Abstract

Abstract
The provision of gerontological care in rural areas represents an increased management challenge. Framed by the Portuguese Northeast reality, this paper reflects on the role and potential of pervasive and mobile computing in the management of gerontological care, specially in rural areas, explaining the potential of fusion between gerontology and technology and presents a conceptual model to frame it. Finally, it presents a software prototype developed for Android smartphones, capable of assist a gerontological care provider in some of their operational practices.

Abstract
UML Sequence Diagrams are used in different domains for specifying the required behaviour of software-based systems. However, the created diagrams are often used only as documentation, and not as a basis for generating subsequent lifecycle artifacts or for automated analysis. Several authors have proposed the transformation of Sequence Diagrams to executable Coloured Petri Nets (CPN), for simulation and testing purposes, but the transformations are not automated or are implemented in an ad-hoc way. To overcome those limitations, we present in this paper an approach to automatically translate Sequence Diagrams to CPN ready for execution with CPN Tools, taking advantage of model-to-model transformation techniques provided by the Eclipse Modelling Framework (EMF). The transformation rules are implemented in the Epsilon Transformation Language. We use the standard UML metamodel provided by EMF and the CPN metamodel provided by CPN Tools, so any Sequence Diagram created with an EMF compliant modelling tool can be transformed. An application example is presented to better illustrate the approach. Copyright

Abstract

Abstract
We propose a new computational model, the scaffolding automaton, which exactly characterises the computational power of parsing expression grammars (PEGs). Using this characterisation we show that: PEGs have unexpected power and semantics. We present several PEGs with surprising behaviour, and languages which, unexpectedly, have PEGs, including a PEG for the language of palindromes whose length is a power of two.PEGs are computationally “universal”, in the following sense: take any computable function; then there exists a computable function such that has a PEG.There can be no pumping lemma for PEGs. There is no total computable function A with the following property: for every well-formed PEG G, there exists such that for every string of size the output is in and has |x|.PEGs are strongly non real-time for Turing machines. There exists a language with a PEG, such that neither it nor its reverse can be recognised by any multi-tape online Turing machine which is allowed to do only steps after reading each input symbol. © 2018, Springer Nature Switzerland AG.

Abstract
An efficient traffic-network is an essential demand for any smart city. Usually, city traffic forms a huge network with millions of locations and trips. Traffic flow prediction using such large data is a classical problem in intelligent transportation system (ITS). Many existing models such as ARIMA, SVR, ANN etc, are deployed to retrieve important characteristics of traffic-network and for forecasting mobility. However, these methods suffer from the inability to handle higher data dimensionality. The tensor-based approach has recently gained success over the existing methods due to its ability to decompose high dimension data into factor components. We present a modified Tucker decomposition method which predicts traffic mobility by approximating very large networks so as to handle the dimensionality problem. Our experiments on two big-city traffic-networks show that our method reduces the forecasting error, for up to 7 days, by around 80% as compared to the existing state of the art methods. Further, our method also efficiently handles the data dimensionality problem as compared to the existing methods. © 2018, Springer Nature Switzerland AG.