The Computer Contradictionary

Ascertain the meaning before consulting this dictionary, warns the author of this collection of deliberately satirical misdefinitions. New computer cultures and their jargons have burgeoned since this book's progenitor, The Devil's DP Dictionary, was published in 1981. This updated version of Stan Kelly-Bootle's romp through the data processing lexicon is a response to the Unix pandemic that has swept academia and government, to the endlessly hyped panaceas offered to the MIS, and to the PC explosion that has brought computer terminology to a hugely bewildered, lay audience.' The original dictionary, a pastiche of Ambrose Bierce's famous work, parried chiefly the mainframe and mini-folklore of the 1950s, 1960s and 1970s. This revision adds over 550 new entries and enhances many of the original definitions. Key targets are a host of new follies crying out for cynical lexicography including: the GUI-Phooey iconoclasts, object orienteering and the piping of BLObs down the Clinton-Gore InfoPike.

Innovations and Advances in Computer Sciences and Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Innovations and Advances in Computer Sciences and Engineering includes selected papers form the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2008) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2008).

Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behavior; comprehensive coverage of all types of algorithms useful for this subject; and applications of various modeling activities (e.g., environmental systems, noise immunity, economic systems, clusterization, and neural networks). It presents recent studies on clusterization and recognition problems, and it includes listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs. Inductive Learning Algorithms for Complex Systems Modeling will be a valuable reference for graduate students, research workers, and scientists in applied mathematics, statistics, computer science, and systems science disciplines. The book will also benefit engineers and scientists from applied fields such as environmental studies, oceanographic modeling, weather forecasting, air and water pollution studies, economics, hydrology, agriculture, fisheries, and time series evaluations.

Society forges ahead in the process of solving various contradictory problems and it is ceaselessly innovating. It is the desire of mankind to use computers and computing networks to help deal with contradictory problems and to conduct innovative activities. Using formal models to discuss object extension and the possibility of change, as well as t

"This book summarizes the challenges inherent in leading distributed teams and explores practices that are emerging to optimize distributed team performance"--Provided by publisher.

With all-new coverage of home, mobile, and wireless issues, migrating from IP chains to IP tables, and protecting your network from users as well as hackers, this book provides immediate and effective Intrusion Detection System techniques. Contains practical solutions for every system administrator working with any Linux system, large or small.

Human error is so often cited as a cause of accidents. There is perception of a 'human error problem'. Solutions are thought to lie in changing the people or their role. The label 'human error', however, is prejudicial and hides more than it reveals about how a system malfunctions. This book takes you behind the label. It explains how human error results from social and psychological judgments by the system's stakeholders that focus only on one facet of a set of interacting contributors.

Researchers and practitioners alike are increasingly turning to search, op timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu tionary computation for the solution of multi-objective problems is a landmark addition to the literature that comes just in the nick of time. Multi-objective evolutionary algorithms (MOEAs) are receiving increasing and unprecedented attention. Researchers and practitioners are finding an irresistible match be tween the popUlation available in most genetic and evolutionary algorithms and the need in multi-objective problems to approximate the Pareto trade-off curve or surface.