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For over half a century, Boris (Boaz) Trakhtenbrot has made seminal contributions to virtually all of the central areas of theoretical computer science. This festschrift volume readily illustrates the profound influence he has had on the field.
The Person 1 Boris Abramovich Trakhtenbrot ( ) - his Hebrew given name is Boaz ( ) - is universally admired as a founding - ther and long-standing pillar of the discipline of computer science. He is the ?eld's preeminent distinguished researcher and a most illustrious trailblazer and disseminator. He is unmatched in combining farsighted vision, unfaltering c- mitment, masterful command of the ?eld, technical virtuosity, aesthetic expr- sion, eloquent clarity, and creative vigor with humility and devotion to students and colleagues. For over half a century, Trakhtenbrot has been making seminal contributions to virtually all of the central aspects of theoretical computer science, inaugur- ing numerous new areas of investigation. He has displayed an almost prophetic ability to foresee directions that are destined to take center stage, a decade or morebeforeanyoneelsetakesnotice.Hehasneverbeentempted toslowdownor limithisresearchtoareasofendeavorinwhichhehasalreadyearnedrecognition and honor. Rather, he continues to probe the limits and position himself at the vanguard of a rapidly developing ?eld, while remaining, as always, unassuming and open-minded.
The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerators that have become increasingly important for mainstream applications.
This accessible compendium examines a collection of significant technology firms that have helped to shape the field of computing and its impact on society. Each company is introduced with a brief account of its history, followed by a concise account of its key contributions. The selection covers a diverse range of historical and contemporary organizations from pioneers of e-commerce to influential social media companies. Features: presents information on early computer manufacturers; reviews important mainframe and minicomputer companies; examines the contributions to the field of semiconductors made by certain companies; describes companies that have been active in developing home and personal computers; surveys notable research centers; discusses the impact of telecommunications companies and those involved in the area of enterprise software and business computing; considers the achievements of e-commerce companies; provides a review of social media companies.
This book presents fundamental contributions to computer science as written and recounted by those who made the contributions themselves. As such, it is a highly original approach to a OC living historyOCO of the field of computer science. The scope of the book is broad in that it covers all aspects of computer science, going from the theory of computation, the theory of programming, and the theory of computer system performance, all the way to computer hardware and to major numerical applications of computers.
This title gives students an integrated and rigorous picture of applied computer science, as it comes to play in the construction of a simple yet powerful computer system.
Why so few African American and Latino/a students study computer science: updated edition of a book that reveals the dynamics of inequality in American schools. The number of African Americans and Latino/as receiving undergraduate and advanced degrees in computer science is disproportionately low. And relatively few African American and Latino/a high school students receive the kind of institutional encouragement, educational opportunities, and preparation needed for them to choose computer science as a field of study and profession. In Stuck in the Shallow End, Jane Margolis and coauthors look at the daily experiences of students and teachers in three Los Angeles public high schools: an overcrowded urban high school, a math and science magnet school, and a well-funded school in an affluent neighborhood. They find an insidious “virtual segregation” that maintains inequality. The race gap in computer science, Margolis discovers, is one example of the way students of color are denied a wide range of occupational and educational futures. Stuck in the Shallow End is a story of how inequality is reproduced in America—and how students and teachers, given the necessary tools, can change the system. Since the 2008 publication of Stuck in the Shallow End, the book has found an eager audience among teachers, school administrators, and academics. This updated edition offers a new preface detailing the progress in making computer science accessible to all, a new postscript, and discussion questions (coauthored by Jane Margolis and Joanna Goode).
Coding and computational thinking (the ability to think like a computer) are among the skills that will serve students well in the future. Coding goes beyond websites and software - it's an essential component in finding solutions to everyday problems. Computational thinking has many applications beyond the computer lab or math class -it teaches reasoning, creativity and expression, and is an innovative way to demonstrate content knowledge and see mathematical processes in action. No-Fear Coding shows K-5 educators how to bring coding into their curriculum by embedding computational thinking skills into activities for every content area. At the same time, embedding these skills helps students prepare for coding in the middle grades as they build their knowledge. To help teachers easily and effectively introduce coding, the book features: Classroom-tested lessons and activities designed for skills progression. Ready-to-implement coding exercises that can be incorporated across the curriculum. Alignment to ISTE and Computer Science Teachers Association (CSTA) standards. Case studies and explorations of technology tools and resources to teach coding.
This textbook is a systematic guide to the steps in setting up a Capability Maturity Model Integration (CMMI) improvement initiative. Readers will learn the project management practices necessary to deliver high-quality software solutions to the customer on time and on budget. The text also highlights how software process improvement can achieve specific business goals to provide a tangible return on investment. Topics and features: supplies review questions, summaries and key topics for each chapter, as well as a glossary of acronyms; describes the CMMI model thoroughly, detailing the five maturity levels; provides a broad overview of software engineering; reviews the activities and teams required to set up a CMMI improvement initiative; examines in detail the implementation of CMMI in a typical organization at each of the maturity levels; investigates the various tools that support organizations in improving their software engineering maturity; discusses the SCAMPI appraisal methodology.
This book constitutes the refereed proceedings of the International Symposium on Logical Foundations of Computer Science, LFCS 2018, held in Deerfield Beach, FL, USA, in January 2018. The 22 revised full papers were carefully reviewed and selected from 22 submissions. The scope of the Symposium is broad and includes constructive mathematics and type theory; homotopy type theory; logic, automata, and automatic structures; computability and randomness; logical foundations of programming; logical aspects of computational complexity; parameterized complexity; logic programming and constraints; automated deduction and interactive theorem proving; logical methods in protocol and program verification; logical methods in program specification and extraction; domain theory logics; logical foundations of database theory; equational logic and term rewriting; lambda andcombinatory calculi; categorical logic and topological semantics; linear logic; epistemic and temporal logics; intelligent and multiple-agent system logics; logics of proof and justification; non-monotonic reasoning; logic in game theory and social software; logic of hybrid systems; distributed system logics; mathematical fuzzy logic; system design logics; and other logics in computer science.