Sobre
Mestre em Ciência da Informação, e Doutorado em Media Digitais.
O foco da dissertação é o desenvolvimento de modelos de metadados específicos para a descrição de dados de investigação.
Mestre em Ciência da Informação, e Doutorado em Media Digitais. O foco da dissertação é o desenvolvimento de modelos de metadados específicos para a descrição de dados de investigação.
Mestre em Ciência da Informação, e Doutorado em Media Digitais.
O foco da dissertação é o desenvolvimento de modelos de metadados específicos para a descrição de dados de investigação.
2023
Autores
Castro, JA; Rodrigues, J; Mena Matos, P; M D Sales, C; Ribeiro, C;
Publicação
IASSIST Quarterly
Abstract
2022
Autores
Maciel, A; Castro, JA; Ribeiro, C; Almada, M; Midão, L;
Publicação
Int. J. Digit. Curation
Abstract
2021
Autores
Leite, B; Abdalrahman, A; Castro, J; Frade, J; Moreira, J; Soares, C;
Publicação
ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2
Abstract
Artificial Intelligence (AI) is continuously improving several aspects of our daily lives. There has been a great use of gadgets & monitoring devices for health and physical activity monitoring. Thus, by analyzing large amounts of data and applying Machine Learning (ML) techniques, we have been able to infer fruitful conclusions in various contexts. Activity Recognition is one of them, in which it is possible to recognize and monitor our daily actions. The main focus of the traditional systems is only to detect pre-established activities according to the previously configured parameters, and not to detect novel ones. However, when applying activity recognizers in real-world applications, it is necessary to detect new activities that were not considered during the training of the model. We propose a method for Novelty Detection in the context of physical activity. Our solution is based on the establishment of a threshold confidence value, which determines whether an activity is novel or not. We built and train our models by experimenting with three different algorithms and four threshold values. The best results were obtained by using the Random Forest algorithm with a threshold value of 0.8, resulting in 90.9% of accuracy and 85.1% for precision.
2020
Autores
Aguiar Castro, JD; Landeira, C; da Silva, JR; Ribeiro, C;
Publicação
Int. J. Digit. Curation
Abstract
2019
Autores
Karimova, Y; Castro, JA; Ribeiro, C;
Publicação
Digital Libraries: Supporting Open Science - 15th Italian Research Conference on Digital Libraries, IRCDL 2019, Pisa, Italy, January 31 - February 1, 2019, Proceedings
Abstract
Researchers are currently encouraged by their institutions and the funding agencies to deposit data resulting from projects. Activities related to research data management, namely organization, description, and deposit, are not obvious for researchers due to the lack of knowledge on metadata and the limited data publication experience. Institutions are looking for solutions to help researchers organize their data and make them ready for publication. We consider here the deposit process for a CKAN-powered data repository managed as part of the IT services of a large research institute. A simplified data deposit process is illustrated here by means of a set of examples where researchers describe their data and complete the publication in the repository. The process is organised around a Dublin Core-based dataset deposit form, filled by the researchers as preparation for data deposit. The contacts with researchers provided the opportunity to gather feedback about the Dublin Core metadata and the overall experience. Reflections on the ongoing process highlight a few difficulties in data description, but also show that researchers are motivated to get involved in data publication activities.
Teses supervisionadas
2020
Autor
Jéssica Alexandra Lopes Barbosa
Instituição
UP-FEUP
2020
Autor
André Filipe da Costa Maciel
Instituição
UP-FEUP
Autor
Yulia Karimova
Instituição
FCT
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