Da leitura à escrita : texto expositivo

Projeto de intervênção apresentado à Escola Superior de Educação para a obtenção do grau de mestre em Didática da Língua Portuguesa em 1º e 2º Ciclos do Ensino Básico

GAIC: Um sistema inteligente e flexível para simulação e apoio à participação de pequenos e médios consumidores na gestão ativa de cargas no âmbito de smart grids

A liberalização dos mercados de energia e a utilização intensiva de produção distribuída tem vindo a provocar uma alteração no paradigma de operação das redes de distribuição de energia elétrica. A continuidade da fiabilidade das redes de distribuição no contexto destes novos paradigmas requer alterações estruturais e funcionais. O conceito de Smart Grid vem permitir a adaptação das redes de distribuição ao novo contexto. Numa Smart Grid os pequenos e médios consumidores são chamados ao plano ativo das participações. Este processo é conseguido através da aplicação de programas de demand response e da existência de players agregadores. O uso de programas de demand response para alcançar benefícios para a rede encontra-se atualmente a ser estudado no meio científico. Porém, existe a necessidade de estudos que procurem benefícios para os pequenos e médios consumidores. O alcance dos benefícios para os pequenos e médios consumidores não é apenas vantajoso para o consumidor, como também o é para a rede elétrica de distribuição. A participação, dos pequenos e médios consumidores, em programas de demand response acontece significativamente através da redução de consumos energéticos. De modo a evitar os impactos negativos que podem provir dessas reduções, o trabalho aqui proposto faz uso de otimizações que recorrem a técnicas de aprendizagem através da utilização redes neuronais artificiais. Para poder efetuar um melhor enquadramento do trabalho com as Smart Grids, será desenvolvido um sistema multiagente capaz de simular os principais players de uma Smart Grid. O foco deste sistema multiagente será o agente responsável pela simulação do pequeno e médio consumidor. Este agente terá não só que replicar um pequeno e médio consumidor, como terá ainda que possibilitar a integração de cargas reais e virtuais. Como meio de interação com o pequeno e médio consumidor, foi desenvolvida no âmbito desta dissertação um sistema móvel. No final do trabalho obteve-se um sistema multiagente capaz de simular uma Smart Grid e a execução de programas de demand response, sSendo o agente representante do pequeno e médio consumidor capaz de tomar ações e reações de modo a poder responder autonomamente aos programas de demand response lançados na rede. O desenvolvimento do sistema permite: o estudo e análise da integração dos pequenos e médios consumidores nas Smart Grids por meio de programas de demand response; a comparação entre múltiplos algoritmos de otimização; e a integração de métodos de aprendizagem. De modo a demonstrar e viabilizar as capacidades de todo o sistema, a dissertação inclui casos de estudo para as várias vertentes que podem ser exploradas com o sistema desenvolvido.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Motivação para o desporto escolar: estudo em alunos do ensino secundário, de uma escola do concelho de Guimarães

Relatório de estágio de mestrado em Ensino de Educação Física nos Ensinos Básico e Secundário

Desenvolvimento de um laboratório pedagógico de vias de comunicação na Universidade do Minho

Dissertação de mestrado integrado em Engenharia Civil

Encontros e desencontros da educação de adultos numa associação de teatro amador

Dissertação de mestrado em Ciências da Educação (área de especialização em Educação de Adultos)

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Ler melhor através dos temas e mitos na literatura cabo-verdiana : uma análise comparativista

Esta dissertação teve como ponto de partida a pergunta “Como se comportaram alguns temas e mitos ao longo da história da literatura de Cabo Verde? Que caminho percorreram?” Para tentar dar-lhe resposta recorremos à metodologia comparativista, com o objetivo de analisar as evoluções e possíveis mudanças ocorridas ao longo dos tempos, a partir do cruzamento transversal entre temas e mitos em épocas diferentes. Foi assim necessário abordar a literatura cabo-verdiana, na sua relação profunda com a história do país, considerando os seus três grandes momentos: um tempo “ indefinido” que vai até 1936, um segundo que vai de 1936 até 1975 e, por último, o que vai desde essa data até à atualidade. Ao longo dessa análise, e a partir de um corpus textual necessariamente circunscrito, fomos detetando as variantes, as diferentes atualizações ocorridas nesta literatura, geralmente provocadas por fatores de ordem geográfica, pela evolução histórico-social, por fatores ideológicos que se prenderam sobretudo com a falta de liberdade vivida até 1975. Esta abordagem comparativista, a análise intertextual que lhe é inerente, proporcionou, não uma simples comparação, mas o desvendar de relações múltiplas existentes entre as obras, o corpus literário escolhido, e os tempos que lhes deram origem, permitindo assim conhecer as partes e, consequentemente o todo através do “desocultamento do oculto”. Constatou-se uma migração dos temas e dos mitos entre as literaturas dos três momentos. Em certos casos, alguns elementos permaneceram, mas com novas representações, noutros assumem significados radicalmente diferentes dados os atuais contextos da sua recepção. Relativamente aos mitos, observou-se a sua crescente dessacralização, em sintonia com o progresso que o país foi conhecendo após a independência nacional. A leitura que fomos fazendo conduziu-nos deste modo a uma viagem ao passado do país. Esta leitura transversal, alicerçada na história, dando conta das transformações ocorridas ao longo dos tempos, mostrou-nos ainda de forma cabal a necessidade de se introduzirem mudanças no ensino da literatura nas escolas cabo-verdianas. Uma mudança que deverá passar por uma abordagem comparativista, que privilegia as relações entre textos de diferentes épocas, a sua perspetivação interdisciplinar com outras formas de expressão, capaz de transformar o aluno-mero-receptor num leitor ativo, implicado numa realidade que lhe diz respeito.

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Turvallisuusosaamisen varmistaminen verkko-oppimistyökalun avulla metsäteollisuusyrityksessä

Diplomityön tavoitteena oli kehittää UPM-Kymmene Oyj:n turvallisuuskoulutukseen verkko-oppimistyökalu, jolla voitaisiin tehostaa yrityksen turvallisuuskoulutusta, ja näin parantamaan turvallisuusosaamista, ja sitä kautta vähentää tapaturmia ja sairaspoissaoloja. Työ toteutettiin pilottiprojektina UPM:n Kymin sellutehtaalla Kuusankoskella. Työtä lähestyttiinhuomioimalla mahdollisimman monipuolisesti ne eri osa alueet, jotka turvallisuuden verkko-oppimishankkeeseen liittyivät. Samalla selvitettiin verkko-oppimiseen liittyviä vahvuuksia ja heikkouksia sekä keinot vahvuuksien hyödyntämiseen ja heikkouksien välttämiseen. Teoreettisessa tarkastelussa painotettiin aikuisdidaktisia lähestymistapoja, järjestelmän käytettävyyttä sekä e-oppimismenetelmien erityispiirteitä. Työn tuloksena saatiin kehitettyä verkko-oppimisympäristö oppimateriaaleineen, joka on laajennettavissa muihin yksiköissä sekä kehitettävissä edelleen. Lisäksi saatiin tutkimustuloksia ja kokemuksia, joita voidaan hyödyntää hankkeen jatkokehityksessä.

El reclutamiento de personal en la nueva sociedad informacional

El artículo plantea un análisis empírico sobre las posibilidades de aplicación de las nuevas tecnologías de la información al proceso de reclutamiento de personal. Las competencias sociales y cognitivas que requieren las nuevas formas de organización de la producción plantean nuevos métodos de aprendizaje y la actualización del desarrollo de capacidades y comportamientos. Se trata de renovar y completar las competencias profesionales en un proceso permanente, que implica la adopción de una política de reclutamiento orientada por la consideración del conocimiento como elemento diferenciador de competitividad empresarial y de creación de riqueza.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.