Da 'portugalidade' à lusofonia

Tese de Doutoramento em Ciências da Comunicação (Especialidade em Teoria da Cultura)

Grupos de facto enquanto fenómeno organizativo plurissocietário: (in)admissibilidade da aplicação analógica dos artigos 501º e 502º do CSC aos grupos de facto qualificados

Dissertação de mestrado em Direito dos Contratos e das Empresas

Late-medieval construction site management at the Monastery of Jerónimos in Lisbon

Through the analysis of the exceptional accounting documents of 1517 related to the construction of the Monastery of Jerónimos (Lisbon), this paper discusses the main characteristics of a new model of construction site organization. In the later Middle Ages we can find, among others, two main models of constructing site organization. One, older and more widespread, consisted in a centralized and pyramidal management model. The other, apparently more recent, was based in the existence of several autonomous teams working simultaneously, each one responsible for building a specific part or section of the building. This paper describes and discusses this new organizational model as it was adopted and implemented by João de Castilho (1470–1552) for the construction of the Monastery of Jerónimos in 1517, probably for the first time in Portugal, but with some parallels in other places in Europe.

A expressão dramática em contexto escolar: um estudo de caso com alunos do 1º ciclo do ensino básico na Região Autónoma da Madeira

Tese de Doutoramento em Estudos da Criança (área de especialização em Educação Dramática).

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.