Dublin City University at CLEF 2007: Cross-Language Speech Retrieval Experiments

The Dublin City University participation in the CLEF 2007 CL-SR English task concentrated primarily on issues of topic translation. Our retrieval system used the BM25F model and pseudo relevance feedback. Topics were translated into English using the Yahoo! BabelFish free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domainspecific translation lexicons can provide improved CLIR effectiveness for this task

Examining the contributions of automatic speech transcriptions and metadata sources for searching spontaneous conversational speech

The searching spontaneous speech can be enhanced by combining automatic speech transcriptions with semantically related metadata. An important question is what can be expected from search of such transcriptions and different sources of related metadata in terms of retrieval effectiveness. The Cross-Language Speech Retrieval (CL-SR) track at recent CLEF workshops provides a spontaneous speech test collection with manual and automatically derived metadata fields. Using this collection we investigate the comparative search effectiveness of individual fields comprising automated transcriptions and the available metadata. A further important question is how transcriptions and metadata should be combined for the greatest benefit to search accuracy. We compare simple field merging of individual fields with the extended BM25 model for weighted field combination (BM25F). Results indicate that BM25F can produce improved search accuracy, but that it is currently important to set its parameters suitably using a suitable training set

Status of HPV vaccine introduction and barriers to country uptake.

During the last 12 years, over 80 countries have introduced national HPV vaccination programs. The majority of these countries are high or upper-middle income countries. The barriers to HPV vaccine introduction remain greatest in those countries with the highest burden of cervical cancer and the most need for vaccination. Innovation and global leadership is required to increase and sustain introductions in low income and lower-middle income countries

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1–3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10–12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species

The impact of conditionality on the welfare rights of EU migrants in the UK

This paper highlights and explores how conditionality operating at three levels (the EU supra-national level, the UK national level and in migrants’ mundane ‘street level’ encounters with social security administrators), come together to restrict and have a negative impact on the social rights of EU migrants living in the UK. Presenting analysis of new data generated in repeat qualitative interviews with 49 EU migrants resident in the UK, the paper makes an original contribution to understanding how the conditionality inherent in macro level EU and UK policy has seriously detrimental effects on the everyday lives of individual EU migrants

A Learning 2.0 Programme: raising library staff awareness of Web 2.0 at Imperial College London

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Welfare conditionality and disabled people in the UK: claimants' perspectives

In order to fully understand the impact of the extension of conditionality in the UK to include people with impairments, it is vital to give voice to those with direct experience of the welfare system. The case studies that follow are taken from interviews carried out as part of a project called Welfare Conditionality: Sanctions, Support and Behaviour Change. This is a major five-year programme of research running from 2013-2018, funded under the Economic and Social Research Council’s Centres and Large Grants Scheme (ESRC grant ES/K002163/2)