El Coruña Corpus Tool: diez años después

In this paper we provide a brief introduction to a new version of the Coruña Corpus Tool. Currently available for Windows, macOS and Linux, the Coruña Corpus Tool is a corpus management tool that facilitates the retrieval of information from an indexed textual repository. Although it works like most concordance programs, its distinguishing feature is that it allows users to search for old or non-standard characters and tags in texts and metadata files, as well as to extract and export specific data for the purposes of research. With a new set of advanced search features and other recent improvements, researchers now have access to functionalities that significantly enhance the previous user experience.En este artículo presentamos una breve introducción a una nueva versión del Coruña Corpus Tool. Actualmente disponible para Windows, macOS y Linux, el Coruña Corpus Tool es una herramienta de gestión de corpus que facilita la recuperación de información desde un repositorio textual indexado. Aunque funciona como la mayoría de los programas de concordancia, su característica distintiva es que permite a los usuarios buscar caracteres y etiquetas antiguos o no estándar en archivos de texto y metadatos, así como extraer y exportar datos específicos con fines de investigación. Con un nuevo conjunto de funciones de búsqueda avanzada y otras mejoras recientes, los investigadores ahora tienen acceso a funcionalidades que mejoran significativamente la experiencia previa del usuario.The research reported here has been funded by the Spanish Ministry of the Economy, Industry and Competitiveness (MINECO), grant number FFI2016-75599-P. This grant is hereby gratefully acknowledged. The second author also acknowledges the support of the Spanish Ministry of Science, Innovation and Universities, grant number FPU014/01724

Distributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach

We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective for managing outliers in the data.Peer ReviewedPostprint (author's final draft

Information retrieval models for recommender systems

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Information retrieval addresses the information needs of users by delivering relevant pieces of information but requires users to convey their information needs explicitly. In contrast, recommender systems offer personalized suggestions of items automatically. Ultimately, both fields help users cope with information overload by providing them with relevant items of information. This thesis aims to explore the connections between information retrieval and recommender systems. Our objective is to devise recommendation models inspired in information retrieval techniques. We begin by borrowing ideas from the information retrieval evaluation literature to analyze evaluation metrics in recommender systems. Second, we study the applicability of pseudo-relevance feedback models to different recommendation tasks. We investigate the conventional top-N recommendation task, but we also explore the recently formulated user-item group formation problem and propose a novel task based on the liquidation oflong tail items. Third, we exploit ad hoc retrieval models to compute neighborhoods in a collaborative filtering scenario. Fourth, we explore the opposite direction by adapting an effective recommendation framework to pseudo-relevance feedback. Finally, we discuss the results and present our concIusions. In summary, this doctoral thesis adapts a series of information retrieval models to recommender systems. Our investigation shows that many retrieval models can be accommodated to deal with different recommendation tasks. Moreover, we find that taking the opposite path is also possible. Exhaustive experimentation confirms that the proposed models are competitive. Finally, we also perform a theoretical analysis of sorne models to explain their effectiveness.[Resumen] La recuperación de información da respuesta a las necesidades de información de los usuarios proporcionando información relevante, pero requiere que los usuarios expresen explícitamente sus necesidades de información. Por el contrario, los sistemas de recomendación ofrecen sugerencias personalizadas de elementos automáticamente. En última instancia, ambos campos ayudan a los usuarios a lidiar con la sobrecarga de información al proporcionarles información relevante. Esta tesis tiene como propósito explorar las conexiones entre la recuperación de información y los sistemas de recomendación. Nuestro objetivo es diseñar modelos de recomendación inspirados en técnicas de recuperación de información. Comenzamos tomando prestadas ideas de la literatura de evaluación en recuperación de información para analizar las métricas de evaluación en los sistemas de recomendación. En segundo lugar, estudiamos la aplicabilidad de los modelos de retroalimentación de pseudo-relevancia a diferentes tareas de recomendación. Investigamos la tarea de recomendar listas ordenadas de elementos, pero también exploramos el problema recientemente formulado de formación de grupos usuario-elemento y proponemos una tarea novedosa basada en la liquidación de los elementos de la larga cola. Tercero, explotamos modelos de recuperación ad hoc para calcular vecindarios en un escenario de filtrado colaborativo. En cuarto lugar, exploramos la dirección opuesta adaptando un método eficaz de recomendación a la retroalimentación de pseudo-relevancia. Finalmente, discutimos los resultados y presentamos nuestras conclusiones. En resumen, esta tesis doctoral adapta varios modelos de recuperación de información para su uso como sistemas de recomendación. Nuestra investigación muestra que muchos modelos de recuperación de información se pueden aplicar para tratar diferentes tareas de recomendación. Además, comprobamos que tomar el camino contrario también es posible. Una experimentación exhaustiva confirma que los modelos propuestos son competitivos. Finalmente, también realizamos un análisis teórico de algunos modelos para explicar su efectividad.[Resumo] A recuperación de información dá resposta ás necesidades de información dos usuarios proporcionando información relevante, pero require que os usuarios expresen explicitamente as súas necesidades de información. Pola contra, os sistemas de recomendación ofrecen suxestións personalizadas de elementos automaticamente. En última instancia, ambos os campos axudan aos usuarios a lidar coa sobrecarga de información ao proporcionarlles información relevante. Esta tese ten como propósito explorar as conexións entre a recuperación de información e os sistemas de recomendación. O naso obxectivo é deseñar modelos de recomendación inspirados en técnicas de recuperación de información. Comezamos tomando prestadas ideas da literatura de avaliación en recuperación de información para analizar as métricas de avaliación nos sistemas de recomendación. En segundo lugar, estudamos a aplicabilidade dos modelos de retroalimentación de seudo-relevancia a diferentes tarefas de recomendación. Investigamos a tarefa de recomendar listas ordenadas de elementos, pero tamén exploramos o problema recentemente formulado de formación de grupos de usuario-elemento e propoñemos unha tarefa nova baseada na liquidación dos elementos da longa cola. Terceiro, explotamos modelos de recuperación ad hoc para calcular veciñanzas nun escenario de filtrado colaborativo. En cuarto lugar, exploramos a dirección aposta adaptando un método eficaz de recomendación á retroalimentación de seudo-relevancia. Finalmente, discutimos os resultados e presentamos as nasas conclusións. En resumo, esta tese doutoral adapta varios modelos de recuperación de información para o seu uso como sistemas de recomendación. A nosa investigación mostra que moitos modelos de recuperación de información pódense aplicar para tratar diferentes tarefas de recomendación. Ademais, comprobamos que tomar o camiño contrario tamén é posible. Unha experimentación exhaustiva confirma que os modelos propostos son competitivos. Finalmente, tamén realizamos unha análise teórica dalgúns modelos para explicar a súa efectividade

Priors for Diversity and Novelty on Neural Recommender Systems

[Abstract] PRIN is a neural based recommendation method that allows the incorporation of item prior information into the recommendation process. In this work we study how the system behaves in terms of novelty and diversity under different configurations of item prior probability estimations. Our results show the versatility of the framework and how its behavior can be adapted to the desired properties, whether accuracy is preferred or diversity and novelty are the desired properties, or how a balance can be achieved with the proper selection of prior estimations.Ministerio de Ciencia, Innovación y Universidades; RTI2018-093336-B-C22Xunta de Galicia; GPC ED431B 2019/03Xunta de Galicia; ED431G/01Ministerio de Ciencia, Innovación y Universidades; FPU17/03210Ministerio de Ciencia, Innovación y Universidades; FPU014/0172

Building High-Quality Datasets for Information Retrieval Evaluation at a Reduced Cost

[Abstract] Information Retrieval is not any more exclusively about document ranking. Continuously new tasks are proposed on this and sibling fields. With this proliferation of tasks, it becomes crucial to have a cheap way of constructing test collections to evaluate the new developments. Building test collections is time and resource consuming: it requires time to obtain the documents, to define the user needs and it requires the assessors to judge a lot of documents. To reduce the latest, pooling strategies aim to decrease the assessment effort by presenting to the assessors a sample of documents in the corpus with the maximum number of relevant documents in it. In this paper, we propose the preliminary design of different techniques to easily and cheapily build high-quality test collections without the need of having participants systems.Ministerio de Ciencia, Innovación y Universidades; RTI2018-093336-B-C22Xunta de Galicia; GPC ED431B 2019/03Xunta de Galicia; ED431G/0

Docencia en sistemas de acceso á información: detección de plaxios, emprego de tecnoloxías avanzadas para desenvolvemento software e achegamento da experiencia na industria á aula

[Resumo] Este artigo presenta as actividades desenvolvidas polo grupo de innovación educativa en Sistemas de Acceso á Información durante o curso 2017/2018. Este grupo, con docencia na Facultade de Informática da Universidade da Coruña, realizou accións en tres liñas de actuación diferentes. A primeira delas, dirixida á mellora da calidade nos métodos de avaliación, consiste no emprego dun protocolo para a detección de plaxios en prácticas de programación. A segunda actividade pretende mellorar a empregabilidade do alumnado e consiste en utilizar unha metodoloxía de aprendizaxe baseada en proxectos xunto cunha serie de ferramentas avanzadas para desenvolvemento software, permitindo recrear a actividade que deberán levar a cabo cando se incorporen ao mundo laboral. Por último, e de cara a aumentar o coñecemento das alternativas profesionais do alumnado, organizáronse unha serie de seminarios e charlas impartidas por profesionais dunha empresa internacional, unha empresa local multidisciplinar e un investigador da contorna académica. A experiencia obtida das diferentes actividades foi satisfactoria e enriquecedora tanto para o alumnado como para o profesorado, que xa baralla melloras de cara aos vindeiros cursos académicos.[Abstract] This paper presents the activities performed by the educative innovation group in Information Access Systems during the academic year 2017/2018. This group, with teaching at the Faculty of Informatics of the University of A Coruña, carried out actions addressing three different topics. The first action was designed to improve the quality of the evaluation methods, and consisted in following a protocol for detecting plagiarism in programming exercises. The second activity aimed to improve the employability of the students and consisted in using a methodology based on project-based learning along with a series of advanced tools for software development, which recreated the activity that the students will carry out when they obtain their first job. Lastly, heading towards a better knowledge about the available professional alternatives, a series of seminars and talks were organized, which were performed by professionals from an international company, a local interdisciplinary company, and a researcher from an academic institution. The experience obtained from the different activities was satisfactory for both students and teachers, who are already considering improvements for the next academic year

ArcDrain: A GIS Add-In for Automated Determination of Surface Runoff in Urban Catchments

ABSTRACT: Surface runoff determination in urban areas is crucial to facilitate ex ante water planning, especially in the context of climate and land cover changes, which are increasing the frequency of floods, due to a combination of violent storms and increased imperviousness. To this end, the spatial identification of urban areas prone to runoff accumulation is essential, to guarantee effective water management in the future. Under these premises, this work sought to produce a tool for automated determination of urban surface runoff using a geographic information systems (GIS). This tool, which was designed as an ArcGIS add-in called ArcDrain, consists of the discretization of urban areas into subcatchments and the subsequent application of the rational method for runoff depth estimation. The formulation of this method directly depends on land cover type and soil permeability, thereby enabling the identification of areas with a low infiltration capacity. ArcDrain was tested using the city of Santander (northern Spain) as a case study. The results achieved demonstrated the accuracy of the tool for detecting high runoff rates and how the inclusion of mitigation measures in the form of sustainable drainage systems (SuDS) and green infrastructure (GI) can help reduce flood hazards in critical zonesThis research was funded by the Spanish Ministry of Science, Innovation, and Universities, with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF), grant number RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE)

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

Rockfall events are one of the most frequent types of mass wasting in mountainous areas, causing service and traffic disruption, as well as infrastructure and human damage. Hence, having accurate tools to model these hazards becomes crucial to prevent fatalities, especially in a context of climate change whereby the effects of these phenomena might be exacerbated. Under this premise, this article concerned the development of a framework for assessing rockfall hazard in mountainous areas. First, a set of factors expected to favor rockfalls were processed and aggregated using spatial analysis tools, yielding a series of hazard maps with which to fit observed data through statistical modeling. The validation process was undertaken with the support of a database containing the number of rocks removed from a mountainous road section located in Cantabria, northern Spain. The results achieved, which demonstrated the accuracy of the proposed approach to reproduce rockfall hazard using frequency data, highlighted the primary role played by factors such as slope, runoff threshold, and precipitation to explain the occurrence of these events. The effects of climate change were considerably influenced by the fluctuations in the projections of precipitation, which limited the variations in the spatial distribution and magnitude of rockfall hazard.This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities, in part by the State General Budget (PGE), and in part by the European Regional Development Fund (ERDF)under Grant RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE). The work of Alejandro Roldan-Valcarce was supported by the Spanish Ministry of Science, Innovation and Universities through a Researcher Formation Fellowship under Grant PRE2019-08945