Learning Robotics for youngsters - the RoboParty experience

Series: "Advances in intelligent systems and computing , ISSN 2194-5357, vol. 417"

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

Álbuns narrativos e aprendizagens no pré-escolar. Um estudo de caso

Tese de Doutoramento em Estudos da Criança (área de especialização em Literatura para a Infância).

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.

Spatial-temporal modellization of the NO2 concentration data through geostatistical tools

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO2 levels, by using geostatisti- cal approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This method- ology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

O professor como construtor do currículo: integração da tecnologia em atividades de aprendizagem de matemática

O desenvolvimento profissional dos professores de matemática, por meio de progra¬mas nacionais e formações contínuas, deve proporcionar experiências que envolvam investigação, pensamento, planeamento, prática e reflexão. No caso da tecnologia, não nos devemos focar nas ferramentas em si, mas no modo como são usadas pelos docentes em contexto de sala de aula. Existem taxonomias de atividades de apren¬dizagem baseadas no conteúdo assentes na ideia do professor como construtor do currículo, que, para integrar com sucesso a tecnologia educativa nas aulas, desenvolve o conhecimento pedagógico e tecnológico do conteúdo (TPACK), e apresenta-se a de matemática. Desse modo, reflete-se, por meio de vários estudos nacionais e internacionais, que as tecnologias deverão ser usadas pelos professores de acordo com objetivos, conteúdos e pedagogias específicas para terem um efeito positivo na aprendizagem dos alunos sobre as atividades baseadas no conteúdo que melhor se enquadram com essas tecnologias.

“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.

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

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Cognitive functioning and efficacy of cognitive intervention in Multiple Sclerosis

Tese de Doutoramento em Psicologia Clínica / Psicologia

A network approach to brain aging through multimodal neuroimaging

Tese de Doutoramento em Ciências da Saúde.

Fueling the bio-economy: european culture collections and microbiology education and training

A survey of European Microbial Biological Resource Centers and their users provided an overview on Microbiology education and training. The results identified future increases in demand despite several shortcomings and gaps in the current offer. Urgent adjustments are needed to match users' needs, integrate innovative programs, and adopt new technologies.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering