Abstract
During the next decades the refurbishment of old buildings will be an essential way to contribute to the global improvement of buildings energy performance indicators. Within this context, the present paper is focused on the use of electrochromic (EC) windows, an emerging technology alternative to shading devices, to control solar gains in buildings located in Mediterranean climates. The optical properties adjustments of the EC glasses are discussed based on the incident solar radiation. The ESP-r building energy simulation software was used to study the energy savings resulting from the application of electrochromic windows, considering the comparison of several windows solutions (single, double-glazing and EC windows) and windows orientations (East, South and West). In addition, different transition ranges for the optical properties of the EC glasses are assessed through the analysis of the energy needs for space heating and cooling. The main conclusion is that EC technology is an effective option in cooling dominated buildings. The impact of EC windows is highly dependent on facade orientation, being a valid option particularly in the cases of the East and West facades. For these facades, the control set point found to be effective corresponds to an incident solar radiation on the glass of 150 W/m(2) to impose a total coloured state. For the South facade the results show no significant advantage of using EC windows.

Abstract
Ensuring the effectiveness factor of usability consists in ensuring that the application allows users to reach their goals and perform their tasks. One of the few means for reaching this goal relies on task analysis and proving the compatibility between the interactive application and its task models. Synergistic execution enables the validation of a system against its task model by co-executing the system and the task model and comparing the behavior of the system against what is prescribed in the model. This allows a tester to explore scenarios in order to detect deviations between the two behaviors. Manual exploration of scenarios does not guarantee a good coverage of the analysis. To address this, we resort to model based testing (MBT) techniques to automatically generate scenarios for automated synergistic execution. To achieve this, we generate, from the task model, scenarios to be co-executed over the task model and the system. During this generation step we explore the possibility of including considerations about user error in the analysis. The automation of the execution of the scenarios closes the process. We illustrate the approach with an example

Abstract
Health care practices increasingly rely on complex technological infrastructure, and new approaches to the integration of information and communication technology in those practices lead to the development of such concepts as integrated clinical environments and smart intensive care units. These concepts refer to hospital settings where therapy relies heavily on inter-operating medical devices, supervised by clinicians assisted by advanced monitoring and co-ordinating software. In order to ensure safety and effectiveness of patient care, it is necessary to specify the requirements of such socio-technical systems in the most rigorous and precise way. This paper presents an approach to the formalization of system requirements for communication networks deployed in integrated clinical environment, based on the higher-order logic language of a theorem-proving environment, the Prototype Verification System.

Abstract
We investigate learning of flexible robot locomotion controllers, i.e., the controllers should be applicable for multiple contexts, for example different walking speeds, various slopes of the terrain or other physical properties of the robot. In our experiments, contexts are desired walking linear speed of the gait. Current approaches for learning control parameters of biped locomotion controllers are typically only applicable for a single context. They can be used for a particular context, for example to learn a gait with highest speed, lowest energy consumption or a combination of both. The question of our research is, how can we obtain a flexible walking controller that controls the robot (near) optimally for many different contexts? We achieve the desired flexibility of the controller by applying the recently developed contextual relative entropy policy search(REPS) method which generalizes the robot walking controller for different contexts, where a context is described by a real valued vector. In this paper we also extend the contextual REPS algorithm to learn a non-linear policy instead of a linear policy over the contexts which call it RBF-REPS as it uses Radial Basis Functions. In order to validate our method, we perform three simulation experiments including a walking experiment using a simulated NAO humanoid robot. The robot learns a policy to choose the controller parameters for a continuous set of forward walking speeds.

Abstract

Abstract
Taxi trip duration affects the efficiency of operation, the satisfaction of drivers, and, mainly, the satisfaction of the customers, therefore, it is an important metric for the taxi companies. Especially, knowing the predicted trip duration beforehand is very useful to allocate taxis to the taxi stands and also finding the best route for different trips. The existence of hyperconnected network can help to collect data from connected taxis in the city environment and use it collaboratively between taxis for a better prediction. As a matter of fact, the existence of high volume of data, for each individual taxi, several models can be generated. Moreover, taking into account the difference between the data collected by taxis, this data can be organized into different levels of hierarchy. However, finding the best level of granularity which leads to the best model for an individual taxi could be computationally expensive. In this paper, the use of metalearning for addressing the problem of selection of the right level of the hierarchy and the right algorithm that generates the model with the best performance for each taxi is proposed. The proposed approach is evaluated by the data collected in the Drive-In project. The results show that metalearning helps the selection of the algorithm with the best performance.