Abstract
Usually, Aspect-Oriented Programming (AOP) languages are an extension of a specific target language (e.g., AspectJ for Java and AspectC++ for C++). This coupling can impose drawbacks such as arbitrary limitations to the aspect language. LARA is a DSL for source-to-source transformations inspired by AOP concepts, and has been designed to be independent of the target language. In this paper we propose techniques to overcome some of the challenges presented by a language-independent approach to source code transformations, and present and discuss possible solutions and their impact. Additionally, we present some of the benefits and opportunities of this approach. We present an evaluation of our approach, show that we can significantly reduce the effort to develop weavers for new target languages and that the proposed techniques contribute to more concise LARA aspects and safer semantics. Copyright 2017 ACM.

Abstract
Technological evolution has led to a significant transformation in tourism organizations, particularly in those who focus their activities on particular themes or segments, such as wine tourism. This can be transposed to Portuguese wine tourism organizations because the majority lack the necessary information and communication technologies (and inherent technologies) to become globally competitive. As highlighted in the literature, for a tourism experience to become memorable it must be emotional and immersive in such a way that the tourist becomes fully involved with the existing surroundings. This leads to the notion of using virtual reality experiences as triggers for the development of wine tourism. Considering the relevance of Portugal's Douro Valley to the country's wine tourism segment, a theoretical model that supports the implementation of multisensory (hence more immersive) virtual wine tourism experiences is developed. While considering the international success of Port wine tourism, this paper also presents a conceptualization of a multisensory virtual Port wine experience that includes a conceptual perspective and a technological solution proposal.

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Integrating wind and solar energy resources poses intermittency to power systems, which faces independent system operators with new technical and economic challenges. This study proposes a novel model to integrate the uncertainties of wind power on the supply side and roof-top solar photovoltaic (PV) on the demand side. To cope with their uncertainties, a demand response (DR) aggregator is proposed, which is enabled to participate in reserve markets. To this end, a new DR model is developed considering both customers' options to reduce and increase load through the DR aggregator. As such, besides improving the existing DR models (load shifting and curtailment), two DR programmes, i.e. load growth and load recovery, are mathematically modelled. Numerical studies indicate the effectiveness of the proposed model to reduce the total operation cost of the system and facilitate the integration of wind power and roof-top PV.

Abstract
Deep transfer learning emerged as a new paradigm in machine learning in which a deep model is trained on a source task and the knowledge acquired is then totally or partially transferred to help in solving a target task. In this paper, we apply the source-target-source methodology, both in its original form and an extended multi-source version, to the problem of cross-sensor biometric recognition. We tested the proposed methodology on the publicly available CSIP image database, achieving state-of-the-art results in a wide variety of cross-sensor scenarios.