BUSCADOR RECOLECTA

This paper introduces a novel, affordable companion robot that has been designed for rehabilitation purposes among the elderly population. The robot is equipped with a camera that records exercises, and an animation screen that delivers clear and easy-to-follow instructions and feedback. To evaluate the device, a machine learning algorithm was used on a dataset of therapy exercises. The results indicate that the robot effectively recognizes gestures and accurately identifies the exercises being performed. This study presents a groundbreaking and cost-effective solution for elderly rehabilitation and has the potential to revolutionize the industry with its cutting-edge technology., This work was partly supported by Universitat Politecnica de Valencia Research Grant PAID-10-19 and PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by 'ERDFA way of making Europe', Consellería d'Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future) and grant from the Research Services of Universitat Politècnica de València (PAIDPD-22).

This paper explores the use of personalized cognitive support through social robots to assist the elderly in maintaining cognitive health and emotional well-being. As aging populations grow, the demand for innovative solutions to address issues like loneliness, cognitive decline, and physical limitations increases. The studied social robots utilize machine learning and advanced sensor technology to deliver real-time adaptive interactions, including cognitive exercises, daily task assistance, and emotional support. Through responsive and personalized features, the robot enhances user autonomy and improves quality of life by monitoring physical and emotional states and adapting to the needs of each user. This study also examines the challenges of implementing assistive robots in home and healthcare settings, offering insights into the evolving role of AI-powered social robots in eldercare., This work was partially supported by grant PID2021-123673OB-C31, TED2021-131295B-C32 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and PROMETEO grant CIPROM/2021/077 from the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital—Generalitat Valenciana.

In this work, we compare different families of fuzzy integrals
in the context of feature aggregation for edge detection. We analyze the
behaviour of the Sugeno and Choquet integral and some of its generalizations.
In addition, we study the influence of the fuzzy measure over
the extracted image features. For testing purposes, we follow the Bezdek
Breakdown Structure for edge detection and compare the different fuzzy
integrals with some classical feature aggregation methods in the literature.
The results of these experiments are analyzed and discussed in
detail, providing insights into the strengths and weaknesses of each approach.
The overall conclusion is that the configuration of the fuzzy measure
does have a paramount effect on the results by the Sugeno integral,
but also that satisfactory results can be obtained by sensibly tuning such
parameter. The obtained results provide valuable guidance in choosing
the appropriate family of fuzzy integrals and settings for specific applications.
Overall, the proposed method shows promising results for edge
detection and could be applied to other image-processing tasks., This work was partially supported with grant PID2021-123673OB-C31 funded by
MCIN/AEI/ 10.13039/501100011033 and by ”ERDF A way of making Europe”,
Conseller´ıa d’Innovaci´o, Universitats, Ciencia i Societat Digital from Comunitat
Valenciana (APOSTD/2021/227) through the European Social Fund (Investing
In Your Future), grant from the Reseach Services of Universitat Polit`ecnica de
Val`encia (PAID-PD-22), FAPERGS/Brazil (Proc. 19/2551-0001279-9, 19/2551-
0001660) and CNPq/Brazil (301618/2019-4, 305805/2021-5, Edital 07/2022),
Programa de Apoio `a Fixa¸c˜ao de Jovens Doutores no Brasil (23/2551-0000126-
8).

Edge detection is a crucial process in numerous stages of computer vision. This field of study has recently gained momentum due to its importance in various applications. The uncertainty, among other characteristics of images, makes it difficult to accurately determine the edge of objects. Furthermore, even the definition of an edge is vague as an edge can be considered as the maximum boundary between two regions with different properties. Given the advancement of research in image discontinuity detection, especially using aggregation and pre-aggregation functions, and the lack of systematic literature reviews on this topic, this paper aims to gather and synthesize the current state of the art of this topic. To achieve this, this paper presents a systematic review of the literature, which selected 24 papers filtered from 428 articles found in computer databases in the last seven years. It was possible to synthesize important related information, which was grouped into three approaches: (i) based on both multiple descriptor extraction and data aggregation, (ii) based on both the aggregation of distance functions and fuzzy C-means, and (iii) based on fuzzy theory, namely type-2 fuzzy and neutrosophic sets. As a conclusion, this review provides interesting gaps that can be explored in future work., This research was partially funded by: the Spanish Ministry of Science (TIN2016-77356-P, PID2019-108392GB-I00 AEI/10.13039/501100011033) ; MCIN/AEI/ 10.13039/501100011033 (grant PID2021-123673OB-C31) ; "ERDF A way of making Europe", Consellería d’Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future) ; Reseach Services of Universitat Politècnica de València (PAID-PD-22) ; FAPERGS/Brazil (Proc. 19/2551-0001279-9, 19/2551-0001660,23/2551-0000126-8) ; CNPq/Brazil (Proc. 301618/2019-4,305805/2021-5).

Versión original extendida en: https://academica-e.unavarra.es/handle/2454/45222, Este artículo es un resumen del trabajo publicado en la revista IEEE Transactions on Fuzzy Systems. En este trabajo, presentamos una contribución a la teoría de las Funciones de Equivalencia Restringida (REF), que permite comparar elementos multivaluados. Extendemos el concepto de REF de L a Ln y presentamos una nueva construcción de similitud en Ln. A partir de esta filosofía se construyen medidas de similitud entre multiconjuntos difusos y se presenta un ejemplo aplicado en el contexto de la difusión anisotrópica de imágenes en color., Proyecto PID2022-136627NB-I00 financiado por MCIN/AEI/10.13039/501100011033/FEDER, UE; Proyectos TED2021-131295B-C32, PID2021-123673OB-C31 del
MCIN/AEI/10.13039/501100011033 Proyecto PROMETEO CIPROM/2021/077 Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital - Generalitat Valenciana Ayuda PAID-06-23 del Vicerrectorado de Investigación de la U. Politécnica de València (UPV)

[EN] One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents communicate with other agents to share their learning model as they are available to the wireless connection range. When deploying a set of agents, it is essential to study whether all the WANET agents will be reachable before the deployment. The paper proposes to explore it by generating a simulation close to the real world using a framework (FIVE) that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study. The paper also presents how and why the concept of artifact has been included in the above-mentioned framework as a way to highlight the importance of some devices used in the environment that have to be located in specific places to ensure the full connection of the system. This inclusion is the first step to allow Digital Twins to be modeled with this framework, now allowing a Digital Shadow of those devices., This work has been developed thanks to the funding of projects: Grant PID2021-123673OBC31 funded by MCIN/AEI/ 10.13039/ 501100011033 and by ERDF A way of making Europe ,
PROMETEO CIPROM/ 2021/077, TED2021-131295B-C32 and Ayudas del Vicerrectorado de Investigacion de la UPV (PAID-PD-22).

[EN] Rural mobility has a lack of innovative proposals in contrast with its urban counterpart. This research aims to bring solutions that ease the implementation of reliable and flexible rural transportation. Demand-responsive transportation is chosen to develop a public transportation service providing interurban trips among several rural settlements. Given the characteristics of rural displacement demand, a novel approach is introduced to optimize the service's economic costs: the dynamic transfer point allocation. The problem is fully formulated and an architecture is introduced describing the workflow of the whole system. Data from an interurban bus transportation service are used to build a case study of a rural area of Valencia, Spain, and develop several examples illustrating the benefits of the proposed approach. The results reveal that the dynamic creation of transfer points can simplify the transportation fleet's itineraries and boost the amount of served travel requests. Finally, a discussion of the benefits and dangers of flexible features in rural transportation is developed, underscoring the need to achieve a balance between dynamic operation and service quality., This work was partially supported with grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe . Pasqual Martí is supported by grant ACIF/2021/259 funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana . Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR

[EN] This paper introduces an innovative approach to predicting bus
ridership andanalysing transportation scenarios through a fusion
of machine learning (ML) techniques and multi-agent simulations.
Utilising a comprehensive dataset from an urban bus system,
we employ ML models to accurately forecast passenger flows,
factoring in diverse variables such as weather conditions. The
novelty of our method lies in the application of these predictions
to generate detailed simulation scenarios, which are meticulously
executed to evaluate the efficacy of public transportation services.
Our research uniquely demonstrates the synergy between ML
predictions and agent-based simulations, offering a robust
tool for optimising urban mobility. The results reveal critical
insights into resource allocation, service efficiency, and potential
improvements in public transport systems. This study significantly
advances the field by providing a practical framework for
transportation providers to optimise services and address longterm challenges in urban mobility., This work is partially supported by grants PID2021-123673 and PDC2022-133161-C32 funded by
MCIN/AEI/ 10.13039/501100011033 and by «ERDF A way of making Europe». Pasqual Martí is supported by grant ACIF/2021/259 funded by the «Conselleria de Innovación, Universidades, Ciencia y
Sociedad Digital de la Generalitat Valenciana». Jaume Jordán is supported by grant IJC2020-045683-I
funded by MCIN/AEI/ 10.13039/501100011033 and by «European Union NextGenerationEU/PRTR».

[EN] The problem of university dropout is a recurring issue in universities that affects students, especially in the first year of studies. The situation is aggravated by the COVID-19 pandemic, which has imposed a virtual education, generating a greater amount of data in addition to historical information, and thus, a greater demand for strategies to design projects based on Educational Data Mining (EDM). To deal with this situation, we present a framework for designing EDM projects based on the construction of a problem tree. The result is the proposal of a framework that merges the six phases of the CRISP-DM methodology with the first stage of the Logical Framework Methodology (LFM) to increase university retention. To illustrate this framework, we have considered the design of a project based on data mining to prevent students from dropping out of a Peruvian university., This work is partially supported by grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". The research was developed thanks to the support of the National University of Moquegua, which provided the information for the creation of the dataset.

[EN] Recent advancements in computer vision have led to humanlevel performance in image recognition tasks, but challenges persist in
real-world applications due to differences in data distribution. This paper
introduces CLARA, a semi-automatic framework designed to continually
retrain computer vision models by incorporating expert annotation of
new data. The system was tested in a production environment with a
waste image classification model and showed improved performance and
robustness against evolving data distributions. This approach encourages collaboration among stakeholders in waste management and utilizes
human-in-the-loop strategies to enhance model accuracy. The framework
is adaptable to various models and tasks and, additionally, supports
fusion techniques that combine outputs from multiple models to improve
prediction robustness and overall system reliability., This work was partially supported with grant tPID2021-
123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way
of making Europe , grant CIPROM/2021/077 from the Conselleria de Innovació, Universitats, Ciencias i Societat Digital GVA - PROMETEO, grant PAID-06-23 by the
Vice Rectorate Office for Research from UPV and project Reciclaje Inteligente y
Cooperativo en Toda la Cadena de Valor Orientado a Una Sociedad Sostenible 360º
(INNEST/2022/180) from AVI.

[EN] Over the last few years, several works have appeared that employ Artificial Intelligence (AI) techniques to improve sustainable development in the agricultural sector. Specifically, these intelligent techniques provide mechanisms and procedures to facilitate decision-making in the agri-food industry. One of the application areas has been the automatic detection of plant diseases. These techniques, mainly based on deep learning models, allow analysing and classifying plants to determine possible diseases facilitating early detection and thus preventing the propagation of the disease. In this way, this paper proposes an Edge-AI device that incorporates the necessary hardware and software components for automatically detecting plant diseases from a set of images of the plant leaf. In this way, the main goal of this work is to design an autonomous device that allows the detection of possible diseases. that can detect potential diseases in plants. This will be achieved by capturing multiple images of the leaves and implementing data fusion techniques to enhance the classification process and improve its robustness. Several tests have been carried out to determine that the use of this device significantly increases the robustness of the classification responses to possible plant diseases., This work was partially supported by grant number PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF A way of making Europe and Consellería d Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future).

[EN] In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method's efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things., This work was partially supported with grant PID2021-123673OB-C31, TED2021-131295BC32 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe ,
PROMETEO grant CIPROM/2021/077 from the Conselleria de Innovación, Universidades, Ciencia y
Sociedad Digital Generalitat Valenciana and Early Research Project grant PAID-06-23 by the Vice
Rectorate Office for Research from Universitat Politècnica de València (UPV).

[EN] Federated Learning (FL) improves the performance of the training phase of machine learning procedures by distributing the model training to a set of clients and recombining the final models in a server. All clients share the same model, each with a subset of the complete dataset, addressing size issues or privacy concerns. However, having a central server generates a bottleneck and weakens the failure tolerance in truly distributed environments.

This work follows the line of applying consensus for FL as a no-centralized approach. Moreover, the paper presents a fully distributed consensus in MAS (multi-agent system) modeling and a new asynchronous consensus in MAS (multi-agent system). The paper also includes some descriptions and tests for implementing such learning algorithms in an actual agent platform, along with simulation results obtained in a case study about electrical production in Australian wind farms., This work has been developed thanks to the funding of projects:
Grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/
501100011033 and by ERDF A way of making Europe
PROMETEO CIPROM/2021/077
TED2021-131295B-C32
Grant of the Vicerrectorado de Investigacion from UPV (PAID-PD22)

[EN] In this work, we introduce a low-rank approach based on the truncated Singular Value Decomposition (SVD) technique to make deep neural networks (DNNs) smaller. Our method focuses on reducing the overall physical size of a neural network model without losing much in terms of its accuracy or increasing its error noticeably. Specifically, we applied our technique to a modified MobileNetV2 model, in the context of automatic plant diseases detection. We chose two of the biggest layers of the model and compressed them. Our goal was to see how this compression affects the breakdown of the layers, the error in rebuilding the layers, and how well the modified model performs. The results showed that the smaller model still predicts very accurately, even with the reduced physical size of its layers. By making these layers smaller, our approach offers a practical way to handle the deployment of large neural networks, especially in devices with limited resources, without compromising their effectiveness., This work was partially supported with grants TED2021-131295B-C32, PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/
501100011033 and by ERDF A way of making Europe , PROMETEO grant CIPROM/2021/077 from the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital - Generalitat Valenciana, Early Research Project grant PAID-06-23
by the Vice Rectorate Office for Research from Universitat Politècnica de València (UPV)

[EN] In recent years, there has been a strong desire to meet the challenge of electrification of vehicles in order to achieve the decarbonization objective. However, as sales of electric vehicles have increased, there is a significant lack of infrastructure to support the charging of this type of vehicle. The infrastructural deficiencies are even more evident in the interurban environment, where the autonomy in kilometers of the battery is a critical issue. To minimize the substantial economic costs involved in installing sufficient charging points to ensure any interurban journey, it is necessary to establish mechanisms that evaluate appropriate locations to deploy the necessary stations. Accordingly, this paper proposes using an evolutionary approach to calculate the most suitable locations in an interurban environment for electric charging stations. For this purpose, different input information is taken into account in the allocation process. The proposed algorithm has been tested using real data from the USA. The results assess the current infrastructure and show the advantages of the locations proposed by the algorithm., This work is partially supported by grant PID2021-123673OB- C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR". Pasqual Marti is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana".

[EN] Despite global efforts in the pursuit of sustainable waste
management practices, significant gaps for improvement persist, as evidenced by Spain¿s recycling rates lagging below European targets. As
part of a larger initiative aiming to build an intelligent and collaborative
environment to promote the Circular Economy, this paper investigates
the potential of Image Classification in developing an intelligent assistant
for waste disposal guidance. Tailored to Spain¿s recycling framework, an
analysis of different state-of-the-art Image Classification architecture¿s
performance on waste sorting is presented. Additionally, the paper explores aggregation techniques to enhance classification accuracy., This work was partially supported with grant PID2021-
123673OB-C31, TED2021-131295B-C32 funded by MCIN/AEI/10.13039/5011
00011033 and by ERDF A way of making Europe , grant CIPROM/2021/077
from the Conselleria de Innovació, Universitats, Ciencias i Societat Digital Generalitat Valenciana - PROMETEO and project Reciclaje Inteligente y Cooperativo en Toda la Cadena de Valor Orientado a Una Sociedad Sostenible 360º
(INNEST/2022/180) from Agencia Valenciana de la Innovación.

[EN] In recent years, several proposals have been based on Artificial Intelligence techniques for automatically detecting the presence of pests and diseases in crops from images usually taken with a camera. By training with pictures of affected crops and healthy crops, artificial intelligence techniques learn to distinguish one from the other. Furthermore, in the long term, it is intended that the tools developed from such approaches will allow the automation and increased frequency of plant analysis, thus increasing the possibility of determining and predicting crop health and potential biotic risks. However, the great diversity of proposed solutions leads us to the need to study them, present possible situations for their improvement, such as image preprocessing, and analyse the robustness of the proposals examined against more realistic pictures than those existing in the datasets typically used. Taking all this into account, this paper embarks on a comprehensive exploration of various AI techniques leveraging leaf images for the autonomous detection of plant diseases. By fostering a deeper understanding of the strengths and limitations of these methodologies, this research contributes to the vanguard of agricultural disease detection, propelling innovation, and fostering the maturation of AI-driven solutions in this critical domain., The authors gratefully acknowledge the financial support of Conselleria d'Innovacio,Universitats,Cienciai Societat Digital from Comunitat Valenciana (APOSTD/2021/227),the European Social Fund (Investing In Your Future), grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe" and grant from the Research Services of Universitat Politecnica de Valencia (PAID-PD-22).

[ES] El transporte de personas y bienes supone un problema complejo a la vez que un servicio esencial en la sociedad moderna. Entre los distintos modos de transporte, el transporte rodado supone ventajas y retos únicos, gracias a su flexibilidad y operación tanto urbana como interurbana. La creciente preocupación social respecto al medio ambiente afecta también al transporte rodado, pues los vehículos a motor son una gran fuente de emisiones de gases de efecto invernadero. Sin embargo, la digitalización de la sociedad y la aparición de nuevos modelos de transporte indican el potencial de mejora del transporte rodado, que podría adaptarse mejor a sus usuarios a la vez que operar de forma más sostenible.

En esta tesis afrontamos la mejora del transporte rodado mediante técnicas de computación e inteligencia artificial. Esto incluye el modelado de sistemas de transporte mediante sistemas multiagente y su posterior simulación virtual. La operación de las flotas de transporte está determinada por la distribución de tareas, la planificación de las acciones de cada vehículo y su posterior coordinación. Exploramos distintas técnicas y desarrollamos propuestas que mejoran la operación de distintos sistemas de transporte rodado considerando tres puntos de vista: el del operador, el del usuario y, finalmente, el de la sostenibilidad. En otras palabras, apuntamos a obtener sistemas con mayor rendimiento económico y calidad de servicio a la par que un reducido impacto medioambiental.

El objetivo de la mejora del transporte rodado se lleva a cabo desde tres frentes. Primero, se propone un marco de trabajo para el modelado efectivo y la simulación de sistemas de transporte. Esta aportación nos sirve como herramienta para la experimentación del resto de la investigación. Después, la investigación se centra en el transporte urbano, caso de uso para el que modelamos la ciudad como un escenario con recursos compartidos. Proponemos el uso de flotas de vehículos descentralizados para una mayor reactividad del sistema. Mediante un modelado de autointerés, se incentiva a los vehículos a proveer de un mejor servicio a los usuarios a la vez que evitan la congestión de los recursos. Finalmente, con la intención de aportar soluciones innovadoras también a las áreas rurales, se adaptan nuestras propuestas previas para el caso de uso del transporte rural interurbano. En este caso, observamos la necesidad de transporte público flexible y adaptado a los usuarios, con especial importancia en su sostenibilidad económica. Nuestras propuestas de sistema siguen estos principios atendiendo al paradigma del transporte adaptable a la demanda.

Los resultados de esta tesis aportan soluciones prácticas para la mejora de distintos sistemas de transporte rodado, contribuyendo a un futuro de movilidad flexible más sostenible y adaptada al usuario. Como aportación en el ámbito de la inteligencia artificial, las técnicas desarrolladas tienen el potencial de ser adaptadas a campos más allá del transporte como soluciones generales para la distribución de tareas y la coordinación de elementos distribuidos.

[EN] In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot Operating System (ROS) and Agrobots-SIM package that extends the existing package Patrolling SIM made for multi-robot patrolling. The Agrobots-SIM package accommodates dynamic multi-robot task allocation and vehicle routing considering limited robot battery autonomy. Moreover, it accommodates the dynamic assignment of implements to robots for the execution of heterogeneous tasks. The system coordinates task assignment and vehicle routing in real time and responds to unforeseen contingencies during simulation considering dynamic updates of the data related to the environment, tasks, implements, and robots. Except for the ROS and Agrobots-SIM package, other crucial components of the architecture include SPADE3 middleware for developing and executing multi-agent decision making and the FIVE framework that allows us to seamlessly define the environment and incorporate the Agrobots-SIM algorithms to be validated into SPADE agents inhabiting such an environment. We compare the proposed simulation architecture with the conventional approach to 3D multi-robot simulation in Gazebo. The functioning of the simulation architecture is demonstrated in several use-case experiments. Even though resource consumption and community support are still an open challenge in Unity3D, the proposed Agri-RO5 architecture gives better results in terms of simulation realism and scalability., This work was partially supported by grants PID2021-123673OB-C32, PID2021-123673OBC33, TED2021-131295B-C31 and TED2021-131295B-C33 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe and the European Union NextGenerationEU/PRTR ,
respectively, as well as the Agrobots Project funded by the Community of Madrid, Spain, and the AGROBOTIX Project funded by the University Rey Juan Carlos.

[EN] This paper presents a novel approach to analyzing trends in federated learning (FL) using automatic semantic keyword clustering. The authors collected a dataset of FL research papers from the Scopus database and extracted keywords to form a collection representing the FL research landscape. They employed natural language processing (NLP) techniques, specifically a pre-trained transformer model, to convert keywords into vector embeddings. Agglomerative clustering was then used to identify major thematic trends and sub-areas within FL. The study provides a granular view of the thematic landscape and captures the broader dynamics of research activity in FL. The key focus areas are divided into theoretical areas and practical applications of FL. The authors make their FL paper dataset and keyword clustering results publicly available. This data-driven approach moves beyond manual literature reviews and offers a comprehensive overview of the current evolution of FL., This research was funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe grant number PID2021-123673OB-C31 and funded by VAE-VADEN UPV grant number TED2021-131295B-C32 and funded by GUARDIA grant number PROMETEO CIPROM/2021/077 and funded by Ayudas del Vicerrectorado de Investigacion de la UPV grant number PAID-PD-22.

[EN] Rural mobility research has been left aside in favor of urban transportation. Rural areas' low demand, the distance among settlements, and an older population on average make conventional public transportation inefficient and costly. This paper assesses the contribution that on -demand mobility has the potential to make to rural areas. First, demand -responsive transportation is described, and the related literature is reviewed to gather existing system configurations. Next, we describe and implement a proposal and test it on a simulation basis. The results show a clear potential of the demand -responsive mobility paradigm to serve rural demand at an acceptable quality of service. Finally, the results are discussed, and the issues of adoption rate and input data scarcity are addressed., This work is partially supported by grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". Pasqual Marti is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana". Jaume Jordan is supported by grant IJC2020- 045683-I funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR".

[EN] Edge detection is a crucial process in numerous stages of computer vision. This field of
study has recently gained momentum due to its importance in various applications. The uncertainty,
among other characteristics of images, makes it difficult to accurately determine the edge of objects.
Furthermore, even the definition of an edge is vague as an edge can be considered as the maximum
boundary between two regions with different properties. Given the advancement of research in
image discontinuity detection, especially using aggregation and pre-aggregation functions, and the
lack of systematic literature reviews on this topic, this paper aims to gather and synthesize the current
state of the art of this topic. To achieve this, this paper presents a systematic review of the literature,
which selected 24 papers filtered from 428 articles found in computer databases in the last seven
years. It was possible to synthesize important related information, which was grouped into three
approaches: (i) based on both multiple descriptor extraction and data aggregation, (ii) based on both
the aggregation of distance functions and fuzzy C-means, and (iii) based on fuzzy theory, namely
type-2 fuzzy and neutrosophic sets. As a conclusion, this review provides interesting gaps that can
be explored in future work., This work was partially supported with grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF A way of making Europe , Consellería d Innovació, Universitats, Ciencia i Societat Digital from Comunitat Valenciana (APOSTD/2021/227) through the European Social Fund (Investing In Your Future), grant from the Reseach Services of Universitat Politècnica de València (PAID-PD-22), FAPERGS/ Brazil (Proc. 19/2551-0001279-9,19/2551-0001660) and CNPq/Brazil (301618/2019-4, 305805/2021-5) Programa de Apoio à Fixação de Jovens Doutores no Brasil (23/2551-0000126-8).

[EN] The modeling of fleet vehicles as self-interested agents brings a realistic perspective to open fleet transportation research. This feature allows us to model the fleet operation from a non-cooperative point of view. In this work, we study parcel delivery in a city with limited resources (roads and charging stations). We designed and implemented a system composed of a multi-agent planner and a game-theoretic coordination algorithm: a Best-Response Fleet Planner. The system allows for the self-organization of the transportation system by coordinating a fleet of self-interested electric vehicles. The system's operation is optimized together with resource usage while preserving the agents' private interests, allowing each agent to plan its actions. The results show that our system has higher scalability than similar approaches, allowing it to function for a considerable number of agents in settings that feature congestion and conflicts. Additionally, overall solution quality is improved compared to other coordination systems, reducing congestion and avoiding unnecessary waiting times., This work is partially supported by Grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe." Pasqual Marti is supported by Grant ACIF/2021/259 funded by the "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana". Jaume Jordan is supported by Grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR".

[EN] This paper presents a federated learning framework for multi-agent robotic systems, leveraging the ROS 2 framework to enable decentralized collaboration in both simulated and real-world environments. Traditional centralized machine learning approaches face challenges such as data privacy concerns, communication overhead, and limited scalability. To address these issues, we propose a federated reinforcement learning architecture where multiple robotic agents train local models and share their knowledge while preserving data privacy. The framework integrates deep reinforcement learning techniques, utilizing Unity for high-fidelity simulation. Experimental evaluations compare our federated approach against classical centralized learning, demonstrating that our proposal improves model generalization, stabilizes reward distribution, and reduces training variance. Additionally, results indicate that increasing the number of robots enhances task efficiency, reducing the number of steps required for successful navigation while maintaining consistent performance. This study highlights the potential of federated learning in robotics, offering a scalable and privacy-preserving approach to distributed multi-agent learning., This work was partially supported by the Spanish government with grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe".

[EN] The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the
inability of approaching more complex natural
language processing tasks. Such is the case
of the automatic evaluation of complete professional argumentative debates. In this paper,
we propose an original hybrid method to automatically predict the winning stance in this
kind of debates. For that purpose, we combine concepts from argumentation theory such
as argumentation frameworks and semantics,
with Transformer-based architectures and neural graph networks. Furthermore, we obtain
promising results that lay the basis on an unexplored new instance of the automatic analysis
of natural language arguments., This work has been developed thanks to
the funding of the following projects:
Grant PID2021-123673OB-C31 funded
by MCIN/AEI/10.13039/501100011033, Grant TED2021-131295B-C32 funded by
AEI/10.13039/501100011033/ European Union
NextGenerationEU/PRTR, Spanish Government
project PID2020-113416RB-I00, and the AI for
Citizen Intelligence Coaching against Disinformation (TITAN) project, funded by the EU Horizon
2020 research and innovation programme under
grant agreement 101070658, and by UK Research
and innovation under the UK governments Horizon
funding guarantee grant numbers 10040483 and
10055990.

[EN] Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper., This work was partially supported by the Spanish Government with grant number PID2021-123673OB-C31 through the European Social Fund (Investing In Your Future).

[EN] We are currently assisting a social paradigm change motivated by the incorporation, more and more
accelerated, of new information technologies (e.g., social networks or content platforms) in our day-today life. The increased availability of online resources in a universal, diverse, and permanent way is
modifying how we consume online information and content. This is especially true when it comes to
children and teenagers. New generations are increasingly adapting to immediacy and communication
through information technologies. Therefore, it is necessary to evolve the educational paradigm to adapt
it to this new social reality. Future learning strategies should consider the latest models of social
communication, adapting them to achieve learning objectives from the perspective of constructive
alignment and the acquisition or improvement of transversal competencies.
In this sense, there are currently several technologies that can be incorporated into the classroom. One
of the emerging technologies is the podcast. Usually used for entertainment (e.g., stories, books, or
radio talks), the podcast is becoming a tool for massive online content distribution. There exist many
advantages to using podcasting for an educative purpose, as the production cost (in terms of time) is
lower than recording a video. From a content consumer perspective, the main advantage is that an
audio-only approach can be used/consumed everywhere and more easily than video media. Some
educational podcasts are available online but generally tend to focus on learning languages or history.
Outside these specific topics, their use in technology subjects is still residual and mainly focuses on
interviews or long expositions. Moreover, in current proposals, the teacher is the one who produces the
podcast, and therefore it is a one-way communication model (from the teacher to the students).
On the other hand, one of the teaching innovation models that is being increasingly used is the flipped
classroom. In the flipped classroom, students are the main protagonists in their learning. They must prepare
the theoretical parts on their own, and the teacher serves as a guide during this learning process.
In this paper, we propose using the podcast in the flipped classroom model, turning the podcast into a
bidirectional tool in which students are both producers and receivers of learning. The proposal consists
of dividing the class into small groups of students (2-4). Each group will record a podcast episode on a
different conceptual part of the lesson. Group members will have to coordinate and share the activities
of recording and searching for content for the podcast. This will encourage transversal skills related to
communication and social skills. These podcasts will then be shared with the rest of the groups so that
everyone can have direct and permanent access to the different sections of the lesson. Creativity will
also be encouraged, allowing students to add the music or sound effects they consider necessary to
enhance the explanation., The authors gratefully acknowledge the financial support of Consellería d'Innovació, Universitats,
Ciencia i Societat Digital from Comunitat Valenciana and the European Social Fund (Investing In Your
Future) (APOSTD/2021/227 and CIPROM/2021/077), the Spanish Ministry of Science (project
PID2021-123673OB-C31) and the Research Services of Universitat Politècnica de València. Jaume
Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by
"European Union NextGenerationEU/PRTR".

[EN] New generations are increasingly becoming more familiar with consuming audio-visual material through
online platforms. Consequently, learning through IT-based tools is becoming more and more common.
Nowadays, learning platforms (e.g., edX or W3Schools) or content platforms (e.g., YouTube) containing
vast amounts of courses and video tutorials are becoming increasingly popular among students. The main
advantage of online learning is that students can access the content from anywhere and whenever they
want, being able to revisit the content to review concepts and improve their level of knowledge. In this way,
learning based on a deep approach and self-learning is promoted since students are the ones who regulate
their learning process by deciding how much time to dedicate and when to do it. Appropriately using this
type of resource can become a very effective tool applied to a flipped classroom model.
In the flipped classroom model, students are active learners since they are in charge of developing the
lesson material both in class and at home. In this type of learning, the teacher assumes the role of guide
assisting during the learning process. A standard methodology in this flipped classroom model consists
of students preparing different parts of the course content and then explaining those parts to their
classmates. In this way, students develop a sense of responsibility toward the rest of their classmates,
creating an environment where they can recognise their shortcomings and take control of their learning
to teach others. In addition, the acquisition of transversal communication skills is encouraged.
With all this in mind, in this article, we describe a case study we are currently carrying out with students
enrolled in the programming course at the Universitat Politècnica de València. Our proposal combines
the flipped classroom model with access to online resources. In this first approach, we have proposed
that the students record a video explaining a part of the lesson or how to solve at least two exercises
step by step. The explanation must be done as if they were content creators, and their audience were
beginner programmers. The students will upload the videos to a private YouTube channel that will only
be accessible to their classmates. In the classroom, the teacher will encourage students to share their
stories and experiences while learning, editing, and recording the videos. This proposal's main objective
is to promote students' engagement in the learning process and offer them learning alternatives through
online content with a closer language that they can access whenever they need it. To motivate
participation, students and teachers will choose the three best videos from all the videos. The three
winners will receive extra points in the evaluation of the course., The authors gratefully acknowledge the financial support of Consellería d'Innovació, Universitats,
Ciencia i Societat Digital from Comunitat Valenciana and the European Social Fund (Investing In Your
Future) (APOSTD/2021/227 and CIPROM/2021/077), the Spanish Ministry of Science (project
PID2021-123673OB-C31) and the Research Services of Universitat Politècnica de València. Jaume
Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/10.13039/501100011033 and by
"European Union NextGenerationEU/PRTR".