Abstract
This paper describes the EmbodiMentor, an interaction concept and metaphor that aims to enable users to embody a different person or character's perspective, specify or modify his/her/its emotional elements and conditioning elements, and experience the resulting changes. Its use case scenario is the education and training of foreign languages and intercultural communication skills, were contextualization and first person experiences in common settings are key for practical skill acquisitions. It was born as the microscience-fiction prototype "Frances can't sleep. She crawls out of bed and with her EmbodiMentor runs through a range of a client's emotional states, pitching to each one. She then falls asleep." The application of the science fiction prototyping concept has been proven a strong approach to develop and investigate innovative applications of emerging technologies. © 2016 ACM.

Abstract
We address optimal control problems for nonlinear systems with pathwise state-constraints. These are challenging nonlinear problems for which the number of discretization points is a major factor determining the computational time. Also, the location of these points has a major impact in the accuracy of the solutions. We propose an algorithm that iteratively finds an adequate time-grid to satisfy some predefined error estimate on the obtained trajectories, which is guided by information on the adjoint multipliers. The obtained results show a highly favorable comparison against the traditional equidistant spaced time grid methods, including the ones using discrete time models. This way, continuous time plant models can be directly used. The discretization procedure can be automated and there is no need to select a priori the adequate time step. Even if the optimization procedure is forced to stop in an early stage, as might be the case in real time problems, we can still obtain a meaningful solution, although it might be a less accurate one. The extension of the procedure to a Model Predictive Control (MPC) context is proposed here. By defining a time dependent accuracy threshold, we can generate solutions that are more accurate in the initial parts of the receding horizon, which are the most relevant for MPC.

Abstract
This paper presents an architecture that supports Quality of Service (QoS) in an Arrowhead-compliant System of Systems (SoS). The Arrowhead Framework supports local cloud functionalities for automation applications, provided by means of a Service Oriented Architecture (SOA), by offering a number of services that ease application development. On such applications the QoS guarantees are required for service fruition, and are themselves requested as services from the framework. To fulfil this objective we start by describing the Arrowhead architecture and the components needed to dynamically in run-time negotiate a system configuration that guarantees the QoS requirements between application services.

Abstract
This work describes SituMan (Situation Manager), a mobile system that makes use of the sensors commonly included in most mobile platforms and a fuzzy inference engine to attempt to infer user context and environment. Such "situation" information, has been used to enhance the behaviour of MoodBuster, another mobile application used in the scope of the mental health domain to collect Ecological Momentary Assessments (EMA). EMA has been used in psychotherapy to minimize the effects of recall bias in the assessment of patient mood, as well as in the recollection of other experiences and behaviours. SituMan can enhance the user experience in the scope of EMA by prompting users in the desired situation, instead of at random or fixed-times, thus reducing obtrusiveness. It can also provide new insight to mental health professionals by summarizing the situations experienced by the patient, further allowing correlation of situation information with patient mood within the same time frame.

Abstract
Usually, a Big Data system has a monitoring system for performance evaluation and error prevention. Although, there are some disadvantages in the way that these tools display the information and its targeted approach to physical components. The main goal is to study visual and interaction mechanisms that allow the representation of monitoring data in grid computing environments, providing the end-user information which can contribute objectively to the system analysis. This paper has the purpose to present the state of the art, carries out an intermediate evaluation of the current work and present the proposed solution.

Abstract
The Web is frequently used as a way to access health information. In the health domain, the terminology can be very specific, frequently assuming a medico-scientific character. This can be a barrier to users who may be unable to understand the retrieved documents. Therefore, it would be useful to automatically assess how well a certain document will be understood by a certain user. In the present work, we analyse whether it is possible to predict the comprehension of documents using document features together with user features, and how well this can be achieved. We use an existing dataset, composed by health documents on the Web and their assessment in terms of comprehension by users, to build two multivariate prediction models for comprehension. Our best model showed very good results, with 96.51% accuracy. Our findings suggest features that can be considered by search engines to estimate comprehension. We found that user characteristics related to web and health search habits, such as the success of the users with Web search and the frequency of the users' health search, are some of the most influential user variables. The promising results obtained with this dataset with manual comprehension assessment will lead us to explore the automatic assessment of document and user characteristics. (C) 2016 The Authors. Published by Elsevier B.V.