Abstract
This paper offers a characterization of performance limits for classification and reconstruction of high-dimensional signals from noisy compressive measurements, in the presence of side information. We assume the signal of interest and the side information signal are drawn from a correlated mixture of distributions/components, where each component associated with a specific class label follows a Gaussian mixture model (GMM). We provide sharp sufficient and/or necessary conditions for the phase transition of the misclassification probability and the reconstruction error in the low-noise regime. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of measurements taken from the signal of interest, the number of measurements taken from the side information signal, and the geometry of these signals and their interplay. © 2015 IEEE.

Abstract
By understanding the need for a social, collaborative and participative learning process, an analysis to the existent learning and pedagogical models was made in order to identify the existence of a basis for the development of an e-learning course implemented over a social network site. As a complement, a systematic literature review on social media adoption and use was made, particularly focusing the adoption for educational purposes and aiming at achieving the set of variables which might impact the implementation of s-learning activities. The combination of these activities resulted in the presentation of a new learning assessment model designed to be applied over e-learning courses implemented on social media. With this in mind, a description on the referred assessment model is presented, thus considering the need for a comprehensive characterization of the inherent assessment process and inherent activities and technologies. Through the execution of the research and pedagogical activities performed in the scope of the present project it was also possible to acknowledge that learning activities performed over social networks sites are a solution for improving student's willingness and commitment towards studying and learning.

Abstract
The emergence of the so-called "European Paradox" shows that increasing Governmental R&D Investment is far from being a 'panacea' for stagnant growth. Surprisingly, Governmental R&D Employment does not contribute to 'mass-market' employment, despite its important role in reducing Youth-Unemployment. Despite the negative side-effect of Governmental R&D Employment on economic growth, University R&D Employment appears to have a quite important role in reducing Unemployment, especially Youth-Unemployment, while it also does not have a downside in terms of economic growth. Technological Capacity enhancement is the most effective instrument for reducing Youth-Unemployment and is a policy with a quite robust effect regarding sustainable economic development.

Abstract
Optimization in current decision support systems has a highly interdisciplinary nature related with the need to integrate different techniques and paradigms for solving real-world complex problems. Computing optimal solutions in many of these problems are unmanageable. Heuristic search methods are known to obtain good results in an acceptable time interval. However, parameters need to be adjusted to allow good results. In this sense, learning strategies can enhance the performance of a system, providing it with the ability to learn, for instance, the most suitable optimization technique for solving a particular class of problems, or the most suitable parameterization of a given algorithm on a given scenario. Hyper-heuristics arise in this context as efficient methodologies for selecting or generating (meta) heuristics to solve NP-hard optimization problems. This paper presents the specification of a hyper-heuristic for selecting techniques inspired in nature, for solving the problem of scheduling in manufacturing systems, based on previous experience. The proposed hyper-heuristic module uses a reinforcement learning algorithm, which enables the system with the ability to autonomously select the meta-heuristic to use in optimization process as well as the respective parameters. A computational study was carried out to evaluate the influence of the hyper-heuristics on the performance of a scheduling system. The obtained results allow to conclude about the effectiveness of the proposed approach. © 2015 AISTI.

Abstract
This chapter introduces Shannon-inspired performance limits associated with the classification of low-dimensional subspaces embedded in a high-dimensional ambient space from compressive and noisy measurements. In particular, it introduces the diversity-discrimination tradeoff that describes the interplay between the number of classes that can be separated by a compressive classifier-measured via the discrimination gain-and the performance of such a classifier-measured via the diversity gain-and the relation of such an interplay to the underlying problem geometry, including the ambient space dimension, the subspaces dimension, and the number of compressive measurements. Such a fundamental limit on performance is derived from a syntactic equivalence between the compressive classification problem and certain wireless communications problems. This equivalence provides an opportunity to cross-pollinate ideas between the wireless information theory domain and the compressive classification domain. This chapter also demonstrates how theory aligns with practice in a concrete application: face recognition from a set of noisy compressive measurements.

Abstract
This work presents an automatic calibration method for a vision based external underwater ground-truth positioning system. These systems are a relevant tool in benchmarking and assessing the quality of research in underwater robotics applications. A stereo vision system can in suitable environments such as test tanks or in clear water conditions provide accurate position with low cost and flexible operation. In this work we present a two step extrinsic camera parameter calibration procedure in order to reduce the setup time and provide accurate results. The proposed method uses a planar homography decomposition in order to determine the relative camera poses and the determination of vanishing points of detected lines in the image to obtain the global pose of the stereo rig in the reference frame. This method was applied to our external vision based ground-truth at the INESC TEC/Robotics test tank. Results are presented in comparison with an precise calibration performed using points obtained from an accurate 3D LIDAR modelling of the environment.