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

Doctoral Program in Computer Science

Ontological representation of tumor-node-metastasis classification and an ontology-driven classifier: a study on colorectal cancer

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

Refinement and variability techniques in model transformation of software requirements

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Maternal attachment representations in relation to emotional availability and discipline behaviour

"Published online: 15 Sep 2015."

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

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.

Intervention program on adolescent’s creativity representations and academic motivation

Creativity and its promotion are widespread concerns in education. However, few efforts have been made to implement intervention programs designed to promote creativity and other related aspects (e.g., academic motivation). The Future Problem Solving Program International (FPSPI), aimed for training creativity representations and creative problem solving skills in young people, has been one of the most implemented programs. This intervention’s materials and activities were adapted for Portuguese students, and a longitudinal study was conducted. The program was implemented during four months, in weekly sessions, by thirteen teachers. Teachers received previous training for the program and during the program’s implementation. Intervention participants included 77 Basic and Secondary Education students, and control participants included 78 equivalent students. Pretest-posttest measures of academic motivation and creativity representations were collected. Results suggest a significant increase, in the intervention group, in motivation and the appropriate representations of creativity. Practical implications and future research perspectives are presented.

Development of a methodology to incorporate risk and uncertainty in electricity power planning

Dissertação de mestrado em Engenharia Industrial

3D representation of the urban evolution of Braga using the cityengine tool

The morphological evolution of the city of Braga has been the subject of several studies focusing on different urban areas in different periods. Using the accumulated knowledge provided by the available archaeological, historical and iconographic data of Braga, from the Roman times to the nineteenth century, we intend to present a working methodology for 3D representation of urban areas and its evolution, using the CityEngine ESRI tool. Different types of graphic and cartographic data will be integrated in an archaeological information system for the characterization of urban buildings. Linking this information system to the rules of characterization of urban spaces through the CityEngine tool, we can create the 3D urban spaces and their changes. The building characterization rules include several parameters of architectural elements that can be dynamically changed according the latest information. This methodology will be applied to the best known areas within of the city allowing the creation of different and dynamic layouts. Considerations about the concepts, challenges and constraints of using the CityEngine tool for recording and representing urban evolution knowledge will be discussed.

Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)

Foreword [to Digital literacy, technology and social inclusion: making sense of one-to-one computer programmes around the world]

(Excerto) In times past, learning to read, write and do arithmetic was to get on course to earn the “writ of emancipation” in society. These skills are still essential today, but are not enough to live in society. Reading and critically understanding the world we live in, with all its complexity, difficulties and challenges, require not only other skills (learning to search for and validate information, reading with new codes and grammar, etc) but, to a certain extent, also metaskills, matrixes and mechanisms that are transversal to the different and new literacies, are necessary. They are needed not just to interpret but equally to communicate and participate in the little worlds that make up our everyday activities as well as, in a broader sense, in the world of the polis, which today is a global world.

Media(ted) discourse and society: rethinking the framework of critical discourse analysis

The analysis of journalistic discourse and its social embeddedness has known significant advances in the last two decades, especially due to the emergence and development of Critical Discourse Analysis. However, three important aspects remain under-researched: the time plane in discourse analysis, the discursive strategies of social actors, and the extra- and supra-textual effects of mediated discourse. Firstly, understanding the biography of public matters requires a longitudinal examination of mediated texts and their social contexts but most forms of analysis of journalistic discourse do not account for the time sequence of texts and its implications. Secondly, as the media representation of social issues is, to a large extent, a function of the discursive construction of events, problems and positions by social actors, the discursive strategies that they employ in a variety of arenas and channels ‘‘before’’ and ‘‘after’’ journalistic texts need to be examined. Thirdly, the fact that many of the modes of operation of discourse are extra- or supra-textual calls for a consideration of various social processes ‘‘outside’’ the text. This paper aims to produce a theoretical and methodological contribution to the integration of these issues in discourse analysis by proposing a framework that combines a textual dimension with a contextual one

Priming of a DNA metabarcoding approach for species identification and inventory in marine macrobenthic communities

Dissertação de mestrado em Molecular Genetics

Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.