Abstract
Games are a part of human life since ancient times, present not only at childhood but throughout most of our adult life. A growing area of research focuses on the development of games for teaching and learning in various areas of expertise. These are called serious games. They intend to capture the attention, to motivate and to encourage user engagement through the use of recreational and entertainment elements, thus facilitating the learning process. This article describes iLearnTest, a framework for developing serious online games which are capable of supporting education of several subjects, allowing for a suitable training of participants, thus preparing them to meet the job market needs. This paper describes the architecture of the framework, the structure of the game and presents some results from performed experiments as to validate the overall approach. (C) 2016 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
Smart grids play an important role in the modernization and optimization of the existing electrical grid, to accomplish the current European Union Energy and Climate targets. Smart grids require distributed applications to manage the grid more efficiently. The performance of the distributed applications impacts on the communications delay time and on the timely interaction with the devices located in the users' Home Area Networks. This paper presents the results of the ENCOURAGE project related to the development of a software platform to support smart grids. The work presented in this paper assesses four different middleware configurations and analyses the results on the delay performance tests. The results show that the mean end-to-end delay is between 310 ms and 453 ms in proper conditions. In terms of operational costs, the optimal configuration enables managing houses with less than 0.25 Euros per month per house. This paper justifies the maturity of the technology to support smart grids, and the possibility to transfer the ENCOURAGE project results to the industry.

Abstract
Thresholding on coherence is a common practice for identifying the surface scatterers that are less affected by decorrelation noise during post-processing and visualisation of the results from multi-temporal InSAR techniques. Simple selection of the points with coherence greater than a specific value is, however, challenged by the presence of spatial dependence among observations. If the discrepancies in the areas of moderate coherence share similar behaviour, it appears important to take into account their spatial correlation for correct inference. Low coherence areas thus could serve as clear indicators of measurement noise or imperfections in mathematical models. Once exhibiting properties of statistical similarity, they allow for detection of observations that could be considered as outliers and trimmed from the dataset. In this paper we propose an approach based on renowned data mining and exploratory data analysis procedures for mitigating the impact of outlying observations in the final results.

Abstract
Anurans (frogs or toads) are closely related to the ecosystem and they are commonly used by biologists as early indicators of ecological stress. Automatic classification of anurans, by processing their calls, helps biologists analyze the activity of anurans on larger scale. Wireless Sensor Networks (WSNs) can be used for gathering data automatically over a large area. WSNs usually set restrictions on computing and transmission power for extending the network's lifetime. Deep Learning algorithms have gathered a lot of popularity in recent years, especially in the field of image recognition. Being an eager learner, a trained Deep Learning model does not need a lot of computing power and could be used in hardware with limited resources. This paper investigates the possibility of using Convolutional Neural Networks with Mel-Frequency Cepstral Coefficients (MFCCs) as input for the task of classifying anuran sounds.

Abstract
There are some obstacles, towards a paperless office. One of them is the collection of signatures, since nearly half of all documents are printed for the sole purpose of collecting them. Digital signatures can have the same legal evidential validity as handwritten signatures, provided they are based on certificates issued by accredited certification authorities and the associated private keys are stored on tamper proof token security devices like smart cards. In this article, we propose a platform for secure digital signature workflow management that integrates secure token based digital signatures with the Enterprise Content Management Alfresco, where each user can associate a set of smart cards to his account. The documents can then be signed with the citizen card or other smart card that has digital signatures capabilities. We have implemented an Alfresco module that allows us to explore several workflow techniques to implement real task secure digital signatures workflows, as people for example do when they pass a paper document between various departments to be signed. Since all users can see the current state of the documents being signed during the entire signage process, important security properties like system trust are preserved. We also describe an external validation web service, that provides a way for users to validate signed documents. The validation service then shows to the user important document security properties like timestamps, certificates attributes and highlights the document integrity in face of the digital signatures that have been collected in the workflows defined by our module in Alfresco.