Committee-Based Profiles for Politician Finding

One step towards breaking down barriers between citizens and politicians is to help people identify those politicians who share their concerns. This paper is set in the field of expert finding and is based on the automatic construction of politicians’ profiles from their speeches on parliamentary committees. These committee-based profiles are treated as documents and are indexed by an information retrieval system. Given a query representing a citizen’s concern, a profile ranking is then obtained. In the final step, the different results for each candidate are combined in order to obtain the final politician ranking. We explore the use of classic combination strategies for this purpose and present a new approach that improves state-of-the-art performance and which is more stable under different conditions. We also introduce a two-stage model where the identification of a broader concept (such as the committee) is used to improve the final politician ranking.This work has been funded by the Spanish Ministerio de Economı́a y Competitividad under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Predicting IR Personalization Performance using Pre-retrieval Query Predictors

Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements.This work has been supported by the Spanish Andalusian “Consejerı́a de Innovación, Ciencia y Empresa” postdoctoral phase of project P09-TIC-4526, the Spanish “Ministerio de Economı́a y Competitividad” projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

Positive unlab ele d learning for building recommender systems in a parliamentary setting

Our goal is to learn about the political interests and preferences of Members of Parliament (MPs) by mining their parliamentary activity in order to develop a recommendation/filtering system to determine how relevant documents should be distributed among MPs. We propose the use of positive unlabeled learning to tackle this problem since we only have information about relevant documents (the interventions of each MP in debates) but not about irrelevant documents and so it is not possible to use standard binary classifiers which have been trained with positive and negative examples. Additionally, we have also developed a new positive unlabeled learning algorithm that compares favorably with: (a) a baseline approach which assumes that every intervention by any other MP is irrelevant; (b) another well-known positive unlabeled learning method; and (c) an approach based on information retrieval methods that matches documents and legislators’ representations. The experiments have been conducted with data from the regional Spanish Andalusian Parliament.This work has been funded by the Spanish “Ministerio de Economía y Competitividad” under projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)

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.Agencia Estatal de Investigación | Ref. PID2019-106758GB-C31Agencia Estatal de Investigación | Ref. PID2020-113230RB-C22FEDER/Junta de Andalucía | Ref. A-TIC-146-UGR2

La inteligencia artificial fue poco utilizada pero relevante en las revisiones sistemáticas sobre el COVID-19: un estudio metodológico

Este trabajo de investigación fue presentado como Trabajo Fin de Grado en la Facultad de Medicina de nuestra Universidad (convocatoria ordinaria, curso 2021/2022) el 13 de junio de 2022.Objective: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID–19 evidence syntheses. Study design: After prospective registration of the review protocol, we automated the download of all open-access COVID–19 systematic reviews in the COVID–19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals’ JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR–2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI. Results: Of the 3 999 COVID–19 reviews, 28 (0.7 %, 95 % CI 0.47-1.03 %) made use of AI. On average, compared to controls (n = 64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs. 3.5, P<0.001), and screened more abstracts per author (302.2 vs. 140.3, P=0.009) and per included study (189.0 vs. 365.8, P<0.001) while inspecting less full texts per author (5.3 vs. 14.0, P=0.005). No differences were found in citation counts (0.5 vs. 0.6, P=0.600), inspected full texts per included study (3.8 vs. 3.4, P=0.481), completion times (74.0 vs. 123.0, P=0.205) or AMSTAR–2 (7.5 vs. 6.3, P=0.119). Conclusion: AI was an underutilized tool in COVID–19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.Objetivo: Un escenario dinámico como una pandemia requiere la rápida producción de revisiones sistemáticas de calidad, que pueden automatizarse utilizando inteligencia artificial (IA). Se evaluó el uso de herramientas de IA en las revisiones sistemáticas sobre COVID–19. Diseño del estudio: Tras el registro prospectivo del protocolo del estudio, automatizamos la descarga de todas las revisiones sistemáticas open-access sobre COVID–19 en la base de datos COVID–19 Living Overview of Evidence, las indexamos en busca de palabras clave relacionadas con la IA y localizamos aquellas que utilizaban herramientas de IA. Comparamos el factor de impacto de sus revistas, las citas por mes recibidas, las cargas de trabajo en screening, el tiempo de elaboración (días desde el registro del protocolo hasta el primer preprint o envío a una revista) y la evaluación metodológica AMSTAR–2 (máximo, 13 puntos) con un grupo control de revisiones sistemáticas que no usaron IA emparejadas por fecha de publicación. Resultados: De las 3999 revisiones sobre COVID–19, 28 (0,7%, IC al 95%: 0,471,03 %) hicieron uso de IA. De media, en comparación con los controles (n = 64), las revisiones con IA se publicaron en revistas con mayor factor de impacto (mediana 8,9 vs. 3,5, P<0,001), y examinaron más abstracts por autor (302,2 vs. 140,3, P=0,009) y por estudio incluido (189,0 vs. 365,8, P<0,001), a la vez que inspeccionaron menos full texts por autor (5,3 vs. 14,0, P=0,005). No se encontraron diferencias en las citas recibidas (0,5 vs. 0,6, P=0,600), en full texts inspeccionados por estudio incluido (3,8 vs. 3,4, P=0,481), en los tiempos de elaboración (74 frente a 123, P=0,205) ni en puntuación AMSTAR–2 (7,5 frente a 6,3, P=0,119). Conclusión: La IA fue una herramienta infrautilizada en las revisiones sistemáticas sobre COVID–19. Su uso, en comparación con las revisiones sin IA, se asoció con una selección más eficiente de la literatura y un mayor impacto de publicación. Hay cabida para la aplicación de la IA en la automatización de las revisiones sistemáticas.La elaboración de este estudio fue becada con una “Beca de Iniciación a la Investigación para Estudiantes de Grado” del Plan Propio de Investigación 2021 de la UGR. El coste de la publicación open-access fue financiado por la Universidad de Granada y el Consorcio de Bibliotecas Universitarias de Andalucía (CBUA)

Cross-sectional study of height and weight in the population of Andalusia from age 3 to adulthood

<p>Abstract</p> <p>Background and objectives</p> <p>In Andalusia there were no studies including a representative sample of children and adolescent population assessing growth and weight increase. Our objectives were to develop reference standards for weight, height and BMI for the Andalusian pediatric population, from 3 to 18 years of age for both genders, and to identify the final adult height in Andalusia.</p> <p>Subjects and methods</p> <p>Two samples were collected. The first included individuals from 3 to 18 years of age (3592 girls and 3605 boys). They were stratified according type of study center, size of population of origin, age (32 categories of 0.5 years) and gender, using cluster sampling. Subjects from >18 to 23 years of age (947 women and 921 men) were sampled in 6 non-university educational centers and several university centers in Granada. Exclusion criteria included sons of non-Spanish mother or father, and individuals with chronic conditions and/or therapies affecting growth. Two trained fellows collected the data through February to December 2004, for the first sample, and through January to May 2005, for the second.</p> <p>Reference curves were adjusted using Cole's LMS method, and the quality of the adjustment was assessed using the tests proposed by Royston. In addition, a sensitivity analysis was applied to the final models obtained.</p> <p>Results</p> <p>Data for 9065 cases (4539 women and 4526 men) were obtained; 79.39% (n = 7197) in the up to 18 years of age group. In the first sampling only 0.07% (3 girls and 2 boys) refused to participate in the study. In addition, 327 students (4.5%) were absent when sampling was done. We present mean and standard deviation fort height, weight and BMI at 0.5 years intervals, from 3 to 23 years of age, for both genders. After adjustment with the different models, percentiles for height, weight (percentiles 3, 5, 10, 25, 50, 75, 90, 95, and 97) and BMI (percentiles 3, 5, 50, 85, 95, and 97) are presented for both genders.</p> <p>Conclusion</p> <p>This is the first study in Andalusia with a representative sample from the child-juvenile population to investigate weight, height and BMI in subjects from 3 to 23 years of age. The great variability observed in the values from sample of 18 to 23 years of age individuals, ensures the inclusion of extreme values, although random sampling was not used. There still is a lack of standard reference values for the Andalusian population younger done 3 years of age.</p

Design rules for antenna placement on MIMO systems

In recent works, it is demonstrated that, depending on the different spatial distributions and distance between elements, there exists a different true polarization diversity (TPD) configuration that provides a high improvement in terms of capacity. This means that it is necessary to choose the appropriate TPD configuration to maximize the multiple-input-multiple-output (MIMO) capacity. In this work, a genetic algorithm is used to optimize the element positions for four new different configurations in combination with the TPD technique. It is shown that, for some configurations, the same polarization option is always found to reach the maximum capacity. Based on this, some novel design rules are provided to maximize MIMO capacity when the area for placing the antennas is very small. This is the case for most of the wireless devices, where the antenna design and location is one of the latest design constraints to be taken into consideration in the device design

Presentaciones orales a un coste razonable

La ponencia describe cómo se ha incorporado en nuestras asignaturas de programación el desarrollo de la competencia de comunicación oral. Sobre la base de una organización docente basada en proyectos que usa la técnica del puzle para el aprendizaje de varios temas, cada alumno debe preparar un vídeo con una presentación oral del tema que le ha sido adjudicado en el puzle. Ese vídeo es entregado a sus compañeros, que deben aprender los contenidos de la presentación y realizar una evaluación formal de su calidad de acuerdo con unos criterios. Cada alumno debe realizar una versión mejorada del vídeo teniendo en cuenta las evaluaciones recibidas. La versión mejorada es calificada por parte de los profesores. La ponencia describe los procedimientos utilizados y algunos resultados de la experiencia.Peer Reviewe

Inferencia de la respuesta afectiva de los espectadores de un video

In this project we propose the automatic analysis of the relation between the audiovisual characteristics of a multimedia production and the impact caused in its audience. With this aim, potential synergies are explored between different areas of knowledge including, among others: audiovisual communication, computer vision, multimodal systems, biometric sensors, social network analysis, opinion mining, and affective computing. Our efforts are oriented towards combining these technologies to introduce novel computational models that could predict the reactions of spectators to multimedia elements across different media and moments. On the one hand, we study the cognitive and emotional response of the spectators while they are watching the media instances, using neuroscience techniques and biometric sensors. On the other hand, we also study the reaction shown by the audience on social networks by relying on the automatic collection and analysis of different metadata related to the media elements, such as popularity, sharing patterns, ratings and commentaries.Este proyecto propone el análisis de la posible dependencia entre el contenido audiovisual de una producción multimedia y el impacto causado por ésta en sus espectadores. Para ello, nos apoyamos en diferentes áreas de conocimiento tales como comunicación audiovisual, visión por computador, sistemas multimodales, sensores biométricos, análisis de redes sociales, análisis de opinión o computación afectiva, entre otras, con el objetivo de diseñar nuevos modelos computacionales que permitan predecir las reacciones de los espectadores de un video de forma transversal a los medios y momentos en que éstas se producen. Trabajamos principalmente con dos tipos de respuesta: la respuesta cognitiva y emocional inmediata de los espectadores durante el visionado, que medimos utilizando técnicas de neurociencia y sensores biométricos, y la reacción expresada en redes sociales, cuyo impacto es cuantificado mediante el análisis automático de diferentes metadatos recabados para dichos videos, tales como popularidad, patrones de compartición, valoraciones y comentarios realizados en las redes.The work leading to these results has been supported by the Spanish Ministry of Economy, Industry and Competitiveness through the ESITUR (MINECO, RTC-2016-5305-7), CAVIAR (MINECO, TEC2017-84593-C2-1-R), and AMIC (MINECO, TIN2017-85854-C4-4-R) projects (AEI/FEDER, UE)

Qué y cómo se evalúa en el TFG del Grado en Ingeniería Informática en España

Este trabajo presenta una comparativa sobre cómo se está realizando la evaluación por competencias en la asignatura de Trabajo Fin de Grado (TFG) en los distintos Grados en Ingeniería Informática a nivel nacional. Para ello, se han consultado todas las guías docentes disponibles, las rúbricas y el informe que debe realizar la comisión evaluadora (si los hubiere). En esta contribución se analizan las diferencias y similitudes encontradas. Tras realizar un análisis cualitativo y cuantitativo, se llega a la conclusión de que, actualmente, la evaluación de las competencias asignadas a la asignatura de TFG es un proceso sujeto a subjetividades y que no refleja la gran mayoría de las competencias que se supone que deben ser evaluadas y calificadas.This work presents a comparison study about how competency-based evaluation is being performed in the senior degree project (Trabajo fin de Grado in Spanish) subject in the computing curricula of the Spanish universities. All available teaching guides, evaluation rubrics and the reports (if any) to be delivered by the corresponding evaluation committees have been checked, both quantitatively and qualitatively. The analysis of results yields the conclusion that, at present, the evaluation of competencies for the senior degree Project is a subjective process that does not reflects most of competencies that, in theory, should be assessed and marked.Este proyecto está financiado por la Universidad de Granada, en la convocatoria de Proyectos de Innovación Docente y Buenas Prácticas del Plan FIDO UGR 2022-2023, modalidad de proyectos coordinados: Proyecto “Cómo escribir tu TFG o TFM de ingeniería Informática y no morir en el intento: dificultades, retos y elaboración de materiales docentes. Ref 22-29”