Percorrer por data de Publicação, começado por "2021-06-24"
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- Changing rituals and practices surrounding COVID-19 related deaths: implications for mental health nursingPublication . Laranjeira, Carlos; Querido, AnaWith a rising number of coronavirus deaths, concerns have been raised over farewell rituals and the grieving processes. This commentary acknowledges the therapeutic potential of mental health nursing to help with the resolution of grief trajectories in the ongoing pandemic.
- Disentangling the effects of ego and task-involving climate perceptions on cohesion in youth sportPublication . Chicau Borrego, Carla; Monteiro, Diogo; Benson, Alex J.; Miguel, Mauro; Teixeira, Eduardo; Silva, CarlosThe present study evaluated how the combination of ego- and task-involving climate perceptions related to youth soccer athletes’ perceptions of team cohesion. We hypothesized that athletes would perceive their group to be less cohesive as ego climate perceptions increased in salience relative to task climate perceptions. In addition, the factor structure and longitudinal measurement invariance of Youth Sport Environment Questionnaire (YSEQ)—Portuguese version was also analyzed. A total of 956 national level youth male soccer athletes’ belonging to 49 different teams participated in the study. Using a prospective design with two time points, the polynomial regression with a response surface analysis indicated that the effect of an ego-involving climate on task cohesion varied as a function of task-involving climate perceptions. Specifically, athletes reported lower levels of task cohesion as ego-involving climate perceptions began to predominate over task-involving climate perceptions. Furthermore, a strong taskinvolving climate buffered against the negative effects of ego-involving climate perceptions on task cohesion. Regarding social cohesion, we only observed a positive linear association between task-involving climate perceptions and social cohesion. Our findings support the validity and reliability of two factors underlying the YSEQ and its longitudinal invariance across time in an elite youth sample. Future studies should strive to replicate these results in other sports and with female athletes. Our results provide insight into how task-involving and ego-involving climate perceptions combine to shape how elite youth athletes view their group environment.
- Exposing Manipulated Photos and Videos in Digital Forensics AnalysisPublication . Ferreira, Sara; Antunes, Mário; Correia, Manuel E.Tampered multimedia content is being increasingly used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are amongst the most recurrent crimes in which manipulated digital photos and videos are the perpetrating and disseminating medium. Criminal investigation has been challenged in applying machine learning techniques to automatically distinguish between fake and genuine seized photos and videos. Despite the pertinent need for manual validation, easy-to-use platforms for digital forensics are essential to automate and facilitate the detection of tampered content and to help criminal investigators with their work. This paper presents a machine learning Support Vector Machines (SVM) based method to distinguish between genuine and fake multimedia files, namely digital photos and videos, which may indicate the presence of deepfake content. The method was implemented in Python and integrated as new modules in the widely used digital forensics application Autopsy. The implemented approach extracts a set of simple features resulting from the application of a Discrete Fourier Transform (DFT) to digital photos and video frames. The model was evaluated with a large dataset of classified multimedia files containing both legitimate and fake photos and frames extracted from videos. Regarding deepfake detection in videos, the Celeb-DFv1 dataset was used, featuring 590 original videos collected from YouTube, and covering different subjects. The results obtained with the 5-fold cross-validation outperformed those SVM-based methods documented in the literature, by achieving an average F1-score of 99.53%, 79.55%, and 89.10%, respectively for photos, videos, and a mixture of both types of content. A benchmark with state-of-the-art methods was also done, by comparing the proposed SVM method with deep learning approaches, namely Convolutional Neural Networks (CNN). Despite CNN having outperformed the proposed DFT-SVM compound method, the competitiveness of the results attained by DFT-SVM and the substantially reduced processing time make it appropriate to be implemented and embedded into Autopsy modules, by predicting the level of fakeness calculated for each analyzed multimedia file.