https://hdl.handle.net/10481/15208 2026-04-26T02:52:22Z https://hdl.handle.net/10481/112952 A Short Review of Arabic Aspect-Based Sentiment Analysis: Methods, Challenges and Future Directions Youseef, Hamza; Baca Ruiz, Luis Gonzaga; Criado-Ramón, David; Pegalajar Jiménez, María Del Carmen The need for Arabic Aspect-Based Sentiment Analysis (ABSA) has grown steadily alongside the expansion of digital content, while the linguistic complexity of Modern Standard Arabic and its diverse dialects introduces significant challenges. However, progress in the field remains constrained by methodological fragmentation, inconsistent task definitions, heterogeneous datasets, and non-standardized evaluation practices. Based on a systematic analysis of 57 studies, this work presents an analytical and interpretive review that moves beyond performance-oriented surveys to examine the methodological foundations of Arabic ABSA research. The review follows a rigorous and transparent study selection process and applies a structured analytical framework to analyze task formulations, dataset characteristics, modeling approaches and evaluation strategies. Our findings reveal persistent challenges, including ambiguous aspect definitions, insufficiently documented annotation protocols, structural annotation biases, and limited robustness across domains and dialects. A heavy reliance on Transformer-based architectures and new Arabic foundation models can create an illusion of progress. Researchers often evaluate these models on small and homogeneous datasets. Consequently, strong in-domain performance obscures limited cross-domain and cross-dialectal generalizability. This study concludes by outlining actionable research directions, emphasizing clearer task standardization, more rigorous annotation guidelines, unified evaluation, and broader dialectal coverage to enhance reproducibility and scalability in Arabic ABSA systems. https://hdl.handle.net/10481/112949 From Prompts to High-Fidelity Prototypes: A Usability Evaluation of Generative AI–Driven Prototyping Tools for Smart Mobile App Design Bustamante Orejuela, John; Quiñonez Ku, Xavier; Pico Valencia, Pablo The integration of Generative Artificial Intelligence (GAI) into software design tools has transformed the early stages of mobile application development, particularly prototype creation from natural-language prompts. This study evaluates the usability and effectiveness of GAI-assisted prototyping tools for generating high-fidelity mobile application prototypes. A controlled laboratory usability study was conducted in which undergraduate Information Technology Engineering students used and evaluated four widely adopted prototyping platforms: Figma, Uizard, Visily, and Stitch. Participants employed these tools to recreate mobile interfaces corresponding to the interaction model of the Duolingo application. The System Usability Scale (SUS) was used to assess perceived usability and effectiveness from the users’ perspective. The results indicate that all evaluated tools enabled rapid prototype generation; however, significant differences emerged in usability, structural fidelity, and perceived control. Figma and Stitch achieved the highest usability scores and demonstrated greater alignment with the reference prototype (82.86 and 80.36, respectively). Visily achieved a favorable usability score (78.57), while Uizard obtained a moderate score (67.14). Although Uizard and Visily exhibited strong automation capabilities and faster initial generation, their outputs required additional manual refinement to achieve higher fidelity and customization. Participant feedback emphasized the importance of output quality, responsiveness, and foundational design knowledge in achieving satisfactory results. Overall, the findings suggest that current GAI-based prototyping tools are effective and valuable in real-world software development contexts. However, their effectiveness appears closely related to the degree of user control, responsiveness, and the ability to iteratively refine AI-generated interface components. 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/10481/112474 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.