Abstract
Typed models of connector/component composition specify interfaces describing ports of components and connectors. Typing ensures that these ports are plugged together appropriately, so that data can flow out of each output port and into an input port. These interfaces typically consider the direction of data flow and the type of values flowing. Components, connectors, and systems are often parameterised in such a way that the parameters affect the interfaces. Typing such connector families is challenging. This paper takes a first step towards addressing this problem by presenting a calculus of connector families with integer and boolean parameters. The calculus is based on monoidal categories, with a dependent type system that describes the parameterised interfaces of these connectors. As an example, we demonstrate how to define n-ary Reo connectors in the calculus. The paper focusses on the structure of connectors-well-connectedness-and less on their behaviour, making it easily applicable to a wide range of coordination and component-based models. A type-checking algorithm based on constraints is used to analyse connector families, supported by a proof-of-concept implementation.

Abstract
The reduction or elimination of incentives for the installation of decentralized generation directly at the customers' premises, favoring self-consumption, can bring significant changes for distribution network operation. According to the new Portuguese law, injection of energy into the distribution grid is discouraged since prosumers receive only 90% of the energy cost in the Iberian Energy Market. In order to lower energy bills, the possibility of storing excess energy is being considered as a possible solution. In this paper, an optimization framework is proposed to model the operation of consumers with renewable-based Distributed Generation (DG) and storage capacity and assess their aggregated effect at the level of the MV grid using a multi-temporal Optimal Power Flow (OPF). The proposed algorithm is then tested in a real Portuguese MV network to evaluate its performance. Finally, a financial viability analysis is performed considering the installation of small PV generators and storage devices at the residential level.

Abstract
This paper proposes a novel case-based reasoning (CBR) approach to support the intelligent management of energy resources in a residential context. The proposed approach analyzes previous cases of consumption reduction in houses, and determines the amount that should be reduced in each moment and in each context, in order to meet the users' needs in terms of comfort while minimizing the energy bill. The actual energy resources management is executed using the SCADA House Intelligent Management (SHIM) system, which schedules the use of the different resources, taking into account the suggested reduction amount. A case study is presented, using data from Brazilian consumers. Several scenarios are considered, representing different combinations concerning the type of house/inhabitants, the season, the type of used energy tariff, the use of Photovoltaic system (PV) generation, and the maximum amount of allowed reduction. Results show that the proposed CBR approach is able to suggest appropriate amounts of energy reduction, which result in significant reductions of the energy bill, while, with the use of SHIM, minimizing the reduction of users' comfort. © 2016 IEEE.

Abstract
Data has increased in a large scale in various fields leading to the coin of the term Big Data. Big data is mainly used to describe enormous datasets that typically includes masses of unstructured data that may need real-time analysis. As human behaviour and personality can be captured through human-computer interaction a massive opportunity opens for providing wellness services. Through the use of interaction data, behavioral biometrics can be obtained. The usage of biometrics has increased due to several factors such as the rise of power and availability of computational power. One of the challenges in this kind of approaches has to do with handling the acquired data. The growing volumes, variety and velocity brings challenges in the tasks of pre-processing, storage and providing analytics. In this sense, the problem can be framed as a Big Data problem. In this work it is intended to provide an architecture that accommodates the data pipeline of data generated by human-computer interaction, providing real time data analytics on behavioral biometrics.

Abstract
Satellite altimetry allows the study of sea-level long-term variability on a global and spatially uniform basis. Here quantile regression is applied to derive robust median regression trends of mean sea level as well as trends in extreme quantiles from radar altimetry time series. In contrast with ordinary least squares regression, which only provides an estimate on the rate of change of the mean of data distribution, quantile regression allows the estimation of trends at different quantiles of the data distribution, yielding a more complete picture of long-term variability. Trends derived from basin-wide averaged regional mean sea level time series are robust and similar for all quantiles, indicating that all parts of the data distribution are changing at the same rate. In contrast, trends are not robust and diverge across quantiles in the case of local time series. Trends are under- (over-)estimated in the western (eastern) equatorial Pacific. Furthermore, trends in the lowermost quantile (0.05) are larger than the median trend in the western Pacific, while trends in the uppermost quantile (0.95) are lower than the median trend in the eastern Pacific. These differences in trends in extreme mean sea level quantiles are explained by the exceptional effect of the strong 1997-1998 El Nino-Southern Oscillation (ENSO) event.

Abstract
Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.