Lecture Series on Computer and on Computational Sciences (LSCCS) aims to provide a medium for the publication of new results and developments of high-level research and education in the field of computer and computational science.In this series, only selected proceedings of conferences in all areas of computer science and computational sciences wil
The aim of IeCCS 2007 is to bring together leading scientists of the international Computer Science community and to attract original research papers of very high quality. The topics to be covered include (but are not limited to): Numerical Analysis, Scientific Computation, Computational Mathematics, Mathematical Software, Programming Techniques and Languages, Parallel Algorithms and its Applications, Symbolic and Algebraic Manipulation, Analysis of Algorithms, Problem Complexity, Mathematical Logic, Formal Languages, Data Structures, Data Bases, Information Systems, Artificial Intelligence, Expert Systems, Simulation and Modeling, Computer Graphics, Software Engineering, Image Processing, Computer Applications, Hardware, Computer Systems Organization, Software, Data, Theory of Computation, Mathematics of Computing, Information Systems, Computing Methodologies, Computer Applications, Computing Milieu (see http://www.ieccs.net/topics.htm).
Many claims are made about how certain tools, technologies, and practices improve software development. But which claims are verifiable, and which are merely wishful thinking? In this book, leading thinkers such as Steve McConnell, Barry Boehm, and Barbara Kitchenham offer essays that uncover the truth and unmask myths commonly held among the software development community. Their insights may surprise you. Are some programmers really ten times more productive than others? Does writing tests first help you develop better code faster? Can code metrics predict the number of bugs in a piece of software? Do design patterns actually make better software? What effect does personality have on pair programming? What matters more: how far apart people are geographically, or how far apart they are in the org chart? Contributors include: Jorge Aranda Tom Ball Victor R. Basili Andrew Begel Christian Bird Barry Boehm Marcelo Cataldo Steven Clarke Jason Cohen Robert DeLine Madeline Diep Hakan Erdogmus Michael Godfrey Mark Guzdial Jo E. Hannay Ahmed E. Hassan Israel Herraiz Kim Sebastian Herzig Cory Kapser Barbara Kitchenham Andrew Ko Lucas Layman Steve McConnell Tim Menzies Gail Murphy Nachi Nagappan Thomas J. Ostrand Dewayne Perry Marian Petre Lutz Prechelt Rahul Premraj Forrest Shull Beth Simon Diomidis Spinellis Neil Thomas Walter Tichy Burak Turhan Elaine J. Weyuker Michele A. Whitecraft Laurie Williams Wendy M. Williams Andreas Zeller Thomas Zimmermann
Software engineering requires specialized knowledge of a broad spectrum of topics, including the construction of software and the platforms, applications, and environments in which the software operates as well as an understanding of the people who build and use the software. Offering an authoritative perspective, the two volumes of the Encyclopedia of Software Engineering cover the entire multidisciplinary scope of this important field. More than 200 expert contributors and reviewers from industry and academia across 21 countries provide easy-to-read entries that cover software requirements, design, construction, testing, maintenance, configuration management, quality control, and software engineering management tools and methods. Editor Phillip A. Laplante uses the most universally recognized definition of the areas of relevance to software engineering, the Software Engineering Body of Knowledge (SWEBOK®), as a template for organizing the material. Also available in an electronic format, this encyclopedia supplies software engineering students, IT professionals, researchers, managers, and scholars with unrivaled coverage of the topics that encompass this ever-changing field. Also Available Online This Taylor & Francis encyclopedia is also available through online subscription, offering a variety of extra benefits for researchers, students, and librarians, including: Citation tracking and alerts Active reference linking Saved searches and marked lists HTML and PDF format options Contact Taylor and Francis for more information or to inquire about subscription options and print/online combination packages. US: (Tel) 1.888.318.2367; (E-mail) [email protected] International: (Tel) +44 (0) 20 7017 6062; (E-mail) [email protected]
A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.
This book brings together some of the best practitioners andthinkers from around the world to discuss the likely future ofinformation and communication technologies for the constructionindustry. It addresses a range of innovative developments, state of the artapplications, research work and theoretical arguments with regardto the use of virtual technologies in design, construction andprocurement. From a future oriented perspective, the book presentswhat can be expected from the next generation of thesetechnologies.
Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes. In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and commu nicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance – this corresponds to the selection of the most suitable algorithm for solving a given instance. The book is suitable for researchers, practitioners, and graduate students working in the areas of optimization, search, constraints, and computational complexity.
These Proceedings represent the work of contributors to the 14th European Conference on e-Learning, ECEL 2015, hosted this year by the University of Hertfordshire, Hatfield, UK on 29-30 October 2015. The Conference and Programme Co-Chairs are Pro-fessor Amanda Jefferies and Dr Marija Cubric, both from the University of Hertfordshire. The conference will be opened with a keynote address by Professor Patrick McAndrew, Director, Institute of Educational Tech-nology, Open University, UK with a talk on "Innovating for learning: designing for the future of education." On the second day the keynote will be delivered by Professor John Traxler, University of Wolverhampton, UK on the subject of "Mobile Learning - No Longer Just e-Learning with Mobiles." ECEL provides a valuable platform for individuals to present their research findings, display their work in progress and discuss conceptual advances in many different branches of e-Learning. At the same time, it provides an important opportunity for members of the EL community to come together with peers, share knowledge and exchange ideas. With an initial submission of 169 abstracts, after the double blind, peer review process there are 86 academic papers,16 Phd Papers, 5 Work in Progress papers and 1 non academic papers in these Conference Proceedings. These papers reflect the truly global nature of research in the area with contributions from Algeria, Australia, Austria, Belgium, Botswana, Canada, Chile, Cov-entry, Czech Republic, Denmark, Egypt, England, Estonia, France, Germany, Ireland, Japan, Kazakhstan, New Zealand, Nigeria, Norway, Oman, Portugal, Republic of Kazakhstan, Romania, Saudi Arabia, Scotland, Singapore, South Africa, Sweden, the Czech Republic, Turkey, Uganda, UK, United Arab Emirates, UK and USA, Zimbabwe. A selection of papers - those agreed by a panel of reviewers and the editor will be published in a special conference edition of the EJEL (Electronic Journal of e-Learning www.ejel.org ).
Multi-objective optimization (MO) is a fast-developing field in computational intelligence research. Giving decision makers more options to choose from using some post-analysis preference information, there are a number of competitive MO techniques with an increasingly large number of MO real-world applications. Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.
