Language and Proofs for Higher-Order SMT (Work in Progress)

Satisfiability modulo theories (SMT) solvers have throughout the years been able to cope with increasingly expressive formulas, from ground logics to full first-order logic modulo theories. Nevertheless, higher-order logic within SMT is still little explored. One main goal of the Matryoshka project, which started in March 2017, is to extend the reasoning capabilities of SMT solvers and other automatic provers beyond first-order logic. In this preliminary report, we report on an extension of the SMT-LIB language, the standard input format of SMT solvers, to handle higher-order constructs. We also discuss how to augment the proof format of the SMT solver veriT to accommodate these new constructs and the solving techniques they require.Comment: In Proceedings PxTP 2017, arXiv:1712.0089

Utilização de um sistema multi-modal para a recolha de informação afetiva

Recognizing, interpreting and processing emotions (Affective Computing) is an emerging field of computer science. Multiple methods of data acquisition and emotion classification exist with different accuracy performances. Despite this, multimodal systems, generally have a higher accuracy than unimodal ones. This dissertation’s goal is to research the current methods of both affective data gathering and emotion classification while developing a multi-modal system, that focuses primarily on the utilisation of non-intrusive methods with potential application in cccupational stress. The system has the purpose of collecting affective data including multiple data gathering methods such as mouse and keyboard utilisation data, ECG data, face and upper body video recordings and computer screen video recordings (for activity detection). For the emotion classification, the Clustering and Random Forest algorithms were utilised. In the exploratory study with the already existent SWELL investigation dataset, we tested the algorithm of Random Forest and an overall accuracy of 89.97% was achieved, which we considered acceptable. In order to validate the final system, a study with eleven participants was conducted. An overall error rate of approximately 65% was achieved with the Random Forest algorithm. For the majority of the participants, the Clustering algorithm did not recognize most of the data above 3% in class 2. The participants also reported in the questionnaires an overall decrease in the stress felt. Therefore, it is possible that the proposed protocol did not induce the desired emotional state (stress) in the participants. The developed multimodal system is functional and can be utilised in other studies with emotional markings gathering.O reconhecimento, a interpretação e o processamento de emoções (Affective Computing) é uma área emergente das aplicações computacionais. Existem vários métodos de aquisição de dados e de classificação de emoções, com precisões distintas, em que os sistemas multimodais apresentam geralmente uma precisão mais elevada do que os unimodais. Nesta dissertação, procuramos investigar os métodos atualmente usados para recolher informação afetiva bem como métodos para a análise da mesma, tendo em vista uma proposta de um sistema multimodal, com foco em métodos não-intrusivos, com potencial aplicação na monitorização de stress ocupacional. O sistema desenvolvido tem como objectivo a recolha de informação afetiva, incluindo várias fontes de dados, como informação sobre utilização do rato e do teclado, dados ECG, vídeo da face e gravações de vídeo do ecrã do computador (para deteção de atividades). Para a classificação de emoções, foram utilizados os algoritmos de Clustering e de Random Forest. Num estudo exploratório, usando o dataset de investigação SWELL, testámos o algoritmo de Random Forest e obtivemos uma precisão global de 89.97% na classificação, o que considerámos satisfatória, uma vez que é comparável com os resultados apresentados na literatura. O sistema desenvolvido foi testado num conjunto de onze participantes. Globalmente, o algoritmo de Random Forest obteve uma taxa de erro de 65%. O algoritmo de Clustering testado não classificou acima de 3% dos dados na classe 2. Quando se avaliaram os questionários de avaliação do estado emocional (aplicados antes e depois do teste ao sistema), verificou-se que os participantes reportaram um decremento na ansiedade sentida depois da realização do estudo. O que pode indicar que o protocolo de recolha de dados apresentado pode não ter induzido os estados emocionais pretendidos (stress) nos participantes O sistema multimodal encontra-se funcional e pode ser aplicado em outros estudos para recolha de marcadores de emoções.Mestrado em Engenharia de Computadores e Telemátic

Automatic segmentation of the second cardiac sound by using wavelets and hidden Markov models

This paper is concerned with the segmentation of the second heart sound (S2) of the phonocardiogram (PCG), in its two acoustic events, aortic (A2) and pulmonary (P2) components. The aortic valve (A2) usually closes before the pulmonary valve (P2) and the delay between these two events is known as “split” and is typically less than 30 miliseconds. S2 splitting, reverse splitting or reverse occurrence of components A2 and P2 are the most important aspects regarding cardiac diagnosis carried out by the analysis of S2 cardiac sound. An automatic technique, based on discrete wavelet transform and hidden Markov models, is proposed in this paper to segment S2, to estimate de order of occurrence of A2 and P2 and finally to estimate the delay between these two components (split). A discrete density hidden Markov model (DDHMM) is used for phonocardiogram segmentation while embedded continuous density hidden Markov models are used for acoustic models, which allows segmenting S2. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second.Centre Algoritm

Proposal of a deterministic model to explain swimming performance.

Swimming is one of the most challenging sports to investigate. Since long, swimming practitioners base their decisions in scientific evidences. It is known that several scientific domains have a significant role in the swimming performance, such as the “Biomechanics”, “Physiology”, “Anthropometrics”, “Motor Control” and “Muscle strength and conditioning”. The nowadays trend in swimming research is the “Interdisciplinary assessment”, which is related to the “holistic approach”. In Sport Sciences, and especially in Biomechanics, a re-new interest also emerged in the last few years for the design and development of deterministic models. Merging both concepts (i.e., “holistic thinking” and “deterministic models”) there is a chance to expand a deterministic model for competitive swimming, including several other scientific domains besides the Biomechanics. With this it is possible to have a deeper understanding of the variables that determine swimming and how they interplay to enhance performance. The aim of this paper was two-folds: (i) to make a revision and an update of the state of the art about the relationships between swimming biomechanics with performance, energetics, anthropometrics, motor control, muscle strength and conditioning; (ii) to design the deterministic model of such relationships