https://hdl.handle.net/10481/15207 2026-04-13T19:08:24Z https://hdl.handle.net/10481/112781 Fractal characterization of restored paintings Ruiz de Miras, Juan; López-Montes, Ana; Vílchez Quero, José Luis; Blanc García, María Rosario; Martín Perandrés, Domingo The fractal dimension (FD) is a quantitative measure of complexity that has been effectively used over the past two decades to analyze paintings for several purposes, including forgery detection, artist classification, characterization of pictorial genres, and analysis of historical periods. However, the potential of FD to characterize the variations that may occur during restoration processes, such as consolidation, cleaning, and reintegration, remains largely unexplored. In this study, we present a novel methodology that combines FD computation on color images with a sliding window approach to generate detailed FD maps of paintings before and after restoration. We applied this methodology to a dataset of twenty-four restored paintings, which includes three types of alterations: craquelure, paint losses, and aged varnishes. Statistical comparisons of FD distributions before and after restoration were conducted using the Wilcoxon rank-sum test and Levene’s test. Our results show a consistent decrease in FD after restoration in paintings affected by craquelure or paint losses, and an increase in FD in aged-varnish paintings following restoration. Additionally, most paintings exhibited increased variance in FD after restoration, regardless of the type of damage. The difference FD maps, obtained by subtracting the post-restoration FD map from the pre-restoration one, revealed the specific areas where restoration had the greatest impact. These findings suggest that the proposed FD-based methodology offers a valuable, image-based tool for restorers, serving as a complementary resource to traditional restoration techniques for assessing the extent of alterations and monitoring applied treatments. Funding for open access publishing: Universidad de Granada/CBUA. This research was partially funded by the Spanish Ministry of Science, Innovation and University MICIU/AEI/10.13039/501100011033 and FEDER EU (grant number PID2024-161348OB-I00). https://hdl.handle.net/10481/112654 Automating the Initial Development of Intent-Based Task-Oriented Dialog Systems Using Large Language Models: Experiences and Challenges Kharitonova, Ksenia; Pérez Fernández, David; Callejas Carrión, Zoraida; Griol Barres, David Building reliable intent-based, task-oriented dialog systems typically requires substantial manual effort: designers must derive intents, entities, responses, and control logic from raw conversational data, then iterate until the assistant behaves consistently. This paper investigates how far large language models (LLMs) can automate this development. In this paper, we use two reference corpora, Let’s Go (English, public transport) and MEDIA (French, hotel booking), to prompt four LLM families (GPT-4o, Claude, Gemini, Mistral Small) and generate the core specifications required by the rasa platform. These include intent sets with example utterances, entity definitions with slot mappings, response templates, and basic dialog flows. To structure this process, we introduce a model- and platform-agnostic pipeline with two phases. The first normalizes and validates LLM-generated artifacts, enforcing cross-file consistency and making slot usage explicit. The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably. Across eight projects, all models required some targeted repairs before training. After applying our pipeline, all reached ≥ 70% task completion (many above 84%), while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth. These results show that, with modest guidance, current LLMs can produce workable end-to-end dialog prototypes directly from raw transcripts. Our main contributions are: (i) a reusable bootstrap method aligned with industry domain-specific languages (DSLs), (ii) a small set of high-impact corrective patterns, and (iii) a simple but effective harness for closed-loop refinement across conversational platforms. https://hdl.handle.net/10481/112592 OnTheEdge: Cuando la computación y las personas están al borde Rodríguez Fórtiz, María José; García Moreno, Francisco Manuel; Bolaños Martinez, Daniel; Garrido Bullejos, José Luis; Hornos Barranco, Miguel Juan; Rodríguez Almendros, María Luisa; Hurtado Torres, María Visitación; Bermúdez Edo, María del Campo En este artículo se describe un proyecto a cargo del grupo MYDASS de la Universidad de Granada, cuyo objetivo es el diseño de una arquitectura IoT con servicios de recogida y procesamiento de datos en el edge/cloud continuum. Las mejoras de capacidades de procesamiento, almacenamiento y comunicaciones en los dispositivos en el edge hacen que podamos plantear aligerar el cloud llevando algunos servicios de pre-procesamiento y almacenamiento al edge. El caso de estudio de aplicación de la arquitectura es la detección de estrés y ansiedad crónicas en personas mayores mientras realizan sus actividades de la vida diaria, observando diferencias de género. En el proyecto se crearán modelos de clasificación de machine learning basados en el análisis de datos fisiológicos y ambientales recogidos de sensores, cuestionarios y pruebas clínicas homologadas, para que los profesionales planifiquen intervenciones personalizadas. https://hdl.handle.net/10481/112591 Predicción de la repetición de visitas en destinos rurales mediante aprendizaje automático: un caso de estudio en Sierra Nevada (España) Durán López, Alberto; Bolaños Martinez, Daniel; Bermúdez Edo, María del Campo; Delgado Márquez, Blanca Luisa A pesar del creciente interés por el turismo repetido como indicador de sostenibilidad rural, existen escasos estudios que integren enfoques predictivos avanzados. Este estudio analiza la capacidad de modelos de machine learning basados en datos de reconocimiento de matrículas (LPR) y encuestas. Tras contextualizar el turismo rural y el fenómeno del “repeat tourism” como motor de sostenibilidad económica y social, se describe una metodología mixta para predecir la repetición de visitas en tres pueblos rurales de Sierra Nevada (Granada). Primero, se realiza una recolección de datos mediante sensores LPR, cuestionarios presenciales e información contextual del Instituto Nacional de Estadística (INE). Segundo, una limpieza y detección de anomalías (Isolation Forest, Recorrido Intercuartílico (IQR), Z-Score). Por último, una transformación de variables seguida de normalización y estandarización. El modelo propuesto es una red neuronal con capas de autoatención, que supera un 3 % en F1-score a algoritmos clásicos (Árboles de Decisión, Regresión Logística, Support Vector Machine) y reduce el tiempo de entrenamiento en un 32 %. Los resultados permiten a ayuntamientos locales, empresas turísticas y comunidades diseñar campañas de fidelización, optimizar tarifas dinámicas y planificar rutas sostenibles ajustadas a picos de demanda. Se concluye que integrar machine learning y datos de LPR proporciona predicciones precisas y eficientes para la gestión de destinos rurales. Este trabajo contribuye a la literatura emergente sobre destinos inteligentes rurales, al demostrar el potencial de técnicas de deep learning con auto-atención aplicadas a muestras pequeñas, una problemática frecuente en contextos rurales. https://hdl.handle.net/10481/112474 Developing and Evaluating With Usability and Accessibility in Mind: A Case Study on Cultural Heritage Information Systems Almeraj, Zainab; López Escudero, Luis; Torres Cantero, Juan Carlos Over the last decade, interest in creating Cultural Heritage Information Systems (CHISystems) to document conservation and preservation efforts has grown globally. However, due to their interactive, multi-lingual, and distributed nature, their users face various usability challenges especially those systems developed or supervised in house by non UX/UI expert software designers and developers. Ongoing efforts to promote adopting usability and digital accessibility best practices are slowly making a difference, but more solutions are needed. This work aims to make international standards for basic usability and accessibility reachable and easy to recognize for researchers. The first of two contributions includes a simple heuristic evaluation framework, UsA11y, to guide in the adoption of digital accessibility and usability principles from early stages of the information system design, development and evaluation. To the best of the authors’ knowledge, there is no simple and concise assessment targeting designers and developers with limited knowledge in UX/UI, especially in niche fields such as cultural heritage. The second contribution includes a usability test and a heuristic evaluation (with UsA11y) on an existing cultural heritage system with users in the field to gain insight into experiences and needs. This work also offers design implications and insights into effectively adopting usability and accessibility for researchers, designers and developers in light of universal design concepts to ensure reliably and sustainably.