2025
Authors
Massaranduba, ABR; Coelho, BFO; Santos Souza, CAd; Viana, GG; Brys, I; Ramos, RP;
Publication
Current Psychology
Abstract
2025
Authors
Mendes, AS; Murciego, AL; Silva, LA; Jiménez-Bravo, DM; Navarro-Cáceres, M; Bernardes, G;
Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2024, PT I
Abstract
Monodic folk music has traditionally been preserved in physical documents. It constitutes a vast archive that needs to be digitized to facilitate comprehensive analysis using AI techniques. A critical component of music score digitization is the transcription of lyrics, an extensively researched process in Optical Character Recognition (OCR) and document layout analysis. These fields typically require the development of specific models that operate in several stages: first, to detect the bounding boxes of specific texts, then to identify the language, and finally, to recognize the characters. Recent advances in vision language models (VLMs) have introduced multimodal capabilities, such as processing images and text, which are competitive with traditional OCR methods. This paper proposes an end-to-end system for extracting lyrics from images of handwritten musical scores. We aim to evaluate the performance of two state-of-the-art VLMs to determine whether they can eliminate the need to develop specialized text recognition and OCR models for this task. The results of the study, obtained from a dataset in a real-world application environment, are presented along with promising new research directions in the field. This progress contributes to preserving cultural heritage and opens up new possibilities for global analysis and research in folk music.
2025
Authors
Schneider, D; Chaves, R; Pimentel, AP; de Almeida, MA; De Souza, JM; Correia, A;
Publication
Proceedings of the 2025 ACM International Conference on Interactive Media Experiences
Abstract
2025
Authors
Cusi, S; Martins, A; Tomasi, B; Puillat, I;
Publication
Abstract
2025
Authors
Mahdi, SS; Caldeira, E; Matthews, H; Vanneste, M; Nauwelaers, N; Yuan, M; Bouritsas, G; Baynam, GS; Hammond, P; Spritz, R; Klein, OD; Bronstein, M; Hallgrimsson, B; Peeters, H; Claes, P;
Publication
IEEE ACCESS
Abstract
Clinical diagnosis of syndromes benefits strongly from objective facial phenotyping. This study introduces a novel approach to enhance clinical diagnosis through the development and exploration of a low-dimensional metric space referred to as the clinical face phenotypic space (CFPS). As a facial matching tool for clinical genetics, such CFPS can enhance clinical diagnosis. It helps to interpret facial dysmorphisms of a subject by placing them within the space of known dysmorphisms. In this paper, a triplet loss-based autoencoder developed by geometric deep learning (GDL) is trained using multi-task learning, which combines supervised and unsupervised learning approaches. Experiments are designed to illustrate the following properties of CFPSs that can aid clinicians in narrowing down their search space: a CFPS can 1) classify syndromes accurately, 2) generalize to novel syndromes, and 3) preserve the relatedness of genetic diseases, meaning that clusters of phenotypically similar disorders reflect functional relationships between genes. The proposed model consists of three main components: an encoder based on GDL optimizing distances between groups of individuals in the CFPS, a decoder enhancing classification by reconstructing faces, and a singular value decomposition layer maintaining orthogonality and optimal variance distribution across dimensions. This allows for the selection of an optimal number of CFPS dimensions as well as improving the classification capacity of the CFPS, which outperforms the linear metric learning baseline in both syndrome classification and generalization to novel syndromes. We further proved the usefulness of each component of the proposed framework, highlighting their individual impact. From a clinical perspective, the unique combination of these properties in a single CFPS results in a powerful tool that can be incorporated into current clinical practices to assess facial dysmorphism.
2025
Authors
Fonseca, MJ; Lopes, S; Garcia, JE; Sousa, BB;
Publication
Smart Innovation, Systems and Technologies
Abstract
This study explores the context of blood donation in Portugal, specifically aiming to understand how communication strategies can effectively recruit young blood donors aged 18 to 24. The research addresses the following question: What is the impact of communication efforts on the recruitment of young blood donors in Portugal? To answer this question, four specific objectives were set: (1) To evaluate the level of awareness among young individuals in this age group regarding blood donation; (2) to analyze and assess the communication strategies employed by the Portuguese Institute of Blood and Transplantation (IPST) to promote blood donation; (3) to investigate the motivations and barriers related to blood donation; and (4) to identify effective communication strategies for encouraging blood donation. To achieve the first objective, which is the primary focus of this article, a content analysis of 14 blood donation campaigns was conducted. For the second objective, an exploratory interview was held with a specialist from the IPST. The third objective is being addressed through a survey involving 390 young individuals, which has already been administered and revealed that over half of the respondents are not blood donors. The findings suggest that future campaigns should adopt more targeted segmentation strategies based on behavioral criteria and make greater use of integrated marketing communication to enhance effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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