Ana Fernández Vilas

A social gamification framework for a K-6 learning platform

As video games, particularly, social games are growing in popularity and number of users, there has been an increasing interest in its potential as innovative teaching tools. Gamification is a new concept intending to use elements from video games in non-game applications. Education is an area with high potential for application of this concept since it seeks to promote peoples motivation and engagement. The research in progress will try to find how to apply social gamification in education, testing and validating the results of that application. To fulfil these objectives, this paper presents the guidelines and main features of a social gamification framework to be applied in an existent K-6 social learning environment.

What's on TV tonight? An efficient and effective personalized recommender system of TV programs

This paper introduces Queveo.tv: a personalized TV program recommendation system. The proposed hybrid approach (combining content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network as comments, tagging, ratings, etc. This web 2.0 application has been devised to enormously simplify the task of selecting what program to watch on TV.

Enhancing Residential Gateways: OSGi Services Composition

We propose a scheme to support the composition of OSGi services as result of orchestrating atomic services at run-time. With this proposal the OSGi potentiality increases because of the huge amount of new services that can be offered as a result of appropriate combinations. To specify the service composition we propose a totally transparent BPEL-style solution which does not break the OSGi standard. However, the syntactic matchmaking provided by the OSGi standard does not enable a flexible enough automatic service composition. Thus, we propose to define a semantic OSGi platform, which combined with the BPEL-style solution fits all the OSGi service composition requirements.

Controlling the smart home from TV

In this paper we introduce a cooperative environment between the interactive digital TV (IDTV) and the networked home with the aim of allowing the interaction between interactive TV applications and the controllers of the in-home appliances in a natural way. Concretely, we have selected the MHP (multimedia home platform) standard, well-known in the IDTV field, and OSGi (open service gateway initiative), the most widely used open platform to set up residential gateways. To overcome the radically different conceptions below these two specifications we define a new kind of application, coined as XbundLET, which acts as a bridge between both worlds allowing MHP applications to use the OSGi services and vice versa, i.e., allowing OSGi applications to take advantage of the MHP functionality. As a further step towards integrated residential gateways, we also propose a semantic description for OSGi services at the smart home that facilitates services discovery and invocation from broadcast MHP applications.

Enhancing Residential Gateways: A Semantic OSGi Platform

The OSGi (open service gateway initiative) service platform specification is the most widely adopted technology for building a control system for the networked home. Three main features contribute to its success. First, it supports various well-known protocols, simplifying communication among home devices. In addition, it defines a cooperative model for applications to dynamically discover and use services provided by others. Finally, it allows flexible remote management of these applications and the services they provide. So, an OSGi solution supports decentralization, diversification, and ubiquity not only in smart homes but in pervasive environments generally.

A simulation study on the performance of differentiation-based methods to detect selected loci using linked neutral markers.

We investigated the performance of two of the most popular differentiation‐based methods to detect loci under selection (dfdist/fdist and bayescan) in order to ascertain the average chromosome map distance between the detected outlier markers and the nearest loci under selection. We used a model of neutral markers genetically linked to selected loci (QTL) controlling a quantitative trait subject to divergent selection in two subpopulations connected by migration. The results are not particularly encouraging because for chromosome lengths above 0.5 morgan, at least 30% of outliers detected were positioned in chromosomes where QTL were absent, clearly denoting false positives. Outliers linked to QTL were on average closer to the nearest QTL than randomly chosen markers, but the methods showed a substantial uncertainty about the genetic association between markers and selected loci, as this association could be shown significantly only in a moderate number of replicates for most scenarios. At equal conditions, bayescan seemed to perform somewhat more efficiently than dfdist/fdist, with little difference between results for dominant and codominant markers.

AVATAR: ENHANCING THE PERSONALIZED TELEVISION BY SEMANTIC INFERENCE ∗

The generalized arrival of Digital TV will lead to a significant increase in the amount of channels and programs available to end users, making it difficult to find interesting programs among a myriad of irrelevant contents. Thus, in this field, automatic content recommenders should receive special attention in the following years to improve assistance to users. Current approaches of content recommenders have significant well-known deficiencies that hamper their wide acceptance. In this paper, a new approach for automatic content recommendation is presented that considerably reduces those deficiencies. This approach, based on the so-called Semantic Web technologies, has been implemented in the AVATAR tool, a hybrid content recommender that makes extensive use of well-known standards, such as TV-Anytime and OWL. Our proposal has been evaluated experimentally with real users, showing significant increases in the recommendation accuracy with respect to other existing approaches.

Recommender Systems for the Social Web

The recommendation of products, content and services cannot be considered newly born, although its widespread application is still in full swing. While its growing success in numerous sectors, the progress of the Social Web has revolutionized the architecture of participation and relationship in the Web, making it necessary to restate recommendation and reconciling it with Collaborative Tagging, as the popularization of authoring in the Web, and Social Networking, as the translation of personal relationships to the Web. Precisely, the convergence of recommendation with the above Social Web pillars is what motivates this book, which has collected contributions from well-known experts in the academy and the industry to provide a broader view of the problems that Social Recommenders might face with. If recommender systems have proven their key role in facilitating the user access to resources on the Web, when sharing resources has become social, it is natural for recommendation strategies in the Social Web era take into account the users point of view and the relationships among users to calculate their predictions. This book aims to help readers to discover and understand the interplay among legal issues such as privacy; technical aspects such as interoperability and scalability; and social aspects such as the influence of affinity, trust, reputation and likeness, when the goal is to offer recommendations that are truly useful to both the user and the provider.

Allelic diversity for neutral markers retains a higher adaptive potential for quantitative traits than expected heterozygosity

The adaptive potential of a population depends on the amount of additive genetic variance for quantitative traits of evolutionary importance. This variance is a direct function of the expected frequency of heterozygotes for the loci which affect the trait (QTL). It has been argued, but not demonstrated experimentally, that long‐term response to selection is more dependent on QTL allelic diversity than on QTL heterozygosity. Conservation programmes, aimed at preserving this variation, usually rely on neutral markers rather than on quantitative traits for making decisions on management. Here, we address, both through simulation analyses and experimental studies with Drosophila melanogaster, the question of whether allelic diversity for neutral markers is a better indicator of a high adaptive potential than expected heterozygosity. In both experimental and simulation studies, we established synthetic populations for which either heterozygosity or allelic diversity was maximized using information from QTL (simulations) or unlinked neutral markers (simulations and experiment). The synthetic populations were selected for the quantitative trait to evaluate the evolutionary potential provided by the two optimization methods. Our results show that maximizing the number of alleles of a low number of markers implies higher responses to selection than maximizing their heterozygosity.

Adaptive Learning Objects for t-learning

IDTV (Interactive Digital TV) opens new learning opportunities for those social groups that would hardly have access to traditional forms of education. However, viewers are not usually active learners, for this reason, education through IDTV should be offered in an attractive way, so as they get engaged in the learning experience. For this to be possible, we need to introduce personalization in the t-learning field. To achieve this goal, we present, in this paper, a proposal of self-adaptive t-learning objects, which show a different behavior depending on users characteristics. These objects are conformant to the ADL SCORM (Sharable Content Object Reference Model) standard for which we propose an extension in order to permit this type of learning objects. We expose as well an authoring tool to these objects, which hides the implementation details to the content creator.