O webmarketing e o perfil de consumidor online: estudo de caso da Delta Q

Mestrado em Controlo de Gestão e dos Negócios

Avaliação da intervenção socioeducativa sobre a violência na escola

A investigação desenvolvida no âmbito do projeto Estratégias de Intervenção socioeducativa em contextos sociais complexos enquadra-se na avaliação das políticas sociais e educativas, em particular no que diz respeito à segurança escolar em contextos marcados pela diversidade e complexidade social e cultural. O processo de avaliação centrou-se na análise das estratégias de intervenção socioeducativa relativas ao problema da violência na escola, desenvolvidas em três escolas de um concelho da Área Metropolitana de Lisboa. Partindo do pressuposto que a violência na escola é um fenómeno multideterminado e multifacetado, a pesquisa centrou-se numa abordagem que enquadra as esferas de intervenção/ação das instituições formais e dos agentes sociais enquanto mecanismos que estruturam e regulam as concepções e práticas de violência na escola. A recolha e sistematização de informação centrou-se, por um lado, nas estratégias de intervenção que têm vindo a ser desenvolvidas localmente pelas escolas, e, por outro, nas perspetivas dos diferentes intervenientes, considerando-se os alunos, osprofessores, as direções escolares e representantes das entidades e instituições locais. Metodologicamente, privilegiou-se o cruzamento de métodos de carácter extensivo e intensivo, combinando técnicas como a Observação Direta, a realização de Entrevistas, de Grupos Focais, de Questionários, e ainda, a Análise de Redes e a Análise Documental. Numa fase posterior, os diversos intervenientes participaram na discussão e análise dos resultados previamente recolhidos, e na validação conjunta de uma metodologia de intervenção que define um conjunto de estratégias gerais de combate às situações de violência na escola e nos territórios educativos. Esta metodologia é o principal produto do projeto e resulta de um processo de avaliação dinâmico e participado. A contribuição que se apresenta no VI Encontro do CIED ocupa-se dos procedimentos de avaliação desenvolvidos no âmbito deste projeto.

An Evaluation of Local Feature Combiners for Robot Visual Localization

In the last decade, local image features have been widely used in robot visual localization. To assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image to those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, we compare several candidate combiners with respect to their performance in the visual localization task. A deeper insight into the potential of the sum and product combiners is provided by testing two extensions of these algebraic rules: threshold and weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance. The voting method, whilst competitive to the algebraic rules in their standard form, is shown to be outperformed by both their modified versions.

Automatic synthesis of VHDL Hardware Components from IOPT Petri Net models

Conferência: 39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON), Vienna, Austria, Nov 10-14, 2013

ECG-based biometrics: A real time classification approach

Behavioral biometrics is one of the areas with growing interest within the biosignal research community. A recent trend in the field is ECG-based biometrics, where electrocardiographic (ECG) signals are used as input to the biometric system. Previous work has shown this to be a promising trait, with the potential to serve as a good complement to other existing, and already more established modalities, due to its intrinsic characteristics. In this paper, we propose a system for ECG biometrics centered on signals acquired at the subject's hand. Our work is based on a previously developed custom, non-intrusive sensing apparatus for data acquisition at the hands, and involved the pre-processing of the ECG signals, and evaluation of two classification approaches targeted at real-time or near real-time applications. Preliminary results show that this system leads to competitive results both for authentication and identification, and further validate the potential of ECG signals as a complementary modality in the toolbox of the biometric system designer.

Optimal offering strategies for wind power producers considering uncertainty and risk

This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn.

Limites no Cruzamento de Géneros: Documentário e Ficção

Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Audiovisual e Multimédia.

Influência da variabilidade dos materiais compósitos na resposta dinâmica de estruturas laminadas

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Mecânica

Mining social individuals movements and friends based on mobile devices

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Sem corpo, sem problemas: narradores e contadores de histórias

Ora combinando técnicas da narração oral, ora enfatizando o uso da linguagem, todos os espectáculos estudados neste ensaio lidam com a palavra antes de olharem para o corpo. A mîse en oralité presente em todas estas criações permitia ao corpo desaparecer em palco e tornar-se (aparentemente) um simples veículo para a palavra. Contudo, é claro que este número de desaparecimento apenas vem acentuar a importância do corpo no acto de contar e de representar.

Severity and exposure associated with tsunami actions in urban waterfronts: the case of Lisbon, Portugal

The Tagus estuary is bordered by the largest metropolitan area in Portugal, the Lisbon capital city council. It has suffered the impact of several major tsunamis in the past, as shown by a recent revision of the catalogue of tsunamis that struck the Portuguese coast over the past two millennia. Hence, the exposure of populations and infrastructure established along the riverfront comprises a critical concern for the civil protection services. The main objectives of this work are to determine critical inundation areas in Lisbon and to quantify the associated severity through a simple index derived from the local maximum of momentum flux per unit mass and width. The employed methodology is based on the mathematical modelling of a tsunami propagating along the estuary, resembling the one occurred on the 1 November of 1755 that followed the 8.5 M-w Great Lisbon Earthquake. The employed simulation tool was STAV-2D, a shallow-flow solver coupled with conservation equations for fine solid phases, and now featuring the novelty of discrete Lagrangian tracking of large debris. Different sets of initial conditions were studied, combining distinct tidal, atmospheric and fluvial scenarios, so that the civil protection services were provided with comprehensive information to devise public warning and alert systems and post-event mitigation intervention. For the most severe scenario, the obtained results have shown a maximum inundation extent of 1.29 km at the AlcA cent ntara valley and water depths reaching nearly 10 m across Lisbon's riverfront.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.

Synthesis of titanate nanofibers co-sensitized with ZnS and Bi2S3 nanocrystallites and their application on pollutants removal

The synthesis of nanocomposite materials combining titanate nanofibers (TNF) with nanocrystalline ZnS and Bi2S3 semiconductors is described in this work. The TNF were produced via hydrothermal synthesis and sensitized with the semiconductor nanoparticles, through a single-source precursor decomposition method. ZnS and Bi2S3 nanoparticles were successfully grown onto the TNF's surface and Bi2S3-ZnS/TNF nanocomposite materials with different layouts. The samples' photocatalytic performance was first evaluated through the production of the hydroxyl radical using terephthalic acid as probe molecule. All the tested samples show photocatalytic ability for the production of this oxidizing species. Afterwards, the samples were investigated for the removal of methylene blue. The nanocomposite materials with best adsorption ability were the ZnS/TNF and Bi2S3ZnS/TNF. The dye removal was systematically studied, and the most promising results were obtained considering a sequential combination of an adsorption-photocatalytic degradation process using the Bi2S3ZnS/TNF powder as a highly adsorbent and photocatalyst material. (C) 2015 Elsevier Ltd. All rights reserved.

How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

Coevolution between two antagonistic species has been widely studied theoretically for both ecologically- and genetically-driven Red Queen dynamics. A typical outcome of these systems is an oscillatory behavior causing an endless series of one species adaptation and others counter-adaptation. More recently, a mathematical model combining a three-species food chain system with an adaptive dynamics approach revealed genetically driven chaotic Red Queen coevolution. In the present article, we analyze this mathematical model mainly focusing on the impact of species rates of evolution (mutation rates) in the dynamics. Firstly, we analytically proof the boundedness of the trajectories of the chaotic attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. By using symbolic dynamics theory, we quantify the complexity of genetically driven Red Queen chaos computing the topological entropy of existing one-dimensional iterated maps using Markov partitions. Co-dimensional two bifurcation diagrams are also built from the period ordering of the orbits of the maps. Then, we study the predictability of the Red Queen chaos, found in narrow regions of mutation rates. To extend the previous analyses, we also computed the likeliness of finding chaos in a given region of the parameter space varying other model parameters simultaneously. Such analyses allowed us to compute a mean predictability measure for the system in the explored region of the parameter space. We found that genetically driven Red Queen chaos, although being restricted to small regions of the analyzed parameter space, might be highly unpredictable.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.