Semantic and pragmatic characterization of learning objects

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

A systematic review of whole class, subject based, pedagogies with reported outcomes for the academic and social inclusion of pupils with special educational needs in mainstream classrooms

Schools across the world have responded to international and national initiatives designed to further the development of inclusive education. In England, there is a statutory requirement for all schools to provide effective learning opportunities for all pupils (QCA, 2000) and children with special educational needs (SEN) are positioned as having a right to be within mainstream classrooms accessing an appropriate curriculum (SENDA, 2001). Previous reviews which have sought to identify classroom practices that support the inclusion of children with SEN have been technically non-systematic and hence a need for a systematic review within this area has been identified (Nind et al., 2004; Rix et al., 2006). This systematic literature review is the last in a series of three

Arguments for exception in US security discourse

In his influential State of Exception, Giorgio Agamben proposes that, even in apparently liberal western democracies, the state will routinely use the contingency of national emergency to suspend civil liberties and justify expansion of military and police powers. We investigated rhetorical strategies deployed in the web pages of US security agencies, created or reformed in the aftermath of the 9/11 events, to determine whether they present argumentation conforming to Agamben’s model. To expose rhetorical content, we examined strategies operating at two levels within our corpus. Argument schemes and underlying warrants were identified through close examination of systematically selected core documents. Semantic fields establishing themes of threat and danger were also explored, using automatic corpus tools to expose patterns of lexical selection established across the whole corpus. The study recovered evidence of rhetoric broadly consistent with the logic predicted by State of Exception theory, but also presented nuanced findings whose interpretation required careful re-appraisal of core ideas within Agamben’s work

Semantic Content Mediation and Acquisition: The Challenge for Semantic e-Business Solutions

A Top Quadrant report situates the Semantic Web within the current Innovation Wave of “Distributed Intelligence”. This is one of the main innovation waves of the last centuries including textile, railway, auto, computer, distributed intelligence (1997-2061) and nanotechnology (2007-2081). The Distributed Intelligence wave started in the late nineties and is expected to peak between 2010 and 2020. The report estimates first return on investments in 2006-7, growing to a market of $40-60 billion in 2010. Funds are coming primary from governments, venture capitalists and industry commercialization. Over the next few years, this is expected to change in favour of industry commercialization

Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

International audienceSocial media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20-29th June 2016. Highlights • Prototype real-time social geodata pipeline for flood events and demonstration dataset • National-scale flood warnings/river levels set 'at-risk areas' in Twitter API queries • Monitoring multiple locations (without keywords) retrieved current, geotagged tweets • Novel application of word embeddings in flooding context identified relevant tweets • Pipeline extracts tweets to visualise using open-source libraries (SciKit Learn/Gensim) Keywords Flood management; Twitter; volunteered geographic information; natural language processing; word embeddings; social geodata. Hardware required: Intel i3 or mid-performance PC with multicore processor and SSD main drive, 8Gb memory recommended. Software required: Python and library dependencies specified in Appendix A1.2.1, (viii) environment.yml Software availability: All source code can be found at GitHub public repositorie