BUSCADOR RECOLECTA

In this paper, based on the analysis of the most widely used dispersion measure in the real context (namely, the variance), we introduce the notion of fuzzy dispersion measure associated to a finite set of data given by fuzzy numbers. This measure is implemented as a fuzzy number, so there is no loss of information caused by any defuzzification. The proposed concept satisfies the usual properties in a genuinely fuzzy sense and it avoids limitations in terms of its geometric shape or its analytical properties: under this conception, it could have a piece of its support in the negative part of the real line. This novel notion can be interpreted as a way of fusing the information included in a fuzzy data set in order to make a decision based on its dispersion. To illustrate the main characteristics of this approach, we present an example of a fuzzy dispersion measure that allows to conclude that this new way to deal this problem is coherent, at least, from the point of view of human intuition., The authors are grateful to their universities. This paper has been supported by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades by Project A-FQM-170-UGR20, and also by Ministerio de Ciencia e Innovación by Projects PID2020-119478GB-I00 and PID2019-108392GB-I00 (AEI/ 10.13039/501100011033).

Ordered Weighted Averaging (OWA) operators are some of the most widely used aggregation functions in classic literature, but their application to fuzzy numbers has been limited due to the complexity of defining a total order in fuzzy contexts. However, the recent notion of admissible order for fuzzy numbers provides an effective method to totally order them by refining a given partial order. Therefore, this paper is devoted to defining OWA operators for fuzzy numbers with respect to admissible orders and investigating their properties. Firstly, we define the OWA operators associated with such admissible orders and then we show their main properties. Afterward, an example is presented to illustrate the applicability of these AOWA operators in linguistic decision-making. In this regard, we also develop an admissible order for trapezoidal fuzzy numbers that can be efficiently applied in practice., This study was funded by National Council for Scientific and Technological Development (CNPq-Brazil) within Projects 301618/2019-4, 312053/2018-5, 311429/2020-3, and 200282/2022-0, by Coordination for the Improvement of Higher Education Personnel (CAPES-Brazil) within the project Capes-Print 88887.363001/2019-00, by FAPERGS/Brazil (Proc. 19/2551-0001660-3), by PID2022-136627NB-I00 of the Spanish Government. A.F. Roldán López de Hierro is grateful to Ministerio de Ciencia e Innovación by Project PID2020-119478 GB-I00 and to Junta de Andalucía Program FEDER Andalucía 2014-2020, Project A-FQM-170-UGR20. This work has been also partially supported by the Spanish Ministry of Economy and Competitiveness through the Spanish National Project PGC2018-099402-B-I00, the FEDER-UJA project 1380637, and by the Spanish Ministry of Science, Innovation and Universities through a Formación de Profesorado Universitario grant (FPU2019/01203).; Funding text 2: This study was funded by National Council for Scientific and Technological Development (CNPq-Brazil) within Projects 301618/2019-4 , 312053/2018-5 , 311429/2020-3 , and 200282/2022-0 , by Coordination for the Improvement of Higher Education Personnel (CAPES-Brazil) within the project Capes-Print 88887.363001/2019-00 , by FAPERGS /Brazil (Proc. 19/2551-0001660-3 ), by PID2022-136627NB-I00 of the Spanish Government . A.F. Roldán López de Hierro is grateful to Ministerio de Ciencia e Innovación by Project PID2020-119478 GB-I00 and to Junta de Andalucía Program FEDER Andalucía 2014¿2020, Project A-FQM-170-UGR20 . This work has been also partially supported by the Spanish Ministry of Economy and Competitiveness through the Spanish National Project PGC2018-099402-B-I00 , the FEDER-UJA project 1380637 , and by the Spanish Ministry of Science, Innovation and Universities through a Formación de Profesorado Universitario grant (FPU2019/01203).

In this paper we introduce a novel methodology to face the problem of finding, for every fuzzy set of the real line, a fuzzy number which can be considered as an approximation of the first one in some reasonable sense. This methodology depends on a wide variety of initial parameters that each researcher may set depending on his/her own interests. The main objective of this new methodology is to ensure that many of the techniques that are currently available for fuzzy numbers can also be extended to the setting of fuzzy sets of the real line which are, in many ways, much more enriching. To do this, we carry out a study of the families of nested sets that can determine fuzzy numbers through their level sets. Next, we describe some of the main properties that this approximation methodology verifies and we show some examples to illustrate how the initial parameters influence the result of the approximation., This manuscript has been partially supported by Junta de Andalucía by Project FQM-365 of the Andalusian CICYE, and also by Projects PID2020-119478GB-I00 and PID2019-108392GB-I00 (AEI/10.13039/501100011033 , Ministerio de Ciencia e Innovación).

Social network analysis is a popular tool to understand the relationships between interacting agents by studying the structural properties of their connections. However, this kind of analysis can miss some of the domain-specific knowledge available in the original information domain and its propagation through the associated network. In this work, we develop an extension of classical social network analysis to incorporate external information from the original source of the network. With this extension we propose a new centrality measure, the semantic value, and a new affinity function, the semantic affinity, that establishes fuzzy-like relationships between the different actors in the network. We also propose a new heuristic algorithm based on the shortest capacity problem to compute this new function. As an illustrative case study, we use the novel proposals to analyze and compare the gods and heroes from three different classical mythologies: 1) Greek; 2) Celtic; and 3) Nordic. We study the relationships of each individual mythology and those of the common structure that is formed when we fuse the three of them. We also compare our results with those obtained using other existing centrality measures and embedding approaches. In addition, we test the proposed measures on a classical social network, the Reuters terror news network, as well as in a Twitter network related to the COVID-19 pandemic. We found that the novel method obtains more meaningful comparisons and results than previous existing approaches in every case., The work of Javier Fumanal-Idocin and Humberto
Bustince was supported by the Project PID2019-108392GB I00 under Grant
AEI/10.13039/501100011033. The work of Oscar Cordón was supported by
the Government of Spain under Grant MCIN/AEI/10.13039/501100011033/
and CONFIA under Grant PID2021-122916NB-I00, including European
Regional Development Funds (ERDF) “FEDER, Una manera de hacer
Europa.” The work of Antonio-Francisco Roldán López-de-Hierro was
supported in part by the Project PID2020-119478GB-I00 and in part
by the FEDER Project under Grant A-FQM-170-UGR20

Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500 m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging Terra + Aqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping., This work was partially supported by DETECTOR (grant no. A-RNM-256-UGR18, Universidad de Granada/FEDER), LifeWatch SmartEcoMountains (grant no. LifeWatch-2019-10-UGR-01, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundación BBVA a Equipos de Investigación Científica 2018), DeepL-ISCO (grant no. A-TIC-458-UGR18, Ministerio de Ciencia e Innovación/FEDER), SMART-DASCI (grant no. TIN2017-89517-P, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BigDDL-CET (grant no. P18-FR-4961, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), RESISTE (grant no. P18-RT-1927, Consejería de Economía, Conocimiento y Universidad from the Junta de Andalucía/FEDER), Ecopotential (grant no. 641762, European Commission), PID2020-119478GB-I00, the Consellería de Educación, Cultura y Deporte de la Generalitat Valenciana, the European Social Fund (grant no. APOSTD/2021/188), the European Research Council (ERC grant no. 647038/BIODESERT), and the Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables – ScaleUp project).

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps., This work is part of the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8, LifeWatch-2019-10-UGR-01 WP-7 and LifeWatch-2019-10-UGR-01 WP-4. This work was also supported by projects A-RNM-256-UGR18, A-TIC-458-UGR18, PID2020-119478GB-I00 and P18-FR-4961. E.G. was supported by the European Research Council grant agreement n° 647038 (BIODESERT) and the Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188). We thank the “Programa de Unidades de Excelencia del Plan Propio” of the University of Granada for partially covering the article processing charge.

Hope speech is the speech that is able to relax hostile environments and that helps, inspires and encourages people in times of illness, stress, loneliness or depression. Its automatic recognition can have a very significant effect fighting against sexual and racial discrimination or fostering less belligerent environments. In contrast to identifying and censoring negative or hate speech, hope speech detection is focused on recognizing and promoting positive speech online. In this paper we present an overview of the IberLEF 2023 shared task, HOPE: Multilingual Hope Speech Detection, consisting of identifying whether texts written in English or Spanish contain hope speech or not. The competition was organized through CodaLab and attracted 50 teams that registered. Finally, 12 submitted results and 8 presented working notes describing their systems., Definimos el discurso de la esperanza como aquel que es capaz de relajar entornos hostiles y que ayuda, inspira y anima a las personas en momentos de enfermedad, estrés, soledad o depresión. Su detección automática puede tener un efecto muy significativo luchando contra la discriminación sexual y racial o fomentando entornos menos beligerantes. A diferencia de la identificación y censura del discurso negativo o de odio, la detección del discurso esperanzador se centra en reconocer y promover el discurso positivo. En este artículo presentamos los resultados de la tarea de IberLEF 2023, HOPE: Detección multilingüe del discurso de la esperanza, que consiste en identificar si textos escritos en inglés o español contienen o no discurso de esperanza. La competición se organizó a través de CodaLab y atrajo a 50 equipos que se inscribieron. Finalmente, 12 equipos presentaron resultados y 8 enviaron artículos describiendo sus sistemas., This work has been partially supported by Project CONSENSO (PID2021-122263OB-C21), Project MODERATES (TED2021-130145B-I00) and Project SocialTox (PDC2022-133146-C21) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR, Project PRECOM (SUBV-00016) funded by the Ministry of Consumer Affairs of the Spanish Government, Project FedDAP (PID2020-116118GA-I00) and Project Trust-ReDaS (PID2020-119478GB-I00) supported by MICINN/AEI/10.13039/501100011033, and WeLee project (1380939, FEDER Andalucía 2014-2020) funded by the Andalusian Regional Government. It is also part of the research projects AIIn-Funds (PDC2021-121112-I00) and LTSWM (TED2021-131167B-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. This work is also part of the research project LaTe4PSP (PID2019-107652RB-I00/AEI/10.13039/501100011033) funded by MCIN/AEI/10.13039/501100011033. Salud María Jiménez-Zafra has been partially supported by a grant from Fondo Social Europeo and the Administration of the Junta de Andalucía (DOC 01073).

Downy mildew is a major disease of grapevine. Conventional methods for assessing crop diseases are time-consuming and require trained personnel. This work aimed to develop and validate a new method to automatically estimate the severity of downy mildew in grapevine leaves using fuzzy logic and computer vision techniques. Leaf discs of two grapevine varieties were inoculated with Plasmopara viticola and subsequently, RGB images were acquired under indoor conditions. Computer vision techniques were applied for leaf disc location in Petri dishes, image pre-processing and segmentation of pre-processed disc images to separate the pixels representing downy mildew sporulation from the rest of the leaf. Fuzzy logic was applied to improve the segmentation of disc images, rating pixels with a degree of infection according to the intensity of sporulation. To validate the new method, the downy mildew severity was visually evaluated by eleven experts and averaged score was used as the reference value. A coefficient of determination (R) of 0.87 and a root mean squared error (RMSE) of 7.61 % was observed between the downy mildew severity obtained by the new method and the visual assessment values. Classification of the severity of the infection into three levels was also attempted, achieving an accuracy of 86 % and an F1 score of 0.78. These results indicate that computer vision and fuzzy logic can be used to automatically estimate the severity of downy mildew in grapevine leaves. A new method has been developed and validated to assess the severity of downy mildew in grapevine. The new method can be adapted to assess the severity of other diseases and crops in agriculture., This work has been developed as part of the project NoPest (Novel Pesticides for a Sustainable Agriculture), which received funding from the European Union Horizon 2020 FET Open program under Grant agreement ID 828940. It was also supported by the Spanish Ministry of Economy and Competitiveness under Grant PID2020-119478GB-I00. Inés Hernández would like to acknowledge the research funding FPI grant 1150/2020 by Universidad de La Rioja and Gobierno de La Rioja.

Jellyfish swarms have a direct negative impact on human enterprise, specially on places dependent on the sun and beach economy. The local economy and the health of bathers may be at risk from the emergence of these gelatinous organisms. Economic losses can be mitigated by monitoring the occurrence of jellyfish on the coast. Due to the lack of jellyfish data, environmental citizen science is presented as an alternative for data collection. In this study, fuzzy logic-based models have been used to modelize the knowledge from citizen comments collected by the Infomedusa app. The effect of local climatological factors such as wind speed and direction on the incidence of jellyfish on the coast was studied. The fuzzy logic-based models showed that winds perpendicular to the coast lead to a higher occurrence of jellyfish swarms in central and eastern Malaga, while winds parallel to the coast have a greater influence in the westernmost coasts. Wind speed has a different effect on jellyfish incidence depending on the study area and wind direction. Data extracted from the Infomedusa app can help to address the historical scarcity of scientific data on jellyfish. This app presents an opportunity for future studies to expand the knowledge about the occurrence of these organisms on the coasts and may contribute to the prediction of onshore arrival., This work was partially supported by the Spanish Ministry of Science and Innovation under Project PID2020-119478GB-I00., Peer reviewed

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional auto-encoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multiclass, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling. © 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society., Authors MJ and EJA acknowledge financial support from the State Agency for Research of the Spanish MCIU through the 'Center of Excellence Severo Ochoa' award to the Instituto de Astrofisica de Andalucia (grant no. SEV-2017-0709). This work is supported by projects A-TIC-434-UGR20 and PID2020-119478GB-I00. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used in this research. We also thank Dr Steven Bamford (University of Nottingham) for the valuable discussions about the Galaxy Zoo project and data.