Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer

Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations

A two-way interactive broadband satellite architecture to break the digital divide barrier

September 24-26, 2007, Turin, Ital

TV-Centric technologies to provide remote areas with two-way satellite broadband access

October 1-2, 2007, Rome, Italy TV-Centric Technologies To Provide Remote Areas With Two-Way Satellite Broadband Acces

Quality assurance for digital learning object repositories: issues for the metadata creation process

Metadata enables users to find the resources they require, therefore it is an important component of any digital learning object repository. Much work has already been done within the learning technology community to assure metadata quality, focused on the development of metadata standards, specifications and vocabularies and their implementation within repositories. The metadata creation process has thus far been largely overlooked. There has been an assumption that metadata creation will be straightforward and that where machines cannot generate metadata effectively, authors of learning materials will be the most appropriate metadata creators. However, repositories are reporting difficulties in obtaining good quality metadata from their contributors, and it is becoming apparent that the issue of metadata creation warrants attention. This paper surveys the growing body of evidence, including three UK-based case studies, scopes the issues surrounding human-generated metadata creation and identifies questions for further investigation. Collaborative creation of metadata by resource authors and metadata specialists, and the design of tools and processes, are emerging as key areas for deeper research. Research is also needed into how end users will search learning object repositories

Challenges to Teaching Credibility Assessment in Contemporary Schooling

Part of the Volume on Digital Media, Youth, and CredibilityThis chapter explores several challenges that exist to teaching credibility assessment in the school environment. Challenges range from institutional barriers such as government regulation and school policies and procedures to dynamic challenges related to young people's cognitive development and the consequent difficulties of navigating a complex web environment. The chapter includes a critique of current practices for teaching kids credibility assessment and highlights some best practices for credibility education

Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks

Fast-AT is an automatic thumbnail generation system based on deep neural networks. It is a fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios. During inference, the appropriate filter is selected depending on the dimensions of the target thumbnail. Unlike most previous work, Fast-AT does not utilize saliency but addresses the problem directly. In addition, it eliminates the need to conduct region search on the saliency map. The model generalizes to thumbnails of different sizes including those with extreme aspect ratios and can generate thumbnails in real time. A data set of more than 70,000 thumbnail annotations was collected to train Fast-AT. We show competitive results in comparison to existing techniques