A multidimensional scaling analysis of musical sounds based on pseudo phase plane

This paper studies musical opus from the point of view of three mathematical tools: entropy, pseudo phase plane (PPP), and multidimensional scaling (MDS). The experiments analyze ten sets of different musical styles. First, for each musical composition, the PPP is produced using the time series lags captured by the average mutual information. Second, to unravel hidden relationships between the musical styles the MDS technique is used. The MDS is calculated based on two alternative metrics obtained from the PPP, namely, the average mutual information and the fractal dimension. The results reveal significant differences in the musical styles, demonstrating the feasibility of the proposed strategy and motivating further developments towards a dynamical analysis of musical sounds.

Investigação em curso no Centro de Investigação de Recursos Naturais (CIRN)

Jornadas "Ciência nos Açores – que futuro? Tema Ciências Naturais e Ambiente", Ponta Delgada, 7-8 de Junho de 2013.

O desenvolvimento profissional docente: a função supervisiva do coordenador de ano do 1º ciclo

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação, especialidade de Supervisão em Educação

A tributação dos fundos de investimento mobiliário em portugal na esfera dos participantes

Mestrado em Contabilidade

Racionalidade de emissão de obrigações subordinadas: Estudo de caso do Banco Comercial do Atlântico (BCA) Cabo Verde

Mestrado em Contabilidade e Gestão de Instituições Financeiras

Cão de Fila de S. Miguel : património cultural imaterial

Dissertação de Mestrado, Património, Museologia e Desenvolvimento, 2 de Outubro de 2015, Universidade dos Açores.

Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data?

Chapter in Book Proceedings with Peer Review First Iberian Conference, IbPRIA 2003, Puerto de Andratx, Mallorca, Spain, JUne 4-6, 2003. Proceedings

Utilização de metodologias multicritério de apoio à decisão como ferramenta de suporte numa empresa de serviços energéticos

Atualmente e devido às conjunturas sócio económicas que as empresas atravessam, é importante maximizar tanto os recursos materiais como humanos. Essa consciência faz com que cada vez mais as empresas tentem que os seus colaboradores possam desempenhar um papel importante no processo de decisão. Cada vez mais a diferença entre o sucesso e o fracasso depende da estratégia que cada empresa opte por envergar. Sendo assim cada atividade desempenhada por um seu colaborador deve estar alinhada com os objetivos estratégicos da empresa. O contexto em que a presente tese se insere tem por base uma pesquisa aos vários métodos multicritério existentes, de forma a que o serviço que seja adjudicado possa ser executado de forma transparente e eficiente, sem nunca descorar a sua otimização. O método de apoio à decisão escolhido foi o Analytic Hierarchy Process (AHP). A necessidade de devolver aos decisores/gestores a melhor solução resultante da aplicação de um método de apoio à decisão numa empresa de serviços energéticos foi a base para a escolha da tese. Dos resultados obtidos conclui-se que a aplicação do método AHP foi adequada, conseguindo responder a todos os objetivos inicialmente propostos. Foi também possível verificar os benefícios que advêm da sua aplicação, que por si só, ajudaram a perceber que é necessário haver uma maior entreajuda e consenso entre as decisões a tomar.

Welcome to the new world disorder: conflict and transformation in Ian McEwan’s saturday

Ian McEwan‘s novel Saturday deals with the complex issues of conflict and transformation in the age of terrorism. The plot presents one internal dilemma and several interpersonal altercations that occur within a mere twenty-four hours: a) Perowne (the protagonist) vs. himself, in face of his ambivalent thoughts regarding British military participation in the war in the Middle East; b) The protagonist vs. Baxter, a ruffian from East End, in the context of a car accident; c) Perowne vs. a fellow anaesthetist, Jay Strauss, during a squash game; d) Perowne‘s daughter, Daisy vs. her grandfather, John Grammaticus, both poets and rivals; e) Perowne‘s family vs. Baxter, who intrudes the protagonist‘s house. In this paper, I exemplify, analyse and discuss how: a) Understanding the causes of what we call evil constitutes an important step towards mutual understanding; b) Both science and arts (which Perowne considers, at first, irrelevant) are important elements in the process of transformation; c) Both personal and interpersonal conflicts are intrinsic to human nature — but they also propitiate healthy changes in behaviour and opinion, through reflection. In order to do so, I resort to Saturday, and to the work of several specialists in the field of conflict management.

A Educação Positiva aplicada à Técnica de Dança Clássica do 3ºAno B da Escola de Dança do Conservatório Nacional

Relatório Final de Estágio apresentado à Escola Superior de Dança, com vista à obtenção do grau de Mestre em Ensino de Dança.

As políticas e práticas culturais no seio da Rede Nacional de Teatros e Cineteatros

Artigo baseado na comunicação proferida no 7º Congresso SOPCOM: Comunicação Global, Cultura e Tecnologia, realizado na Faculdade de Letras da Universidade do Porto, Porto, Portugal, 15 -17 dezembro de 2011

Informal learning recognition through a cloud ecosystem

Learning and teaching processes, like all human activities, can be mediated through the use of tools. Information and communication technologies are now widespread within education. Their use in the daily life of teachers and learners affords engagement with educational activities at any place and time and not necessarily linked to an institution or a certificate. In the absence of formal certification, learning under these circumstances is known as informal learning. Despite the lack of certification, learning with technology in this way presents opportunities to gather information about and present new ways of exploiting an individual’s learning. Cloud technologies provide ways to achieve this through new architectures, methodologies, and workflows that facilitate semantic tagging, recognition, and acknowledgment of informal learning activities. The transparency and accessibility of cloud services mean that institutions and learners can exploit existing knowledge to their mutual benefit. The TRAILER project facilitates this aim by providing a technological framework using cloud services, a workflow, and a methodology. The services facilitate the exchange of information and knowledge associated with informal learning activities ranging from the use of social software through widgets, computer gaming, and remote laboratory experiments. Data from these activities are shared among institutions, learners, and workers. The project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Extraction automatique d´associations lexicales à partir de corpora

The automatic acquisition of lexical associations from corpora is a crucial issue for Natural Language Processing. A lexical association is a recurrent combination of words that co-occur together more often than expected by chance in a given domain. In fact, lexical associations define linguistic phenomena such as idiomes, collocations or compound words. Due to the fact that the sense of a lexical association is not compositionnal, their identification is fundamental for the realization of analysis and synthesis that take into account all the subtilities of the language. In this report, we introduce a new statistically-based architecture that extracts from naturally occurring texts contiguous and non contiguous. For that purpose, three new concepts have been defined : the positional N-gram models, the Mutual Expectation and the GenLocalMaxs algorithm. Thus, the initial text is fisrtly transformed in a set of positionnal N-grams i.e ordered vectors of simple lexical units. Then, an association measure, the Mutual Expectation, evaluates the degree of cohesion of each positional N-grams based on the identification of local maximum values of Mutual Expectation. Great efforts have also been carried out to evaluate our metodology. For that purpose, we have proposed the normalisation of five well-known association measures and shown that both the Mutual Expectation and the GenLocalMaxs algorithm evidence significant improvements comparing to existent metodologies.