Examinando por Autor "Salazar, C."

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    Autonomous recommender system architecture for virtual learning environments
    (Elsevier B.V., 2020-01-01) Monsalve-Pulido, J.; Aguilar, J.; Montoya, E.; Salazar, C.; Monsalve-Pulido, J.; Aguilar, J.; Montoya, E.; Salazar, C.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture. © 2020 The Authors
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    Comparison and evaluation of different methods for the feature extraction from educational contents
    (MDPI Multidisciplinary Digital Publishing Institute, 2020-01-01) Aguilar, J.; Salazar, C.; Velasco, H.; Monsalve-Pulido, J.; Montoya, E.; Aguilar, J.; Salazar, C.; Velasco, H.; Monsalve-Pulido, J.; Montoya, E.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
    This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The utilization of the features/descriptors generated by them are tested in three types of educational digital resources (scientific publications, learning objects, patents), a paraphrase corpus and two use cases: in an information retrieval context and in an educational recommendation system. For this analysis are used unsupervised metrics to determine the feature quality proposed by each one, which are two similarity functions and the entropy. In addition, the paper presents tests of the techniques for the classification of paraphrases. The experiments show that according to the type of content and metric, the performance of the feature extraction methods is very different; in some cases are better than the others, and in other cases is the inverse. © 2020 by the authors.
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    A simple but efficient voice activity detection algorithm through Hilbert transform and dynamic threshold for speech pathologies
    (IOP PUBLISHING LTD, 2016-01-01) Ortiz, P.D.; Villa, L.F.; Salazar, C.; Quintero, O.L.; Ortiz, P.D.; Villa, L.F.; Salazar, C.; Quintero, O.L.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    A simple but efficient voice activity detector based on the Hilbert transform and a dynamic threshold is presented to be used on the pre-processing of audio signals. The algorithm to define the dynamic threshold is a modification of a convex combination found in literature. This scheme allows the detection of prosodic and silence segments on a speech in presence of non-ideal conditions like a spectral overlapped noise. The present work shows preliminary results over a database built with some political speech. The tests were performed adding artificial noise to natural noises over the audio signals, and some algorithms are compared. Results will be extrapolated to the field of adaptive filtering on monophonic signals and the analysis of speech pathologies on futures works.