Computer Science

Due to the exponential growth of textual information available on the Web, end users need to be able to access information in summary form – and without losing the most important information in the document when generating the summaries. Automatic generation of extractive summaries from a single document has traditionally been given the task of extracting the most relevant sentences from the original document. The methods employed generally allocate a score to each sentence in the document, taking into account certain features. The most relevant sentences are then selected, according to the score obtained for each sentence. These features include the position of the sentence in the document, its similarity to the title, the sentence length, and the frequency of the terms in the sentence. However, it has still not been possible to achieve a quality of summary that matches that performed by humans and therefore methods continue to be brought forward that aim to improve on the results. This paper addresses the generation of extractive summaries from a single document as a binary optimization problem where the quality (fitness) of the solutions is based on the weighting of individual statistical features of each sentence – such as position, sentence length and the relationship of the summary to the title, combined with group features of similarity between candidate sentences in the summary and the original document, and among the candidate sentences of the summary. This paper proposes a method of extrac-tive single-document summarization based on genetic operators and guided local search, called MA-Sin-gleDocSum. A memetic algorithm is used to integrate the own-population-based search of evolutionary algorithms with a guided local search strategy. The proposed method was compared with the state of the art methods UnifiedRank, DE, FEOM, NetSum, CRF, QCS, SVM, and Manifold Ranking, using ROUGE measures on the datasets DUC2001 and DUC2002. The results showed that MA-SingleDocSum outperforms the state of the art methods.

To carry out effective teaching/learning processes, lecturers in a variety of educational institutions frequently need support. They therefore resort to advice from more experienced lecturers, to formal training processes such as specializations, master or doctoral degrees, or to selftraining. High costs in time and money are invariably involved in the processes of formal training, while self-training and advice each bring their own specific risks (e.g. of following new trends that are not fully evaluated or the risk of applying techniques that are inappropriate in specific contexts).This paper presents a system that allows lecturers to define their best teaching strategies for use in the context of a specific class. The context is defined by: the specific characteristics of the subject being treated, the specific objectives that are expected to be achieved in the classroom session, the profile of the students on the course, the dominant characteristics of the teacher, and the classroom environment for each session, among others. The system presented is the Recommendation System of Pedagogical Patterns (RSPP). To construct the RSPP, an ontology representing the pedagogical patterns and their interaction with the fundamentals of the educational process was defined. A web information system was also defined to record information on courses, students, lecturers, etc.; an option based on a unified hybrid model (for content and collaborative filtering) of recommendations for pedagogical patterns was further added to the system. RSPP features a minable view, a tabular structure that summarizes and organizes the information registered in the rest of the system as well as facilitating the task of recommendation. The data recorded in the minable view is taken to a latent space, where noise is reduced and the essence of the information contained in the structure is distilled. This process makes use of Singular Value Decomposition (SVD), commonly used by information retrieval and recommendation systems. Satisfactory results both in the accuracy of the recommendations and in the use of the general application open the door for further research and expand the role of recommender systems in educational teacher support processes.

Actualmente el aprendizaje móvil (mLearning) esta siendo objeto de múltiples investigaciones en diversas instituciones del mundo, cada una de ellas esta realizando proyectos de investigación, desarrollo y aplicación de esta nueva y alternativa forma de enseñar y aprender. En este artículo se presentan las características más importantes del mLearning que se han reportado en algunos de esos proyectos, luego se presentan algunas implicaciones pedagógicas, teóricas y practicas de este tipo de ambientes y una descripción de las características más importantes de los dispositivos y de las conexiones inalámbricos, finalmente se presentan algunos proyectos y avances en el área.

Este artículo presenta los resultados principales de un proyecto de investigación que se enmarca en el programa Computadores para Educar. Se presenta el resumen de una propuesta de incorporación de la Dinámica de Sistemas en la educación básica primaria, basada en un método didáctico soportada en el aprendizaje conceptual y colectivo denominada "Método Conceptual-Dinámico" y el uso de una herramienta software denominada "DINAMICO". Este último involucró la unión del modelado de dinámica de sistemas y autómatas celulares. Finalmente, se presenta los resultados preliminares de una experiencia aplicada a 160 estudiantes y 25 docentes de instituciones educativas de la región sur pacifica colombiana con resultados favorables.

The Harmony Search Algorithm is a meta-heuristic algorithm which bases its operation on the musical improvisation process. HS has been applied to many optimization problems, showing its efficiency compared to other meta-heuristics and mathematical optimization techniques. This paper reviews the development and evolution of the algorithm over the last 10 years, outlining the different areas of application and the most important improvements along the way

Based on SCORM sequencing and navigation specifications, a learning management system has been developed. The system has intelligent tutoring system capabilities that allow contents, presentation and navigation to be adapted according to the learner's requirements. In order to achieve that development, two concepts were put together: multi-agent systems and data mining techniques (especially the ID3 algorithm). All the implementation code was developed in VS.Net, which implied building a supporting framework for agents. The results of a pilot test were favorable.

The clustering of web search has become a very interesting research area among academic and scientific communities involved in information retrieval. Clustering of web search result systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for web document clustering already exist, but results show there is room for more to be done. This paper introduces a new description-centric algorithm for clustering of web results called IFCWR. IFCWR initially selects a maximum estimated number of clusters using Forgy's strategy, then it iteratively merges clusters until results cannot be improved. Every merge operation implies the execution of Fuzzy C-Means for clustering results of web search and the calculus of Bayesian Information Criterion for automatically evaluating the best solution and number of clusters. IFCWR was compared against other established web document clustering algorithms, among them: Suffix Tree Clustering and Lingo. Comparison was executed on AMBIENT and MORESQUE datasets, using precision, recall, fmeasure, SSL k and other metrics. Results show a considerable improvement in clustering quality and performance.