DIGITAL LIBRARIES AND LEARNING OBJECTS REPOSITORIES

Discusses the technological changes that affects learning organizations as well as the human, technical, legal and sustainable aspects regarding learning objects repositories creation, maintenance and use. It presents concepts of information objects and learning objects, the functional requirements needed to their storage at Learning Management Systems. The role of Metadata is reviewed concerning learning objects creation and retrieval, followed by considerations about learning object repositories models, community participation/collaborative strategies and potential derived metrics/indicators. As a result of this desktop research, it can be said that not only technical competencies are critical to any learning objects repository implementation, but it urges that an engaged community of interest be establish as a key to support a learning object repository project. On that matter, researchers are applying Activity Theory (Vygostky, Luria y Leontiev) in order to seek joint perceptions and actions involving learning objects repository users, curators and managers, perceived as critical assets to a successful proposal.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Result on speech perception after conversion from Spectra (R) to Freedom (R)

New technology in the Freedom (R) speech processor for cochlear implants was developed to improve how incoming acoustic sound is processed; this applies not only for new users, but also for previous generations of cochlear implants. Aim: To identify the contribution of this technology - the Nucleus 22 (R) - on speech perception tests in silence and in noise, and on audiometric thresholds. Methods: A cross-sectional cohort study was undertaken. Seventeen patients were selected. The last map based on the Spectra (R) was revised and optimized before starting the tests. Troubleshooting was used to identify malfunction. To identify the contribution of the Freedom (R) technology for the Nucleus22 (R), auditory thresholds and speech perception tests were performed in free field in soundproof booths. Recorded monosyllables and sentences in silence and in noise (SNR = 0dB) were presented at 60 dBSPL. The nonparametric Wilcoxon test for paired data was used to compare groups. Results: Freedom (R) applied for the Nucleus22 (R) showed a statistically significant difference in all speech perception tests and audiometric thresholds. Conclusion: The reedom (R) technology improved the performance of speech perception and audiometric thresholds of patients with Nucleus 22 (R).

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012