Algorithms and data structures Books

Book SynopsisTable of ContentsPreface xiii Introduction xix Part I The Fun Part Chapter 1 Is Data Analytics Right for Me? 3 What Does a Data Analyst Do Every Day? 4 Hours/Time 6 In-Person Data Jobs 9 What Makes a Good Analyst? 10 Planning 12 Organization 13 Critical Thinking/Strategy 14 Collaboration/Communication 15 What Tools Should I Learn? 17 Excel/Google Sheets 17 SQL 19 Tableau/Power BI 21 Python 24 R 25 Which Entry-Level Tech Job Is Right for Me? 25 What’s Next 29 Chapter 2 Understanding the Paths into Data 31 How Hard Is It to Become a Data Analyst? 32 What Are My Options for Getting into Data Analytics? 34 Transitioning from an Analyst-Adjacent Role 35 Getting a Degree 35 Boot Camps 36 When a Boot Camp May Be the Right Option for You 37 How to Pick a Good Boot Camp 38 DIY Approach 40 How I Decided on the DIY Approach 41 Chapter 3 Designing Your Data Analyst Roadmap 45 Can You Shows Me Your Data Analyst Roadmap? 46 Building Your Roadmap 46 Step 1: Skill Development 47 Step 2: Building a Portfolio 49 Step 3: Getting Yourself Ready to Job Search 52 How Do I Choose the Best Course? 53 What Makes a Good Course 55 Learning Styles 55 Budget 56 Support 57 Interests 58 Time Constraints 59 Getting Started for Free 60 When Not to Pick a Course: How to Avoid Course Hopping 61 Chapter 4 My Experience with Data Analytics Courses 63 The Beginning 63 The Google Certificates Course 64 Learning SQL 65 Learning Tableau and R 68 Finishing the Course 70 What Came Next 72 Changing Careers 72 Course Hopping: When Is Taking Another Course Worth It? 73 Part II The Scary Part 77 Chapter 5 Introduction to Portfolios 79 What Is a Data Analytics Portfolio? 79 Can I See an Example? 80 Why Do I Need a Portfolio? 81 As an Analyst 81 As a Job Seeker 82 If I Have Experience from Another Job, Do I Still Need a Portfolio? 83 Chapter 6 Portfolio Project FAQ 85 How Do I Find Free Data? 86 Maven Analytics 87 Real World Fake Data 89 Your Data 89 Data from Me! 90 SQL Practice 91 Other Places 92 Can You Tell Me More about Completing Projects? 93 How Do I Get Started on Projects? 93 Does My Project Need to Be Original and Industry Specific? 95 How Do I Know When a Project Is Ready? 96 Where Do I Publish and Store My Work? 96 How Many Projects Do I Need? 98 Should I Share My Work Publicly? 99 Project Time! 100 Chapter 7 Portfolio Project Handbook 101 Project Levels: What Separates a Beginner from an Intermediate Project? 102 First Project 102 Beginner Project 103 Intermediate Project 103 Regular Tableau User 104 Guided Projects 104 New Year’s Eve Resolutions Project 104 Case Study: New Year’s Eve Resolutions Project 105 Semi-Structured Case Study with Hints 106 Final Thoughts 108 Help Desk Project 108 Case Study: Help Desk Project 109 Semi-Structured Prompts 109 Pizza Sales Project 111 Case Study: Pizza Sales Project 111 Dirty Data + Case Study 112 Dirty Data + Case Study + Hints 113 Clean Data + Case Study 114 Semi-Structured Case Study with Hints 115 Busy Times 116 Pizzas During Peak Periods 116 Best- and Worst-Selling Pizzas 116 Average Order Value 117 Seating Capacity 117 Final Thoughts 118 SQL Project Creation Advice 119 From the Portfolio to the Job Search 121 Getting in the Mindset for Projects 122 Part III The Hard Part 125 Chapter 8 Starting Your Job Search 127 How Do I Know When I Am Ready to Start My Job Search? 127 Where and How Should I Look for Jobs? 129 Searching Posts 129 Job Titles 130 Where Can I Find Salary Information? 131 What Is the Data Analyst Career Progression? 131 Chapter 9 Résumé Building and Setting Your Public Image 137 How Do I Write a Résumé? 138 Length 138 Technical Skills 141 Relevant History 142 Formatting 142 Use Metrics 143 How Do I Optimize My LinkedIn? 144 History 144 Connections 146 Headline 149 Profile Photo 150 Can You Tell Me How to Network? 151 What Is Networking (and What Is It Not)? 151 Networking and Messaging on LinkedIn 153 Messaging Jobs Directly 154 Networking Events 156 Interviewing 157 Bonus Tip: An Idea for Your First LinkedIn Post 158 Chapter 10 Stages of Data Interviews 161 Why Do Interviews Take So Long? 161 Can You Tell Me More about the Interview Stages? 162 Phone Screen 163 Meeting the Hiring Manager 165 Behavioral Interview 166 Technical Interview 166 Panel Interview 169 Culture Fit 170 Follow-up 171 How I Handled Some Common How-Tos 172 Tell Me about Yourself 173 How to Come Up with Good Questions 175 Resources 180 Teal 180 Maven Analytics 181 Content Creators/Small Businesses 182 Working with Data Creators 183 Using AI 184 Chapter 11 How to Use ChatGPT to Aid Your Job Search 185 Writing a Résumé 185 Writing Cover Letters 186 Practicing for Interviews 186 Phone Screen 186 Technical Interview 189 Behavioral Interview 189 Writing Follow-Up Emails 191 Be Specific 192 Chapter 12 My Job Search 195 “Open to Work?” 195 Beginning to Search 197 Getting Reponses (and Rejections) 200 Pivoting 202 Interviewing 204 Decision Day 207 Part IV The Bonus Part 209 Chapter 13 After the Job Offer 211 Starting the Job 212 Dealing with Imposter Syndrome 213 Steps to Success 214 What It’s Like Working Remotely 215 Some Things About Tech That Surprised Me 217 121s 217 Home Office Stipend 217 Company Party/Offsites 218 Meetings 218 Referrals 219 Layoffs 220 Problem‐ Solving 221 Travel 222 Data Has Changed My Life 224 Chapter 14 Preparing for/Recovering from a Layoff 225 Don’t Ignore Red Flags 225 Resumes and Networking—Restarting the Job Search 226 Updating my Portfolio 229 The Layoff 230 Adjusting for Your Situation 235 Closing Thoughts 236 Appendix A Data Analytics Roadmap Checklist 239 Appendix B Tableau Tips 241 Appendix C My Data Analyst Journey 249 Acknowledgments 257 About the Author 259 Index 261

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Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data

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Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data

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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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Book SynopsisUncovers the surprising ways math shapes our livesfrom whom we date to what we learn. How do dating sites match compatible partners? What do cell phones and sea coasts have in common? And why do computer scientists keep ant colonies? Jeff Suzuki answers these questions and more in Patently Mathematical, which explores the mathematics behind some of the key inventions that have changed our world. In recent years, patents based on mathematics have been issued by the thousandsfrom search engines and image recognition technology to educational software and LEGO designs. Suzuki delves into the details of cutting-edge devices, programs, and products to show how even the simplest mathematical principles can be turned into patentable ideas worth billions of dollars. Readers will discover whether secure credit cards are really secure how improved data compression made streaming video services like Netflix a hit the mathematics behind self-correcting golf balls why Google is such an effectiTrade ReviewPatently Mathematical by Jeff Suzuki is a chronicle of the various patents based on mathematical algorithm applications. Each of his twelve chapters itemizes a specific family of patents along with pertinent anecdotes and suitable-for-the-general-reader examples illustrating how the algorithms work . . . Suzuki's book is a kaleidoscopic guided tour of the patented mathematical innovations that by and large now distinctly characterize the twenty-first century with respect to past eras.—Andrew James Simoson, MathSciNetTable of ContentsAcknowledgments Introduction. My Billion-Dollar Blunder Chapter 1: The Informational Hokey PokeyChapter 2: The Trillion-Dollar EquationChapter 3: A Picture Is a Thousand WordsChapter 4: If You Like Piña ColadasChapter 5: The Education RevolutionChapter 6: Forget Your Password? Forget Your Password!Chapter 7: The Company We KeepChapter 8: The Best of All Possible WorldsChapter 9: The Complete SagaChapter 10: Complexity from SimplicityChapter 11: RSA . . .Chapter 12: . . . Is PasséEpilogueBibliographyIndex

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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 SynopsisIn the ten years since the publication of the best-selling first edition, more than 1,000 graph theory papers have been published each year. Reflecting these advances, Handbook of Graph Theory, Second Edition provides comprehensive coverage of the main topics in pure and applied graph theory. This second editionover 400 pages longer than its predecessorincorporates 14 new sections. Each chapter includes lists of essential definitions and facts, accompanied by examples, tables, remarks, and, in some cases, conjectures and open problems. A bibliography at the end of each chapter provides an extensive guide to the research literature and pointers to monographs. In addition, a glossary is included in each chapter as well as at the end of each section. This edition also contains notes regarding terminology and notation.With 34 new contributors, this handbook is the most comprehensive single-source guide to graph theory. It emphasizes quick aTrade ReviewPraise for the First Edition:… a fine guide to various literatures, especially for topics like Ramsey theory … . Many first-rate mathematicians have contributed, making the exposition's quality high overall. …. Highly recommended.—CHOICE, January 2005, Vol. 42, No. 05Praise for the First Edition:… a fine guide to various literatures, especially for topics like Ramsey theory … . Many first-rate mathematicians have contributed, making the exposition's quality high overall. …. Highly recommended.—CHOICE, January 2005, Vol. 42, No. 05Table of ContentsIntroduction to Graphs. Graph Representation. Directed Graphs. Connectivity and Traversability. Colorings and Related Topics. Algebraic Graph Theory. Topological Graph Theory. Analytic Graph Theory. Graphical Measurement. Graphs in Computer Science. Networks and Flows. Communication Networks. Natural Science and Processes. Index.

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Book SynopsisWhat identity means in an algorithmic age: how it works, how our lives are controlled by it, and how we can resist itAlgorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline. Algorithms create and recreate us, using our data to assign and reassign our gender, race, sexuality, and citizenship status. They can recognize us as celebrities or mark us as terrorists. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities wTrade ReviewWe Are Datais a gem!... This finely crafted book should help us to take a giant collective leap forward. * International Journal of Communication *We Are Dataspells out the implications of being made of data in the digital age: our new & algorithmic identity. John Cheney-Lippold shows how algorithmic logics that undergird the architecture, regulation, monetization, and uses of the Internet have changed the nature of human experience and identity. Through witty and accessible examples, he eloquently lays out the social and political consequences of transcoding lived identity into measurable types in our new world. Clearly written, carefully researched, timely and intelligent,We Are Datais a compelling and much-needed book. -- Alexandra Juhasz,Chair, Film Department, Brooklyn CollegeJohn Cheney-Lippolds deft examination of & measurable typesthe categories by which we are known and assessed, based on our datasheds light on contemporary societys encounter with information systems to scrutiny, and with those eager to identify us for their own ends.We Are Data goes beyond naming possible harms. It helps us think differently about what it means to be & seen by marketers, algorithms, or the NSA as members of shifting categoriesidentifications that structure us and our encounter with the world, but that we have little power to shape. -- Tarleton Gillespie,author of Wired Shut: Copyright and the Shape of Digital CultureThis book sparkles with brilliant insights. It offers us tools and a vocabulary through which we can think about the layers of identities that our data-conjured ghosts inhabit. I dont think I fully grasped the complexity of what these clouds of commercial data did with us and to us until I read We Are Data. -- Siva Vaidhyanathan,author of The Googlization of Everything—and Why We Should WorryWe Are Data is an inspiring and thought-provoking book to read, especially for those interested in the social, political, and cultural aspects of data. It draws on a wide range of well-known literature in the field of Internet and algorithm studies and further engages deeply with the philosophical aspects of the presented themes. * Mobile Media and Communication *If knowledge is indeed the means by which we can begin to challenge the digital status quo, then Cheney-Lippold has done much to forearm us by so capably elucidating the problem. * LSE Review of Books *The text moves beyond overdone topics of online privacy to look at how the lack of privacy of our data impacts identities It is the most appropriate for social science researchers and students. * Choice *We Are Data shows us just how powerful data can be and how that data affects who we are and who we can be. Cheney-Lippold addresses how data is (and always has been) a part of our lives through the discussionof categorization, control, subjectivity, and privacy. * Technical Communication *A heady and rewarding explanation of our lives in the data age. [Cheney-Lippold's] discussion of privacy...will fascinate many. Essential reading for anyone who cares about the internet's extraordinary impact on each of us and on our society. * Starred Kirkus Reviews *

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Book SynopsisA revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for black girlswhat will you find? Big Booty and other sexually explicit terms are likely to come up as top search terms. But, if you type in white girls, the results are radically different. The suggested porn sites and un-moderated discussions about why black women are so sassy or why black women are so angry presents a disturbing portrait of black womanhood in modern society.In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilegTrade Review"Rather than being a neutral arbiter that sorts content by quality, Noble argues that search engines are easily gamed in ways that reflect discriminatory practices. Even without malevolent actors, search engines may be perpetuating racist stereotypes." * Chicago Tribune *"Nobles thesis is a new tune in the ever-louder chorus that, in light of the dominance of the big tech companies, is singing for 'protections and attention that work in service of the public'." * The Financial Times *"[P]resents convincing evidence of the need for closer scrutiny and regulation of search engine[s].A thought-provoking, well-researched work." * Library Journal *"Noble argues...that the web is ...a machine of oppression...[Her] central insight - that nothing about internet search and retrieval is political neutral - is made...through the accumulation of alarming and disturbing examples. [She] makes a compelling case that pervasive racism online inflames racist violence IRL." * Los Angeles Review of Books *"A distressing account of algorithms run amok." * Kirkus Reviews *"Algorithms of Oppressionis a wakeup call to bring awareness to the biases of the internet, and should motivate all concerned people to ask why those biases exist, and who they benefit." * New York Journal of Books *"Noble offers a compelling look into the structure of digitized informationmost of it driven by advertising revenueand how it perpetuates racist assumptions and ideologies." * Pacific Standard *"Noble makes a strong case that present technologies and search engines are not just imperfect, but they enact actual harm to people and communities." * Popmatters.com *"50 Best Book of 2018 So Far, "There's been a growing swell of concern in the academic community about the stranglehold that commercial (for-profit) search engines have over access to information in our world. Safiya Umoja Noble builds on this body of work...to demonstrate that search engines, and in particular Google, are not simply imperfect machines, but systems designed by humans in ways that replicate the power structures of the western countries where they are built, complete with all the sexism and racism that are built into those structures." * Popmatters.com *"Noble demolishes the popular assumption that Google is a values-free tool with no agenda...She astutely questions the wisdom of turning so much of our data and intellectual capital over to a corporate monopoly.Nobles study should prompt some soul-searching about our reliance on commercial search engines and about digital social equity." * STARRED Booklist *"Nobles incisive work centers around the fact that, at present, Googles search engine promotes structural inequality through multiple examples and that this is not just a & design problem but an inherent political problem that has shaped the entirety of twentieth-century technology design. In addition to her illustrative examples and incisive criticism, Noble offers practicable policy solutions." * Metascience *"In Algorithms of Oppression, [Noble] offers her readers a lens to discover, analyze, and critique the search engine algorithms that perpetuate stereotypes and racist beliefs[This] book will be of great interest to academic librarians who teach information literacy courses, as well as students and faculty in computer science, ethnic studies, gender studies, and mass communications." * Choice *"A good read for anyone interested in how bias can be expressed by lines of code. Even those already familiar with the issues will find new insight in the connections and impact Noble outlines. The book is accessible even to those who are not well-versed in the technology of search engines." -- The International Journal of Information, Diversity, & Inclusion""Algorithms of Oppression succeeds as a critical intervention, one with a clear commitment to engaged scholarship that should lead to policy changes as well as changes in a field too white, American and male. For readers of this journal, the book is a powerful example of the vital contributions of Black Feminist Technology Studies... Noble demonstrates that engaged, intersectional and accessible writing can and indeed does make a difference." " -- The International Journal of Press/Politics"Often assumed by both developers and the general public to be value-neutral, the algorithmic structures through which human beings create, organize, and access content online are, Noble effectively argues, inescapably shaped by the logics of oppression that shape our interconnected lives … Algorithms provides a strong introduction, with concrete and replicable examples of algorithmic oppression, for those beginning to think critically about our internet-centric information ecosystem. For those already steeped in the rapidly growing literature of critical librarian and information studies, Algorithms will be a valuable addition to our corpus of texts that blend theory and practice, both documenting the problematic nature of where we are and the possibility of where we might arrive in future if we fight, collectively, to make it so." -- New England Archivists"Algorithms of Oppression offers a sobering portrait of the impact of our reliance on quick, freely accessible searches. Foregrounding her discussion in the context of the technological mechanisms and decision‐makers that drive results, Noble forces the reader to confront the rarely discussed risks and long‐term costs associated with easy‐to‐access, corporate‐sponsored information." -- Teachers College Record"All search results are not created equal. Through deft analyses of software, society, and superiority, Noble exposes both the motivations and mathematics that make a & technologically redlined internet. Read this book to understand how supposedly race neutral zeros and ones simply dont add up." -- Matthew W. Hughey,Author of White Bound: Nationalists, Antiracists, and the Shared Meanings of Race"Safiya Noble has produced an outstanding book that raises clear alarms about the ways Google quietly shapes our lives, minds, and attitudes. Noble writes with urgency and clarity. This book is essential for anyone hoping to understand our current information ecosystem." -- Siva Vaidhyanathan,Author of The Googlization of Everything — and Why We Should Worry"Safiya Nobles compelling and accessible book is an impressive survey of the impact of search and other algorithms on our understandings of racial and gender identity. Her study raises crucial questions regarding the power and control of algorithms, and is essential reading for understanding the way media works in the contemporary moment." -- Sarah Banet-Weiser,Author of Authentic™: The Politics of Ambivalence in a Brand Culture"Algorithms of Oppression shines a light not only on the way that new technologies both reaffirm hegemonies of the past and impose constraints on our futures, but also on how we ourselves are interpellated daily and voluntarily into these algorithmic processes." * This Year’s Work in Critical and Cultural Theory *"Illustrates not only how the platforms and programmes we use in our daily life are created and built within a specific economic, racial, and gendered context, but that that context and those platforms enact and reinforce oppressive social relationships as we use them." * Archifacts *

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Book SynopsisA revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for black girlswhat will you find? Big Booty and other sexually explicit terms are likely to come up as top search terms. But, if you type in white girls, the results are radically different. The suggested porn sites and un-moderated discussions about why black women are so sassy or why black women are so angry presents a disturbing portrait of black womanhood in modern society. In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminaTrade ReviewRather than being a neutral arbiter that sorts content by quality, Noble argues that search engines are easily gamed in ways that reflect discriminatory practices. Even without malevolent actors, search engines may be perpetuating racist stereotypes. * Chicago Tribune *Nobles thesis is a new tune in the ever-louder chorus that, in light of the dominance of the big tech companies, is singing for 'protections and attention that work in service of the public'. * The Financial Times *[P]resents convincing evidence of the need for closer scrutiny and regulation of search engine[s].A thought-provoking, well-researched work. * Library Journal *Noble argues...that the web is ...a machine of oppression...[Her] central insight - that nothing about internet search and retrieval is political neutral - is made...through the accumulation of alarming and disturbing examples. [She] makes a compelling case that pervasive racism online inflames racist violence IRL. * Los Angeles Review of Books *A distressing account of algorithms run amok. * Kirkus Reviews *Algorithms of Oppressionis a wakeup call to bring awareness to the biases of the internet, and should motivate all concerned people to ask why those biases exist, and who they benefit. * New York Journal of Books *Noble offers a compelling look into the structure of digitized informationmost of it driven by advertising revenueand how it perpetuates racist assumptions and ideologies. * Pacific Standard *Noble makes a strong case that present technologies and search engines are not just imperfect, but they enact actual harm to people and communities. * Popmatters.com *50 Best Book of 2018 So Far, "There's been a growing swell of concern in the academic community about the stranglehold that commercial (for-profit) search engines have over access to information in our world. Safiya Umoja Noble builds on this body of work...to demonstrate that search engines, and in particular Google, are not simply imperfect machines, but systems designed by humans in ways that replicate the power structures of the western countries where they are built, complete with all the sexism and racism that are built into those structures. * Popmatters.com *Noble demolishes the popular assumption that Google is a values-free tool with no agenda...She astutely questions the wisdom of turning so much of our data and intellectual capital over to a corporate monopoly.Nobles study should prompt some soul-searching about our reliance on commercial search engines and about digital social equity. * STARRED Booklist *Nobles incisive work centers around the fact that, at present, Googles search engine promotes structural inequality through multiple examples and that this is not just a & design problem but an inherent political problem that has shaped the entirety of twentieth-century technology design. In addition to her illustrative examples and incisive criticism, Noble offers practicable policy solutions. * Metascience *In Algorithms of Oppression, [Noble] offers her readers a lens to discover, analyze, and critique the search engine algorithms that perpetuate stereotypes and racist beliefs[This] book will be of great interest to academic librarians who teach information literacy courses, as well as students and faculty in computer science, ethnic studies, gender studies, and mass communications. * Choice *A good read for anyone interested in how bias can be expressed by lines of code. Even those already familiar with the issues will find new insight in the connections and impact Noble outlines. The book is accessible even to those who are not well-versed in the technology of search engines. -- The International Journal of Information, Diversity, & Inclusion"Algorithms of Oppression succeeds as a critical intervention, one with a clear commitment to engaged scholarship that should lead to policy changes as well as changes in a field too white, American and male. For readers of this journal, the book is a powerful example of the vital contributions of Black Feminist Technology Studies... Noble demonstrates that engaged, intersectional and accessible writing can and indeed does make a difference." -- The International Journal of Press/PoliticsOften assumed by both developers and the general public to be value-neutral, the algorithmic structures through which human beings create, organize, and access content online are, Noble effectively argues, inescapably shaped by the logics of oppression that shape our interconnected lives … Algorithms provides a strong introduction, with concrete and replicable examples of algorithmic oppression, for those beginning to think critically about our internet-centric information ecosystem. For those already steeped in the rapidly growing literature of critical librarian and information studies, Algorithms will be a valuable addition to our corpus of texts that blend theory and practice, both documenting the problematic nature of where we are and the possibility of where we might arrive in future if we fight, collectively, to make it so. -- New England ArchivistsAlgorithms of Oppression offers a sobering portrait of the impact of our reliance on quick, freely accessible searches. Foregrounding her discussion in the context of the technological mechanisms and decision‐makers that drive results, Noble forces the reader to confront the rarely discussed risks and long‐term costs associated with easy‐to‐access, corporate‐sponsored information. -- Teachers College RecordAll search results are not created equal. Through deft analyses of software, society, and superiority, Noble exposes both the motivations and mathematics that make a & technologically redlined internet. Read this book to understand how supposedly race neutral zeros and ones simply dont add up. -- Matthew W. Hughey,Author of White Bound: Nationalists, Antiracists, and the Shared Meanings of RaceSafiya Noble has produced an outstanding book that raises clear alarms about the ways Google quietly shapes our lives, minds, and attitudes. Noble writes with urgency and clarity. This book is essential for anyone hoping to understand our current information ecosystem. -- Siva Vaidhyanathan,Author of The Googlization of Everything — and Why We Should WorrySafiya Nobles compelling and accessible book is an impressive survey of the impact of search and other algorithms on our understandings of racial and gender identity. Her study raises crucial questions regarding the power and control of algorithms, and is essential reading for understanding the way media works in the contemporary moment. -- Sarah Banet-Weiser,Author of Authentic™: The Politics of Ambivalence in a Brand CultureAlgorithms of Oppression shines a light not only on the way that new technologies both reaffirm hegemonies of the past and impose constraints on our futures, but also on how we ourselves are interpellated daily and voluntarily into these algorithmic processes. * This Year’s Work in Critical and Cultural Theory *Illustrates not only how the platforms and programmes we use in our daily life are created and built within a specific economic, racial, and gendered context, but that that context and those platforms enact and reinforce oppressive social relationships as we use them. * Archifacts *

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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 SynopsisGain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science classes of data science problems, data science techniques and their applications and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practiceprovides a common base of reTable of ContentsPart One: Fundamentals1. Introduction: The Data Science Process2. Data Science and your business 3. Monks vs. Cowboys: Data Science CulturesPart Two: Classes of Problems4. Classification 5. Regression6. Natural Language Processing 7. Clustering8. Anomaly Detection9. Recommendations10. Computer Vision11. Sequential Decision Making Part Three: Techniques & Technologies12. Overview13. Data Capture14. Data Preparation15. Data Visualization16. Machine Learning17. Inference18. Other tools and services19. Reference Architecture20. Monks vs. Cowboys: PraxisPart Four: Building Teams and Executing Projects21. The Skills Framework22. Building and structuring the team23. Data Science Projects Appendix FAQs

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Book SynopsisThis witty book helps you understand how data compression algorithms work-in theory and practice-so you can choose the best solution among all the available compression tools.

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Book SynopsisMachine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.

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Book SynopsisWith this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior.

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Book SynopsisIn this practical book, author George Heineman (Algorithms in a Nutshell) provides concise and informative descriptions of key algorithms that improve coding. Software developers, testers, and maintainers will discover how algorithms solve computational problems creatively.

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Book SynopsisIn recent years, the United Kingdom's Home Office has started using automated systems to make immigration decisions. These systems promise faster, more accurate, and cheaper decision-making, but in practice they have exposed people to distress, disruption, and even deportation. This book identifies a pattern of risky experimentation with automated systems in the Home Office. It analyses three recent case studies including: a voice recognition system used to detect fraud in English-language testing; an algorithm for identifying ‘risky’ visa applications; and automated decision-making in the EU Settlement Scheme. The book argues that a precautionary approach is essential to ensure that society benefits from government automation without exposing individuals to unacceptable risks.Table of ContentsForeword - Catherine O’Regan 1. The Home Office Laboratory 2. Testing Systems 3. The Brexit Prototype 4. Category Errors 5. Precautionary Measures

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Book SynopsisDonald Knuth's influence in computer science ranges from the invention of methods for translating and defining programming languages to the creation of the TeX and METAFONT systems for desktop publishing. His award-winning textbooks have become classics; his scientific papers are widely referenced and stand as milestones of development over a wide range of topics. The present volume, which is the fourth in a series of his collected works, is devoted to an important subfield of Computer Science that Knuth founded in the 1960s and still considers his main life's work. This field, to which he gave the name Analysis of Algorithms, deals with quantitative studies of computer techniques, leading to methods for understanding and predicting the efficiency of computer programs. More than 30 of the papers that helped to shape this field are reprinted and updated in the present collection, together with historical material that has not previously been published.Table of Contents1. An almost linear recurrence; 2. The problem of compatible representatives; 3. The analysis of algorithms; 4. Mathematical analysis of algorithms; 5. The average height of planted plane trees; 6. An experiment in optimal sorting; 7. Shellsort with three increments; 8. The dangers of computer science theory; 9. Optimum measurement points for program frequency counts; 10. Ordered Hash tables; 11. Recurrence relations based on minimization; 12. Estimating the efficiency of backtrack programs; 13. An analysis of alpha-beta pruning; 14. Linear probing and graphs; 15. Activity in an interleaved memory; 16. Notes on generalized Dedekind sums; 17. Analysis of the subtractive algorithm for greatest common divisors; 18. Complexity results for bandwidth minimization; 19. Analysis of a simple factorization algorithm; 20. The complexity of nonuniform random number generation; 21. A trivial algorithm whose analysis isn't; 22. Evaluation of Porter's constant; 23. The expectant linearity of a simple equivalence algorithm; 24. Deletions that preserve randomness; 25. The average time for carry propogation; 26. A terminological proposal; 27. An analysis of optimum caching; 28. Optimal prepaging and font caching; 29. The distribution of continued fraction approximations; 30. The toilet paper problem; 31. A recurrence related to trees; 32. Stable husbands; 33. Postscript about NP-hard problems; 34. Nested satisfiability; 35. Textbook examples of recursion; 36. An exact analysis of stable allocation; 37. Big omicron and big omega and big theta.

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Book SynopsisAlmost half of all software projects fail to deliver on key requirements. Behavior-Driven Development (BDD) reduces these costly failures by building a shared understanding of how an application should work. Behavior Driven Development in Action, Second Edition teaches communication skills, collaborative practices, and automation tools that ensure everyone from developers to non-technical stakeholders are in agreement on the goals of a project. Revised and expanded in a second edition, the book contains new techniques for incorporating BDD into large-scale development practices such as Agile and DevOps, as well as updating examples for the latest versions of Java. about the technology You can't write good software if you don't understand what it's supposed to do. Behavior-Driven Development (BDD) encourages developers, quality teams, and non-technical stakeholders to collaborate, using conversation and concrete examples to make sure everyone agrees how an application should work and what features really matter. With a body of best practices and sophisticated tools for requirement analysis and test automation, BDD has become a mainstream practice for keeping projects on track and avoiding cancellation. what's inside BDD theory and practice How BDD will affect your team BDD for acceptance, integration, and unit testing Automating web services Reporting and living documentation about the reader For all development teams. No experience with BDD required. Examples written in Java.

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Book SynopsisFrom finance to artificial intelligence, genetic algorithms are a powerful tool with a wide array of applications. But you don't need an exotic new language or framework to get started; you can learn about genetic algorithms in a language you're already familiar with. Join us for an in-depth look at the algorithms, techniques, and methods that go into writing a genetic algorithm. From introductory problems to real-world applications, you'll learn the underlying principles of problem solving using genetic algorithms. Evolutionary algorithms are a unique and often overlooked subset of machine learning and artificial intelligence. Because of this, most of the available resources are outdated or too academic in nature, and none of them are made with Elixir programmers in mind. Start from the ground up with genetic algorithms in a language you are familiar with. Discover the power of genetic algorithms through simple solutions to challenging problems. Use Elixir features to write genetic algorithms that are concise and idiomatic. Learn the complete life cycle of solving a problem using genetic algorithms. Understand the different techniques and fine-tuning required to solve a wide array of problems. Plan, test, analyze, and visualize your genetic algorithms with real-world applications. Open your eyes to a unique and powerful field - without having to learn a new language or framework. What You Need: You'll need a macOS, Windows, or Linux distribution with an up-to-date Elixir installation.

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Book SynopsisHow do we understand the world around us? How do we solve problems? Often the answer to these questions follows a certain pattern, an algorithm if you wish. This is the case when our analytical left-brain side is at work. However, there are also elements in our behaviour where intelligence appears to follow a more elusive path, which cannot easily be characterised as a specific sequence of steps. Is Intelligence an Algorithm? offers an insight into intelligence as it functions in nature, like human or animal intelligence, but also sheds light on modern developments in the field of artificial intelligence, proposing further architectural solutions for the creation of a so-called global Webmind.

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Book Synopsis Geomatics is a field of science that has been intimately intertwined with our daily lives for almost 30 years, to the point where we often forget all the challenges it entails. Who does not have a navigation application on their phone or regularly engage with geolocated data? What is more, in the coming decades, the accumulation of geo-referenced data is expected to increase significantly. This book focuses on the notion of the imperfection of geographic data, an important topic in geomatics. It is essential to be able to define and represent the imperfections that are encountered in geographical data. Ignoring these imperfections can lead to many risks, for example in the use of maps which may be rendered inaccurate. It is, therefore, essential to know how to model and treat the different categories of imperfection. A better awareness of these imperfections will improve the analysis and the use of this type of data. Table of ContentsPart 1. Bases and Concepts 1. Imperfection and Geographic Information, François Pinet, Mireille Batton-Hubert and Eric Desjardin. 2. Imperfection of Geographic Information: Concepts and Terminologies, Rodolphe Devillers, Eric Desjardin and Cyril De Runz. 3. The Origins of Imperfection in Geographic Data, Jean-Michel Follin, Jean-François Girres, Ana-Maria Olteanu-Raimond and David Sheeren. 4. Integrity and Trust of Geographic Information, Clément Iphar, Benjamin Costé, Aldo Napoli, Cyril Ray and Rodolphe Devillers. Part 2. Representation 5. Formalisms and Representations of Imperfect Geographic Objects, Mireille Batton-Hubert and François Pinet. 6. Representing Diagrams of Imperfect Geographic Objects, François Pinet and Cyril De Runz. Part 3. Reasoning and Treatment 7. Algebraic Reasoning for Uncertain Data, Florence Le Ber. 8. Reasoning in Modal Logic for Uncertain Data, Elisabeth Gavignet and Nadine Cullot. 9. Reviewing the Qualifiers of Imperfection in Geographic Information, Giovanni Fusco and Andrea Tettamanzi. 10. The Features of Decision Aid and? Analysis Processes in Geography: How to Grasp Complexity, Uncertainty, and Risks?, Myriam Merad.

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Book SynopsisAlmost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.Table of Contents1. Some Definitions. 2. Difficulty of the Difficulty. 3. Landscape Typology. 4. LandGener. 5. Test Cases. 6. Difficulty vs Dimension. 7. Exploitation and Exploration vs Difficulty. 8. The Explo2 Algorithm. 9. Balance and Perceived Difficulty.

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Book SynopsisData structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.Table of ContentsPreface ix Acknowledgments xv Chapter 1. Introduction 1 1.1. History of algorithms 3 1.2. Definition, structure and properties of algorithms 4 1.2.1. Definition 4 1.2.2. Structure and properties 4 1.3. Development of an algorithm 5 1.4. Data structures and algorithms 6 1.5. Data structures -- definition and classification 7 1.5.1. Abstract data types 7 1.5.2. Classification 9 1.6. Algorithm design techniques 9 1.7. Organization of the book 11 Chapter 2. Analysis of Algorithms 13 2.1. Efficiency of algorithms 13 2.2. Apriori analysis 15 2.3. Asymptotic notations 17 2.4. Time complexity of an algorithm using the O notation 19 2.5. Polynomial time versus exponential time algorithms 20 2.6. Average, best and worst case complexities 21 2.7. Analyzing recursive programs 23 2.7.1. Recursive procedures 23 2.7.2. Apriori analysis of recursive functions 27 2.8. Illustrative problems 31 Chapter 3. Arrays 45 3.1. Introduction 45 3.2. Array operations 46 3.3. Number of elements in an array 46 3.3.1. One-dimensional array 46 3.3.2. Two-dimensional array 47 3.3.3. Multidimensional array 47 3.4. Representation of arrays in memory 48 3.4.1. One-dimensional array 49 3.4.2. Two-dimensional arrays 51 3.4.3. Three-dimensional arrays 52 3.4.4. N-dimensional array 53 3.5. Applications 54 3.5.1. Sparse matrix 54 3.5.2. Ordered lists 55 3.5.3. Strings 56 3.5.4. Bit array 58 3.6. Illustrative problems 60 Chapter 4. Stacks 71 4.1. Introduction 71 4.2. Stack operations 72 4.2.1. Stack implementation 73 4.2.2. Implementation of push and pop operations 74 4.3. Applications 76 4.3.1. Recursive programming 76 4.3.2. Evaluation of expressions 79 4.4. Illustrative problems 83 Chapter 5. Queues 101 5.1. Introduction 101 5.2. Operations on queues 102 5.2.1. Queue implementation 102 5.2.2. Implementation of insert and delete operations on a queue 103 5.2.3. Limitations of linear queues 105 5.3. Circular queues 106 5.3.1. Operations on a circular queue 106 5.3.2. Implementation of insertion and deletion operations in circular queue 109 5.4. Other types of queues 112 5.4.1. Priority queues 112 5.4.2. Deques 117 5.5. Applications 119 5.5.1. Application of a linear queue 119 5.5.2. Application of priority queues 120 5.6. Illustrative problems 125 Chapter 6. Linked Lists 143 6.1. Introduction 143 6.1.1. Drawbacks of sequential data structures 143 6.1.2. Merits of linked data structures 145 6.1.3. Linked lists -- structure and implementation 145 6.2. Singly linked lists 147 6.2.1. Representation of a singly linked list 147 6.2.2. Insertion and deletion in a singly linked list 149 6.3. Circularly linked lists 155 6.3.1. Representation 155 6.3.2. Advantages of circularly linked lists over singly linked lists 155 6.3.3. Disadvantages of circularly linked lists 156 6.3.4. Primitive operations on circularly linked lists 158 6.3.5. Other operations on circularly linked lists 159 6.4. Doubly linked lists 160 6.4.1. Representation of a doubly linked list 161 6.4.2. Advantages and disadvantages of a doubly linked list 162 6.4.3. Operations on doubly linked lists 163 6.5. Multiply linked lists 166 6.6. Unrolled linked lists 171 6.6.1. Retrieval of an element 172 6.6.2. Insert an element 172 6.6.3. Delete an element 173 6.7. Self-organizing lists 175 6.8. Applications 175 6.8.1. Addition of polynomials 176 6.8.2. Sparse matrix representation 178 6.9. Illustrative problems 182 Chapter 7. Linked Stacks and Linked Queues 201 7.1. Introduction 201 7.1.1. Linked stack 202 7.1.2. Linked queues 203 7.2. Operations on linked stacks and linked queues 203 7.2.1. Linked stack operations 203 7.2.2. Linked queue operations 204 7.2.3. Algorithms for Push/Pop operations on a linked stack 205 7.2.4. Algorithms for insert and delete operations in a linked queue 206 7.3. Dynamic memory management and linked stacks 209 7.4. Implementation of linked representations 214 7.5. Applications 216 7.5.1. Balancing symbols 216 7.5.2. Polynomial representation 218 7.6. Illustrative problems 222 References 241 Index 243 Summaries of other volumes 245

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Book SynopsisData structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.Table of ContentsPreface ix Acknowledgments xv Chapter 8 Trees and Binary Trees 1 8.1 Introduction 1 8.2 Trees: definition and basic terminologies 1 8.2.1 Definition of trees 1 8.2.2 Basic terminologies of trees 2 8.3 Representation of trees 3 8.4 Binary trees: basic terminologies and types 6 8.4.1 Basic terminologies 6 8.4.2 Types of binary trees 7 8.5 Representation of binary trees 8 8.5.1 Array representation of binary trees 8 8.5.2 Linked representation of binary trees 10 8.6 Binary tree traversals 11 8.6.1 Inorder traversal 12 8.6.2 Postorder traversal 16 8.6.3 Preorder traversal 19 8.7 Threaded binary trees 22 8.7.1 Linked representation of a threaded binary tree 24 8.7.2 Growing threaded binary trees 24 8.8 Applications 25 8.8.1 Expression trees 26 8.8.2 Traversals of an expression tree 27 8.8.3 Conversion of infix expression to postfix expression 27 8.8.4 Segment trees 31 8.9 Illustrative problems 42 Chapter 9 Graphs 61 9.1 Introduction 61 9.2 Definitions and basic terminologies 63 9.3 Representations of graphs 75 9.3.1 Sequential representation of graphs 76 9.3.2 Linked representation of graphs 80 9.4 Graph traversals 81 9.4.1 Breadth first traversal 81 9.4.2 Depth first traversal 83 9.5 Applications 87 9.5.1 Single source shortest path problem 87 9.5.2 Minimum cost spanning trees 90 9.6 Illustrative problems 97 Chapter 10 Binary Search Trees and AVL Trees 115 10.1 Introduction 115 10.2 Binary search trees: definition and operations 115 10.2.1 Definition 115 10.2.2 Representation of a binary search tree 116 10.2.3 Retrieval from a binary search tree 117 10.2.4 Why are binary search tree retrievals more efficient than sequential list retrievals? 118 10.2.5 Insertion into a binary search tree 120 10.2.6 Deletion from a binary search tree 122 10.2.7 Drawbacks of a binary search tree 125 10.2.8 Counting binary search trees 128 10.3 AVL trees: definition and operations 130 10.3.1 Definition 131 10.3.2 Retrieval from an AVL search tree 132 10.3.3 Insertion into an AVL search tree 133 10.3.4 Deletion from an AVL search tree 141 10.3.5 R category rotations associated with the delete operation 146 10.3.6 L category rotations associated with the delete operation 150 10.4 Applications 151 10.4.1 Representation of symbol tables in compiler design 151 10.5 Illustrative problems 154 Chapter 11 B Trees and Tries 175 11.1 Introduction 175 11.2 m-way search trees: definition and operations 176 11.2.1 Definition 176 11.2.2 Node structure and representation 176 11.2.3 Searching an m-way search tree 178 11.2.4 Inserting into an m-way search tree 178 11.2.5 Deleting from an m-way search tree 179 11.2.6 Drawbacks of m-way search trees 184 11.3 B trees: definition and operations 184 11.3.1 Definition 184 11.3.2 Searching a B tree of order m 186 11.3.3 Inserting into a B tree of order m 186 11.3.4 Deletion from a B tree of order m 190 11.3.5 Height of a B tree of order m 194 11.4 Tries: definition and operations 195 11.4.1 Definition and representation 195 11.4.2 Searching a trie 197 11.4.3 Insertion into a trie 197 11.4.4 Deletion from a trie 198 11.4.5 Some remarks on tries 200 11.5 Applications 200 11.5.1 File indexing 201 11.5.2 Spell checker 203 11.6 Illustrative problems 204 Chapter 12 Red-Black Trees and Splay Trees 215 12.1 Red-black trees 215 12.1.1 Introduction to red-black trees 215 12.1.2 Definition 216 12.1.3 Representation of a red-black tree 219 12.1.4 Searching a red-black tree 220 12.1.5 Inserting into a red-black tree 220 12.1.6 Deleting from a red-black tree 228 12.1.7 Time complexity of search, insert and delete operations on a red-black tree 236 12.2 Splay trees 236 12.2.1 Introduction to splay trees 236 12.2.2 Splay rotations 237 12.2.3 Some remarks on amortized analysis of splay trees 242 12.3 Applications 244 12.4 Illustrative problems 245 References 261 Index 263 Summaries of other volumes 265

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Book SynopsisCryptography is now ubiquitous – moving beyond the traditional environments, such as government communications and banking systems, we see cryptographic techniques realized in Web browsers, e-mail programs, cell phones, manufacturing systems, embedded software, smart buildings, cars, and even medical implants. Today's designers need a comprehensive understanding of applied cryptography. After an introduction to cryptography and data security, the authors explain the main techniques in modern cryptography, with chapters addressing stream ciphers, the Data Encryption Standard (DES) and 3DES, the Advanced Encryption Standard (AES), block ciphers, the RSA cryptosystem, public-key cryptosystems based on the discrete logarithm problem, elliptic-curve cryptography (ECC), digital signatures, hash functions, Message Authentication Codes (MACs), and methods for key establishment, including certificates and public-key infrastructure (PKI). Throughout the book, the authors focus on communicating the essentials and keeping the mathematics to a minimum, and they move quickly from explaining the foundations to describing practical implementations, including recent topics such as lightweight ciphers for RFIDs and mobile devices, and current key-length recommendations. The authors have considerable experience teaching applied cryptography to engineering and computer science students and to professionals, and they make extensive use of examples, problems, and chapter reviews, while the book’s website offers slides, projects and links to further resources. This is a suitable textbook for graduate and advanced undergraduate courses and also for self-study by engineers.The authors' website (http://www.crypto-textbook.com/) provides extensive notes, slides, video lectures; the authors' YouTube channel (https://www.youtube.com/channel/UC1usFRN4LCMcflV7UjHNuQg) includes video lectures.Trade ReviewFrom the reviews: "The authors have succeeded in creating a highly valuable introduction to the subject of applied cryptography. I hope that it can serve as a guide for practitioners to build more secure systems based on cryptography, and as a stepping stone for future researchers to explore the exciting world of cryptography and its applications." (Bart Preneel, K.U.Leuven) "The material is very well presented so it is clear to understand. The necessary amount of mathematics is used and complete yet simple examples are used by the authors to help the reader understand the topics. ... [The authors] appear to fully understand the concepts and follow a very good pedagogical process that helps the reader not only understand the different topics but motivate you to perform some of the exercises at the end of each chapter and browse some of the reference materials. I fully recommend this book to any software developer/designer working or considering working on a project that requires security." (John Canessa) "The book presents a panoramic of modern Cryptography with a view to practical applications. ... The book is well written, many examples and figures through it illustrate the theory and the book's website offers links and supplementary information. The book also discusses the implementation in software and hardware of the main algorithms described." (Juan Tena Ayuso, Zentralblatt MATH, Vol. 1190, 2010)Table of ContentsIntroduction to Cryptography and Data Security.- Stream Ciphers.- The Data Encryption Standard (DES) and Alternatives.- The Advanced Encryption Standard (AES).- More About Block Ciphers.- to Public-Key Cryptography.- The RSA Cryptosystem.- Public-Key Cryptosystems Based on the Discrete Logarithm Problem.- Elliptic Curve Cryptosystems.- Digital Signatures.- Hash Functions.- Message Authentication Codes (MACs).- Key Establishment.

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Book SynopsisShortlisted for the 2018 Baillie Gifford Prize and the 2018 Royal Society Investment Science Book Prize "A beautifully accessible guide.…One of the best books yet written on data and algorithms." —Times (UK)Trade Review"With refreshing simplicity, Fry explains what AI, machine learning and complicated algorithms really mean." -- Guardian"Fascinating and funny. I learned something on every page." -- Tom Chivers - Buzzfeed"An action-packed read during which you will be outraged, provoked, and challenged." -- Cathy O’Neil, author of Weapons of Math Destruction"This short, sharp book on the power and dangers of algorithms offers one of the clearest explanations of a complex subject." -- Financial Times"Hannah Fry is one of the best STEM explainers and popularizers today." -- Forbes"For a reader unfamiliar with the technical aspects of AI, this book offers among the best lay explanations of how algorithms work." -- Science"Hannah Fry makes algorithms sound not only quite interesting but an idea that we must understand better as they dominate more and more of our daily lives in ways we see and in many ways we don’t." -- Amazon Book Review"Mixing mathematics and storytelling, this book asks the big questions about algorithms and humans—and their future together." -- Literary Hub"A well-constructed tour of technology and its discontents?timely, too, given the increasing prominence of AI in our daily lives." -- Kirkus Reviews"A lucid and timely analysis." -- Booklist (starred review)

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Book SynopsisAn exhilarating, elegant memoir and a significant polemic on how computers and algorithms shape our understanding of the world and of who we are   Bitwise is a wondrous ode to the computer lan­guages and codes that captured technologist David Auerbach’s imagination. With a philoso­pher’s sense of inquiry, Auerbach recounts his childhood spent drawing ferns with the pro­gramming language Logo on the Apple IIe, his adventures in early text-based video games, his education as an engineer, and his contribu­tions to instant messaging technology devel­oped for Microsoft and the servers powering Google’s data stores. A lifelong student of the systems that shape our lives—from the psy­chiatric taxonomy of the Diagnostic and Statistical Manual to how Facebook tracks and profiles its users—Auerbach reflects on how he has experienced the algorithms that taxonomize human speech, knowledge, and behavior and that compel us to do the same.  Into this exquisitely crafted, wide-ranging memoir of a life spent with code, Auerbach has woven an eye-opening and searing examina­tion of the inescapable ways in which algo­rithms have both standardized and coarsened our lives. As we engineer ever more intricate technology to translate our experiences and narrow the gap that divides us from the ma­chine, Auerbach argues, we willingly erase our nuances and our idiosyncrasies—precisely the things that make us human.

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Book SynopsisMETA-HEURISTIC ALGORITHMS FOR ADVANCED DISTRIBUTED SYSTEMS Discover a collection of meta-heuristic algorithms for distributed systems in different application domains Meta-heuristic techniques are increasingly gaining favor as tools for optimizing distributed systemsgenerally, to enhance the utility and precision of database searches. Carefully applied, they can increase system effectiveness, streamline operations, and reduce cost. Since many of these techniques are derived from nature, they offer considerable scope for research and development, with the result that this field is growing rapidly. Meta-Heuristic Algorithms for Advanced Distributed Systems offers an overview of these techniques and their applications in various distributed systems. With strategies based on both global and local searching, it covers a wide range of key topics related to meta-heuristic algorithms. Those interested in the latest developments in distributed system

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Book SynopsisQuantum computing is on the horizon, ready to impact everything from scientific research to encryption and security. But you don’t need a physics degree to get started in quantum computing. Quantum Computing for Developers shows you how to leverage your existing Java skills into writing your first quantum software so you’re ready for the revolution. Rather than a hardware manual or academic theory guide, this book is focused on practical implementations of quantum computing algorithms. Using Strange, a Java-based quantum computer simulator, you’ll go hands-on with quantum computing’s core components including qubits and quantum gates as you write your very first quantum code. Key Features · An introduction to the core concepts of quantum computing · Qubits and quantum gates · Superposition, entanglement, and hybrid computing · Quantum algorithms including Shor’s, Deutsch-jozsa, and Grover’s search For Java developers at all levels who want an early start in quantum computing. No advanced math knowledge required. About the technology Whilst quantum hardware is still on the edge of development, the underlying principles for writing quantum software are well-established. Right now developers can utilize quantum simulators, like Java-based Strange, to try quantum experiments on any platform that runs the JVM. Johan Vosis a cofounder of Gluon, a Java technology company that aims to offer Java solutions for all platforms including desktop, embedded, and mobile apps, and connect them to the cloud. He is a Java Champion and holds an MSc in Mining Engineering and a PhD in Applied Physics.

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Book SynopsisThis fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to utilize the TensorFlow library to rapidly build powerful ML models. You’ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges. New and revised content expands coverage of core machine learning algorithms and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. Key Features · Visualizing algorithms with TensorBoard · Understanding and using neural networks · Reproducing and employing predictive science · Downloadable Jupyter Notebooks for all examples · Questions to test your knowledge · Examples use the super-stable 1.14.1 branch of TensorFlow Developers experienced with Python and algebraic concepts like vectors and matrices. About the technology TensorFlow, Google’s library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow’s end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML. Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he’s faced at NASA, including building an implementation of Google’s Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval. Nishant Shukla wrote the first edition of Machine Learning with TensorFlow.Trade Review'A practical, no-nonsense, original approach to machine learning.' Alain Couniot, Sopra Steria Benelux 'An excellent book for readers who want to learn TensorFlow and machine learning.' Bhagvan Kommadi, ValueMomentum 'A great way to learn the ins and outs of TensorFlow, from the fundamentals to autoencoders, CNNs, and sequence-to-sequence models.' Ariel Gamiño, GLG 'Full of practical examples illustrating the concepts in a clear, progressive approach. This book is worth your while!' Alain Lompo, ISO-GRUPPETable of Contentstable of contents PART 1. YOUR MACHINE-LEARNING RIG READ IN LIVEBOOK 1A MACHINE-LEARNING ODYSSEY READ IN LIVEBOOK 2TENSORFLOW ESSENTIALS PART 2. CORE LEARNING ALGORITHMS READ IN LIVEBOOK 3LINEAR REGRESSION AND BEYOND READ IN LIVEBOOK 4USING REGRESSION FOR CALL-CENTER VOLUME PREDICTION READ IN LIVEBOOK 5A GENTLE INTRODUCTION TO CLASSIFICATION READ IN LIVEBOOK 6SENTIMENT CLASSIFICATION: LARGE MOVIE-REVIEW DATASET READ IN LIVEBOOK 7AUTOMATICALLY CLUSTERING DATA READ IN LIVEBOOK 8INFERRING USER ACTIVITY FROM ANDROID ACCELEROMETER DATA READ IN LIVEBOOK 9HIDDEN MARKOV MODELS READ IN LIVEBOOK 10PART-OF-SPEECH TAGGING AND WORD-SENSE DISAMBIGUATION PART 3. THE NEURAL NETWORK PARADIGM READ IN LIVEBOOK 11A PEEK INTO AUTOENCODERS READ IN LIVEBOOK 12APPLYING AUTOENCODERS: THE CIFAR-10 IMAGE DATASET READ IN LIVEBOOK 13REINFORCEMENT LEARNING READ IN LIVEBOOK 14CONVOLUTIONAL NEURAL NETWORKS READ IN LIVEBOOK 15BUILDING A REAL-WORLD CNN: VGG -FACE AND VGG -FACE LITE READ IN LIVEBOOK 16RECURRENT NEURAL NETWORKS READ IN LIVEBOOK 17LSTMS AND AUTOMATIC SPEECH RECOGNITION READ IN LIVEBOOK 18SEQUENCE-TO-SEQUENCE MODELS FOR CHATBOTS READ IN LIVEBOOK 19UTILITY LANDSCAPE READ IN LIVEBOOK APPENDIX A: INSTALLATION INSTRUCTIONS

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Book SynopsisMetaheuristic Algorithms in Maritime Operations Optimization focuses on the seaside and port side problems regarding the maritime transportation. The book reviews and introduces the most important problems regarding the shipping network design, long-term and short-term scheduling and planning problems in both bulk and container shipping as well as liquid maritime transportation. Application of meta heuristic algorithm is important for these problems, as most of them are hard and time-consuming to be solved optimally. Table of ContentsIntroduction ix Chapter 1. A Review of Maritime Operations 1 1.1. Maritime transportation 1 1.2. Types of ships and cargo 3 1.3. Containerization 5 1.4. Handling equipment in seaports 8 1.4.1. Quay cranes 10 1.4.2. Vehicles 11 1.4.3. Storage equipment 13 1.5. Optimization of maritime operations 15 1.6. Conclusion 19 Chapter 2. Metaheuristic Algorithms 21 2.1. Basics of metaheuristics 21 2.2. Simulated annealing algorithm 22 2.3. Tabu search algorithm 26 2.4. Genetic algorithms 28 2.5. Particle swarm optimization 32 2.6. Ant colony optimization 34 2.7. Conclusion 37 Chapter 3. Metaheuristics for Ship Operations 39 3.1. Ship routing problem 39 3.1.1. Formulation of the tramp SRP 42 3.1.2. A simple GA for the tramp SRP 46 3.1.3. An ACO for the liner SRP 48 3.2. Green maritime transportations 51 3.2.1. Mathematical formulation of GSRP 54 3.2.2. A PSO for the GSRP 59 3.3. Conclusions 62 Chapter 4. Optimization of Seaside Operations 65 4.1. Berth allocation problem 65 4.1.1. Formulation of the BAP 68 4.1.2. Representation of the BAP solution 70 4.1.3. An SA algorithm for the BAP 72 4.1.4. A bi-objective GA for the BAP 74 4.2. BAP in bulk seaports 76 4.2.1. Formulation of the BAP in bulk seaports 77 4.2.2. SA for the BAP in bulk seaports 80 4.3. Quay crane scheduling problem 82 4.3.1. An MILP for the QCSP 84 4.3.2. Genetic algorithms for the QCSP 87 4.3.3. Tabu search for the QCSP 90 4.3.4. Double cycling QCSP 92 4.4. Integrated berth allocation problem 95 4.4.1. A parallel GA for the B∩ 98 4.4.2. PSO for the B∩ 101 4.4.3. A simple hybrid GA for the B&CSP 108 4.5. Conclusions 111 Chapter 5. Problems in Yard Operations 113 5.1. Storage space allocation problem 113 5.1.1. Formulation of the SSAP 116 5.1.2. GA for the SSAP 120 5.2. Yard crane scheduling 124 5.2.1. Mathematical formulation of the YCSP 126 5.2.2. A hybrid TS algorithm for the YCSP 130 5.3. Intra-terminal transportation 135 5.3.1. Formulation of the dispatching of multi-load vehicles 139 5.3.2. A Tabu search for the vehicle routing 142 5.3.3. Formulation of the vehicle scheduling problem 147 5.3.4. GA for the vehicle scheduling problem 150 5.4. Integrated storage space allocation problem 153 5.4.1. A PSO for integrated SSAP–VSP 155 5.4.2. GA for the integrated SSAP–VSP and dispatching of YCs 158 5.5. Integrated scheduling of handling equipment 161 5.5.1. Mathematical formulation of the 3D scheduling 163 5.5.2. SA for the 3D scheduling 172 5.5.3. A GA for the 3D scheduling 176 5.6. Conclusions 179 Conclusion 181 Bibliography 185 Index 207

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