https://reunir.unir.net/handle/123456789/9600 2024-11-08T21:24:12Z 2024-11-08T21:24:12Z Redondo-García, José Luis Botón-Fernández, Vicente Lozano-Tello, Adolfo https://reunir.unir.net/handle/123456789/9607 2019-12-03T10:49:48Z

Linked Data Methodologies for Managing Information about Television Content Redondo-García, José Luis; Botón-Fernández, Vicente; Lozano-Tello, Adolfo OntoTV is a television information management system designed for improving the quality and quantity of the information available in the current television platforms. In order to achieve this objective, OntoTV (1) collects the information offered by the broadcasters, (2) integrates it into a ontology-based data structure, (3) extracts extra data from alternative television sources, and (4) makes possible for the user to perform queries over the stored information. This document shows the way Linked Data methodologies have been applied in OntoTV system, and the improvements in the data consumption and publication processes that have been obtained as result. On the one hand, the possibility of accessing to information available in the Web of Data has made possible to offer more complete descriptions about the programs, as well as more detailed guides than those obtained by using classic collection methods. On the other hand, as the information of the television programs and channels is published according to the Linked Data philosophy, it becomes available not only for OntoTV clients, but also for other agents able to access Linked Data resources, who could offer the viewer more fresh and innovative features. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:49:48Z No. of bitstreams: 1 IJIMAI20121_6_5.pdf: 1129975 bytes, checksum: 476f7c52062460a2528613c34f055765 (MD5); Made available in DSpace on 2019-12-03T10:49:48Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_5.pdf: 1129975 bytes, checksum: 476f7c52062460a2528613c34f055765 (MD5)

Planet, Santiago Iriondo, Ignasi https://reunir.unir.net/handle/123456789/9606 2019-12-03T10:36:45Z

Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech Planet, Santiago; Iriondo, Ignasi The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Nave-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:36:45Z No. of bitstreams: 1 IJIMAI20121_6_6.pdf: 382450 bytes, checksum: 1a3a08599d1b3bc8d031977afa6e70ca (MD5); Made available in DSpace on 2019-12-03T10:36:45Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_6.pdf: 382450 bytes, checksum: 1a3a08599d1b3bc8d031977afa6e70ca (MD5)

Kumar Singh, Deepak Srinivas, K. Bhagwan Das, D. https://reunir.unir.net/handle/123456789/9605 2019-12-03T10:16:53Z

A Useful Metaheuristic for Dynamic Channel Assignment in Mobile Cellular Systems Kumar Singh, Deepak; Srinivas, K.; Bhagwan Das, D. The prime objective of a Channel Assignment Problem (CAP) is to assign appropriate number of required channels to each cell in a way to achieve both efficient frequency spectrum utilization and minimization of interference effects (by satisfying a number of channel reuse constraints). Dynamic Channel Assignment (DCA) assigns the channels to the cells dynamically according to traffic demand, and hence, can provide higher capacity (or lower call blocking probability), fidelity and quality of service than the fixed assignment schemes. Channel assignment algorithms are formulated as combinatorial optimization problems and are NP-hard. Devising a DCA, that is practical, efficient, and which can generate high quality assignments, is challenging. Though Metaheuristic Search techniques like Evolutionary Algorithms, Differential Evolution, Particle Swarm Optimization prove effective in the solution of Fixed Channel Assignment (FCA) problems but they still require high computational time and therefore may be inefficient for DCA. A number of approaches have been proposed for the solution of DCA problem but the high complexity of these proposed approaches makes them unsuitable/less efficient for practical use. Therefore, this paper presents an effective and efficient Hybrid Discrete Binary Differential Evolution Algorithm (HDB-DE) for the solution of DCA Problem Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:16:53Z No. of bitstreams: 1 IJIMAI20121_6_1.pdf: 419918 bytes, checksum: 9ed933dd981ea6a03123333d2ea35da4 (MD5); Made available in DSpace on 2019-12-03T10:16:53Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_1.pdf: 419918 bytes, checksum: 9ed933dd981ea6a03123333d2ea35da4 (MD5)

Correia, Bruno Urbano, Paulo Moniz, Luís https://reunir.unir.net/handle/123456789/9604 2019-12-03T10:06:57Z

DEVELOP-FPS: a First Person Shooter Development Tool for Rule-based Scripts Correia, Bruno; Urbano, Paulo; Moniz, Luís We present DEVELOP-FPS, a software tool specially designed for the development of First Person Shooter (FPS) players controlled by Rule Based Scripts. DEVELOP-FPS may be used by FPS developers to create, debug, maintain and compare rule base player behaviours, providing a set of useful functionalities: i) for an easy preparation of the right scenarios for game debugging and testing; ii) for controlling the game execution: users can stop and resume the game execution at any instant, monitoring and controlling every player in the game, monitoring the state of each player, their rule base activation, being able to issue commands to control their behaviour; and iii) to automatically run a certain number of game executions and collect data in order to evaluate and compare the players performance along a sufficient number of similar experiments. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T10:06:57Z No. of bitstreams: 1 IJIMAI20121_6_7.pdf: 728274 bytes, checksum: 0182356e8f8c0e92428053ee088d9f56 (MD5); Made available in DSpace on 2019-12-03T10:06:57Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_7.pdf: 728274 bytes, checksum: 0182356e8f8c0e92428053ee088d9f56 (MD5)

León, Rafael Rainer, J. Javier Rojo, José Manuel Galán, Ramón https://reunir.unir.net/handle/123456789/9603 2019-12-03T09:57:24Z

Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot León, Rafael; Rainer, J. Javier; Rojo, José Manuel; Galán, Ramón We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T09:57:24Z No. of bitstreams: 1 IJIMAI20121_6_2.pdf: 620104 bytes, checksum: 45501369bacf46d3e346b60e77c414de (MD5); Made available in DSpace on 2019-12-03T09:57:24Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_2.pdf: 620104 bytes, checksum: 45501369bacf46d3e346b60e77c414de (MD5)

Romero, Lucía Gutiérrez, Milagros Caliusco, María Laura https://reunir.unir.net/handle/123456789/9602 2019-12-03T09:44:11Z

Conceptualizing the e-Learning Assessment Domain using an Ontology Network Romero, Lucía; Gutiérrez, Milagros; Caliusco, María Laura During the last year, approaches that use ontologies, the backbone of the Semantic Web technologies, for different purposes in the assessment domain of e-Learning have emerged. One of these purposes is the use of ontologies as a mean of providing a structure to guide the automated design of assessments. The most of the approaches that deal with this problem have proposed individual ontologies that model only a part of the assessment domain. The main contribution of this paper is an ontology network, called AONet, that conceptualizes the e-assessment domain with the aim of supporting the semi-automatic generation of it. The main advantage of this network is that it is enriched with rules for considering not only technical aspects of an assessment but also pedagogic. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T09:44:11Z No. of bitstreams: 1 IJIMAI20121_6_3.pdf: 759584 bytes, checksum: f2e6244d28e91f90c7505bf579dc25ad (MD5); Made available in DSpace on 2019-12-03T09:44:11Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_3.pdf: 759584 bytes, checksum: f2e6244d28e91f90c7505bf579dc25ad (MD5)

Rombaldo Jr, Carlos Alberto Alves Souza, Solange N de Souza, Luiz Sergio https://reunir.unir.net/handle/123456789/9601 2019-12-03T09:29:26Z

O-ODM Framework for Object-Relational Databases Rombaldo Jr, Carlos Alberto; Alves Souza, Solange N; de Souza, Luiz Sergio Object-Relational Databases introduce new features which allow manipulating objects in databases.At present, many DBMS offer resources to manipulate objects in database, but most application developers just map class to relations tables, failing to exploit the O-R model strength. The lack of tools that aid the database project contributes to this situation. This work presents O-ODM (Object-Object Database Mapping), a persistent framework that maps objects from OO applications to database objects.Persistent Frameworks have been used to aid developers, managing all access to DBMS. This kind of tool allows developers to persist objects without solid knowledge about DBMSs and specific languages, improving the developers' productivity, mainly when a different DBMS is used. The results of some experiments using O-ODM are shown. Submitted by Administrador Re-UNIR Re-UNIR (reunir@unir.net) on 2019-12-03T09:29:26Z No. of bitstreams: 1 IJIMAI20121_6_4.pdf: 490822 bytes, checksum: 44f43d5d74961dbc4c1ac0049089c818 (MD5); Made available in DSpace on 2019-12-03T09:29:26Z (GMT). No. of bitstreams: 1 IJIMAI20121_6_4.pdf: 490822 bytes, checksum: 44f43d5d74961dbc4c1ac0049089c818 (MD5)