Abstract
The incredible appeals of smartphones and the unprecedented progress in the development of mobile and wireless networks in recent years have enabled ubiquitous availability of myriad media contents. Consequently, it has become problematic for mobile users to find relevant media items. However, context awareness has been proposed as a means to help mobile users find relevant media items anywhere and at any time. The contribution of this paper is the presentation of a context-aware media recommendation framework for smart devices (CAMR). CAMR supports the integration of context sensing, recognition, and inference, using classification algorithms, an ontology-based context model and user preferences to provide contextually relevant media items to smart device users. This paper describes CAMR and its components, and demonstrates its use to develop a context-aware mobile movie recommendation on Android smart devices. Experimental evaluations of the framework, via an experimental context-aware mobile recommendation application, confirm that the framework is effective, and that its power consumption is within acceptable range. © 2014 Springer International Publishing Switzerland.

Abstract
This paper intends to analyze the performance of an electric bus for three different routes in the city of Oporto. The objective is to study the correlation between the type of route a bus travels and the amount of energy it spends in performing it. Besides energy, the potential of the recovering part of it through regenerative braking will also be discussed. It is expected that long and demanding routes, e.g. those encompassing meandering paths, will make the bus use more energy. To accomplish these objectives, a simulation platform was built upon both a nanoscopic representation of the electrical bus and its microscopic interaction with the road traffic network while performing different journeys through specific routes. Simulation results are compared and discussed, as well as are directions for future work identified. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Scientific Committee

Abstract
Different XML formats are widely used for data exchange and processing, being often necessary to mutually convert between them. Standard XML transformation languages, like XSLT or XQuery, are unsatisfactory for this purpose since they require writing a separate transformation for each direction. Existing bidirectional transformation languages mean to cover this gap, by allowing programmers to write a single program that denotes both transformations. However, they often 1) induce a more cumbersome programming style than their traditionally unidirectional relatives, to establish the link between source and target formats, and 2) offer limited configurability, by making implicit assumptions about how modifications to both formats should be translated that may not be easy to predict. This paper proposes a bidirectional XML update language called BIFLUX (BIdirectional FunctionaL Updates for XML), inspired by the FLUX XML update language. Our language adopts a novel bidirectional programming by update paradigm, where a program succinctly and precisely describes how to update a source document with a target document, in an intuitive way, such that there is a unique "inverse" source query for each update program. BIFLUX extends FLUX with bidirectional actions that describe the connection between source and target formats. We introduce a core BIFLUX language, with a clear and well-behaved bidirectional semantics and a decidable static type system based on regular expression types.

Abstract
The ability to find and count subgraphs of a given network is an important non trivial task with multidisciplinary applicability. Discovering network motifs or computing graphlet signatures are two examples of methodologies that at their core rely precisely on the subgraph counting problem. Here we present the g-trie, a data-structure specifically designed for discovering subgraph frequencies. We produce a tree that encapsulates the structure of the entire graph set, taking advantage of common topologies in the same way a prefix tree takes advantage of common prefixes. This avoids redundancy in the representation of the graphs, thus allowing for both memory and computation time savings. We introduce a specialized canonical labeling designed to highlight common substructures and annotate the g-trie with a set of conditional rules that break symmetries, avoiding repetitions in the computation. We introduce a novel algorithm that takes as input a set of small graphs and is able to efficiently find and count them as induced subgraphs of a larger network. We perform an extensive empirical evaluation of our algorithms, focusing on efficiency and scalability on a set of diversified complex networks. Results show that g-tries are able to clearly outperform previously existing algorithms by at least one order of magnitude.

Abstract
Most current research data management solutions rely on a fixed set of descriptors (e.g. Dublin Core Terms) for the description of the resources that they manage. These are easy to understand and use, but their semantics are limited to general concepts, leaving out domain-specific metadata. The textual values for descriptors are easily indexed through free-text indexes, but faceted search and dataset interlinking becomes limited. From the point of view of the relational database schema modeler, designing a more flexible metadata model represents a non-trivial challenge because it means representing entities with attributes unknown at the time of modeling and that can change in time. Those traits, combined with the presence of hierarchies among the entities, can make the relational schema quite complex. This work demonstrates the approaches followed by current opensource platforms and proposes a graph-based model for achieving modular, ontology-based metadata for interlinked data assets in the Semantic Web. The proposed model was implemented in a collaborative research data management platform currently under development at the University of Porto. © 2014 ACM.

Abstract
This paper describes the application of Evolutionary Particle Swarm Optimization, EPSO, to the optimization of the short term operation of hydro stations in market environment. The maximization of the revenues of hydro stations, namely pumping stations, is gaining increasing attention by generation companies. However, this is typically a complex problem given the non linear relation between the power, the flow and the head, the temporal coupling between stations in cascade and the increasing number of pumping stations. The EPSO based algorithm displayed a very good performance in terms of the quality of the final operation plan as well as regarding the speed of convergence and the robustness of the algorithm.