Telefónica

About: Telefónica is a based out in . It is known for research contribution in the topics: The Internet & Quality of service. The organization has 2119 authors who have published 3157 publications receiving 71556 citations. The organization is also known as: Telefonica & Telefónica S.A..

A break in the clouds: towards a cloud definition

Abstract: This paper discusses the concept of Cloud Computing to achieve a complete definition of what a Cloud is, using the main characteristics typically associated with this paradigm in the literature. More than 20 definitions have been studied allowing for the extraction of a consensus definition as well as a minimum definition containing the essential characteristics. This paper pays much attention to the Grid paradigm, as it is often confused with Cloud technologies. We also describe the relationships and distinctions between the Grid and Cloud approaches.

I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system

Abstract: User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

Abstract: In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease

Abstract: Background: Patients with chronic obstructive pulmonary disease (COPD) often present with severe acute exacerbations requiring hospital treatment. However, little is known about the prognostic consequences of these exacerbations. A study was undertaken to investigate whether severe acute exacerbations of COPD exert a direct effect on mortality. Methods: Multivariate techniques were used to analyse the prognostic influence of acute exacerbations of COPD treated in hospital (visits to the emergency service and admissions), patient age, smoking, body mass index, co-morbidity, long term oxygen therapy, forced spirometric parameters, and arterial blood gas tensions in a prospective cohort of 304 men with COPD followed up for 5 years. The mean (SD) age of the patients was 71 (9) years and forced expiratory volume in 1 second was 46 (17)%. Results: Only older age (hazard ratio (HR) 5.28, 95% CI 1.75 to 15.93), arterial carbon dioxide tension (HR 1.07, 95% CI 1.02 to 1.12), and acute exacerbations of COPD were found to be independent indicators of a poor prognosis. The patients with the greatest mortality risk were those with three or more acute COPD exacerbations (HR 4.13, 95% CI 1.80 to 9.41). Conclusions: This study shows for the first time that severe acute exacerbations of COPD have an independent negative impact on patient prognosis. Mortality increases with the frequency of severe exacerbations, particularly if these require admission to hospital.