From the reviews: "This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry. ... ... The book is well organized and lucidly written; a timely contribution by two founders of the field. It clearly demonstrates that computational geometry in the plane is now a fairly well-understood branch of computer science and mathematics. It also points the way to the solution of the more challenging problems in dimensions higher than two." #Mathematical Reviews#1 "... This remarkable book is a comprehensive and systematic study on research results obtained especially in the last ten years. The very clear presentation concentrates on basic ideas, fundamental combinatorial structures, and crucial algorithmic techniques. The plenty of results is clever organized following these guidelines and within the framework of some detailed case studies. A large number of figures and examples also aid the understanding of the material. Therefore, it can be highly recommended as an early graduate text but it should prove also to be essential to researchers and professionals in applied fields of computer-aided design, computer graphics, and robotics." #Biometrical Journal#2
The study of formal languages and of related families of automata has long been at the core of theoretical computer science. Until recently, the main reasons for this centrality were connected with the specification and analy sis of programming languages, which led naturally to the following ques tions. How might a grammar be written for such a language? How could we check whether a text were or were not a well-formed program generated by that grammar? How could we parse a program to provide the structural analysis needed by a compiler? How could we check for ambiguity to en sure that a program has a unique analysis to be passed to the computer? This focus on programming languages has now been broadened by the in creasing concern of computer scientists with designing interfaces which allow humans to communicate with computers in a natural language, at least concerning problems in some well-delimited domain of discourse. The necessary work in computational linguistics draws on studies both within linguistics (the analysis of human languages) and within artificial intelligence. The present volume is the first textbook to combine the topics of formal language theory traditionally taught in the context of program ming languages with an introduction to issues in computational linguistics. It is one of a series, The AKM Series in Theoretical Computer Science, designed to make key mathematical developments in computer science readily accessible to undergraduate and beginning graduate students.
In the 1930s, mathematical logicians studied the notion of "effective comput ability" using such notions as recursive functions, A-calculus, and Turing machines. The 1940s saw the construction of the first electronic computers, and the next 20 years saw the evolution of higher-level programming languages in which programs could be written in a convenient fashion independent (thanks to compilers and interpreters) of the architecture of any specific machine. The development of such languages led in turn to the general analysis of questions of syntax, structuring strings of symbols which could count as legal programs, and semantics, determining the "meaning" of a program, for example, as the function it computes in transforming input data to output results. An important approach to semantics, pioneered by Floyd, Hoare, and Wirth, is called assertion semantics: given a specification of which assertions (preconditions) on input data should guarantee that the results satisfy desired assertions (postconditions) on output data, one seeks a logical proof that the program satisfies its specification. An alternative approach, pioneered by Scott and Strachey, is called denotational semantics: it offers algebraic techniques for characterizing the denotation of (i. e. , the function computed by) a program-the properties of the program can then be checked by direct comparison of the denotation with the specification. This book is an introduction to denotational semantics. More specifically, we introduce the reader to two approaches to denotational semantics: the order semantics of Scott and Strachey and our own partially additive semantics.
Voting difficulties hung over America's presidential election in 2000 like a dark cloud. Hanging chads, a butterfly ballot, and the Supreme Court remain the most vivid memories of that political donnybrook. Passage of 2002's Help America Vote Act sparked further interest in the physical process of casting a ballot, yet several recent contests still produced confusion at the polls. A solution to at least some of those problems may be found in new technology, but such innovations carry their own concerns and questions. V oting Technology is the first book to investigate in a scientific and authoritative manner how voters respond to the new equipment. The authors—an interdisciplinary group of experts in American elections, political behavior, human-computer interaction, and human factors psychology—assess five commercially available voting systems, each one representing a specific class based on shared design principles, as well as a prototype system not currently available. They evaluate the systems against different criteria (including ease of use, speed, and accuracy) using field experiments, laboratory experiments, and expert reviews. The results reveal the good and bad about the new systems, including specific features that contribute to clarity, confusion, or error. Going beyond the concern with spoiled ballots, they determine whether voters actually cast their ballots for the candidates they intended to support. They address fundamental questions of whether voters like and trust the equipment and whether the various systems are equally usable by all voters. Their research also opens up an entirely new line of inquiry by asking about the interaction between ballot format and voter behavior. The concluding chapter pulls together best practices that will guide manufacturers of voting systems, ballot designers, election officials, political observers, and of course, voters. In a political system based on free exercise of personal choice, the least w
This book offers a comprehensive introduction to relational (SQL) and non-relational (NoSQL) databases. The authors thoroughly review the current state of database tools and techniques, and examine coming innovations. The book opens with a broad look at data management, including an overview of information systems and databases, and an explanation of contemporary database types: SQL and NoSQL databases, and their respective management systems The nature and uses of Big Data A high-level view of the organization of data management Data Modeling and Consistency Chapter-length treatment is afforded Data Modeling in both relational and graph databases, including enterprise-wide data architecture, and formulas for database design. Coverage of languages extends from an overview of operators, to SQL and and QBE (Query by Example), to integrity constraints and more. A full chapter probes the challenges of Ensuring Data Consistency, covering: Multi-User Operation Troubleshooting Consistency in Massive Distributed Data Comparison of the ACID and BASE consistency models, and more System Architecture also gets from its own chapter, which explores Processing of Homogeneous and Heterogeneous Data; Storage and Access Structures; Multi-dimensional Data Structures and Parallel Processing with MapReduce, among other topics. Post-Relational and NoSQL Databases The chapter on post-relational databases discusses the limits of SQL – and what lies beyond, including Multi-Dimensional Databases, Knowledge Bases and and Fuzzy Databases. A final chapter covers NoSQL Databases, along with Development of Non-Relational Technologies, Key-Value, Column-Family and Document Stores XML Databases and Graphic Databases, and more The book includes more than 100 tables, examples and illustrations, and each chapter offers a list of resources for further reading. SQL & NoSQL Databases conveys the strengths and weaknesses of relational and non-relational approaches, and shows how to undertake development for big data applications. The book benefits readers including students and practitioners working across the broad field of applied information technology. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Since the 2000 presidential election, the United States has been embroiled in debates about electronic voting. Critics say the new technologies invite tampering and fraud. Advocates say they enhance the accuracy of vote counts and make casting ballots easier--and ultimately foster greater political participation. Electronic Elections cuts through the media spin to assess the advantages and risks associated with different ways of casting ballots--and shows how e-voting can be the future of American democracy. Elections by nature are fraught with risk. Michael Alvarez and Thad Hall fully examine the range of past methods and the new technologies that have been created to try to minimize risk and accurately reflect the will of voters. Drawing upon a wealth of new data on how different kinds of electronic voting machines have performed in recent elections nationwide, they evaluate the security issues that have been the subject of so much media attention, and examine the impacts the new computer-based solutions is having on voter participation. Alvarez and Hall explain why the benefits of e-voting can outweigh the challenges, and they argue that media coverage of the new technologies has emphasized their problems while virtually ignoring their enormous potential for empowering more citizens to vote. The authors also offer ways to improve voting technologies and to develop more effective means of implementing and evaluating these systems. Electronic Elections makes a case for how e-voting can work in the United States, showing why making it work right is essential to the future vibrancy of the democratic process.
Composition and the Rhetoric of Science: Engaging the Dominant Discourse calls for instructors of first-year writing courses to employ primary scientific discourse in their teaching and for rhetoricians of science to think about teaching scientific discourse as a literacy skill. Author Michael J. Zerbe argues that inclusion of scientific discourse is crucial because of this rhetoric’s status as the dominant discourse in western culture. The volume draws on Lyotard, Žižek, Foucault, and Althusser to argue that while important theorists such as these have recognized the dominance of scientific discourse, rhetoric and composition has not—to its detriment. The textillustrates that scientific discourse remains a miniscule part of the enterprise of rhetoric and composition and thus the field is not fulfilling its mission of providing students with the writing and reading skills they need to live and work in a science- and technology-dependent society. Zerbe provides an analysis of science popularizations and demonstrates how these works can be used to contextualize primary scientific research. He also presents three pedagogical scenarios, each built around a carefully chosen, accessible example of scientific discourse, that demonstrate how articles from scientific journals can be used in writing courses. Only by gaining a meaningful fluency in this discourse—one that is not offered by science textbooks—can a more sophisticated scientific literacy be assured. Composition and the Rhetoric of Science effectively explores the relatively limited amount of work done in rhetoric and composition on scientific discourse and questions this state of affairs. Zerbe presents for the first time cultural studies and science literacy as gateways for incorporating scientific discourse into first-year writing courses.
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