79,832 research outputs found
Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results
In this paper, automated user verification techniques for smartphones are
investigated. A unique non-commercial dataset, the University of Maryland
Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication
research is introduced. This paper focuses on three sensors - front camera,
touch sensor and location service while providing a general description for
other modalities. Benchmark results for face detection, face verification,
touch-based user identification and location-based next-place prediction are
presented, which indicate that more robust methods fine-tuned to the mobile
platform are needed to achieve satisfactory verification accuracy. The dataset
will be made available to the research community for promoting additional
research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201
Solutions to Detect and Analyze Online Radicalization : A Survey
Online Radicalization (also called Cyber-Terrorism or Extremism or
Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing
concern to the society, governments and law enforcement agencies around the
world. Research shows that various platforms on the Internet (low barrier to
publish content, allows anonymity, provides exposure to millions of users and a
potential of a very quick and widespread diffusion of message) such as YouTube
(a popular video sharing website), Twitter (an online micro-blogging service),
Facebook (a popular social networking website), online discussion forums and
blogosphere are being misused for malicious intent. Such platforms are being
used to form hate groups, racist communities, spread extremist agenda, incite
anger or violence, promote radicalization, recruit members and create virtual
organi- zations and communities. Automatic detection of online radicalization
is a technically challenging problem because of the vast amount of the data,
unstructured and noisy user-generated content, dynamically changing content and
adversary behavior. There are several solutions proposed in the literature
aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we
review solutions to detect and analyze online radicalization. We review 40
papers published at 12 venues from June 2003 to November 2011. We present a
novel classification scheme to classify these papers. We analyze these
techniques, perform trend analysis, discuss limitations of existing techniques
and find out research gaps
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