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1,235 results on '"Costa, Rogério"'

1. Cybersecurity in Industry 5.0: Open Challenges and Future Directions

Author

Santos, Bruno, Costa, Rogério Luís C., and Santos, Leonel

Subjects
Computer Science - Cryptography and Security
Abstract

Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations., Comment: 7 pages, 2 figures, Accepted for PST2024 : 21st Annual International Conference on Privacy, Security, and Trust

Published
2024

3. Reconstructing Spatiotemporal Data with C-VAEs

Author

Ribeiro, Tiago F. R., Silva, Fernando, and Costa, Rogério Luís de C.

Subjects
Computer Science - Databases, Computer Science - Machine Learning
Abstract

The continuous representation of spatiotemporal data commonly relies on using abstract data types, such as \textit{moving regions}, to represent entities whose shape and position continuously change over time. Creating this representation from discrete snapshots of real-world entities requires using interpolation methods to compute in-between data representations and estimate the position and shape of the object of interest at arbitrary temporal points. Existing region interpolation methods often fail to generate smooth and realistic representations of a region's evolution. However, recent advancements in deep learning techniques have revealed the potential of deep models trained on discrete observations to capture spatiotemporal dependencies through implicit feature learning. In this work, we explore the capabilities of Conditional Variational Autoencoder (C-VAE) models to generate smooth and realistic representations of the spatiotemporal evolution of moving regions. We evaluate our proposed approach on a sparsely annotated dataset on the burnt area of a forest fire. We apply compression operations to sample from the dataset and use the C-VAE model and other commonly used interpolation algorithms to generate in-between region representations. To evaluate the performance of the methods, we compare their interpolation results with manually annotated data and regions generated by a U-Net model. We also assess the quality of generated data considering temporal consistency metrics. The proposed C-VAE-based approach demonstrates competitive results in geometric similarity metrics. It also exhibits superior temporal consistency, suggesting that C-VAE models may be a viable alternative to modelling the spatiotemporal evolution of 2D moving regions., Comment: Update acknowledgments to include published article information

Published
2023
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4. Territory

Author

da Costa, Rogério Haesbaert, primary and Halvorsen, Sam, additional

Published
2024
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10. Supply and Demand Matching of VET Skills -- A Regional Case Study

Author

Lopes, Ana Sofia, Rebelo, Isabel, Santos, Rui, Costa, Rogério, and Ferreira, Vitor

Abstract

The proper alignment between supply of Vocational Education and Training (VET) and labour market demand is critical to successfully achieve VET's main benefits, such as reduction of academic dropout rates, unemployment reduction and economic growth. This article presents the study carried out in Leiria region (Portugal) to define VET areas priorities at the secondary education level. A mixed methodology was applied by using both quantitative (employment microdata, employers survey and employment offers analysis) and qualitative (interviews, focus groups and documents analysis) instruments and techniques. A diverse group of stakeholders (e.g. regional policymakers and private and public sectors' employers) were heard in order to anticipate the market's needs for skills. The results, obtained through the various methodologies, point to the same skills' priorities and reveal several mismatches between demand and supply of qualifications. Thus, this study helps policymakers to prioritize areas of education and VET providers to design courses aligned with the real needs of the region's labour market. Additionally, several challenges are identified, as the need of a better dialogue between the VET system and employers. By increasing the quality of VET supply, our policy suggestions contribute to reduce future demand-supply imbalances, and therefore, to reduce unemployment, control the demographic problem, increase the companies' expansion and promote regional development.

Published
2023
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23. Why- and How-Provenance in Distributed Environments

Author

Pintor, Paulo, Costa, Rogério Luís de Carvalho, Moreira, José, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Strauss, Christine, editor, Cuzzocrea, Alfredo, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2022
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24. Automatic Quality Improvement of Data on the Evolution of 2D Regions

Author

de C. Costa, Rogério Luís, Moreira, José, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Bohan, editor, Yue, Lin, editor, Jiang, Jing, editor, Chen, Weitong, editor, Li, Xue, editor, Long, Guodong, editor, Fang, Fei, editor, and Yu, Han, editor

Published
2022
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31. Constructivist Online Learning Environment Survey (COLLES): evaluating of hybrid learning in Residency Programs.

Author

Mueller, Vanessa, da Fonseca Simas, Keith Bullia, de Castro Berry, Maria Cardoso, Crespi Alecio, Gabriela da Silva, Pria Balejo, Rodrigo Dalla, Modos Figueira, Desirée Ariane, Wanderley da Costa, Rogério, and Cardoso Torres, Márcia Regina

Subjects
CAREER development, CONSTRUCTIVISM (Education), RESIDENTS (Medicine), BLENDED learning, INFORMATION technology
Abstract

The research aimed to assess the effectiveness of blended learning in the Medical Residency Programs in Family and Community Medicine and Multiprofessional Residency in Family Health at the SESAU/Fiocruz. A cross-sectional observational study was conducted, utilizing a mixed-methods approach with the Constructivist Online Learning Environment Survey (COLLES) among enrolled residents in the first and second years. The study included 88 residents who showed a high level of agreement regarding the relevance of the programs to their professional practices, highlighting significant challenges in the process. Implementing blended learning emerged as an innovative and effective strategy in Residency Programs, evidencing a positive connection between theory and practice. Despite challenges in interactivity, the results suggest the need for strategies to enhance collaboration among residents, streamlining the learning experience. The use of blended learning is a promising tool in Residency Programs, contributing significantly to professional development. [ABSTRACT FROM AUTHOR]

Published
2024
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32. The General Data Protection Regulation and Log Pseudonymization

Author

Varanda, Artur, Santos, Leonel, Costa, Rogério Luís de C., Oliveira, Adail, Rabadão, Carlos, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Woungang, Isaac, editor, and Enokido, Tomoya, editor

Published
2021
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