10,764 research outputs found
A GIS-based multi-criteria evaluation framework for uncertainty reduction in earthquake disaster management using granular computing
One of the most important steps in earthquake disaster management is the prediction of probable damages which is called earthquake vulnerability assessment. Earthquake vulnerability assessment is a multicriteria problem and a number of multi-criteria decision making models have been proposed for the problem. Two main sources of uncertainty including uncertainty associated with experts‘ point of views and the one associated with attribute values exist in the earthquake vulnerability assessment problem. If the uncertainty in these two sources is not handled properly the resulted seismic vulnerability map will be unreliable. The main objective of this research is to propose a reliable model for earthquake vulnerability assessment which is able to manage the uncertainty associated with the experts‘ opinions. Granular Computing (GrC) is able to extract a set of if-then rules with minimum incompatibility from an information table. An integration of Dempster-Shafer Theory (DST) and GrC is applied in the current research to minimize the entropy in experts‘ opinions. The accuracy of the model based on the integration of the DST and GrC is 83%, while the accuracy of the single-expert model is 62% which indicates the importance of uncertainty management in seismic vulnerability assessment problem. Due to limited accessibility to current data, only six criteria are used in this model. However, the model is able to take into account both qualitative and quantitative criteria
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
This paper adapts topic models to the psychometric testing of MOOC students
based on their online forum postings. Measurement theory from education and
psychology provides statistical models for quantifying a person's attainment of
intangible attributes such as attitudes, abilities or intelligence. Such models
infer latent skill levels by relating them to individuals' observed responses
on a series of items such as quiz questions. The set of items can be used to
measure a latent skill if individuals' responses on them conform to a Guttman
scale. Such well-scaled items differentiate between individuals and inferred
levels span the entire range from most basic to the advanced. In practice,
education researchers manually devise items (quiz questions) while optimising
well-scaled conformance. Due to the costly nature and expert requirements of
this process, psychometric testing has found limited use in everyday teaching.
We aim to develop usable measurement models for highly-instrumented MOOC
delivery platforms, by using participation in automatically-extracted online
forum topics as items. The challenge is to formalise the Guttman scale
educational constraint and incorporate it into topic models. To favour topics
that automatically conform to a Guttman scale, we introduce a novel
regularisation into non-negative matrix factorisation-based topic modelling. We
demonstrate the suitability of our approach with both quantitative experiments
on three Coursera MOOCs, and with a qualitative survey of topic
interpretability on two MOOCs by domain expert interviews.Comment: 12 pages, 9 figures; accepted into AAAI'201
Flow-based Influence Graph Visual Summarization
Visually mining a large influence graph is appealing yet challenging. People
are amazed by pictures of newscasting graph on Twitter, engaged by hidden
citation networks in academics, nevertheless often troubled by the unpleasant
readability of the underlying visualization. Existing summarization methods
enhance the graph visualization with blocked views, but have adverse effect on
the latent influence structure. How can we visually summarize a large graph to
maximize influence flows? In particular, how can we illustrate the impact of an
individual node through the summarization? Can we maintain the appealing graph
metaphor while preserving both the overall influence pattern and fine
readability?
To answer these questions, we first formally define the influence graph
summarization problem. Second, we propose an end-to-end framework to solve the
new problem. Our method can not only highlight the flow-based influence
patterns in the visual summarization, but also inherently support rich graph
attributes. Last, we present a theoretic analysis and report our experiment
results. Both evidences demonstrate that our framework can effectively
approximate the proposed influence graph summarization objective while
outperforming previous methods in a typical scenario of visually mining
academic citation networks.Comment: to appear in IEEE International Conference on Data Mining (ICDM),
Shen Zhen, China, December 201
Community Structure Characterization
This entry discusses the problem of describing some communities identified in
a complex network of interest, in a way allowing to interpret them. We suppose
the community structure has already been detected through one of the many
methods proposed in the literature. The question is then to know how to extract
valuable information from this first result, in order to allow human
interpretation. This requires subsequent processing, which we describe in the
rest of this entry
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