Open University of Catalonia

About: Open University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Collaborative learning & Educational technology. The organization has 1943 authors who have published 4646 publications receiving 64200 citations. The organization is also known as: Universitat Oberta de Catalunya & UOC.

Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases

Abstract: In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.

The regional distribution of unemployment: What do micro-data tell us?

Abstract: Regional disparities in unemployment rates are large and persistent. The literature provides evidence of their magnitude and evolution, as well as evidence of the role of certain economic, demographic and environmental factors in explaining the gap between regions of low and high unemployment. Most of these studies, however, adopt an aggregate approach and so do not account for the individual characteristics of the unemployed and employed in each region. This paper, by drawing on micro-data from the Spanish wave of the Labour Force Survey, seeks to remedy this shortcoming by analysing regional differentials in unemployment rates. An appropriate decomposition of the regional gap in the average probability of being unemployed enables us to distinguish between the contribution of differences in the regional distribution of individual characteristics from that attributable to a different impact of these characteristics on the probability of unemployment. Our results suggest that the well-documented disparities in regional unemployment are not just the result of regional heterogeneity in the distribution of individual characteristics. Non-negligible differences in the probability of unemployment remain after controlling for this type of heterogeneity, as a result of differences across regions in the impact of the observed characteristics. Among the factors considered in our analysis, regional differences in the endowment and impact of an individual’s education are shown to play a major role.(This abstract was borrowed from another version of this item.)

ChaLearn Looking at People 2015 challenges: Action spotting and cultural event recognition

Abstract: Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two challenges and the obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Abstract: The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.