Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Diffusional kurtosis imaging using a fast heuristic constrained linear least squares algorithm: a plugin for OsiriX

Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation

Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

About using serious games to teach (Portuguese) sign language

Sign language is the form of communication used by Deaf people, which, in most cases have been learned since childhood. The problem arises when a non-Deaf tries to contact with a Deaf. For example, when non-Deaf parents try to communicate with their Deaf child. In most cases, this situation tends to happen when the parents did not have time to properly learn sign language. This dissertation proposes the teaching of sign language through the usage of serious games. Currently, similar solutions to this proposal do exist, however, those solutions are scarce and limited. For this reason, the proposed solution is composed of a natural user interface that is intended to create a new concept on this field. The validation of this work, consisted on the implementation of a serious game prototype, which can be used as a source for learning (Portuguese) sign language. On this validation, it was first implemented a module responsible for recognizing sign language. This first stage, allowed the increase of interaction and the construction of an algorithm capable of accurately recognizing sign language. On a second stage of the validation, the proposal was studied so that the pros and cons can be determined and considered on future works.

Improving Learners’ Motivation through Feedback in English Language Teaching and Learning at Polytechnic School in Benguela, Angola

Currently, it is widely perceived among the English as a Foreign Language (EFL) teaching professionals, that motivation is a central factor for success in language learning. This work aims to examine and raise teachers’ awareness about the role of assessment and feedback in the process of language teaching and learning at polytechnic school in Benguela to develop and/or enhance their students’ motivation for learning. Hence the paper defines and discusses the key terms and, the techniques and strategies for an effective feedback provision in the context under study. It also collects data through the use of interview and questionnaire methods, and suggests the assessment and feedback types to be implemented at polytechnic school in Benguela

The Visual and Virtual in Contemporary European Learning Contexts

Special issue of Anthropology in Action originated from the Working Images Conference, a joint meeting of TAN and VAN EASA networks

Learning new tricks: Corticostriatal dynamics during novel skill learning

Learning novel actions and skills is a prevalent ability across multiple species and a critical feature for survival and competence in a constantly changing world. Novel actions are generated and learned through a process of trial and error, where an animal explores the environment around itself, generates multiple patterns of behavior and selects the ones that increase the likelihood of positive outcomes. Proper adaptation and execution of the selected behavior requires the coordination of several biomechanical features by the animal. Cortico-basal ganglia circuits and loops are critically involved in the acquisition, learning and consolidation of motor skills.(...)

Definição do conceito de blended learning. Proposta metodológica no quadro da terminologia de base conceptual

Sustentado numa abordagem conceptual da Terminologia, o trabalho de investigação que a seguir se desenvolve visa propor um cenário de resposta à questão: como se define o blended learning no contexto educativo de Ensino Superior Pós-Bolonha? A necessidade de compreender, delimitar e harmonizar o conceito de blended learning no panorama actual do Ensino Superior, resulta do pressuposto de que muito embora proliferem descrições de práticas e modelos para a sua operacionalização de reconhecida qualidade - tal como sucede com outros conceitos sob alçada da educação a distância - a reflexão teórica é ainda insuficiente. Com efeito, para responder à questão supra-colocada, propõe-se o desenho de uma metodologia para construção de uma definição intensional do conceito de blended learning que herde, subsuma e melhore o conhecimento existente, identificado através da análise de texto para fins onomasiológicos e de um processo de elicitação de conhecimento tácito e de negociação discursiva junto de sujeitos especializados. A proposta de desenho metodológico que neste trabalho de esboça escora-se globalmente em três etapas: (1) etapa exploratória do domínio-objecto de estudo; (2) etapa de análise onamasiológica de evidência textual e discursiva; (3) etapa de modelização e de validação de resultados. Pretende-se, em primeiro lugar, através do estudo do espaço conceptual das modalidades de educação que se situam no continuum presença-distância sistematizar e ordenar as visões analisadas, propondo representações de educação presencial, educação a distância, e-learning, educação online, aprendizagem enriquecida por tecnologias e ainda de outras modalidades emergentes. As representações assumem o carácter de proposta aberta e decorrem da necessidade de uma primeira ordenação no sentido de topografrar, delineando - a um nível macro - o possível lugar do conceito de blended learning naquele cenário. Num segundo momento, aprofunda-se e circunscreve-se a análise, depurando a evidência observada, agora reduzida a um conjunto de contextos ricos em informação conceptual – um corpus escrito e oral de definições e descrições de blended learning – identificando candidatas a características essenciais e candidatas a características acidentais. Num terceiro momento, procede-se à modelização do conhecimento, encapsulando-a numa proposta de definição sujeita a um processo iterativo e reflexivo, constituído por um conjunto de ciclos de investigação-acção, os quais reflectem a sequência de interacções entre o terminólogo e sujeitos especializados. Defender-se-á que a experimentação deste desenho revela a produtividade de uma sequência cíclica entre a análise textual e discursiva para fins onomasiológicos, a interacção colaborativa e a introspecção. Por outras palavras, embora a natureza do estudo realizado não permita a generalização, para além da relação diádica de mediação que o terminológo estabelece com o especialista, defende-se a produtividade de um procedimento de acção-reflexão autónomo, solitário e introspectivo, no âmbito da qual o terminólogo se afirma como sujeito conceptualizador, decisor e interventor. Resultam deste percurso uma proposta de definição e de descrição de blended learning em língua portuguesa que acreditamos poder servir diferentes actores da comunidade académica envolvida neste domínio de especialidade.

Management consulting labs-Galp loyalty learning experience

The purpose of this project was to analyze Galp’s loyalty approach in the Portuguese fuel market given the industry context, namely the entry of hypermarket and the resulting increase in competitiveness. The team performed analyses based on analytical models, qualitative research and internal interviews in order to assess Galp’s potential in the field of loyalty and consumers’ behavior. The final recommendations were based on incremental improvements to the Galp’s existing loyalty tool and an innovative paradigm change of the approach to loyalty.

EDP - A new estimation algorithm

The aim of this work project is to analyze the current algorithm used by EDP to estimate their clients’ electrical energy consumptions, create a new algorithm and compare the advantages and disadvantages of both. This new algorithm is different from the current one as it incorporates some effects from temperature variations. The results of the comparison show that this new algorithm with temperature variables performed better than the same algorithm without temperature variables, although there is still potential for further improvements of the current algorithm, if the prediction model is estimated using a sample of daily data, which is the case of the current EDP algorithm.

Implementação de um Sistema b-Learning na Escola Secundária do Nambambe-Lubango

Esta dissertação tem como objecto de estudo a implementação de um sistema b-learning na Escola Secundaria do Nambambe -Lubango, uma vez que a instituição reúne condições para a sua materialização, ainda que não a tenha. Por isso, pensámos fazer um estudo pormenorizado sobre o tema, a fim de descrever e explicar as condições existentes para a sua implantação. O Sistema a ser utilizado é o Moodle, com todas as suas ferramentas e potencialidades, nomeadamente: o fórum e a avaliação, que poderão complementar as actividades presenciais com maior rigor e originalidade. A metodologia empregue é o estudo de caso com vista a explicar a implantação do b-learning que é a modalidade aceite para ser efectuada nesta instituição, a julgar pelas vantagens que ela oferece e os benefícios que trará para o processo de ensino-aprendizagem, mormente para os professores e alunos em termos de tecnologia, informação e conhecimento, libertando-se da infoexclusão digital. Com a pesquisa bibliográfica referente à temática e aplicação de um questionário dirigido aos professores e alunos que, depois de analisados os dados, podemos concluir que os inqueridos revelam competências básicas para a execução do projecto e os benefícios que este sistema oferecer para qualquer aprendente, tanto durante a vida como ao longo da vida.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.

Blogues. Reflexões em ambientes de e-learning pela prática

Esta dissertação tem como referência o trabalho que realizamos no Ministério Público de Pernambuco, onde temos a oportunidade de observar e conhecer os serviços de acolhimento institucional para crianças e adolescentes, bem como da atuação de outros atores que atuam nesta área de medida protetiva, no Estado de Pernambuco. Aqui articularemos esta prática com os achados históricos, teóricos e legais vigentes em busca de estabelecer estratégias e ações de intervenção, para atuarmos neste contexto que geograficamente tem pontos de articulações distantes. Buscamos neste estudo analisar todos os lados de uma construção para a execução de uma plataforma que possa oferecer um serviço de capacitação em e-learning aos profissionais que atuam na área protetiva. Para isto precisaremos identificar o perfil destes profissionais e reconhecer que se, mesmo diante das dificuldades de sua profissão, eles se dispõem em participar de capacitação on-line voltado para a prática. Analisou-se o blog como um sistema adequado para se trabalhar com e-learning recorrendo à literatura para identificar quais os critérios, parâmetros e indicadores que faz um blog institucional de qualidade. Dando corpo ao nosso intento, faremos um percurso pelas teorias da aprendizagem buscando consistência para investigarmos se novas abordagens como o Conectivismo, responde ou não aos nossos anseios de formação continuada na prática e pela prática.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.