapter 1 Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls 1 OpenCV 2 The Basic OpenCV Code 2 The IplImage Data Structure 3 Reading and Writing Images 6 Image Display 7 An Example 7 Image Capture 10 Interfacing with the AIPCV Library 14 Website Files 18 References 18 Chapter 2 Edge-Detection Techniques 21 The Purpose of Edge Detection 21 Traditional Approaches and Theory 23 Models of Edges 24 Noise 26 Derivative Operators 30 Template-Based Edge Detection 36 Edge Models: The Marr-Hildreth Edge Detector 39 The Canny Edge Detector 42 The Shen-Castan (ISEF) Edge Detector 48 A Comparison of Two Optimal Edge Detectors 51 Color Edges 53 Source Code for the Marr-Hildreth Edge Detector 58 Source Code for the Canny Edge Detector 62 Source Code for the Shen-Castan Edge Detector 70 Website Files 80 References 82 Chapter 3 Digital Morphology 85 Morphology Defined 85 Connectedness 86 Elements of Digital Morphology — Binary Operations 87 Binary Dilation 88 Implementing Binary Dilation 92 Binary Erosion 94 Implementation of Binary Erosion 100 Opening and Closing 101 MAX — A High-Level Programming Language for Morphology 107 The ‘‘Hit-and-Miss’’ Transform 113 Identifying Region Boundaries 116 Conditional Dilation 116 Counting Regions 119 Grey-Level Morphology 121 Opening and Closing 123 Smoothing 126 Gradient 128 Segmentation of Textures 129 Size Distribution of Objects 130 Color Morphology 131 Website Files 132 References 135 Chapter 4 Grey-Level Segmentation 137 Basics of Grey-Level Segmentation 137 Using Edge Pixels 139 Iterative Selection 140 The Method of Grey-Level Histograms 141 Using Entropy 142 Fuzzy Sets 146 Minimum Error Thresholding 148 Sample Results From Single Threshold Selection 149 The Use of Regional Thresholds 151 Chow and Kaneko 152 Modeling Illumination Using Edges 156 Implementation and Results 159 Comparisons 160 Relaxation Methods 161 Moving Averages 167 Cluster-Based Thresholds 170 Multiple Thresholds 171 Website Files 172 References 173 Chapter 5 Texture and Color 177 Texture and Segmentation 177 A Simple Analysis of Texture in Grey-Level Images 179 Grey-Level Co-Occurrence 182 Maximum Probability 185 Moments 185 Contrast 185 Homogeneity 185 Entropy 186 Results from the GLCM Descriptors 186 Speeding Up the Texture Operators 186 Edges and Texture 188 Energy and Texture 191 Surfaces and Texture 193 Vector Dispersion 193 Surface Curvature 195 Fractal Dimension 198 Color Segmentation 201 Color Textures 205 Website Files 205 References 206 Chapter 6 Thinning 209 What Is a Skeleton? 209 The Medial Axis Transform 210 Iterative Morphological Methods 212 The Use of Contours 221 Choi/Lam/Siu Algorithm 224 Treating the Object as a Polygon 226 Triangulation Methods 227 Force-Based Thinning 228 Definitions 229 Use of a Force Field 230 Subpixel Skeletons 234 Source Code for Zhang-Suen/Stentiford/Holt Combined Algorithm 235 Website Files 246 References 247 Chapter 7 Image Restoration 251 Image Degradations — The Real World 251 The Frequency Domain 253 The Fourier Transform 254 The Fast Fourier Transform 256 The Inverse Fourier Transform 260 Two-Dimensional Fourier Transforms 260 Fourier Transforms in OpenCV 262 Creating Artificial Blur 264 The Inverse Filter 270 The Wiener Filter 271 Structured Noise 273 Motion Blur — A Special Case 276 The Homomorphic Filter — Illumination 277 Frequency Filters in General 278 Isolating Illumination Effects 280 Website Files 281 References 283 Chapter 8 Classification 285 Objects, Patterns, and Statistics 285 Features and Regions 288 Training and Testing 292 Variation: In-Class and Out-Class 295 Minimum Distance Classifiers 299 Distance Metrics 300 Distances Between Features 302 Cross Validation 304 Support Vector Machines 306 Multiple Classifiers — Ensembles 309 Merging Multiple Methods 309 Merging Type 1 Responses 310 Evaluation 311 Converting Between Response Types 312 Merging Type 2 Responses 313 Merging Type 3 Responses 315 Bagging and Boosting 315 Bagging 315 Boosting 316 Website Files 317 References 318 Chapter 9 Symbol Recognition 321 The Problem 321 OCR on Simple Perfect Images 322 OCR on Scanned Images — Segmentation 326 Noise 327 Isolating Individual Glyphs 329 Matching Templates 333 Statistical Recognition 337 OCR on Fax Images — Printed Characters 339 Orientation — Skew Detection 340 The Use of Edges 345 Handprinted Characters 348 Properties of the Character Outline 349 Convex Deficiencies 353 Vector Templates 357 Neural Nets 363 A Simple Neural Net 364 A Backpropagation Net for Digit Recognition 368 The Use of Multiple Classifiers 372 Merging Multiple Methods 372 Results From the Multiple Classifier 375 Printed Music Recognition — A Study 375 Staff Lines 376 Segmentation 378 Music Symbol Recognition 381 Source Code for Neural Net Recognition System 383 Website Files 390 References 392 Chapter 10 Content-Based Search — Finding Images by Example 395 Searching Images 395 Maintaining Collections of Images 396 Features for Query by Example 399 Color Image Features 399 Mean Color 400 Color Quad Tree 400 Hue and Intensity Histograms 401 Comparing Histograms 402 Requantization 403 Results from Simple Color Features 404 Other Color-Based Methods 407 Grey-Level Image Features 408 Grey Histograms 409 Grey Sigma — Moments 409 Edge Density — Boundaries Between Objects 409 Edge Direction 410 Boolean Edge Density 410 Spatial Considerations 411 Overall Regions 411 Rectangular Regions 412 Angular Regions 412 Circular Regions 414 Hybrid Regions 414 Test of Spatial Sampling 414 Additional Considerations 417 Texture 418 Objects, Contours, Boundaries 418 Data Sets 418 Website Files 419 References 420 Systems 424 Chapter 11 High-Performance Computing for Vision and Image Processing 425 Paradigms for Multiple-Processor Computation 426 Shared Memory 426 Message Passing 427 Execution Timing 427 Using clock() 428 Using QueryPerformanceCounter 430 The Message-Passing Interface System 432 Installing MPI 432 Using MPI 433 Inter-Process Communication 434 Running MPI Programs 436 Real Image Computations 437 Using a Computer Network — Cluster Computing 440 A Shared Memory System — Using the PC Graphics Processor 444 GLSL 444 OpenGL Fundamentals 445 Practical Textures in OpenGL 448 Shader Programming Basics 451 Vertex and Fragment Shaders 452 Required GLSL Initializations 453 Reading and Converting the Image 454 Passing Parameters to Shader Programs 456 Putting It All Together 457 Speedup Using the GPU 459 Developing and Testing Shader Code 459 Finding the Needed Software 460 Website Files 461 References 461 Index 465

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Book SynopsisAn object-oriented learning framework for creating good software design. Bruno Preiss presents readers with a modern, object-oriented perspective for looking at data structures and algorithms, clearly showing how to use polymorphism and inheritance, and including fragments from working and tested programs.Table of ContentsAlgorithm Analysis. Asymptotic Notation. Foundational Data Structures. Data Types and Abstraction. Stacks, Queues and Deques. Ordered Lists and Sorted Lists. Hashing, Hash Tables and Scatter Tables. Trees. Search Trees. Heaps and Priority Queues. Sets, Multisets and Partitions. Dynamic Storage Allocation. Algorithmic Patterns and Problem Solvers. Sorting Algorithms and Sorters. Graphs and Graph Algorithms. Appendices. Index.

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Book SynopsisTrade Review"A welcome addition to the growing literature on the digital economy and change." * Choice *"Stimulating." * Paradigm Explorer *

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Book SynopsisTrade Review"Winner of the Bronze Medal in Business Technology, Axiom Business Book Awards""I loved this book. . . . Trading at the Speed of Light is an amazing, detailed account of why material reality matters for virtual outcomes, and conversely, in the financial markets. Everybody with the slightest interest in modern finance should read it."---Diane Coyle, Enlightened Economist

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Book SynopsisAn introduction to learning how to protect ourselves and organise against Big DataTrade Review'A call to arms [...] sets out a clear, persuasive argument for the need to challenge the power of platforms and systems, and details the tools to do so. A thought-provoking read' -- Prof. Rob Kitchin, Maynooth University‘The first non-technical guidebook on how to live with location data and it is a truly radical response for our times. Spatial data for us, not about us’ -- Jeremy W. Crampton, Professor of Urban Data Analysis, Newcastle University‘Brilliantly traces the closed loops of spatial data and suggests new escape routes, reminding us that our data can be remade to tell different stories’ -- Professor Kate Crawford, author of ‘Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence’'The book that I’ve long been waiting for, one that takes a material approach to the data geographies informing and being informed by technologies of everyday life’ -- Erin McElroy, Assistant Professor of American and Digital Studies at the University of Texas at Austin and cofounder of the Anti-Eviction Mapping Project'Data Power is an activist handbook wrapped in a theoretical treatise inside a media manifesto. The authors have a lively set of suggestions that provide a welcome antidote to the temptations of resignation and complacency' -- Mark Andrejevic, Professor in the School of Media, Film, and Journalism at Monash UniversityTable of ContentsList of Figures and Tables Series Preface Acknowledgments List of Abbreviations Introduction: Technology and the Axes of Hope and Fear 1. Life in the Age of Big Data 2. What Are Our Data, and What Are They Worth? 3. Existing Everyday Resistances 4. Contesting the Data Spectacle 5. Our Data Are Us, So Make Them Ours Epilogue Notes Bibliography Index

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Book Synopsis

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Book SynopsisData Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.Table of ContentsAcknowledgements xix Preface xxi References xxxi Part I Preliminaries 1 1 Tasks 3 1.1 Introduction 3 1.2 Inductive learning tasks 5 1.3 Classification 9 1.4 Regression 14 1.5 Clustering 16 1.6 Practical issues 19 1.7 Conclusion 20 1.8 Further readings 21 References 22 2 Basic statistics 23 2.1 Introduction 23 2.2 Notational conventions 24 2.3 Basic statistics as modeling 24 2.4 Distribution description 25 2.5 Relationship detection 47 2.6 Visualization 62 2.7 Conclusion 65 2.8 Further readings 66 References 67 Part II Classification 69 3 Decision trees 71 3.1 Introduction 71 3.2 Decision tree model 72 3.3 Growing 76 3.4 Pruning 90 3.5 Prediction 103 3.6 Weighted instances 105 3.7 Missing value handling 106 3.8 Conclusion 114 3.9 Further readings 114 References 116 4 Naïve Bayes classifier 118 4.1 Introduction 118 4.2 Bayes rule 118 4.3 Classification by Bayesian inference 120 4.4 Practical issues 125 4.5 Conclusion 131 4.6 Further readings 131 References 132 5 Linear classification 134 5.1 Introduction 134 5.2 Linear representation 136 5.3 Parameter estimation 145 5.4 Discrete attributes 154 5.5 Conclusion 155 5.6 Further readings 156 References 157 6 Misclassification costs 159 6.1 Introduction 159 6.2 Cost representation 161 6.3 Incorporating misclassification costs 164 6.4 Effects of cost incorporation 176 6.5 Experimental procedure 180 6.6 Conclusion 184 6.7 Further readings 185 References 187 7 Classification model evaluation 189 7.1 Introduction 189 7.2 Performance measures 190 7.3 Evaluation procedures 213 7.4 Conclusion 231 7.5 Further readings 232 References 233 Part III Regression 235 8 Linear regression 237 8.1 Introduction 237 8.2 Linear representation 238 8.3 Parameter estimation 242 8.4 Discrete attributes 250 8.5 Advantages of linear models 251 8.6 Beyond linearity 252 8.7 Conclusion 258 8.8 Further readings 258 References 259 9 Regression trees 261 9.1 Introduction 261 9.2 Regression tree model 262 9.3 Growing 263 9.4 Pruning 274 9.5 Prediction 277 9.6 Weighted instances 278 9.7 Missing value handling 279 9.8 Piecewise linear regression 284 9.9 Conclusion 292 9.10 Further readings 292 References 293 10 Regression model evaluation 295 10.1 Introduction 295 10.2 Performance measures 296 10.3 Evaluation procedures 303 10.4 Conclusion 309 10.5 Further readings 309 References 310 Part IV Clustering 311 11 (Dis)similarity measures 313 11.1 Introduction 313 11.2 Measuring dissimilarity and similarity 313 11.3 Difference-based dissimilarity 314 11.4 Correlation-based similarity 321 11.5 Missing attribute values 324 11.6 Conclusion 325 11.7 Further readings 325 References 326 12 k-Centers clustering 328 12.1 Introduction 328 12.2 Algorithm scheme 330 12.3 k-Means 334 12.4 Beyond means 338 12.5 Beyond (fixed) k 342 12.6 Explicit cluster modeling 343 12.7 Conclusion 345 12.8 Further readings 345 References 347 13 Hierarchical clustering 349 13.1 Introduction 349 13.2 Cluster hierarchies 351 13.3 Agglomerative clustering 353 13.4 Divisive clustering 361 13.5 Hierarchical clustering visualization 364 13.6 Hierarchical clustering prediction 366 13.7 Conclusion 369 13.8 Further readings 370 References 371 14 Clustering model evaluation 373 14.1 Introduction 373 14.2 Per-cluster quality measures 376 14.3 Overall quality measures 385 14.4 External quality measures 393 14.5 Using quality measures 397 14.6 Conclusion 398 14.7 Further readings 398 References 399 Part V Getting Better Models 401 15 Model ensembles 403 15.1 Introduction 403 15.2 Model committees 404 15.3 Base models 406 15.4 Model aggregation 420 15.5 Specific ensemble modeling algorithms 431 15.6 Quality of ensemble predictions 448 15.7 Conclusion 449 15.8 Further readings 450 References 451 16 Kernel methods 454 16.1 Introduction 454 16.2 Support vector machines 457 16.3 Support vector regression 473 16.4 Kernel trick 482 16.5 Kernel functions 484 16.6 Kernel prediction 487 16.7 Kernel-based algorithms 489 16.8 Conclusion 494 16.9 Further readings 495 References 496 17 Attribute transformation 498 17.1 Introduction 498 17.2 Attribute transformation task 499 17.3 Simple transformations 504 17.4 Multiclass encoding 510 17.5 Conclusion 521 17.6 Further readings 521 References 522 18 Discretization 524 18.1 Introduction 524 18.2 Discretization task 525 18.3 Unsupervised discretization 530 18.4 Supervised discretization 533 18.5 Effects of discretization 551 18.6 Conclusion 553 18.7 Further readings 553 References 556 19 Attribute selection 558 19.1 Introduction 558 19.2 Attribute selection task 559 19.3 Attribute subset search 562 19.4 Attribute selection filters 568 19.5 Attribute selection wrappers 588 19.6 Effects of attribute selection 593 19.7 Conclusion 598 19.8 Further readings 599 References 600 20 Case studies 602 20.1 Introduction 602 20.2 Census income 605 20.3 Communities and crime 631 20.4 Cover type 640 20.5 Conclusion 654 20.6 Further readings 655 References 655 Closing 657 A Notation 659 A.1 Attribute values 659 A.2 Data subsets 659 A.3 Probabilities 660 B R packages 661 B.1 CRAN packages 661 B.2 DMR packages 662 B.3 Installing packages 663 References 664 C Datasets 666 Index 667

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