Abstract
Faced with the need to quantify software (un)reliability in the presence of faults, the semantics of state-based systems is urged to evolve towards quantified (e.g. probabilistic) nondeterminism. When one is approaching such semantics from a categorical perspective, this inevitably calls for some technical elaboration, in a monadic setting. This paper proposes that such an evolution be undertaken without sacrificing the simplicity of the original (qualitative) definitions, by keeping quantification implicit rather than explicit. The approach is a monad lifting strategy whereby, under some conditions, definitions can be preserved provided the semantics moves to another category. The technique is illustrated by showing how to introduce probabilism in an existing software component calculus, by moving to a suitable category of matrices and using linear algebra in the reasoning. The paper also addresses the problem of preserving monadic strength in the move from original to target (Kleisli) categories, a topic which bears relationship to recent studies in categorial physics.

Abstract
In this paper we present a surface plasmon resonance (SPR) based fiber sensor for measurement of refractive index (RI) at different concentration of glycerol and acetone. The sensing head of fiber probe was fabricated by depositing aluminum (Al) on the unclad portion of multi-mode fiber (MMF). The experimental result shows the sensitivity obtained in power measurement of SPR fiber probe was -106.95 dBm/RIU and -408.90 dBm/RIU for glycerol and acetone. Due to the small size and good sensitivity low cost of our SPR based fiber sensor have many commercial and practical uses. © 2015 IEEE.

Abstract
Markov Logic is an expressive and widely used knowledge representation formalism that combines logic and probabilities, providing a powerful framework for inference and learning tasks. Most Markov Logic implementations perform inference by transforming the logic representation into a set of weighted propositional formulae that encode a Markov network, the ground Markov network. Probabilistic inference is then performed over the grounded network. Constructing, simplifying, and evaluating the network are the main steps of the inference phase. As the size of a Markov network can grow rather quickly, Markov Logic Network (MLN) inference can become very expensive, motivating a rich vein of research on the optimization of MLN performance. We claim that parallelism can have a large role on this task. Namely, we demonstrate that widely available Graphics Processing Units (GPUs) can be used to improve the performance of a state-of-the-art MLN system, Tuffy, with minimal changes. Indeed, comparing the performance of our GPU-based system, TuGPU, to that of the Alchemy, Tuffy and RockIt systems on three widely used applications shows that TuGPU is up to 15x times faster than the other systems.

Abstract
In this paper it is presented a music application for people with intellectual disabilities, called "Professor Piano". We created this application to be a solution for music education for this group of people. For that we present the development and implementation of the app. We choose the virtual piano and the mobile devices as the basis for our solution. It was conducted an assessment of the current status and features of mobile applications also using this paradigm, from which we concluded that, currently, there is not a virtual piano application oriented to people with intellectual disabilities so we design, develop and tested a new application, the "Professor Piano". To validate the "Professor Piano" application approach, we evaluated the application usage by a group of people with intellectual disabilities, without having too much user experience with mobile technologies, with the aim to measure the effectiveness, efficiency and satisfaction. We registered the following variables: success in a conclusion of a level (effectiveness); the percentage of correct notes played versus all notes of that level (efficiency); and the motivation at the end of the experience (satisfaction). The results obtained shows the interest and motivation of the users in playing with the application. In the four tests, three persons completed and wanted to continue the testing experience. This results also shows the importance of using an intuitive design and also of displaying the score at the end of each level, giving an extra boost to the user to replay or advance to the next level.

Abstract
Many spreadsheets in the wild do not have documentation nor categorization associated with them. This makes difficult to apply spreadsheet research that targets specific spreadsheet domains such as financial or database. We introduce with this paper a methodology to automatically classify spreadsheets into different domains. We exploit existing data mining classification algorithms using spreadsheet-specific features. The algorithms were trained and validated with cross-validation using the EUSES corpus, with an up to 89% accuracy. The best algorithm was applied to the larger Enron corpus in order to get some insight from it and to demonstrate the usefulness of this work.

Abstract