Parallel Monte Carlo algorithms for information retrieval

In any data mining applications, automated text and text and image retrieval of information is needed. This becomes essential with the growth of the Internet and digital libraries. Our approach is based on the latent semantic indexing (LSI) and the corresponding term-by-document matrix suggested by Berry and his co-authors. Instead of using deterministic methods to find the required number of first "k" singular triplets, we propose a stochastic approach. First, we use Monte Carlo method to sample and to build much smaller size term-by-document matrix (e.g. we build k x k matrix) from where we then find the first "k" triplets using standard deterministic methods. Second, we investigate how we can reduce the problem to finding the "k"-largest eigenvalues using parallel Monte Carlo methods. We apply these methods to the initial matrix and also to the reduced one. The algorithms are running on a cluster of workstations under MPI and results of the experiments arising in textual retrieval of Web documents as well as comparison of the stochastic methods proposed are presented. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.

Memory for allergies and health foods: how younger and older adults strategically remember critical health information

Objectives. While older adults often display memory deficits, with practice they can sometimes selectively remember valuable information at the expense of less value information. We examined age-related differences and similarities in memory for health-related information under conditions where some information was critical to remember. Method. In Experiment 1, participants studied three lists of allergens, ranging in severity from 0 (not a health risk) to 10 (potentially fatal), with the instruction that it was particularly important to remember items to which a fictional relative was most severely allergic. After each list, participants received feedback regarding their recall of the high-value allergens. Experiment 2 examined memory for health benefits, presenting foods that were potentially beneficial to the relative’s immune system. Results. While younger adults exhibited better overall memory for the allergens, both age groups in Experiment 1 developed improved selectivity across the lists, with no evident age differences in severe allergen recall by List 2. Selectivity also developed in Experiment 2, although age differences for items of high health benefit were present. Discussion. The results have implications for models of selective memory in older age, and for how aging influences the ability to strategically remember important information within health-related contexts.

Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.