Abstract
This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)-capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid.

Abstract
We construct functional encryption (FE) schemes for the orthogonality (OFE) relation where each ciphertext encrypts some vector (Formula Presented) and each decryption key, associated to some vector (Formula Presented), allows to determine if (Formula Presented) is orthogonal to (Formula Presented) or not. Motivated by compelling applications, we aim at schemes which are function hidding, i.e. (Formula Presented) is not leaked. Our main contribution are two such schemes, both rooted in existing constructions of FE for inner products (IPFE), i.e., where decryption keys reveal the inner product of (Formula Presented) and (Formula Presented). The first construction builds upon the very efficient IPFE by Kim et al. (SCN 2018) but just like the original scheme its security holds in the generic group model (GGM). The second scheme builds on recent developments in the construction of efficient IPFE schemes in the standard model and extends the work of Wee (TCC 2017) in leveraging these results for the construction of FE for Boolean functions. Conceptually, both our constructions can be seen as further evidence that shutting down leakage from inner product values to only a single bit for the orthogonality relation can be done with little overhead, not only in the GGM, but also in the standard model. We discuss potential applications of our constructions to secure databases and provide efficiency benchmarks. Our implementation shows that the first scheme is extremely fast and ready to be deployed in practical applications. © 2019, Springer Nature Switzerland AG.

Abstract
Mobile applications are increasingly present in our daily lives. Being increasingly dependent on apps, we all want to make sure apps work as expected. One way to increase confidence and quality of software is through testing. However, the existing approaches and tools still do not provide sufficient solutions for testing mobile apps with features different from the ones found in desktop or web applications. In particular, there are guidelines that mobile developers should follow and that may be tested automatically but, as far as we know, there are no tools that are able do it. The iMPAcT tool combines exploration, reverse engineering and testing to check if mobile apps follow best practices to implement specific behavior called UI Patterns. Examples of UI Patterns within this catalog are: orientation, background-foreground, side drawer, tab-scroll, among others. For each of these behaviors (UI Patterns), the iMPAcT tool has a corresponding Test Pattern that checks if the UI Pattern implementation follows the guidelines. This paper presents an extension to iMPAcT tool. It enables to test if Android apps work properly after receiving an incoming call, i.e., if the state of the screen after the call is the same as before getting the call. It formalizes the problem, describes the overall approach, describes the architecture of the tool and reports an experiment performed over 61 public mobile apps.

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Abstract
The evolution of technology has led to a set of technologies suitable for mobility and ubiquity scenarios. Phenomena of wireless computing, mobile devices and, generally, miniaturized sensors and actuators embedded in physical spaces are a today's reality. This phenomenon has opened the door to a set of opportunities for reengineering the way we perceive a given fact or situation and the way we act on it. Regarding the delivery of information to users of a given physical space, there is now the possibility of transforming the mechanisms of interaction between the space and the user. This change allows the user to see the physical space, that surrounds him, to adapt to himself and to provide him contextualized and personalized information according to his profile of interest. This approach can improve the way we manage customer relationships in a given business context. This paper overview the state of the art of smart spaces and analyses the potential of indoor positioning systems and techniques, and propose a conceptual model for the detection of users in physical spaces and the consequent adaptation of a smart physical space to provide information aligned with the user's profile of interest and according to is privacy rules.

Abstract