Abstract
O movimento é primordial para o desenvolvimento multilateral da criança e através dos jogos e brincadeiras contribui para a melhora das relações interpessoais. Já sua ausência pode limitar o desenvolvimento motor e influenciar em características da personalidade, cognição e emoções. O jogo constitui-se como a essência e razão da infância que através do seu universo imaginário pode melhorar sua percepção corporal, linguagem, autoeficácia, autoestima e autoconfiança. A autoconfiança é uma característica essencial da vida, já que se trata da capacidade de se ver bem-sucedido. Assim, o indivíduo confiante tem uma atitude positiva, desafia-se e assume riscos. O objetivo do estudo foi identificar a presença de capacidades empreendedoras, nomeadamente a autoconfiança em crianças do 1 ciclo do ensino Básico. Participaram no estudo 18 alunos (9 sexo feminino e 9 do sexo masculino) e 19 alunos (9 do sexo feminino e 10 do sexo masculino) do 3 ano de escolaridade de uma escola da área urbana de Braga, com idades entre 6 e 9 anos. Foi utilizado como instrumento um jogo construído e organizado, dividido em equipas que deveriam escolher entre 3 percursos constituídos por barreiras colocadas em diferentes alturas. Ao concluir o percurso foi atribuído pontuações de acordo com a dificuldade, sendo para o mais fácil 1 e 2 pontos, médio 2 e 3 pontos e mais difícil 3 e 4 pontos. Ao término do jogo a pontuação foi contada em voz alta pelo mediador para definir a equipe vencedora. Foram utilizadas análises descritivas para os comportamentos observados e atribuída as seguintes classificações: i) sim, verifica-se o comportamento; ii) Não, verifica-se comportamento oposto e iii) Não observado, não se verifica o comportamento. Os resultados em relação a subcategoria: I - Não se intimida mesmo havendo a possibilidade de ser confrontado com opiniões diferentes, foi constatado que a maioria das crianças apresentavam comportamentos, sejam eles positivos ou negativos. As meninas parecem ficar menos incomodadas com a possibilidade de serem confrontadas pelos colegas, relativamente a idade tanto as crianças de 6/7 quanto as de 8/9 apresentaram comportamentos positivos, sendo no mais novos onde mais comportamentos não observados ocorreram. Para a subcategoria II- Tem uma atitude positiva e confiante, foram observados resultados similares, sendo que a maioria das crianças apresentaram comportamento positivo, para ambos os géneros e referente a idade um pequeno destaque para os alunos mais velhos. Desta forma, a autoconfiança foi observada em contexto do jogo pois os comportamento foram maioritariamente positivos. para além de essencial na componente social e emocional, a autoconfiança é também uma características essenciais do perfil empreendedor.

Abstract
Catalytic computation, defined by Buhrman, Cleve, Koucký, Loff and Speelman (STOC 2014), is a space-bounded computation where in addition to our working memory we have an exponentially larger auxiliary memory which is full; the auxiliary memory may be used throughout the computation, but it must be restored to its initial content by the end of the computation. Motivated by the surprising power of this model, we set out to study the non-deterministic version of catalytic computation. We establish that non-deterministic catalytic log-space is contained in ZPP, which is the same bound known for its deterministic counterpart, and we prove that non-deterministic catalytic space is closed under complement (under a standard derandomization assumption). Furthermore, we establish hierarchy theorems for non-deterministic and deterministic catalytic computation. © Harry Buhrman, Michal Koucký, Bruno Loff, and Florian Speelman; licensed under Creative Commons License CC-BY.

Abstract
Epilepsy diagnosis is typically performed through 2Dvideo-EEG monitoring, relying on the viewer's subjective interpretation of the patient's movements of interest. Several attempts at quantifying seizure movements have been performed in the past using 2D marker-based approaches, which have several drawbacks for the clinical routine (e.g. occlusions, lack of precision, and discomfort for the patient). These drawbacks are overcome with a 3D markerless approach. Recently, we published the development of a single-bed 3Dvideo-EEG system using a single RGB-D camera (Kinect v1). In this contribution, we describe how we expanded the previous single-bed system to a multi-bed departmental one that has been managing 6.61 Terabytes per day since March 2016. Our unique dataset collected so far includes 2.13 Terabytes of multimedia data, corresponding to 278 3Dvideo-EEG seizures from 111 patients. To the best of the authors' knowledge, this system is unique and has the potential of being spread to multiple EMUs around the world for the benefit of a greater number of patients. © 2018 European Federation for Medical Informatics (EFMI) and IOS Press.

Abstract

Abstract
Reducing the dimensionality of datasets is a fundamental step in the task of building a classification model. Feature selection is the process of selecting a smaller subset of features from the original one in order to enhance the performance of the classification model. The problem is known to be NP-hard, and despite the existence of several algorithms there is not one that outperforms the others in all scenarios. Due to the complexity of the problem usually feature selection algorithms have to compromise the quality of their solutions in order to execute in a practicable amount of time. Parallel computing techniques emerge as a potential solution to tackle this problem. There are several approaches that already execute feature selection in parallel resorting to synchronous models. These are preferred due to their simplicity and capability to use with any feature selection algorithm. However, synchronous models implement pausing points during the execution flow, which decrease the parallel performance. In this paper, we discuss the challenges of executing feature selection algorithms in parallel using asynchronous models, and present a feature selection algorithm that favours these models. Furthermore, we present two strategies for an asynchronous parallel execution not only of our algorithm but of any other feature selection approach. The first strategy solves the problem using the distributed memory paradigm, while the second exploits the use of shared memory. We evaluate the parallel performance of our strategies using up to 32 cores. The results show near linear speedups for both strategies, with the shared memory strategy outperforming the distributed one. Additionally, we provide an example of adapting our strategies to execute the Sequential forward Search asynchronously. We further test this version versus a synchronous one. Our results revealed that, by using an asynchronous strategy, we are able to save an average of 7.5% of the execution time.

Abstract
Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the Proplm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance-up to 80% faster-than its original algorithm, regardless of the number of attributes, objects and densities