Efficiency analysis of information technology and online social networks management : an integrated DEA-Model assessment

This paper analyses the relationship between productive efficiency and online-social-networks (OSN) in Spanish telecommunications firms. A data-envelopment-analysis (DEA) is used and several indicators of business ?social Media? activities are incorporated. A super-efficiency analysis and bootstrapping techniques are performed to increase the model?s robustness and accuracy. Then, a logistic regression model is applied to characterise factors and drivers of good performance in OSN. Results reveal the company?s ability to absorb and utilise OSNs as a key factor in improving the productive efficiency. This paper presents a model for assessing the strategic performance of the presence and activity in OSN.

Active learning and generic competences in an operating systems course

The Bologna Declaration and the implementation of the European Higher Education Area are promoting the use of active learning methodologies. The aim of this study is to evaluate the effects obtained after applying active learning methodologies to the achievement of generic competences as well as to the academic performance. This study has been carried out at the Universidad Politécnica de Madrid, where these methodologies have been applied to the Operating Systems I subject of the degree in Technical Engineering in Computer Systems. The fundamental hypothesis tested was whether the implementation of active learning methodologies (cooperative learning and problem based learning) favours the achievement of certain generic competences (‘teamwork’ and ‘planning and time management’) and also whether this fact improved the academic performance of our students. The original approach of this work consists in using psychometric tests to measure the degree of acquired student’s generic competences instead of using opinion surveys, as usual. Results indicated that active learning methodologies improve the academic performance when compared to the traditional lecture/discussion method, according to the success rate obtained. These methods seem to have as well an effect on the teamwork competence (the perception of the behaviour of the other members in the group) but not on the perception of each students’ behaviour. Active learning does not produce any significant change in the generic competence ‘planning and time management'.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.