Spanish society's perceptions about socially responsible investing

The debate surrounding the financial needs of investors and the impact on society of investment is considered to be an important research topic due to the growth of socially responsible financial markets. The objective of this research is to study the perception of the Spanish public about socially responsible investing (SRI) criteria and real-life investment needs. To examine the Spanish perception of SRI, we conducted a field survey. The results show that SRI is in an early stage and Spanish investors need more exact information regarding social, environmental, and governance criteria in order to invest in socially responsible companies and products.

O Tratamento da Violência na Informação de Serviço Público: o Caso da RTP

Dissertação apresentada à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

O teatro de revista no Estado Novo: a década de 30 do século XX: dissertação

Dissertação submetida ao departamento de Teatro da Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro - especialização em Artes Performativas vertente Teatro-Música.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.