Abstract
The wine sector is a billion euro business and therefore subjected to multiple attempts of fraudulent practices. This requires the development of rapid and reliable methods to detect such situations. Several methodologies have been developed based on the chemical profiles of the wines, but they are limited due to the environmental conditions that cannot be controlled. The use of DNA-based detection systems are an emergent research field that have been extended to a wide variety of food products and are still the most reliable methods for varietal identification. However these methods are not suitable for geographical determination. Soil related fingerprints have a primary role considering that there is a relationship between the elemental composition of wine and the composition of the provenance soil. WineBioCode is a project aiming to define the best strategy for wine authenticity based on a multidisciplinary approach. Two DNA-based strategies have been developed based on Real-time PCR and a label free optical biosensor platform. Both platforms enabled successful identification of specific DNA-targets when applied to Vitis vinifera L., and can be applied throughout the grape-wine chain. The methods are complementary and can be used in different situations, according to the requirements. The geographical evaluation has been assessed by the strontium 875r1865r isotope ratio determination involving soil evaluation in the vineyards followed by its assay in the wine samples. The results are being integrated in order to establish the best procedure to be undertaken for wine fingerprinting, including varietal composition and geographical origin, therefore fulfilling the requirements of the geographical denominations in wine certification.

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In the past few years, cable-driven robots have received some attention by the scientific community and the industry. They have special characteristics that made them very reliable to operate with the level of safeness that is required by different environments, such as, handling of hazardous materials in construction sites. This paper presents a cable-driven robot called SPIDERobot, that was developed for automated construction of architectural projects. This robot has a rotating claw and it is controlled by a set of 4 cables that allow 4 degrees of freedom. In addition to the robot, this paper introduces a Dynamic Control System (DCS) that controls the positioning of the robot and assures that the length of cables is always within a safe value. Results show that traditional force-feasible approaches are more influenced by the pulling forces or the geometric arrangement of all cables and their positioning is significantly less accurate than the DCS. Therefore, the architecture of the SPIDERobot is designed to enable an easily scaling up of the solution to higher dimensions for operating in realistic environments.

Abstract
Syndromic surveillance systems continuously monitor multiple pre-diagnostic daily streams of indicators from different regions with the aim of early detection of disease outbreaks. The main objective of these systems is to detect outbreaks hours or days before the clinical and laboratory confirmation. The type of data that is being generated via these systems is usually multivariate and seasonal with spatial and temporal dimensions. The algorithm What's Strange About Recent Events (WSARE) is the state-of-the-art method for such problems. It exhaustively searches for contrast sets in the multivariate data and signals an alarm when find statistically significant rules. This bottom-up approach presents a much lower detection delay comparing the existing top-down approaches. However, WSARE is very sensitive to the small-scale changes and subsequently comes with a relatively high rate of false alarms. We propose a new approach called EigenEvent that is neither fully top-down nor bottom-up. In this method, we instead of top-down or bottom-up search, track changes in data correlation structure via eigenspace techniques. This new methodology enables us to detect both overall changes (via eigenvalue) and dimension-level changes (via eigenvectors). Experimental results on hundred sets of benchmark data reveals that EigenEvent presents a better overall performance comparing state-of-the-art, in particular in terms of the false alarm rate.

Abstract
Following the proliferation of Augmented Reality technologies and applications in mobile devices it is becoming clear that AR techniques have matured and are ready to be used for large audiences. This poses several new multimedia interaction and usability problems that need to be identified and studied. AR problems are no longer exclusively about rendering superimposed virtual geometry or finding ways of performing GPS or computer vision registration. It is important to understand how to keep users engaged with AR and in what occasions it is suitable to use it. Additionally how should graphical user interfaces be designed so that the user can interact with AR elements while pointing a mobile device to a specific real world area? Finally what is limiting AR applications from reaching an even broader acceptance and usage level? This position paper identifies several interaction problems in today's multimedia AR applications, raising several pressing issues and proposes several research directions. Copyright