Measuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach

Promoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Resumen de la tarea Rest-Mex en IberLEF 2023: Investigaci´on sobre An´alisis de Sentimiento para Textos Tur´ısticos Mexicanos

This paper presents the framework and results of the Rest-Mex task at IberLEF 2023, focusing on sentiment analysis and text clustering of tourist texts. The study primarily focuses on texts related to tourist destinations in Mexico, although this edition included data from Cuba and Colombia for the first time. The sentiment analysis task aims to predict the polarity of opinions expressed by tourists, classifying the type of place visited, whether it’s a tourist attraction, hotel, or restaurant, as well as the country it is located in. On the other hand, the text clustering task aims to classify news articles related to tourism in Mexico. For both tasks, corpora were built using Spanish opinions extracted from TripAdvisor and news articles from Mexican media. This article compares and discusses the results obtained by the participants in both sub-tasks. Additionally, a method is proposed to measure the easiness of a multi-class text classification corpus, along with an approach for system selection in a possible late fusion scheme.Este artículo presenta el marco y los resultados de la tarea Rest-Mex en IberLEF 2023, que se enfoca en el análisis de sentimiento y agrupamiento de textos turísticos. El estudio se centra principalmente en textos relacionados con destinos turísticos en México, aunque esta edición incluyó datos de Cuba y Colombia por primera vez. La tarea de análisis de sentimiento tiene como objetivo predecir la polaridad de opiniones expresadas por turistas, clasificando el tipo de lugar visitado, ya sea un atractivo turístico, un hotel o un restaurante, así como el país en el que se encuentra. Por otro lado, la tarea de agrupamiento de textos busca clasificar noticias relacionadas con el turismo en México. Para ambas tareas, se construyeron corpus utilizando opiniones en español extraídas de TripAdvisor y noticias de medios mexicanos. En este artículo, se comparan y discuten los resultados obtenidos por los participantes en ambas sub tareas. Además, se propone un método para medir la facilidad de un corpus de clasificación textual multi-clase, así como un enfoque para la selección de sistemas en un posible esquema de fusión tardía.The authors thank the Mexican Academy of Tourism Research (AMIT) for their support of the project ”Creation of a labeled database related to tourist destinations for training artificial intelligence models for classifying relevant topics” through the call ”I Research Projects 2022”, which originated this work

Vector-based word representations for sentiment analysis: a comparative study

New applications of text categorization methods like opinion mining and sentiment analysis, author profiling and plagiarism detection requires more elaborated and effective document representation models than classical Information Retrieval approaches like the Bag of Words representation. In this context, word representation models in general and vector-based word representations in particular have gained increasing interest to overcome or alleviate some of the limitations that Bag of Words-based representations exhibit. In this article, we analyze the use of several vector-based word representations in a sentiment analysis task with movie reviews. Experimental results show the effectiveness of some vector-based word representations in comparison to standard Bag of Words representations. In particular, the Second Order Attributes representation seems to be very robust and effective because independently the classifier used with, the results are good.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

Desarrollo de productos avanzados para la misión SEOSAT/Ingenio

Revista oficial de la Asociación Española de Teledetección[EN] SEOSAT/Ingenio is the future Spanish Earth Observation high spatial resolution mission in the optical domain. While Level 1 products, at-sensor geo-referenced radiances, are in an advanced phase of development under the framework of an industrial contractor, Level 2 products must be developed by the users. This fact limits the use of the satellite images only to the scientific community, restricting their use in other applications. The need to alleviate this limitation motivated this work, developed under the framework of a coordinate project, which aimed at offering a list of Level2 products to the Ingenio/SEOSAT user community. In this paper, we present the different methodologies developed to produce the proposed Level2 products, from surface reflectance at nominal sensor spatial resolution to images with higher spatial resolution or the possibility to create spatial and temporal mosaics. On the one side, for the surface reflectance product, we proposed an atmospheric correction algorithm based on using the spatial information, linked to a cloud screening algorithm and including morphological and topographic shadow corrections. On the other side, to enhance the image spatial resolution, we applied different fusion techniques using the multispectral and the panchromatic band, as well as some of the so-called “super-resolution” techniques. Finally, we provided different tools to develop spatial mosaics and temporal composites, directed to users interested on the exploitation of the Ingenio/ SEOSAT images.[ES] SEOSAT/Ingenio es la futura misión española de observación de la Tierra en el óptico en alta resolución es-pacial. Mientras que los productos de imagen a Nivel 1, radiancias geo-referenciadas a nivel de sensor, se encuentran en una fase avanzada de desarrollo existiendo para ello un contrato industrial, los productos de Nivel 2 deben ser de-sarrollados por los propios usuarios. Este hecho limita el uso de las imágenes a la comunidad científica, restringiendo sus posibles aplicaciones fuera de ésta. Así pues, bajo el marco de un proyecto coordinado y motivados por ofrecer productos de Ingenio/SEOSAT de Nivel 2 a disposición de cualquier usuario, se origina y desarrolla este trabajo. En este artículo se presentan los diferentes procesos desarrollados para la elaboración de productos a Nivel 2, desde reflectividades en superficie a la resolución nominal del sensor hasta imágenes con información espacial realzada y la posibilidad de crear mosaicos espaciales y compuestos temporales. Por una parte, en el caso de los productos de reflectividad en superficie se propone una técnica de corrección atmosférica basada en el uso de la información es-pacial, previo enmascaramiento de las nubes y una exhaustiva corrección de sombras morfológicas y/o topográficas. Por otra parte, para el realce de la información espacial, han sido evaluados diferentes métodos basados en la fusión de bandas multiespectrales con una banda pancromática así como la aplicación de técnicas llamadas de “Super-re-solución”. Finalmente, se proporcionan las herramientas necesarias para la realización de mosaicos tanto espaciales como temporales para todo tipo de usuarios interesados en la explotación de las imágenesEste artículo ha sido posible gracias al proyecto coordinado “Generación de Productos de Nivel 2 para la Misión INGENIO/SEOSAT”, ESP2013- 48458-C4-1-P, subvencionado por el Ministerio de Economia y Competitividad dentro del Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia.Sabater, N.; Ruiz-Verdú, A.; Delegido, J.; Fernández-Beltrán, R.; Latorre-Carmona, P.; Pla, F.; González-Audícana, M.... (2016). Development of advanced products for the SEOSAT/Ingenio mission. Revista de Teledetección. (47):23-40. https://doi.org/10.4995/raet.2016.6569SWORD234047Blesius, L., & Weirich, F. (2005). The use of the Minnaert correction for land‐cover classification in mountainous terrain. International Journal of Remote Sensing, 26(17), 3831-3851. doi:10.1080/01431160500104194de Lussy, F., Kubik, P., Greslou, D., Pascal, V., Gigord, P., Cantou, J. P. 2005. Pleiades-HR image system products and quality. Proceedings of ISPRS Hannover Workshop 2005: High-Resolution Earth Imaging for Geospatial Information.Do, M. N., & Vetterli, M. (2005). The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091-2106. doi:10.1109/tip.2005.859376Weisheng Dong, Lei Zhang, Guangming Shi, & Xiaolin Wu. (2011). Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization. IEEE Transactions on Image Processing, 20(7), 1838-1857. doi:10.1109/tip.2011.2108306Freedman, G., & Fattal, R. (2011). Image and video upscaling from local self-examples. ACM Transactions on Graphics, 30(2), 1-11. doi:10.1145/1944846.1944852Grodecki, J., & Dial, G. (2003). Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials. Photogrammetric Engineering & Remote Sensing, 69(1), 59-68. doi:10.14358/pers.69.1.59Liu, J. G. (2000). Smoothing Filter-based Intensity Modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461-3472. doi:10.1080/014311600750037499Marini, A., Reina Barragan, F.J., Crippa, G., Harnisch, B., Fuente, I., Lopez, M., Cabeza, I., Zorita, D. 2014. SEOSAT/INGENIO – A Spanish High-spatial-resolution optical mission. International Conference on Space Optics. Tenerife, Spain, 7-10 octubre.Mekler, Y., & Kaufman, Y. J. (1982). Contrast reduction by the atmosphere and retrieval of nonuniform surface reflectance. Applied Optics, 21(2), 310. doi:10.1364/ao.21.000310Otazu, X., Gonzalez-Audicana, M., Fors, O., & Nunez, J. (2005). Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43(10), 2376-2385. doi:10.1109/tgrs.2005.856106Pons, X., Pesquer, L., Cristóbal, J., & González-Guerrero, O. (2014). Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images. International Journal of Applied Earth Observation and Geoinformation, 33, 243-254. doi:10.1016/j.jag.2014.06.002Sun, J., Xu, Z., Shum, H. Y. 2008. Image super-resolution using gradient profile prior. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-8.VICENTESERRANO, S., PEREZCABELLO, F., & LASANTA, T. (2008). Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sensing of Environment, 112(10), 3916-3934. doi:10.1016/j.rse.2008.06.011Villa, G., Montoro, M.A. 1993. Ajuste radiométrico conjunto de varias imágenes de satélite para la realización de mosaicos de ortoimágenes. En Actas de la V Reunión Científica de la Asociación Espa-ola de Teledetección. Las Palmas de Gran Canaria, Espa-a, 10 a 12 de Noviembre, pp. 385- 394.Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G. A., … Wald, L. (2015). A Critical Comparison Among Pansharpening Algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2565-2586. doi:10.1109/tgrs.2014.2361734Wald, L., Ranchin, T., Mangolini, M. 1997. Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering & Remote Sensing, 63(6), 691-699.Zhang, Y., 2004. Understanding Image Fusion. Photogrammetric Engineering & Remote Sensing, 70(6), 657-661.Zhou, J., Civco, D. L., & Silander, J. A. (1998). A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International Journal of Remote Sensing, 19(4), 743-757. doi:10.1080/01431169821597

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

This article belongs to the Special Issue Addressing the Growing Burden of Chronic Diseases and Multimorbidity: Characterization and InterventionsThe current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.This study was performed in the framework of FAIR4Health, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’, and by REDISSEC (RD16/0001/0005) and RICAPPS (RD21/0016/0019) from Carlos III National Institute of Health. This work was also supported by Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health [Río Hortega Program, grant number CM19/00164].Peer reviewe

COVID-19 outbreaks in a transmission control scenario: challenges posed by social and leisure activities, and for workers in vulnerable conditions, Spain, early summer 2020

Severe acute respiratory syndrome coronavirus 2 community-wide transmission declined in Spain by early May 2020, being replaced by outbreaks and sporadic cases. From mid-June to 2 August, excluding single household outbreaks, 673 outbreaks were notified nationally, 551 active (>6,200 cases) at the time. More than half of these outbreaks and cases coincided with: (i) social (family/friends’ gatherings or leisure venues) and (ii) occupational (mainly involving workers in vulnerable conditions) settings. Control measures were accordingly applied

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’.Peer reviewe

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research