Representing clinical documents to support automatic retrieval of evidence from the Cochrane Library

The overall aim of our research is to develop a clinical information retrieval system that retrieves systematic reviews and underlying clinical studies from the Cochrane Library to support physician decision making. We believe that in order to accomplish this goal we need to develop a mechanism for effectively representing documents that will be retrieved by the application. Therefore, as a first step in developing the retrieval application we have developed a methodology that semi-automatically generates high quality indices and applies them as descriptors to documents from The Cochrane Library. In this paper we present a description and implementation of the automatic indexing methodology and an evaluation that demonstrates that enhanced document representation results in the retrieval of relevant documents for clinical queries. We argue that the evaluation of information retrieval applications should also include an evaluation of the quality of the representation of documents that may be retrieved. ©2010 IEEE.

Image retrieval on the Honeycomb Image Browser

Efficient and effective approaches of dealing with the vast amount of visual information available nowadays are highly sought after. This is particularly the case for image collections, both personal and commercial. Due to the magnitude of these ever expanding image repositories, annotation of all images images is infeasible, and search in such an image collection therefore becomes inherently difficult. Although content-based image retrieval techniques have shown much potential, such approaches also suffer from various problems making it difficult to adopt them in practice. In this paper, we follow a different approach, namely that of browsing image databases for image retrieval. In our Honeycomb Image Browser, large image databases are visualised on a hexagonal lattice with image thumbnails occupying hexagons. Arranged in a space filling manner, visually similar images are located close together enabling large image datasets to be navigated in a hierarchical manner. Various browsing tools are incorporated to allow for interactive exploration of the database. Experimental results confirm that our approach affords efficient image retrieval. © 2010 IEEE.

Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

Evaluating the spatial transferability and temporal repeatability of remote-sensing-based lake water quality retrieval algorithms at the European scale: a meta-analysis approach

Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods.

Using domain-specific knowledge to improve information retrieval performance

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Aproximaciones a los usos y apropiación de Twitter en bibliotecas universitarias de Argentina

El artículo analiza los principales usos de la plataforma Twitter por parte de bibliotecas universitarias en Argentina. Luego de la revisión bibliográfica, se exponen los procedimientos metodológicos empleados para identificar las instituciones que cuentan con esta herramienta comunicacional en la actualidad y los usos que se hace de ella a partir de ítems tales como: datos institucionales básicos, visibilidad y accesibilidad de la cuenta de Twitter en la Web de la biblioteca, momento de inicio de la actividad, volumen histórico de tuits, seguidores y siguiendo, así como cantidad y tipo de publicaciones realizadas en el período del relevamiento de datos. En los resultados se observa que un escaso número de bibliotecas adoptaron Twitter y que la apropiación de la herramienta muestra en general usos no planificados más centrados en la difusión que la interacción con usuarios. Se proponen estudios complementarios para conocer rutinas laborales de los bibliotecarios al respecto

Aproximaciones a los usos y apropiación de Twitter en bibliotecas universitarias de Argentina

El artículo analiza los principales usos de la plataforma Twitter por parte de bibliotecas universitarias en Argentina. Luego de la revisión bibliográfica, se exponen los procedimientos metodológicos empleados para identificar las instituciones que cuentan con esta herramienta comunicacional en la actualidad y los usos que se hace de ella a partir de ítems tales como: datos institucionales básicos, visibilidad y accesibilidad de la cuenta de Twitter en la Web de la biblioteca, momento de inicio de la actividad, volumen histórico de tuits, seguidores y siguiendo, así como cantidad y tipo de publicaciones realizadas en el período del relevamiento de datos. En los resultados se observa que un escaso número de bibliotecas adoptaron Twitter y que la apropiación de la herramienta muestra en general usos no planificados más centrados en la difusión que la interacción con usuarios. Se proponen estudios complementarios para conocer rutinas laborales de los bibliotecarios al respecto

Aproximaciones a los usos y apropiación de Twitter en bibliotecas universitarias de Argentina

El artículo analiza los principales usos de la plataforma Twitter por parte de bibliotecas universitarias en Argentina. Luego de la revisión bibliográfica, se exponen los procedimientos metodológicos empleados para identificar las instituciones que cuentan con esta herramienta comunicacional en la actualidad y los usos que se hace de ella a partir de ítems tales como: datos institucionales básicos, visibilidad y accesibilidad de la cuenta de Twitter en la Web de la biblioteca, momento de inicio de la actividad, volumen histórico de tuits, seguidores y siguiendo, así como cantidad y tipo de publicaciones realizadas en el período del relevamiento de datos. En los resultados se observa que un escaso número de bibliotecas adoptaron Twitter y que la apropiación de la herramienta muestra en general usos no planificados más centrados en la difusión que la interacción con usuarios. Se proponen estudios complementarios para conocer rutinas laborales de los bibliotecarios al respecto

The Conversation: Why TTIP will live on – but not for the EU

The Transatlantic Trade and Investment Partnership – also known as TTIP – could be the next casualty in the Brexit fallout. But not in the way you might expect. The controversial trade agreement between the EU and United States could well fall apart, only for the UK to pick up the pieces for its own trade deal.

The Conversation: Why leaving the euro is back on the agenda in the Greek election

Greece leaving the euro is old news. Since the former Greek prime minister, Alexis Tsipras, agreed to a third bailout in July, the perception of Grexit as an immediate threat has subsided – or at least disappeared from commentary. Nonetheless, while appetite for Grexit outside Greece has abated, the traumatic seven months of wrangling over its bailout with Europe produced a significant domestic demand for a return to the national currency.

El impacto de la presencia online en las cuentas de Twitter de las universidades vascas y navarras

Las Universidades han tenido que adaptarse a los nuevos modelos de comunicación surgidos en la época de Internet. Dentro de estos nuevos paradigmas las redes sociales han irrumpido y Twitter se ha establecido como una de las más importantes. El objetivo de esta investigación es demostrar que existe una relación entre la presencia online de una Universidad, definida por la cantidad de información disponible en Internet, y su cuenta en Twitter. Para ello se analizó la relación entre la presencia online y los perfiles oficiales de las cinco universidades del País Vasco y Navarra. Los resultados demostraron la existencia de una correlación significativa entre la presencia online de las instituciones y el número de seguidores de sus respectivas cuentas. En segundo lugar, esta investigación se planteó si Twitter puede servir para potenciar la presencia online de una Universidad. Es por eso que se formuló una segunda hipótesis que buscaba analizar si tener varias cuentas en Twitter aumentaría la presencia online de las Universidades. Los hallazgos para esta segunda hipótesis demostraron una correlación muy significativa entre tener varios perfiles en Twitter y la presencia online de las Universidades. Así queda demostrada la importancia de la presencia online para las cuentas de Twitter y la relevancia de Twitter a la hora de potenciar la presencia online de los centros.

Leveraging Stratification in Twitter Sampling

With Tweet volumes reaching 500 million a day, sampling is inevitable for any application using Twitter data. Realizing this, data providers such as Twitter, Gnip and Boardreader license sampled data streams priced in accordance with the sample size. Big Data applications working with sampled data would be interested in working with a large enough sample that is representative of the universal dataset. Previous work focusing on the representativeness issue has considered ensuring the global occurrence rates of key terms, be reliably estimated from the sample. Present technology allows sample size estimation in accordance with probabilistic bounds on occurrence rates for the case of uniform random sampling. In this paper, we consider the problem of further improving sample size estimates by leveraging stratification in Twitter data. We analyze our estimates through an extensive study using simulations and real-world data, establishing the superiority of our method over uniform random sampling. Our work provides the technical know-how for data providers to expand their portfolio to include stratified sampled datasets, whereas applications are benefited by being able to monitor more topics/events at the same data and computing cost.