Abstract
Due to its general usage in modern buildings, WiFi has been studied as a promising good low-cost solution to build localization services, but many other approaches are possible. Both industry and academy are putting considerable efforts in studying affordable and accurate alternatives, and no de facto standard exists yet. While technological diversity can be good in terms of flexibility, allowing distinct solutions to be used in distinct contexts according to accuracy needs or cost constraints, it is also a big obstacle to the goal of building a global positioning system. So one important requirement that has yet to be addressed is this diversity, allowing for multiple localization systems to be easily plugged into a standard and global localization service. In this paper, a solution to build a global localization service able to integrate various heterogeneous localization systems is presented and discussed. A prototype implementation built on top of DNS is proposed. The DNS subtree structure, as well as the Resource Records able to store the localization information, are presented and justified. The results obtained from prototype testing have proven that DNS is a valid and good candidate to support a global Localization Service.

Abstract
Identifying the user's intent behind a query is a key challenge in Information Retrieval. This information may be used to contextualize the search and provide better search results to the user. The automatic identification of queries targeting a search for health information allows the implementation of retrieval strategies specifically focused on the health domain. In this paper, two kinds of automatic methods to identify and classify health queries based on domain-specific terminology are proposed. Besides evaluating these methods, we compare them with a method that is based on co-occurrence statistics of query terms with the word "health". Although the best overall result was achieved with a variant of the co-occurrence method, the method based on domain-specific frequencies that generates a continuous output outperformed most of the other methods. Moreover, this method also allows the association of queries to the semantic tree of the Unified Medical Language System and thereafter their classification into appropriate subcategories.

Abstract
CAO is a domain-specific imperative language for cryptography, offering a rich mathematical type system and crypto-oriented language constructions. We describe the design and implementation of a deductive verification platform for CAO and demonstrate that the development time of such a complex verification tool could be greatly reduced by building on the Jessie plug-in included in the Frama-C framework. We discuss the interesting challenges raised by the domain-specific characteristics of CAO, and describe how we tackle these problems in our design. We base our presentation on real-world examples of CAO code, extracted from the open-source code of the NaCl cryptographic library, and illustrate how various cryptography-relevant security properties can be verified.

Abstract
Non-instrumental dimensions, the aspects of a product that go beyond its ability to help achieve goals efficiently, are increasingly important in User Experience (UX) research. These dimensions, which include qualities like aesthetics and symbolism, are mainly assessed by self-reports, research has shown. However, respondents can provide wrong answers, willingly, due to concerns like social desirability and self-presentation, or unwillingly, due to the inability to access their inner states. We explored if one implicit measuring method, the Implicit Association Test (IAT), can be used to complement or replace self-report measures. Participants completed six IATs and explicit measures to determine their attitudes toward products represented by pictures of their interfaces. Two non-instrumental dimensions were assessed: valence and self-identification. Overall, implicit and explicit measures displayed a medium correlation. When comparing the correlations between the IATs for the two assessed dimensions and the corresponding explicit measures, similar strong effects were found. This suggests that the IAT bears further exploration as a complement or alternative to selfreport methods. Copyright © IARIA, 2014.

Abstract
In this paper it is proposed an interrogation system based on OTDR for fiber loop mirror intensity sensors. The system has been characterized in order to obtain its maximum dynamic range. The technique demonstrated good linearity with a - 13.3 dB/mm slope. A 0.027 mm resolution was achieved. The proposed interrogation system permits multiplexing of around 10 sensors and showed to be an alternative technique for multiplexing and remote sensing.

Abstract
Automated tracking of axonal neurotransmitter vesicles is a challenging problem in neuroscience. The present vesicle tracking is typically performed manually over confocal microscopy images. NeuronDynamics is a method designed to automate and speed-up the characterization of global vesicle movement in neurons while yielding high accuracy and precision results (similar or better than expert clinicians). For a set of fluorescent-marked vesicles "films", NeuronDynamics performs a two stage approach: 1) Training: the system asks the user to mark a set of vesicles and the position of the cellular body; 2) Detection & tracking: based on the previous training, the system runs a Bayesian classifier over the image sequence to detect and classify vesicles and their movements (speed and direction). The obtained results were compared to another state-of-the-art method (FluoTracker), and were found greatly higher in accuracy, sensitivity, specificity and precision. Although NeuronDynamics is a semi-automated process, it is significantly faster than manual tracking and can be adapted to be used for similar approaches for other biological samples.