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Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

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This study characterized fishing activity in the reservoir of the Hydroelectric Power Plant of Itá in Brazil. The reservoir is located in the Upper Uruguay River, which forms the border between the states of Santa Catarina and Rio Grande do Sul. To analyze fishing activity and the composition

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Copyright © 2010 Seham A. Gad ELhak et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Parkinson’s disease (PD) is one of the neurodegenerative diseases which we can by certainty identify its pathology, however, this confidence disappeares when talking about the cause. A long history of trials, suggestions, and theories tried linking PD to a specific causation. In this paper, a new suggestion is trying to find its way, could it be toxicology? Can we—in the future—look to PD as an occupational disease, in fact, many clues point to the possible toxic responsibility—either total or partial—in causing this disease. Searching for possible toxic causes for PD would help in designing perfect toxic models in animals. 1.

Two studies were conducted to analyze how hope, resilience, optimism, and efficacy individually and as a composite higher-order factor pre-dicted work performance and satisfaction. Results from Study 1 provided psychometric support for a new survey measure designed to assess each of these 4 facets, as well as a composite factor. Study 2 results indi-cated a significant positive relationship regarding the composite of these 4 facets with performance and satisfaction. Results from Study 2 also indicated that the composite factor may be a better predictor of per-formance and satisfaction than the 4 individual facets. Limitations and practical implications conclude the article. In a special issue of the American Psychologist, Sheldon and King (2001, p. 216) point out that “Positive psychology revisits the ‘aver-age person ’ with an interest in finding out what works, what is right, and what is improving. ” Like positive psychology, which does not claim to have discovered the importance of positivity (e.g., see Peterson, 2006), the recently emerging positive organizational behavior field recognizes that much of the early history (e.g., Herzberg, 1966; Maslow, 1954; McGregor, 1960) and contemporary theories and research (e.g., job satisfaction, organizational commitment, positive affectivity, core self-evaluations, organizational citizenship, intrinsic motivation, humor, self-determination, organizational justice, among others) are positively oriented. Nevertheless, analogous to, and drawing from, the positive psy-chology literature, the term positive organizational behavior is intended to

Factor analysis has been one of the most powerful and flexible tools for assessment of multivariate dependence and codependence. Loosely speaking, it could be argued that the origin of its success rests in its very exploratory nature, where various kinds of data-relationships amongst the variables at study can be iteratively verified and/or refuted. Bayesian inference in factor analytic models has received renewed attention in recent years, partly due to computational advances but also partly to applied focuses generating factor structures as exemplified by recent work in financial time series modeling. The focus of our current work is on exploring questions of uncertainty about the number of latent factors in a multivariate factor model, combined with methodological and computational issues of model specification and model fitting. We explore reversible jump MCMC methods that build on sets of parallel Gibbs sampling-based analyses to generate suitable empirical proposal distributions and that address the challenging problem of finding e#cient proposals in high-dimensional models. Alternative MCMC methods based on bridge sampling are discussed, and these fully Bayesian MCMC approaches are compared with a collection of popular model selection methods in empirical studies.

This e-book is devoted to Global Optimization algorithms, which are methods for finding solutions of high quality for an incredible wide range of problems. We introduce the basic concepts of optimization and discuss features which make optimization problems difficult and thus, should be considered when trying to solve them. In this book, we focus on