Utilizando Sistemas Recomendadores para Predecir Ratings en TV

En este trabajo se presenta un método que, utilizando sistemas recomendadores, predice ratings de TV a partir de los perfiles de los telespectadores recibidos desde sus receptores de televisión digital. La predicción está basada en un recomendador de contenidos audiovisuales, aplicado sobre grupos de televidentes, que utiliza clasificaciones multidimensionales extraídas de la norma TV-Anytime. Se presentan dos aplicaciones ilustrativas, con el objetivo común de maximizar la calificación en dos contextos distintos: un primer algoritmo para configurar la parrilla de programación semanal de una estación de televisión, y otro que decide en tiempo real entre un conjunto de contenidos cuál de ellos debe ser transmitido en un momento determinado por una estación de televisión para maximizar su audiencia inmediata

Predicción de Audiencias de Televisión basada en Sistemas Recomendadores

En este trabajo se presenta un método para la predicción de audiencias de televisión a partir de los perfiles de los telespectadores recibidos desde sus receptores de televisión digital. La predicción está basada en un recomendador de contenidos audiovisuales, aplicado sobre grupos de televidentes, que utiliza clasificaciones multidimensionales extraídas de la norma TV-Anytime. Se presentan dos aplicaciones ilustrativas, con el objetivo común de maximizar la calificación en dos contextos distintos: un primer algoritmo para configurar la parrilla de programación semanal de una estación de televisión, y otro que decide en tiempo real entre un conjunto de contenidos cuál de ellos debe ser transmitido en un momento determinado por una estación de televisión para maximizar su audiencia inmediata

A crowdsourcing recommendation model for image annotations in cultural heritage platforms

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.Agencia Estatal de Investigación | Ref. TIN2017-87604-RXunta de Galicia | Ref. ED431B 2020/3

Complexities underlying the breeding and deployment of Dutch elm disease resistant elms

Dutch elm disease (DED) is a vascular wilt disease caused by the pathogens Ophiostoma ulmi and Ophiostoma novo-ulmi with multiple ecological phases including pathogenic (xylem), saprotrophic (bark) and vector (beetle flight and beetle feeding wound) phases. Due to the two DED pandemics during the twentieth century the use of elms in landscape and forest restoration has declined significantly. However new initiatives for elm breeding and restoration are now underway in Europe and North America. Here we discuss complexities in the DED 'system' that can lead to unintended consequences during elm breeding and some of the wider options for obtaining durability or 'field resistance' in released material, including (1) the phenotypic plasticity of disease levels in resistant cultivars infected by O. novo-ulmi; (2) shortcomings in test methods when selecting for resistance; (3) the implications of rapid evolutionary changes in current O. novo-ulmi populations for the choice of pathogen inoculum when screening; (4) the possibility of using active resistance to the pathogen in the beetle feeding wound, and low attractiveness of elm cultivars to feeding beetles, in addition to resistance in the xylem; (5) the risk that genes from susceptible and exotic elms be introgressed into resistant cultivars; (6) risks posed by unintentional changes in the host microbiome; and (7) the biosecurity risks posed by resistant elm deployment. In addition, attention needs to be paid to the disease pressures within which resistant elms will be released. In the future, biotechnology may further enhance our understanding of the various resistance processes in elms and our potential to deploy trees with highly durable resistance in elm restoration. Hopefully the different elm resistance processes will prove to be largely under durable, additive, multigenic control. Elm breeding programmes cannot afford to get into the host-pathogen arms races that characterise some agricultural host-pathogen systems

Sporadic cloud-based mobile augmentation on the top of a virtualization layer: a case study of collaborative downloads in VANETs

Current approaches to Cloud-based Mobile Augmentation (CMA) leverage (cloud-based) resources to meet the requirements of rich mobile applications, so that a terminal (the so-called application node or AppN) can borrow resources lent by a set of collaborator nodes (CNs). In the most sophisticated approaches proposed for vehicular scenarios, the collaborators are nearby vehicles that must remain together near the application node because the augmentation service is interrupted when they move apart. This leads to disruption in the execution of the applications and consequently impoverishes the mobile users’ experience. This paper describes a CMA approach that is able to restore the augmentation service transparently when AppNs and CNs separate. The functioning is illustrated by a NaaS model where the AppNs access web contents that are collaboratively downloaded by a set of CNs, exploiting both roadside units and opportunistic networking. The performance of the resulting approach has been evaluated via simulations, achieving promising results in terms of number of downloads, average download times, and network overheadMinisterio de Educación y Ciencia | Ref. TIN2017-87604-

COVID-19 vaccine failure

COVID-19 affects the population unequally with a higher impact on aged and immunosuppressed people. Hence, we assessed the effect of SARS-CoV-2 vaccination in immune compromised patients (older adults and oncohematologic patients), compared with healthy counterparts. While the acquired humoral and cellular memory did not predict subsequent infection 18 months after full immunization, spectral and computational cytometry revealed several subsets within the CD8+ T-cells, B-cells, NK cells, monocytes and CD45RA+ CCR7- Tγδ cells differentially expressed in further infected and non-infected individuals not just following immunization, but also prior to that. Of note, up to 7 subsets were found within the CD45RA+ CCR7- Tγδ population with some of them being expanded and other decreased in subsequently infected individuals. Moreover, some of these subsets also predicted COVID-induced hospitalization in oncohematologic patients. Therefore, we hereby have identified several cellular subsets that, even before vaccination, strongly related to COVID-19 vulnerability as opposed to the acquisition of cellular and/or humoral memory following vaccination with SARS-CoV2 mRNA vaccines.This study has been funded through Programa Estratégico Instituto de Biología y Genética Molecular (IBGM Junta de Castilla y León. Ref. CCVC8485), Junta de Castilla y León (Proyectos COVID 07.04.467B04.74011.0) and the European Commission – NextGenerationEU (Regulation EU 2020/2094), through CSIC's Global Health Platform (PTI Salud Global; SGL21-03-026 and SGL2021-03-038)N