Abstract
PVSio-web is a graphical environment for facilitating the design and evaluation of interactive (human-computer) systems. Using PVSio-web, one can generate and evaluate realistic interactive prototypes from formal models. PVSio-web has been successfully used over the last two years for analyzing commercial, safety-critical medical devices. It has been used to create training material for device developers and device users. It has also been used for medical device design, by both formal methods experts and non-technical end users. This paper presents the latest release of PVSio-web 2.0, which will be part of the next PVS distribution. The new tool architecture is discussed, and the rationale behind its design choices are presented. © Springer International Publishing Switzerland 2015.

Abstract
In a multienergy system, there are different types of dependencies among the energy carriers. Internal dependencies refer to possible changes in the energy source in the presence of energy converters and storage, and are managed by the system operator through the control strategies applied to the equipment. External dependencies (EDs) are due to the choice of the energy supply according to customer preferences when alternative solutions are available. This paper introduces a new model of EDs within a multigeneration representation based on energy hubs. EDs are addressed through a stochastic model in order to take into account the possible uncertainty in the customers' decisions. This model is then used to introduce carrier-based demand response (DR) in which the user participates in DR programs aimed at promoting the shifting among different energy sources by preserving the service provided to the end users. The results obtained from the new model in deterministic and stochastic cases indicate the appropriateness and usefulness of the proposed approach.

Abstract
Metaheuristics are very useful to achieve good solutions in reasonable execution times. Sometimes they even obtain optimal solutions. However, to achieve near-optimal solutions, the appropriate tuning of parameters is required. This paper presents a Racing based learning module proposal for an autonomous parameter tuning of Metaheuristics. After a literature review on Metaheuristics parameter tuning and Racing approaches, the learning module is presented. A computational study for the resolution of the Scheduling problem is also presented. Comparing the preliminary obtained results with previous published results allow to conclude about the effectiveness and efficiency of this proposal. © 2015 AISTI.

Abstract
Standard Work is a very useful tool of the Lean Production paradigm to specify standards and to establish the best methods and sequences for each process and for each worker, helping to reduce wastes. Despite its usefulness, this tool is often underused, neglected and misunderstood. Apart from that, there is a lack of literature about Standard Work. It is possible to find many papers on Lean Production in general and on some Lean tools, but there are few that focus on or describe in detail this particular tool. The main purpose of this paper is to prove the validity and show the importance and the applicability of Standard Work. The main aspects and ideas to understand this tool are described, as well as the benefits of applying it in a real world manufacturing environment. In this paper, an application example is given on a production unit of an elevators company, following the Action-Research methodology. The main findings are that Standard Work is effectively a good tool to normalize work procedures, allowing increased flexibility and production, and decreased wastes and assembly errors.

Abstract
Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. The project VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6125 features extracted with openSMILE toolkit plus 160 Teager Energy Operator (TEO) features. We use a Mutual Information (MI) filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.4% for generalisation accuracy.

Abstract
This paper addresses the problem of bottom following by the MARES Autonomous Underwater Vehicle, and presents derivation of a controller able to cope with the peculiarities of such problem. In specific, the main requirement for the controller is the existence of no overshoot both in the depth and pitch outputs of the system. The existence of such time-domain requirements motivates the use of Eigenstructure Assignment techniques in the formulation of the controller. Simulation results obtained with a dynamic model of the MARES AUV are presented and discussed, indicating the validity of the proposed approach.