Abstract
Legged robots are being the target of several studies and research. The idea is to develop machines that present characteristics approximate to the ones observed in biological living creatures. However this objective is still relatively far away and the development of prototypes for these studies is expensive and time consuming, which leads to the creation of models that allow the realization of the intended studies in software. These models should include the main characteristics of biological creatures relevant for locomotion studies. Given this, the presented work describes the development of a quadruped robot model in MATLAB/Simmechanics (TM). This model is intended to be used in the development of gaits for legged robots based on Central Pattern Generators. With this purpose in mind, the model was developed in a way to accept different gaits by direct introduction of the angular positions of the knee and hip joints. Various parameters of the robot are also easily changed through a configuration file that accompanies the model. This paper presents the model of a robot with flexible body, its legs and its hip and knee joints. The model of a feet-ground interaction was also modelled using a theoretic model described in the literature.

Abstract
Visual Odometry is one of the most powerful, yet challenging, means of estimating robot ego-motion. By grounding perception to the static features in the environment, vision is able, in principle, to prevent the estimation bias rather common in other sensory modalities such as inertial measurement units or wheel odometers. We present a novel approach to ego-motion estimation of a mobile robot by using a 6D Visual Odometry Probabilistic Approach. Our approach exploits the complementarity of dense optical flow methods and sparse feature based methods to achieve 6D estimation of vehicle motion. A dense probabilistic method is used to robustly estimate the epipolar geometry between two consecutive stereo pairs; a sparse feature stereo approach to estimate feature depth; and an Absolute Orientation method like the Procrustes to estimate the global scale factor. We tested our proposed method on a known dataset and compared our 6D Visual Odometry Probabilistic Approach without filtering techniques against a implementation that uses the well known 5-point RANSAC algorithm. Moreover, comparison with an Inertial Measurement Unit (RTK-GPS) is also performed, for providing a more detailed evaluation of the method against ground-truth information.

Abstract
In this paper a vision based groundtruth system for underwater applications is presented. The proposed system as an external validation perception and localization mechanism for underwater trials in the INESC TEC/ISEP underwater robotics test tank. It is comprised by a stereo camera pair with external synchronization and a image processing and data recording host computer. The cameras are disposed in a rigid baseline calibrated using scenario key points. Two target detection algorithms were tested and their results are discussed. One is based on template matching techniques allowing the tracking of arbitrary targets without particular markers and the other on color segmentation with the target vehicle equipped with light markers. Also an example trajectory of a small ROV motion in the task is also presented.

Abstract
Filtering tweets relevant to a given entity is an important task for online reputation management systems. This contributes to a reliable analysis of opinions and trends regarding a given entity. In this paper we describe our participation at the Filtering Task of RepLab 2013. The goal of the competition is to classify a tweet as relevant or not relevant to a given entity. To address this task we studied a large set of features that can be generated to describe the relationship between an entity and a tweet. We explored different learning algorithms as well as, different types of features: text, keyword similarity scores between enti-ties metadata and tweets, Freebase entity graph and Wikipedia. The test set of the competition comprises more than 90000 tweets of 61 entities of four distinct categories: automotive, banking, universities and music. Results show that our approach is able to achieve a Reliability of 0.72 and a Sensitivity of 0.45 on the test set, corresponding to an F-measure of 0.48 and an Accuracy of 0.908.

Abstract
The robotics community is concerned with the ability to infer and compare the results from researchers in areas such as vision perception and multi-robot cooperative behavior. To accomplish that task, this paper proposes a real-time indoor visual ground truth system capable of providing accuracy with at least more magnitude than the precision of the algorithm to be evaluated. A multi-camera architecture is proposed under the ROS (Robot Operating System) framework to estimate the 3D position of objects and the implementation and results were contextualized to the Robocup Middle Size League scenario.

Abstract
This paper aims to provide an assessment of urban quality of life (QoL) of European cities from the perspective of qualified human resources. The competitiveness of cities relies increasingly in their capacity to attract highly educated workers, as they are important assets for firms when choosing a location. Qualified human resources, on the other hand, tend to value QoL over other urban features. This is why policymakers and urban planners need to evaluate QoL of cities and be provided with tools that can guide action to improvements in this area. We assess urban QoL by means of a composite indicator constructed using data envelopment analysis, based on Urban Audit data and Mercer's framework of analysis, to give account of 246 European cities. Besides presenting a ranking of the best and the worst scores of QoL, this methodology allows benchmarking strategies.