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Publicações

2025

Evaluation of cortical lateralization for identifying Parkinson’s disease patients using electroencephalographic signals and machine learning

Autores
Massaranduba, ABR; Coelho, BFO; Santos Souza, CAd; Viana, GG; Brys, I; Ramos, RP;

Publicação
Current Psychology

Abstract

2025

Evaluation of Lyrics Extraction from Folk Music Sheets Using Vision Language Models (VLMs)

Autores
Mendes, AS; Murciego, AL; Silva, LA; Jiménez-Bravo, DM; Navarro-Cáceres, M; Bernardes, G;

Publicação
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

AI-mediated Collaborative Crowdsourcing for Social News Curation: The Case of Acropolis

Autores
Schneider, D; Chaves, R; Pimentel, AP; de Almeida, MA; De Souza, JM; Correia, A;

Publicação
Proceedings of the 2025 ACM International Conference on Interactive Media Experiences

Abstract

2025

Access opportunities to a unique long term deep sea infrastructure

Autores
Cusi, S; Martins, A; Tomasi, B; Puillat, I;

Publicação

Abstract
EMSO ERIC is a unique European distributed marine Research Infrastructure dedicated to the observation and study of the deep ocean in the long term in fixed regional areas. It provides different services of which access to its infrastructure by external users -engineers, scientists and researchers-, working both in the public and private sectors. The aim of this service, called physical access, is to facilitate access to instrumented platforms deployed at different sites across the European seas, from the seabed to the surface, in order to perform experiments in geosciences and engineering in real ocean conditions. Depending on the logistics and availability of each site, users may deploy their own platforms, instruments, systems or technologies to be tested by the existing equipment that, in this case, can provide reference measurements. Users may also deploy their own systems on the existing EMSO platforms, either in standalone mode or connected to them, receiving power and, in some cases, being able to transmit data by satellite or by cable, depending on the site. Projects requiring the use of several EMSO sites are also accepted. The host EMSO Regional Facility provides logistics and technical support in order to deploy and recover the systems, access the data and it may also offer training and co-development. EMSO ERIC launches the physical access call on a yearly basis and evaluates the received project proposals every two months. Access is free of charge and funding is available for travel, consumables, shipping, operations and hardware adaptations needed to run the project. Since 2022, when the first call was launched, ten projects with varied topics have been funded and are in different phases of execution.

2025

A 3D Clinical Face Phenotype Space of Genetic Syndromes Using a Triplet-Based Singular Geometric Autoencoder

Autores
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;

Publicação
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

The Attitude of Young Portuguese Youth Toward Blood Donation Advertising Campaigns—an Exploratory Approach

Autores
Fonseca, MJ; Lopes, S; Garcia, JE; Sousa, BB;

Publicação
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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