Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.

Architecture of a multi-modal dialogue system oriented to multilingual question-answering

In this paper, a proposal of a multi-modal dialogue system oriented to multilingual question-answering is presented. This system includes the following ways of access: voice, text, avatar, gestures and signs language. The proposal is oriented to the question-answering task as a user interaction mechanism. The proposal here presented is in the first stages of its development phase and the architecture is presented for the first time on the base of the experiences in question-answering and dialogues previously developed. The main objective of this research work is the development of a solid platform that will permit the modular integration of the proposed architecture.

Rotenone induces degeneration of photoreceptors and impairs the dopaminergic system in the rat retina

Rotenone is a widely used pesticide and a potent inhibitor of mitochondrial complex I (NADH-quinone reductase) that elicits the degeneration of dopaminergic neurons and thereby the appearance of a parkinsonian syndrome. Here we have addressed the alterations induced by rotenone at the functional, morphological and molecular levels in the retina, including those involving both dopaminergic and non-dopaminergic retinal neurons. Rotenone-treated rats showed abnormalities in equilibrium, postural instability and involuntary movements. In their outer retina we observed a loss of photoreceptors, and a reduced synaptic connectivity between those remaining and their postsynaptic neurons. A dramatic loss of mitochondria was observed in the inner segments, as well as in the axon terminals of photoreceptors. In the inner retina we observed a decrease in the expression of dopaminergic cell molecular markers, including loss of tyrosine hydroxylase immunoreactivity, associated with a reduction of the dopaminergic plexus and cell bodies. An increase in immunoreactivity of AII amacrine cells for parvalbumin, a Ca2+-scavenging protein, was also detected. These abnormalities were accompanied by a decrease in the amplitude of scotopic and photopic a- and b-waves and an increase in the b-wave implicit time, as well as by a lower amplitude and greater latency in oscillatory potentials. These results indicate that rotenone induces loss of vision by promoting photoreceptor cell death and impairment of the dopaminergic retinal system.

Modelado de estrategias de personalización en OO-H

The consideration of personalization politics in the context of any web application modelling method obliges to the revision of its different modelling activities, which must be adapted to take into account the information regarding the user (usually gathered in a user model) to define aspects such as navigation or presentation. Additionally, they must provide a set of techniques to populate such user model. Finally, and because of the rapid pace at which personalization politics usually change, the modelling process should provide support not only for static personalization rules (known at design time) but also for the definition or change of these rules once the application has been deployed. This article presents, in the context of the Object Oriented Hypermedia Method (OO-H), a personalization framework that fulfils these requirements, and is organized around four main concepts: (1) a set of design activities that capture the personalization requirements known at design time, (2) a mechanism for the specification of personalization rules, defined by means of an XML template, that decouples the definition of the personalization model from the remaining models, (3) an execution architecture that supports the change at execution time of these rules and (4) an extensible repository that includes a set of register mechanisms for the user activity in the system. The possibility of extension of this repository facilitates its adaptation to the particular characteristics of any particular application.

COMPENDIUM: A text summarization system for generating abstracts of research papers

This article analyzes the appropriateness of a text summarization system, COMPENDIUM, for generating abstracts of biomedical papers. Two approaches are suggested: an extractive (COMPENDIUM E), which only selects and extracts the most relevant sentences of the documents, and an abstractive-oriented one (COMPENDIUM E–A), thus facing also the challenge of abstractive summarization. This novel strategy combines extractive information, with some pieces of information of the article that have been previously compressed or fused. Specifically, in this article, we want to study: i) whether COMPENDIUM produces good summaries in the biomedical domain; ii) which summarization approach is more suitable; and iii) the opinion of real users towards automatic summaries. Therefore, two types of evaluation were performed: quantitative and qualitative, for evaluating both the information contained in the summaries, as well as the user satisfaction. Results show that extractive and abstractive-oriented summaries perform similarly as far as the information they contain, so both approaches are able to keep the relevant information of the source documents, but the latter is more appropriate from a human perspective, when a user satisfaction assessment is carried out. This also confirms the suitability of our suggested approach for generating summaries following an abstractive-oriented paradigm.

Una aproximación a la recomendación de artículos científicos según su grado de especificidad

En este artículo se presenta un método para recomendar artículos científicos teniendo en cuenta su grado de generalidad o especificidad. Este enfoque se basa en la idea de que personas menos expertas en un tema preferirían leer artículos más generales para introducirse en el mismo, mientras que personas más expertas preferirían artículos más específicos. Frente a otras técnicas de recomendación que se centran en el análisis de perfiles de usuario, nuestra propuesta se basa puramente en el análisis del contenido. Presentamos dos aproximaciones para recomendar artículos basados en el modelado de tópicos (Topic Modelling). El primero de ellos se basa en la divergencia de tópicos que se dan en los documentos, mientras que el segundo se basa en la similitud que se dan entre estos tópicos. Con ambas medidas se consiguió determinar lo general o específico de un artículo para su recomendación, superando en ambos casos a un sistema de recuperación de información tradicional.