93,889 research outputs found
Escaping the Trap of too Precise Topic Queries
At the very center of digital mathematics libraries lie controlled
vocabularies which qualify the {\it topic} of the documents. These topics are
used when submitting a document to a digital mathematics library and to perform
searches in a library. The latter are refined by the use of these topics as
they allow a precise classification of the mathematics area this document
addresses. However, there is a major risk that users employ too precise topics
to specify their queries: they may be employing a topic that is only "close-by"
but missing to match the right resource. We call this the {\it topic trap}.
Indeed, since 2009, this issue has appeared frequently on the i2geo.net
platform. Other mathematics portals experience the same phenomenon. An approach
to solve this issue is to introduce tolerance in the way queries are understood
by the user. In particular, the approach of including fuzzy matches but this
introduces noise which may prevent the user of understanding the function of
the search engine.
In this paper, we propose a way to escape the topic trap by employing the
navigation between related topics and the count of search results for each
topic. This supports the user in that search for close-by topics is a click
away from a previous search. This approach was realized with the i2geo search
engine and is described in detail where the relation of being {\it related} is
computed by employing textual analysis of the definitions of the concepts
fetched from the Wikipedia encyclopedia.Comment: 12 pages, Conference on Intelligent Computer Mathematics 2013 Bath,
U
Contributions in computational intelligence with results in functional neuroimaging
This thesis applies computational intelligence methodologies to study functional brain images. It is a state-of-the-art application relative to unsupervised learning domain to functional neuroimaging. There are also contributions related to computational intelligence on topics relative to clustering validation and spatio-temporal clustering analysis. Speci_cally, there are the presentation of a new separation measure based on fuzzy sets theory to establish the validity of the fuzzy clustering outcomes and the presentation of a framework to approach the parcellation of functional neuroimages taking in account both spatial and temporal patterns. These contributions have been applied to neuroimages obtained with functional Magnetic Resonance Imaging, using both active and passive paradigm and using both in-house data and fMRI repository. The results obtained shown, globally, an improvement on the quality of the neuroimaging analysis using the methodological contributions proposed
Time Aware Knowledge Extraction for Microblog Summarization on Twitter
Microblogging services like Twitter and Facebook collect millions of user
generated content every moment about trending news, occurring events, and so
on. Nevertheless, it is really a nightmare to find information of interest
through the huge amount of available posts that are often noise and redundant.
In general, social media analytics services have caught increasing attention
from both side research and industry. Specifically, the dynamic context of
microblogging requires to manage not only meaning of information but also the
evolution of knowledge over the timeline. This work defines Time Aware
Knowledge Extraction (briefly TAKE) methodology that relies on temporal
extension of Fuzzy Formal Concept Analysis. In particular, a microblog
summarization algorithm has been defined filtering the concepts organized by
TAKE in a time-dependent hierarchy. The algorithm addresses topic-based
summarization on Twitter. Besides considering the timing of the concepts,
another distinguish feature of the proposed microblog summarization framework
is the possibility to have more or less detailed summary, according to the
user's needs, with good levels of quality and completeness as highlighted in
the experimental results.Comment: 33 pages, 10 figure
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises
The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques
Characterizing Transgender Health Issues in Twitter
Although there are millions of transgender people in the world, a lack of
information exists about their health issues. This issue has consequences for
the medical field, which only has a nascent understanding of how to identify
and meet this population's health-related needs. Social media sites like
Twitter provide new opportunities for transgender people to overcome these
barriers by sharing their personal health experiences. Our research employs a
computational framework to collect tweets from self-identified transgender
users, detect those that are health-related, and identify their information
needs. This framework is significant because it provides a macro-scale
perspective on an issue that lacks investigation at national or demographic
levels. Our findings identified 54 distinct health-related topics that we
grouped into 7 broader categories. Further, we found both linguistic and
topical differences in the health-related information shared by transgender men
(TM) as com-pared to transgender women (TW). These findings can help inform
medical and policy-based strategies for health interventions within transgender
communities. Also, our proposed approach can inform the development of
computational strategies to identify the health-related information needs of
other marginalized populations
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