Abstract
Frequently software testing tends to be neglected at the beginning of the projects, only performed on the late stage. However, it is possible to benefit from combining requirement with testing specification activities. On one hand, acceptance tests specification will require less manual effort since they are defined or generated automatically from the requirements specification. On the other hand, the requirements specification itself will end up having higher quality due to the use of a more structured language, reducing typical problems such as ambiguity, inconsistency and incorrectness. This research proposes an approach that promotes the practice of tests specification since the very beginning of projects, and its integration with the requirements specification itself. It is a model-driven approach that contributes to maintain the requirements and tests alignment, namely between requirements, test cases, and low-level automated test scripts. To show the applicability of the approach, two complementary languages are adopted: the ITLingo RSL that is particularly designed to support both requirements and tests specification; and the Robot language, which is a low-level keyword-based language for the specification of test scripts. The approach includes model-to-model transformation techniques, such as test cases into test scripts transformations. In addition, these test scripts are executed by the Robot test automation framework.

Abstract
The segmentation of retinal vessels in fundus images has been heavily focused in the past years, given their relevance in the diagnosis of several health conditions. Even though the recent advent of deep learning allowed to foster the performance of computer-based algorithms in this task, further improvement concerning the detection of vessels while suppressing background noise has clinical significance. Moreover, the best performing state-of-the-art methodologies conduct patch-based predictions. This, put together with the preprocessing techniques used in those methodologies, may hinder their use in screening scenarios. Thus, in this paper, we explore a fully convolutional setting that takes raw fundus images and allows to combine patch-based training with global image prediction. Our experiments on the DRIVE, STARE and CHASEDB1 databases show that the proposed methodology achieves state-of-the-art performance in the first and the last, allowing at the same time much faster segmentation of new images.

Abstract
A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth's crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto-rheological dampers. The inclusion of magneto-rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain-emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto-rheological actuator such that the building's frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levels.

Abstract
Automated negotiation plays a crucial role in the decision support for bilateral energy transactions. In fact, an adequate analysis of past actions of opposing negotiators can improve the decision-making process of market players, allowing them to choose the most appropriate parties to negotiate with in order to increase their outcomes. This paper proposes a new model to estimate the expected prices that can be achieved in bilateral contracts under a specific context, enabling adequate risk management in the negotiation process. The proposed approach is based on an adaptation of the Q-Learning reinforcement learning algorithm to choose the best scenario (set of forecast contract prices) from a set of possible scenarios that are determined using several forecasting and estimation methods. The learning process assesses the probability of occurrence of each scenario, by comparing each expected scenario with the real scenario. The final chosen scenario is the one that presents the higher expected utility value. Besides, the learning method can determine which is the best scenario for each context, since the behaviour of players can change according to the negotiation environment. Consequently, these conditions influence the final contract price of negotiations. This approach allows the supported player to be prepared for the negotiation scenario that is the most probable to represent a reliable approximation of the actual negotiation environment.

Abstract

Abstract
Urban living labs (ULL) are a new concept which involves users in innovation and development and are regarded as a way of meeting the innovation challenges faced by information and communication technology (ICT) service providers. The chapter focuses on the role of urban living labs in entrepreneurship, energy and governance of smart cities, where it is performed the relationship between innovations, governance, and renewable energy. The methodology proposed will focus on content analysis and on the exploration of some European examples of implemented ULL, namely Amsterdam, Helsinki, Stockholm and Copenhagen. The contributions of the present research should be the consolidation of knowledge about the impact of ULL on innovation and development of smart cities regarding the concepts of renewable energy, smart governance and entrepreneurship.