Language-based multimedia information retrieval

This paper describes various methods and approaches for language-based multimedia information retrieval, which have been developed in the projects POP-EYE and OLIVE and which will be developed further in the MUMIS project. All of these project aim at supporting automated indexing of video material by use of human language technologies. Thus, in contrast to image or sound-based retrieval methods, where both the query language and the indexing methods build on non-linguistic data, these methods attempt to exploit advanced text retrieval technologies for the retrieval of non-textual material. While POP-EYE was building on subtitles or captions as the prime language key for disclosing video fragments, OLIVE is making use of speech recognition to automatically derive transcriptions of the sound tracks, generating time-coded linguistic elements which then serve as the basis for text-based retrieval functionality

SemWeB Semantic Web Browser – Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks

Imagine a Web browser that can understand the context of a Web page and recommends related semantic hyperlinks in any Web domain. In addition, imagine this browser also understands your browsing needs and personalizes information for you. The aim of our research is to achieve this in open Web environment using Semantic Web technologies and adaptive hypermedia techniques. In this paper, we discuss a novel Semantic Web browser, SemWeB, which utilizes linked data for context-based hyperlink recommendation and uses a behavior-based and an ontology-driven user modeling architecture for personalization on Web documents. The aim of this research is to bring the gap between the technology and user needs using Semantic Web technologies in Web browsing

Building a Corpus of 2L English for Automatic Assessment: the CLEC Corpus

In this paper we describe the CLEC corpus, an ongoing project set up at the University of Cádiz with the purpose of building up a large corpus of English as a 2L classified according to CEFR proficiency levels and formed to train statistical models for automatic proficiency assessment. The goal of this corpus is twofold: on the one hand it will be used as a data resource for the development of automatic text classification systems and, on the other, it has been used as a means of teaching innovation techniques

The RFID PIA – developed by industry, agreed by regulators

This chapter discusses the privacy impact assessment (PIA) framework endorsed by the European Commission on February 11th, 2011. This PIA, the first to receive the Commission's endorsement, was developed to deal with privacy challenges associated with the deployment of radio frequency identification (RFID) technology, a key building block of the Internet of Things. The goal of this chapter is to present the methodology and key constructs of the RFID PIA Framework in more detail than was possible in the official text. RFID operators can use this article as a support document when they conduct PIAs and need to interpret the PIA Framework. The chapter begins with a history of why and how the PIA Framework for RFID came about. It then proceeds with a description of the endorsed PIA process for RFID applications and explains in detail how this process is supposed to function. It provides examples discussed during the development of the PIA Framework. These examples reflect the rationale behind and evolution of the text's methods and definitions. The chapter also provides insight into the stakeholder debates and compromises that have important implications for PIAs in general.Series: Working Papers on Information Systems, Information Business and Operation