Abstract
Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.

Abstract
Movement quantification of the human body is presently used for analyzing deficits resulting from Central Nervous System (CNS) pathologies or exploring the insights of the human motor system behaviour. Following our previous work on 2D movement quantification of epileptic seizures, we now present a feasibility study for a newly developed 3D technique. In order to validate this new 3D approach we made a comparison with the previous method. Both techniques were tested in two different datasets: a simple motor execution performed by a volunteer and a complex motor motion induced by a real epileptic seizure. The results obtained showed, as expected, the superior robustness and precision of the 3D approach but also confirmed the validity of the 2D method, given certain constraints. We conclude that the newly developed 3D system will highly improve our capacity of pursuing the clinical research on quantitative characterization of seizure semiology to support epilepsy diagnosis.

Abstract
In this article, we detail a system that provides contributes for analyzing and characterizing interactions that occur between participants of online communities. We adapted and applied the Social Network Analysis methodology to online discussion forums to create a dynamical interaction graph. The graph can be embedded in learning managements systems and accessed through a web page. The functionality of the system provides a suitable environment to characterize the interactions between actors and their participations in discussion forums. In the article we describe the use of the system in two real-world situations. Our conclusions lead to the verification and the rapid identification of some important situations that occur in learning communities, such as: the location of actors more or less active; distinction of positions and roles; identification of different ways of organization/interaction in groups; characterization of the interactions of a group or of a community as a whole © 2012 IEEE.

Abstract
Localisation techniques have long been of major importance for safety systems and a lot of research has been conducted in the distributed computing field regarding its functionality and reliability. In the specific scenario of long yet narrow tunnels existing at CERN, localisation methods will enable a number of applications and processes to substantially reduce human intervention. In this article, we evaluate the use of fingerprinting techniques with GSM signal available throughout the LHC tunnel via a radiating cable and compare some methods to estimate the location. In the tests, 16 variants of the K-Nearest Neighbour algorithm, employing different distance weighting methods and fingerprint grouping functions, are taken into consideration and their performance is assessed with a specific rating algorithm. The existing GSM infrastructure and tunnel conditions seem to be favourable to the adoption of these fingerprinting methods. Nevertheless, significant variations in the signal have been observed which might be traced back to the presence of bulky equipment and different operational states of the accelerator. The performance limits of these fingerprinting methods are discussed for the current scenario and, based on that, an outlook for future research is given aiming at improving the system's accuracy under such challenging conditions. © 2012 Copyright Taylor and Francis Group, LLC.

Abstract
Every day, thousands of first responders work to save the lives of others, sometimes without the adequate surveillance of health conditions. The VitalResponder is a project that aims at monitoring and control teams of first responders in emergency scenarios, using mobile technologies to capture and use real-time data to support real-time coordination. In this paper we present a system to capture, process, and display the vital signs of team members, which are made available to a first responders' team leader, for coordination and monitoring. The system addresses specific requirements of the field action, such as the mobility of actors, combining two of the most recent mobile technologies: the iPad (for the coordination view) and Android OS-based smartphones (for real-time sensor data acquisition). © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Abstract
Assessing the quality of the clustering process is fundamental in unsupervised clustering. In literature we can find three different clustering validity techniques: external criteria; internal criteria and relative criteria. In this paper, we focus on external criteria and present an algorithm that allows the implementation of external measures to assess clustering quality when the structure of the data set is unknown. To obtain an automatic partition of a data set and to reflect how documents must be grouped according to human intuition we use internal information present in data like descriptions provide by the users as tags and the distance between documents. The results show an evident correlation between manual and automatic classes indicating it is acceptable to use an automatic partition. In addition to presenting an alternative to finding the structure of the data set using meta-data such as tags, we also wanted to test the impact of their integration in the k-means++ algorithm and verify how it influences the quality of the formed clusters, suggesting a model of integration based on the occurrence of tags in document content. The experimental results indicate a positive impact when external measures are calculated, although there was no apparent correlation between the weight assigned to the tags and the quality of the obtained clusters.