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Universal access on equal terms to audiovisual content is a key point for the full inclusion of people with disabilities in activities of daily life. As a real challenge for the current Information Society, it has been detected but not achieved in an efficient way, due to the fact that current access solutions are mainly based in the traditional television standard and other not automated high-cost solutions. The arrival of new technologies within the hybrid television environment together with the application of different artificial intelligence techniques over the content will assure the deployment of innovative solutions for enhancing the user experience for all. In this paper, a set of different tools for image enhancement based on the combination between deep learning and computer vision algorithms will be presented. These tools will provide automatic descriptive information of the media content based on face detection for magnification and character identification. The fusion of this information will be finally used to provide a customizable description of the visual information with the aim of improving the accessibility level of the content, allowing an efficient and reduced cost solution for all. [ABSTRACT FROM AUTHOR]

Content personalisation is one of the main aims of the mobile media delivery business models, as a new way to improve the user's experience. In broadcasting networks, the content is sent 'one to many', so a complete personalisation where the user may select any content is not possible. But using the mobile bidirectional return channel (e.g. UMTS connection) visual targeted advertising can be performed in a simple way: by off-line storing the advertisement for selectively replacing the normal broadcasted advertisement. In fact, these concepts provide powerful methods to increase the value of the service, mainly in mobile environments. In this article we present a novel intelligent content personalisation system for targeted advertising over mobile broadcasting networks and terminals, based on user profiling and clustering, as a new solution where the use of content personalisation represents the competitive advantage over traditional advertising. [ABSTRACT FROM AUTHOR]

One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user. [ABSTRACT FROM AUTHOR]

This paper examines power relationships in the classroom. Using the concepts of rhizome, schizophrenia and deterritorialisation of Deleuze and Guattari, a semesterlong ethnographic work was conducted in the course Sex, Culture and Society at the University of Los Andes in Bogotá. The course studies discourses on sex, gender and sexuality in order to deconstruct heteronormative formations of identity. A feministic pedagogic approach allows relationships within the classroom to develop through traditional university learning methodologies. The paper explores this nontraditional approach and its results. [ABSTRACT FROM AUTHOR]