Ampliar y descubrir contenidos en Inteligencia Artificial mediante el uso de agregadores de enlaces, votaciones y karma

El tener que acotar y delimitar la serie de contenidos que abordar en un curso es una dificultad a la que debemos enfrentarnos a la hora de planificar la docencia de una materia. Esto es especialmente problemático en materias ya de por sí extensas como las relacionadas con la Inteligencia Artificial y supone tener que omitir partes importantes, tanto teóricas como aplicadas, de un campo en constante evolución. En este trabajo presentamos un intento de mitigar estos problemas haciendo uso de agregadores sociales de enlaces, como digg, reddit o meneane, que permiten que los alumnos exploren por sí mismos, descubran y compartan sus impresiones respecto a aspectos de la materia Inteligencia Artificial que en las clases presenciales no se pueden tratar en profundidad. En nuestro caso hemos implantado nuestro propio agregador de enlaces proponiendo una actividad complementaria cuya evaluación ha sacado provecho de los mecanismos de reputación, o karma, en los que este tipo de herramientas sociales basan su funcionamiento.Peer Reviewe

Entorno de integración continua para la docencia práctica de Java EE

El presente trabajo propone un entorno de trabajo ba-sado en integración continua y describe la experiencia docente de su implementación con herramientas Java EE en las asignaturas de un itinerario de desarrollo de software implantado en un Máster Universitario en Ingeniería Informática. Se propone un entorno lo más cercano posible a la práctica real en las empresas de desarrollo software que no sólo acerca al alumno a la industria, sino que facilita la implementación de la metodología de aprendizaje basado en proyectos software colaborativos, ya que trata de resolver los problemas típicos, como son una baja frecuencia en las integraciones, problemas en el mantenimiento de un código fuente común, reparto de tareas equitativas, interdependencias entre las tareas, etc.The present work proposes a continuous integration environment and the teaching experience implementing it with Java EE in the subjects of a software development speciality inside a Software Engineering Master's Degree. We propose a working environment similar to those found in software development companies that not only brings students closer to industry, but also eases the implementation project-based learning via collaborative software development, given that it tries to solve typical problems, such as low integration frequency, problems to maintain a common code base, equitable distribution of tasks, task interdependencies, etc

DSBOX: herramienta docente para el diseño y simulación de entornos de red virtualizados

En este trabajo se describe DSBOX, una herramienta gráfica para el diseño y simulación empleando virtualización de pequeñas redes de computadores. Se describe el contexto que ha dado origen a esta herramienta y se detalla su arquitectura y las optimizaciones que se han realizado con el fin de dotar a los alumnos de una herramienta operativa que les facilite la realización de prácticas relacionadas con la seguridad informática y la administración de sistemas. Se presenta también un repositorio de actividades prácticas que hacen uso de la herramienta desarrollada.This paper describes DSBOX, a graphical tool for the design and simulation of small computer networks, employing virtualization software. We describe the context where this tool was created and its architecture and the optimizations that have been made in order to provide our students with a tool giving support to teaching activities in computer security and systems management. Finally, a repository with practical exer-cises using this tool is also described

Information Retrieval and Machine Learning Methods for Academic Expert Finding

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.Spanish “Agencia Estatal de Investigación” under grants PID2019-106758GB-C31 and PID2020-113230RB-C22Spanish “FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under grant A-TIC-146-UGR20European Regional Development Fund (ERDF-FEDER

COLE Experiments at CLEF 2003 - Spanish Monolingual Track

In this our second participation in the CLEF Spanish monolingual track, we have continued applying Natural Language Processing techniques for single word and multi-word term con- ation. Two dierent conation approaches have been tested. The rst approach is based on the lemmatization of the text in order to avoid inectional variation. Our second approach consists of the employment of syntactic dependencies as complex index terms, in an attempt to solve the problems derived from syntactic variation and, in this way, to obtain more precise terms. Such dependencies are obtained through a shallow parser based on cascades of nitestate transducers

COLE experiments at CLEF 2002 Spanish monolingual track

c○Springer-Verlag Abstract. In this our first participation in CLEF, we have applied Natural Language Processing techniques for single word and multiword term conflation. We have tested several approaches at different levels of text processing in our experiments: firstly, we have lemmatized the text to avoid inflectional variation; secondly, we have expanded the queries through synonyms according to a fixed threshold of similarity; thirdly, we have employed morphological families to deal with derivational variation; and fourthly, we have tested a mixed approach based on the employment of such families and syntactic dependencies to deal with the syntactic content of the document.

Two hierarchical text categorization approaches for bioasq semantic indexing challenge

Abstract. This paper describes our participation in the BioASQ semantic indexing challenge with two hierarchical text categorization systems. Both systems originated from previous research in thesaurus topic assignment applied on small domains from the legal document management field. One of the described systems employs a classical top-down approach based on a collection of local classifiers. The other system builds a Bayesian network induced by the thesaurus structure and contents, taking into account descriptor labels and related terms. We describe the adaptations required to deal with a large thesaurus like MeSH and a huge document collection and discuss the results obtained in the BioASQ challenge and the limitations of both approaches.

COLE experiments at CLEF 2002 Spanish monolingual track

In this our first participation in CLEF, we have applied Natural Language Processing techniques for single word and multi-word term conflation. We have tested several approaches at different levels of text processing in our experiments: firstly, we have lemmatized the text to avoid inflectional variation; secondly, we have expanded the queries through synonyms according to a fixed threshold of similarity; and thirdly, we have tested a mixed approach based on the employment of productive derivational morphology to solve derivational variation and syntactic dependencies to deal with the syntactic content of the document