82,105 research outputs found
The Blogosphere at a Glance — Content-Based Structures Made Simple
A network representation based on a basic wordoverlap
similarity measure between blogs is introduced.
The simplicity of the representation renders
it computationally tractable, transparent and insensitive
to representation-dependent artifacts. Using
Swedish blog data, we demonstrate that the representation,
in spite of its simplicity, manages to capture
important structural properties of the content
in the blogosphere. First, blogs that treat similar
subjects are organized in distinct network clusters.
Second, the network is hierarchically organized as
clusters in turn form higher-order clusters: a compound
structure reminiscent of a blog taxonomy
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
An adaptive technique for content-based image retrieval
We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search
Pushing Your Point of View: Behavioral Measures of Manipulation in Wikipedia
As a major source for information on virtually any topic, Wikipedia serves an
important role in public dissemination and consumption of knowledge. As a
result, it presents tremendous potential for people to promulgate their own
points of view; such efforts may be more subtle than typical vandalism. In this
paper, we introduce new behavioral metrics to quantify the level of controversy
associated with a particular user: a Controversy Score (C-Score) based on the
amount of attention the user focuses on controversial pages, and a Clustered
Controversy Score (CC-Score) that also takes into account topical clustering.
We show that both these measures are useful for identifying people who try to
"push" their points of view, by showing that they are good predictors of which
editors get blocked. The metrics can be used to triage potential POV pushers.
We apply this idea to a dataset of users who requested promotion to
administrator status and easily identify some editors who significantly changed
their behavior upon becoming administrators. At the same time, such behavior is
not rampant. Those who are promoted to administrator status tend to have more
stable behavior than comparable groups of prolific editors. This suggests that
the Adminship process works well, and that the Wikipedia community is not
overwhelmed by users who become administrators to promote their own points of
view
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
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