2020
Authors
Vasconcelos, PB; Ribeiro, RP;
Publication
ICPEC
Abstract
This paper reports on the use of property-based testing for providing feedback to C programming exercises. Test cases are generated automatically from properties specified in a test script; this not only makes it possible to conduct many tests (thus potentially find more mistakes), but also allows simplifying failed tests cases automatically. We present some experimental validation gathered for an introductory C programming course during the fall semester of 2018 that show significant positive correlations between getting feedback during the semester and the student's results in the final exam. We also discuss some limitations regarding feedback for undefined behaviors in the C language. 2012 ACM Subject Classification Social and professional topics ! Student assessment; Software and its engineering ! Software testing and debugging; Software and its engineering ! Domain specific languages.
2020
Authors
Rocha, A; Adeli, H; Reis, LP; Costanzo, S; Orovic, I; Moreira, F;
Publication
WorldCIST (1)
Abstract
2020
Authors
Paulo Morais, EP; Cunha, CR; Gomes, JP;
Publication
EDUCATION EXCELLENCE AND INNOVATION MANAGEMENT: A 2025 VISION TO SUSTAIN ECONOMIC DEVELOPMENT DURING GLOBAL CHALLENGES
Abstract
With the rapid expansion of Information and Communication Technologies (ICT) throughout almost all aspects of tourism and hospitality industries, the task of integrating technology into higher education curriculum is a need. This paper analyzes the relevance given by the various Portuguese and Spanish institutions of higher education to ICT in their degrees. The analysis carried out was done in degree courses operating in academic year 2018/2019, in Portuguese and Spanish universities and polytechnics. A comparison was also made with the reality of 2012/2013.
2020
Authors
Carbas, B; Machado, N; Oppolzer, D; Ferreira, L; Queiroz, M; Brites, C; Rosa, EAS; Barros, AIRNA;
Publication
ANTIOXIDANTS
Abstract
Phaseolus vulgaris L. is the most commonly consumed legume in the world, given its high vegetable protein content, phenolic compounds, and antioxidant properties. It also represents one of the most sustainable, low-carbon and sources of food available at present to man. This study aims to identify the nutrients, antinutrients, phenolic composition, and antioxidant profile of 10 common bean cultivars (Arikara yellow, butter, cranberry, red kidney, navy, pinto, black, brown eyed, pink eyed, and tarrestre) from two harvest years, thereby assessing the potential of each cultivar for specific applications in the food industry. Navy and pink eyed beans showed higher potential for enrichment of foodstuffs and gluten-free products due to their higher protein and amino acid contents. Additionally, red kidney, cranberry and Arikara yellow beans had the highest content of phenolic compounds and antioxidant properties, which can act as functional ingredients in food products, thus bringing health benefits. Our study highlights the potential of using specific bean cultivars in the development of nutrient-enriched food and as functional ingredients in diets designed for disease prevention and treatment.
2020
Authors
Gomes, AD; Ferreira, MS; Bierlich, J; Kobelke, J; Rothhardt, M; Bartelt, H; Frazão, O;
Publication
Optics InfoBase Conference Papers
Abstract
We discuss the novel concept of harmonics of the Vernier effect for optical fiber sensors as a tool to break the limits of conventional optical Vernier effect currently used. The new effect provides enhancements scalable with the harmonic order. © 2021 The Author(s).
2020
Authors
Guimaraes, N; Miranda, F; Figueira, A;
Publication
INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING
Abstract
Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.
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