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

Doctoral Program in Computer Science

Avaliação do desempenho de fundos de investimento de ouro nos EUA: influência dos diferentes regimes de mercado na seletividade e market-timing

Dissertação de mestrado em Finanças

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.

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.

Investigations on relational control of time perception

Tese de Doutoramento em Psicologia Básica

Childhood history of abuse and child abuse potential: the role of parent’s gender and timing of childhood abuse

It has been suggested that being physically abused leads to someone becoming a perpetrator of abuse which could be associated to parents' gender, timing of the physical abuse and specific socio-demographic variables. This study aims to investigate the role the parents' gender, timing of childhood abuse and socio-demographic variables on the relationship between parents' history of childhood physical abuse and current risk for children. The sample consisted of 920 parents (414 fathers, 506 mothers) from the Portuguese National Representative Study of Psychosocial Context of Child Abuse and Neglect who completed the Childhood History Questionnaire and the Child Abuse Potential Inventory. The results showed that fathers had lower current potential risk of becoming physical abuse perpetrators with their children than mothers although they did not differed in their physical victimization history. Moreover, the risk was higher in parents (both genders) with continuous history of victimization than in parents without victimization. Prediction models showed that for fathers and mothers separately similar socio-demographic variables (family income, number of children at home, employment status and marital status) predicted the potential risk of becoming physical abuses perpetrators. Nevertheless, the timing of victimization was different for fathers (before 13 years old) and mothers (after 13 years old). Then our study targets specific variables (timing of physical abuse, parents' gender and specific socio-demographic variables), which may enable professionals to select groups of parents at greater need of participating in abuse prevention programs.