Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Medir o impacto nas ciências sociais: pontos de referência teóricos

Os modos de fazer pesquisa científica têm vindo a alterar-se, em função das mudanças pelas quais passa o Ensino Superior, assim como os sistemas de ciência e tecnologia, no seu todo. Na Europa vivem-se tempos de grandes questionamentos acerca do modo de proceder relativamente à definição de metodologias de avaliação e/ou financiamento público à investigação individual e/ou coletiva. A tendência geral, neste contexto, é para aperfeiçoar as metodologias de avaliação, através da definição de indicadores que permitam medir as várias dimensões do trabalho científico. No campo específico da avaliação, prevê-se cada vez mais a avaliação do impacto das pesquisas. Ocorre que, apesar do número e da amplitude dos indicadores que vão sendo propostos, a definição de impacto resulta numa tarefa difícil. Não só porque a realidade medida nem toda é perfeitamente traduzível em algo tangível, mas também porque existem diferenças significativas nos modos como as diferentes áreas produzem conhecimento. Neste artigo, pretende-se refletir sobre esse conceito de impacto, nomeadamente ao nível das ciências sociais, evidenciando aquelas que são as principais conclusões e reflexões de estudos e escritos mais recentes.

Task clustering on ETL systems – A pattern-oriented approach

Usually, data warehousing populating processes are data-oriented workflows composed by dozens of granular tasks that are responsible for the integration of data coming from different data sources. Specific subset of these tasks can be grouped on a collection together with their relationships in order to form higher- level constructs. Increasing task granularity allows for the generalization of processes, simplifying their views and providing methods to carry out expertise to new applications. Well-proven practices can be used to describe general solutions that use basic skeletons configured and instantiated according to a set of specific integration requirements. Patterns can be applied to ETL processes aiming to simplify not only a possible conceptual representation but also to reduce the gap that often exists between two design perspectives. In this paper, we demonstrate the feasibility and effectiveness of an ETL pattern-based approach using task clustering, analyzing a real world ETL scenario through the definitions of two commonly used clusters of tasks: a data lookup cluster and a data conciliation and integration cluster.

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.