Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

“Learning-by-producing” in the fashion value chain

Proceedings da AUTEX 2015, Bucareste, Roménia.

La formación b-learning en el máster de español segunda lengua y lengua extranjera de la Universidade do Minho: innovación pedagógica y desafíos educativos

En el actual marco de creciente innovación pedagógica, debido entre otros factores, a la irrupción de nuevas herramientas informáticas, la enseñanza a distancia (e-learning y/o b-learning) va ocupando cada vez más espacio en la oferta educativa de diversas instituciones. En esta dirección, en la Universidade do Minho, y concretamente en el Área de Estudos Espanhóis e Hispano-Americanos,[1] hemos dedicado considerables esfuerzos a la ampliación de nuestra oferta desde 2010: primero en la elaboración e implementación del Curso de Formación Especializada en Español Lengua Extranjera, modalidad b-learning (3 ediciones; 2010-2013), y, actualmente, con el Máster Universitario en Español Lengua Segunda / Lengua Extranjera (vid. www.melsle.ilch.uminho.pt), también b-learning. En las siguientes páginas, nos proponemos compartir una serie de experiencias y reflexiones que han ido surgiendo durante estos años acerca de la formación universitária de profesores de Español Lengua Extranjera, en general, con recurso a la modalidade b-learning; para ello, nos centraremos en los siguientes aspectos: (i) caracterización general y problematización de la enseñanza a distancia en la Universidade do Minho; (ii) descripción del Máster Universitario en Español Lengua Segunda / Lengua Extranjera, acerca del cual detallaremos algunas prácticas adoptadas, relacionadas com la enseñanza e-learning como, por ejemplo, (iii) la coordinación pedagógica o (iv) los enfoques metodológicos adoptados a partir de la experiencia de una Unidad Curricular concreta.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

O currículo da formação em engenharia no âmbito do processo de bolonha: desenvolvimento de competências e perfil profissional na perspetiva dos docentes, dos estudantes e dos profissionais

Tese de Doutoramento em Ciências da Educação (área de especilização em Desenvolvimento Curricular).

As universidades e a participação pública em ciência. Perceções e práticas de cientistas, profissionais de comunicação e cidadãos em Portugal e Espanha

Tese de doutoramento em Ciência da Comunicação.

PDMS encasing system for integrated lab-on-chip Ag/AgCl reference electrodes

Poly(dimethylsiloxane) (PDMS) is an organosilicon polymer widely used in the fabrication of microfluidic systems to integrate biochips. In this study, we propose the use of an adapted PDMS mould for the creation of a miniaturized, reusable, reference electrode for in-chip electrochemical measurements. Through its integrated microfluidic system it is possible to replenish internal buffer solutions, unclog critical junctions and treat the electrode’s surface, assuring a long term reuse of the same device. Planar Ag/AgCl reference electrodes were microfabricated over a passivated p-type Silicon Wafer. The PDMS mould, containing an integrated microfluidic system, was fabricated based on patterned SU-8 mould, which includes a lateral horizontal inlet access point. Surface oxidation was used for irreversible permanent bondage between flat surfaces. The final result was planar Ag/AgCl reference electrode with integrated microfluidic that allows for electrochemical analysis in biochips

An integrated model for simulation of construction phasing of arch concrete dams

Dissertação de mestrado integrado em Civil Engineering

“Learning through Radio, Learning for Life!”: Notas sobre o desenvolvimento de uma rádio participativa online

(Excerto) Olhando para o percurso do RadioActive, há uma ideia que parece ser transversal a todo o projeto. Referimo-nos a um princípio que chamaríamos de “identificação” e que foi determinante – é determinante – nos processos de investigação participativa. Falamos da identificação dos investigadores com os princípios da investigação-ação, da identificação das intervenções com as particularidades de cada contexto. Da imprescindível e progressiva identificação dos participantes com o projeto. Na verdade, sem esta multifacetada identificação é impossível pensar em resultados sustentáveis e persistentes. Investigadores e demais participantes têm de sentir que o projeto é “seu”, que os objetivos são “seus”, embora o façam necessariamente a velocidades diferentes. A aprendizagem, neste âmbito, expande-se sempre de dentro para fora, emerge dos interesses do sujeito e não de uma estrutura pré-concebida e imposta pelos que chegam (Ravenscroft et al., 2011), neste caso, os investigadores. Uma das diferenças das pesquisas participativas em relação às tradicionais é, precisamente, a atuação coletiva e não solitária do investigador. Os pesquisadores fazem parte de um processo participatório em que estão envolvidos numa estrutura (Cammarota & Fine, 2008: 5). Paulo Freire é o autor primordial em todos os projetos e países onde a RA101 foi aplicada. As suas concepções em torno da investigação-ação participativa tentam apontar sempre para uma ação e também para uma reflexão sobre os processos.

Longitudinal evaluation, acceptability and long-term retention of knowledge on a horizontally integrated organic and functional systems course

Undergraduate medical education is moving from traditional disciplinary basic science courses into more integrated curricula. Integration models based on organ systems originated in the 1950s, but few longitudinal studies have evaluated their effectiveness. This article outlines the development and implementation of the Organic and Functional Systems (OFS) courses at the University of Minho in Portugal, using evidence collected over 10 years. It describes the organization of content, student academic performance and acceptability of the courses, the evaluation of preparedness for future courses and the retention of knowledge on basic sciences. Students consistently rated the OFS courses highly. Physician tutors in subsequent clinical attachments considered that students were appropriately prepared. Performance in the International Foundations of Medicine examination of a self-selected sample of students revealed similar performances in basic science items after the last OFS course and 4 years later, at the moment of graduation. In conclusion, the organizational and pedagogical approaches of the OFS courses achieve high acceptability by students and result in positive outcomes in terms of preparedness for subsequent training and long-term retention of basic science knowledge.

Avaliação de desempenho no Theatro Circo: criação de um Balanced Scorecard

Projeto de mestrado em Estudos de Gestão

A utilização de materiais didáticos diversificados como estratégia de motivação

Relatório de estágio de mestrado em Ensino da Filosofia no Ensino Secundário