Abstract

Abstract
Context-aware pervasive applications can improve user experiences by tracking people in their surroundings. Such systems use multiple sensors to gather information regarding people and devices. However, when developing novel user experiences, researchers are left to building foundation code to support multiple network-connected sensors, a major hurdle to rapidly developing and testing new ideas. We introduce Creepy Tracker, an open-source toolkit to ease prototyping with multiple commodity depth cameras. It automatically selects the best sensor to follow each person, handling occlusions and maximizing interaction space, while providing full-body tracking in scalable and extensible manners. It also keeps position and orientation of stationary interactive surfaces while offering continuously updated point-cloud user representations combining both depth and color data. Our performance evaluation shows that, although slightly less precise than marker-based optical systems, Creepy Tracker provides reliable multi-joint tracking without any wearable markers or special devices. Furthermore, implemented representative scenarios show that Creepy Tracker is well suited for deploying spatial and context-aware interactive experiences.

Abstract
Surveillance missions in vast, difficult access environments are responsible for logistic difficulties in comparison to using an in loco monitoring team. For this and many other reasons, solutions with robotic platforms such as unmanned aerial vehicles (UAVs), present economic advantages. © 1986-2012 IEEE.

Abstract
This paper addresses the problem of decision making in the context of financial markets, more specifically, the problem of forecasting the correct trading action for a certain future horizon. We study and compare two alternative ways of addressing these forecasting tasks: (a) using standard numeric prediction models to forecast the variation on the prices of the target asset and, on a second stage, transform these numeric predictions into a decision according to some predefined decision rules; and (b) use models that directly forecast the right decision thus ignoring the intermediate numeric forecasting task. The objective of our study is to determine if both strategies provide identical results or if there is any particular advantage worth being considered that may distinguish each alternative in the context of financial markets.

Abstract
Deaf people face serious communication problems. The use of smartphones has been explored as a solution for breaking communication barriers and enhancing their communication, by providing access to basic services. This paper explores the usage of iconographic interfaces in smartphones as a means for contributing to further autonomy for deaf people. We applied the model for asynchronous and non-verbal communication through iconographic and interactive flows to develop the MyCarMobile application, a travel assistance android mobile application for deaf people. Our research explores a solution which enables travel assistance services without involving audio, using an iconographic interface to report road accidents. A user centered design approach was applied in the development of the prototype and usability tests were performed with eleven deaf users, in order to validate the mobile application. The results revealed a good performance and user satisfaction when interacting with the application.

Abstract
Context. Canary is the multi-object adaptive optics (MOAO) on-sky pathfinder developed in the perspective of multi-object spectrograph on extremely large telescopes (ELTs). In 2013, Canary was operated on-sky at the William Herschel telescope (WHT), using three off-axis natural guide stars (NGS) and four off-axis Rayleigh laser guide stars (LGS), in open-loop, with the on-axis compensated turbulence observed with a H-band imaging camera and a Truth wave-front sensor (TS) for diagnostic purposes. Aims. Our purpose is to establish a reliable and accurate wave-front error breakdown for LGS MOAO. This will enable a comprehensive analysis of Canary on-sky results and provide tools for validating simulations of MOAO systems for ELTs. Methods. To evaluate the MOAO performance, we compared the Canary on-sky results running in MOAO, in single conjugated adaptive optics (SCAO) and in ground layer adaptive optics (GLAO) modes, over a large set of data acquired in 2013. We provide a statistical study of the seeing. We also evaluated the wave-front error breakdown from both analytic computations, one based on a MOAO system modelling and the other on the measurements from the Canary TS. We have focussed especially on the tomographic error and we detail its vertical error decomposition. Results. We show that Canary obtained 30.1%, 21.4% and 17.1% H-band Strehl ratios in SCAO, MOAO and GLAO respectively, for median seeing conditions with 0.66? of total seeing including 0.59? at the ground. Moreover, we get 99% of correlation over 4500 samples, for any AO modes, between two analytic computations of residual phase variance. Based on these variances, we obtain a reasonable Strehl-ratio (SR) estimation when compared to the measured IR image SR. We evaluate the gain in compensation for the altitude turbulence brought by MOAO when compared to GLAO.