Abstract
This paper presents the performance analysis of a sensing configuration of refractive index, based on surface plasmon resonance (SPR) in microstructured D-type optical fiber with a thin gold layer, using the finite-element method. The configuration is analyzed in terms of the loss and distribution Poynting vector. The results are compared with a conventional SPR D-type optical fiber sensor for refractive index measurement. The simulation results show an improvement of the sensitivity and resolution (10 x 10(3) nm/RIU and 9.8 x 10(-6) RIU, respectively, when considering an accurately spectral variation detection of 0.1 nm).

Abstract
The rising challenges in the fields of iris and face recognition are leading to a renewed interest in the area. In recent years the focus of research has turned towards alternative traits to aid in the recognition process under less constrained image acquisition conditions. The present work assesses the potential of the periocular region as an alternative to both iris and face in such scenarios. An automatic modeling of SIFT descriptors, regardless of the number of detected keypoints and using a GMM-based Universal Background Model method, is proposed. This framework is based on the Universal Background Model strategy, first proposed for speaker verification, extrapolated into an image-based application. Such approach allows a tight coupling between individual models and a robust likelihood-ratio decision step. The algorithm was tested on the UBIRIS.v2 and the MobBIO databases and presented state-of-the-art performance for a variety of experimental setups.

Abstract
Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire's shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.

Abstract
The current level of technological evolution and the mass use of social networks sites (SNS), both by individual users and by organizations, brings to light a window of opportunity for the use of these technologies in educational activities. By analysing the existing scientific literature that focus on the adoption of SNS both at individual and organizational level, we were able to identify a gap with regard to the use of these technologies for educational purposes, making room to the need of creating a global perspective on the phenomenon through a systematic literature review. In this article we present a global characterization on the adoption of SNS for educational activities, and for that we addressed not only the perspective of educational institutions, but also the perspective of both students and teachers. Some final thoughts on future work are also presented in the final section of the article.

Abstract
Today there are different teams specializing in different areas such as shipwrecked rescue, searching for mines, environmental monitoring, border surveillance, traffic control, search and rescue and harbor protecting. Robotic systems and unmanned vehicles can provide additional capabilities and new innovative solutions that contribute to these applications. This paper presents the Robotic Exercises 2014 (REX'14) and the lessons learned with various field experiments performed with multiple unnamed systems in the context of the Portuguese Navy concept of operations. During the REX'2014 multiple experiments and systems were operated. Autonomy and environment characterization and assessment missions were performed with autonomous surface vehicles such as the ROAZ autonomous surface vehicle or with autonomous underwater vehicle MARES. Autonomy and system validation was performed for fast water jet propelled surface systems such as the SWIFT autonomous surface vehicle and the ICARUS unmanned rescue capsule, wind propulsion tests were also performed with unnamed surface vehicles and new maritime wireless communication protocols were tested.

Abstract
Ensemble learning algorithms often benefit from pruning strategies that allow to reduce the number of individuals models and improve performance. In this paper, we propose a Metalearning method for pruning bagging ensembles. Our proposal differs from other pruning strategies in the sense that allows to prune the ensemble before actually generating the individual models. The method consists in generating a set characteristics from the bootstrap samples and relate them with the impact of the predictive models in multiple tested combinations. We executed experiments with bagged ensembles of 20 and 100 decision trees for 53 UCI classification datasets. Results show that our method is competitive with a state-of-the-art pruning technique and bagging, while using only 25% of the models.