37,809 research outputs found
Investigating the information-seeking behaviour of academic lawyers: From Ellis's model to design.
Information-seeking is important for lawyers, who have access to many dedicated electronic resources.However there is considerable scope for improving the design of these resources to better support information-seeking. One way of informing design is to use information-seeking models as theoretical lenses to analyse usersâ behaviour with existing systems. However many models, including those informed by studying lawyers, analyse information-seeking at a high level of abstraction and are only likely to lead to broad-scoped design insights. We illustrate that one potentially useful (and lowerlevel) model is Ellisâs - by using it as a lens to analyse and make design suggestions based on the information-seeking behaviour of twenty-seven academic lawyers, who were asked to think aloud whilst using electronic legal resources to find information for their work. We identify similar information-seeking behaviours to those originally found by Ellis and his colleagues in scientific domains, along with several that were not identified in previous studies such as âupdatingâ (which we believe is particularly pertinent to legal information-seeking). We also present a refinement of Ellisâs model based on the identification of several levels that the behaviours were found to operate at and the identification of sets of mutually exclusive subtypes of behaviours
A Study of Realtime Summarization Metrics
Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for real-time and streaming conditions.
The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems
Analyzing large-scale DNA Sequences on Multi-core Architectures
Rapid analysis of DNA sequences is important in preventing the evolution of
different viruses and bacteria during an early phase, early diagnosis of
genetic predispositions to certain diseases (cancer, cardiovascular diseases),
and in DNA forensics. However, real-world DNA sequences may comprise several
Gigabytes and the process of DNA analysis demands adequate computational
resources to be completed within a reasonable time. In this paper we present a
scalable approach for parallel DNA analysis that is based on Finite Automata,
and which is suitable for analyzing very large DNA segments. We evaluate our
approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog
(2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results
on a dual-socket shared-memory system with 24 physical cores show speed-ups of
up to 17.6x. Our approach is up to 3x faster than a pattern-based parallel
approach that uses the RE2 library.Comment: The 18th IEEE International Conference on Computational Science and
Engineering (CSE 2015), Porto, Portugal, 20 - 23 October 201
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
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Using information behaviors to evaluate the functionality and usability of electronic resources: From Ellis's model to evaluation
Information behavior (IB) research involves examining how people look for and use information, often with the sole purpose of gaining insights into the behavior displayed. However, it is also possible to examine IB with the purpose of using the insights gained to design new tools or improve the design of existing tools to support information seeking and use. This approach is advocated by David Ellis who, over two decades ago, presented a model of information seeking behaviors and made suggestions for how electronic tools might be designed to support these behaviors. Ellis also recognized that IBs might be used as the basis for evaluating as well as designing electronic resources. In this article, we present the IB evaluation methods. These two novel methods, based on an extension of Ellis's model, use the empirically observed IBs of lawyers as a framework for structuring user-centered evaluations of the functionality and usability of electronic resources. In this article, we present the IB methods and illustrate their use through the discussion of two examples. We also discuss benefits and limitations, grounded in specific features of the methods
BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer
For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical modelsâ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the systemâs use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
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