Abstract
The SPHERE instrument, dedicated to high contrast imaging on VLT, has been routinely operated for more than 3 years, over a large range of conditions and producing observations from visible to NIR. A central part of the instrument is the high order adaptive optics system, named SAXO, designed to deliver high Strehl image quality with a balanced performance budget for bright stars up to magnitude R=9. We take benefit now from the very large set of observations to revisit the assumptions and analysis made at the time of the design phase: We compare the actual AO behavior as a function of expectations. The data set consists of the science detector data, for both coronagraphic images and non-coronagraphic PSF calibrations, but also of AO internal data from the high frequency sensors and statistics computations from the real-time computer which are systematically archived, and finally of environmental data, monitored at VLT level. This work is supported and made possible by the SPHERE Data Center infrastructure hosted at Grenoble which provides an efficient access and the capability for the homogeneous analysis of this large and statistically-relevant data set. We review in a statistical manner the actual AO performance as a function of external conditions for different regimes and we discuss the possible performance metrics, either derived from AO internal data or directly from the high contrast images. We quantify the dependency of the actual performance on the most relevant environmental parameters. By comparison to earlier expectations, we conclude on the reliability of the usual AO modeling. We propose some practical criteria to optimize the queue scheduling and the expression of observer requirements; finally, we revisit what could be the most important AO specifications for future high contrast imagers as a function of the primary science goals, the targets and the turbulence properties.

Abstract
The quality of consumer-oriented health information on the Web is usually assessed through the medical certification of websites. These tools are built upon quality indicators but, so far, no standard set of indicators has been defined. The objective of the present study is to explore the popularity of specific document features and their influence on the quality of health web documents, using HON code as ground truth. A set of top-ranked health documents retrieved from a major search engine was characterized in a univariate analysis, and then used in a bivariate analysis to seek features that affect documents' quality. The univariate analysis provides insights into the characteristics of the overall population of the health web documents. The bivariate analysis reveals strong relations between documents' quality and a set of features (namely split content, videos, images, advertisements, English language) that are potential quality indicators. We characterized health web documents and identified specific document features that can be used to assess whether the information in such documents is trustworthy. The main contribution of this work is to provide other features as candidate indicators of quality. Non-health professionals can use these indicators in automatic and manual assessments of health content.

Abstract
As Collaborative Filtering becomes increasingly important in both academia and industry recommendation solutions, it also becomes imperative to study the algorithm selection task in this domain. This problem aims at inding automatic solutions which enable the selection of the best algorithms for a new problem, without performing full-ledged training and validation procedures. Existing work in this area includes several approaches using Metalearning, which relate the characteristics of the problem domain with the performance of the algorithms. This study explores an alternative approach to deal with this problem. Since, in essence, the algorithm selection problem is a recommendation problem, we investigate the use of Collaborative Filtering algorithms to select Collaborative Filtering algorithms. The proposed approach integrates subsampling landmarkers, a data characterization approach commonly used in Metalearning, with a Collaborative Filtering methodology, named CF4CF. The predictive performance obtained by CF4CF using benchmark recommendation datasets was similar or superior to that obtained with Metalearning.

Abstract
The advent of small and low-cost Unmanned Aerial Vehicles (UAVs) is paving the way to use swarms of UAVs to perform missions such as aerial video monitoring and infrastructure inspection. Within a swarm, UAVs communicate by means of a Flying Multi-hop Network (FMN), which due to its dynamics induces frequent changes of network topology and quality of the links. Recently, UAVs have also been used to provide Internet access and enhance the capacity of existing networks in Temporary Events. This brings up additional routing challenges not yet addressed, in order to provide always-on and high capacity paths able to meet the Quality of Service expected by the users. This paper presents RedeFINE, a centralized routing solution for FMNs that selects high-capacity paths between UAVs and avoids communications disruptions, by defining in advance the forwarding tables and the instants they shall be updated in the UAVs; this represents a major step forward with respect to traditional routing protocols. The performance evaluation of RedeFINE shows promising results, especially regarding Throughput and Packet Delivery Ratio, when compared with state of the art routing solutions.

Abstract
Power and energy systems are being subject to relevant changes, mostly due to the large increase of distributed generation. These changes include the deregulation of electricity markets, which has become a more competitive marketplace due to the increase of the number of players based on renewable energy sources. This paper proposes a new portfolio optimization model for the participation in multiple alternative/complementary market opportunities, considering the risk management. The proposed model considers electricity as the asset to be negotiated. The risk is measured using the prediction error of electricity prices. A case study based on real data from Iberian electricity market-MIBEL assesses the results of the proposed model, using a particle swarm based optimization. Results show that using the proposed portfolio optimization model, market players are able to balance their market participation strategies depending on their risk aversion and profit seeking nature. © 2018 Power Systems Computation Conference.

Abstract
Complex cyber-physical systems are more and more a set of components working tightly coupled, with little or no human intervention. Assessing the correctness of these systems by testing components individually, one-by-one, is obviously not sufficient, being required to also test and validate the overall system. KhronoSim is a modular and extensible platform for testing cyber-physical systems in closed-loop, which enables the integration of simulation models and platform emulators to build a closed loop test environment. This paper presents the emulator module of KhronoSim, developed to integrate the well-known QEMU emulator in the closed-loop testing platform. © 2018 IEEE.