Abstract
Applications developed over the cloud coordinate several, often anonymous, computational resources, distributed over different execution nodes, within flexible architectures. Coordination models able to represent quantitative data provide a powerful basis for their analysis and validation. This paper extends IMCReo, a semantic model for Stochastic Reo based on interactive Markov chains, to enhance its scalability, by regarding each channel and node, as well as interface components, as independent stochastic processes that may (or may not) synchronise with the rest of the coordination circuit.

Abstract

Abstract
Many water boiler manufacturers are not able to detect the occurrence of failures in the machines they produce before they can pose inconvenience and sometimes danger for costumers and workers. Moreover, the number of boilers that have to be monitored, are many times in the range of the thousands or even millions, proportionaly to the number of costumers a company possesses. The detection of these failures in real time, would provide a significant improvement to the perception that consumers have of a certain company, since, if these failures occur, maintenance services can be deployed almost as soon as a failure happens. In this paper, an application prototype capable of monitoring and preventing failures in domestic water boilers, on the y, is presented. This application evaluates measurements which are performed by sensors within the boilers, and identifies the ones that greatly differ from those received previously, as new data arrives, detecting tendencies which might illustrate the occurrence of a failure. The incremental local outlier factor is used with an approach based on the interquatile range measure to detect the outlier factors that should be analysed.

Abstract
There is a demand for new models of computation intelligence for the recognition of the environment and the obstacles in each moment and the sharing of this information with other users providing temporary dangers notifications, which can enhance blind navigation experience and autonomy. We identified an opportunity to contribute with an integrated strategy to develop a solution to improve the blind autonomy and quality of life. We are looking for solutions to problems that have emerged from the accumulated experience in blind navigation systems research. The main objective of this paper is to present a conceptual model that works based on data obtained from sensors on passive monitoring, worn by bystanders that can combine and correlate the inference patterns that match the obstacles and/or dangers. The model has retro-feedback mechanisms, allowing the sensors to search and pervasively validate the existence of obstacle, ensuring the temporary basis of these risks.

Abstract
In Public Transportation systems, accurate representation of information has been key for users to take the more advantages of the services and fulfil their travel needs. The Spider Map is a particular schematic representation to illustrate all travel possibilities from a given geographical location. Recently the Spider Map interaction and generation process automation have been studied, although there are yet different possibilities to explore in the technological approach and interactive solution fields. This research proposes the Cobweb solution, dissecting the components of an Interactive Spider Map, focusing on the interactive dynamic potential, the ways to represent information, challenging the existing standard. An implementation of this solution is evaluated using carefully designed user tests to validate the design decisions, taking into consideration different interaction alternatives for each phase of the map generation. The results show improvements regarding the traditional alternative, with positive user response, valuing highly context awareness and information features.

Abstract
In this paper an identification method for statespace LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure.