Sobre o Desenvolvimento do Talento no Desporto: Um Contributo dos Modelos Teóricos do Desenvolvimento Desportivo

ABSTRACT: With this article, we aim to offer a conceptual synthesis of some of the most important developments in past decades on the subject of talent in sport, while also helping sports stakeholders, particularly managers and coaches, to recognize and apply these conclusions in their practices. The article starts with a brief historical review, which explores how there has been a shift from a talent detection perspective to a talent development perspective and to a holistic vision of athletes and their background context. Secondly, the article presents an overview of the main theoretical models put forward in literature on sport psychology, including career-transition-based models and talent-and-expertise-based models. Finally, as the conceptual model most widely referred to in literature, a detailed analysis of the Development Model of Sports Participation (Côté, Baker & Abernethy, 2007), is made, especially with regard to development processes relating to standards of practice (e.g. diversification and specialization) and psychosocial influences, aspects that form the basis of all-round athlete development.

Classifying And Predicting Software Security Vulnerabilities based on Reproducibility

Security defects are common in large software systems because of their size and complexity. Although efficient development processes, testing, and maintenance policies are applied to software systems, there are still a large number of vulnerabilities that can remain, despite these measures. Some vulnerabilities stay in a system from one release to the next one because they cannot be easily reproduced through testing. These vulnerabilities endanger the security of the systems. We propose vulnerability classification and prediction frameworks based on vulnerability reproducibility. The frameworks are effective to identify the types and locations of vulnerabilities in the earlier stage, and improve the security of software in the next versions (referred to as releases). We expand an existing concept of software bug classification to vulnerability classification (easily reproducible and hard to reproduce) to develop a classification framework for differentiating between these vulnerabilities based on code fixes and textual reports. We then investigate the potential correlations between the vulnerability categories and the classical software metrics and some other runtime environmental factors of reproducibility to develop a vulnerability prediction framework. The classification and prediction frameworks help developers adopt corresponding mitigation or elimination actions and develop appropriate test cases. Also, the vulnerability prediction framework is of great help for security experts focus their effort on the top-ranked vulnerability-prone files. As a result, the frameworks decrease the number of attacks that exploit security vulnerabilities in the next versions of the software. To build the classification and prediction frameworks, different machine learning techniques (C4.5 Decision Tree, Random Forest, Logistic Regression, and Naive Bayes) are employed. The effectiveness of the proposed frameworks is assessed based on collected software security defects of Mozilla Firefox.