Abstract

Abstract
Attention is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner's attention affects learning results and can define the success or failure of a student. The negative effects are especially significant when carrying out long or demanding tasks, as often happens in an assessment. This paper presents a monitoring system using computer peripheral devices. Two classes were monitored, a regular one and an assessment one. Results show that it is possible to measure attentiveness in a non-intrusive way.

Abstract
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).

Abstract
Angiogenesis, the formation of blood vessels from pre-existing ones, is a key event in pathology, including cancer progression, but also in homeostasis and regeneration. As the phenotype of endothelial cells (ECs) is continuously regulated by local biomechanical forces, studying endothelial behaviour in altered gravity might contribute to new insights towards angiogenesis modulation. This study aimed at characterizing EC behaviour after hypergravity exposure (more than 1g), with special focus on cytoskeleton architecture and capillary-like structure formation. Herein, human umbilical vein ECs (HUVECs) were cultured under two-dimensional and three-dimensional conditions at 3g and 10g for 4 and 16 h inside the large diameter centrifuge at the European Space Research and Technology Centre (ESTEC) of the European Space Agency. Although no significant tendency regarding cytoskeleton organization was observed for cells exposed to high g's, a slight loss of the perinuclear localization of beta-tubulin was observed for cells exposed to 3g with less pronounced peripheral bodies of actin when compared with 1g control cells. Additionally, hypergravity exposure decreased the assembly of HUVECs into capillary-like structures, with a 10g level significantly reducing their organization capacity. In conclusion, short-term hypergravity seems to affect EC phenotype and their angiogenic potential in a time and g-level-dependent manner.

Abstract
Built-in sensors in most modern smartphones open multiple opportunities for novel context-aware applications. Although the Human Activity Recognition field seized such opportunity, many challenges are yet to be addressed, such as the differences in movement by people doing the same activities. This paper exposes empirical research on Online Semi-supervised Learning (OSSL), an under-explored incremental approach capable of adapting the classification model to the user by continuously updating it as data from the user's own input signals arrives. Ultimately, we achieved an average accuracy increase of 0.18 percentage points (PP) resulting in a 82.76% accuracy model with Naive Bayes, 0.14 PP accuracy increase resulting in a 83.03% accuracy model with a Democratic Ensemble, and 0.08 PP accuracy increase resulting in a 84.63% accuracy model with a Confidence Ensemble. These models could detect 3 stationary activities, 3 active activities, and all transitions between the stationary activities, totaling 12 distinct activities.

Abstract
Typed models of connector/component composition specify interfaces describing ports of components and connectors. Typing ensures that these ports are plugged together appropriately, so that data can flow out of each output port and into an input port. These interfaces typically consider the direction of data flow and the type of values flowing. Components, connectors, and systems are often parameterised in such a way that the parameters affect the interfaces. Typing such connector families is challenging. This paper takes a first step towards addressing this problem by presenting a calculus of connector families with integer and boolean parameters. The calculus is based on monoidal categories, with a dependent type system that describes the parameterised interfaces of these connectors. As an example, we demonstrate how to define n-ary Reo connectors in the calculus. The paper focusses on the structure of connectors-well-connectedness-and less on their behaviour, making it easily applicable to a wide range of coordination and component-based models. A type-checking algorithm based on constraints is used to analyse connector families, supported by a proof-of-concept implementation.