9,841 research outputs found
Benchmarking the Privacy-Preserving People Search
People search is an important topic in information retrieval. Many previous
studies on this topic employed social networks to boost search performance by
incorporating either local network features (e.g. the common connections
between the querying user and candidates in social networks), or global network
features (e.g. the PageRank), or both. However, the available social network
information can be restricted because of the privacy settings of involved
users, which in turn would affect the performance of people search. Therefore,
in this paper, we focus on the privacy issues in people search. We propose
simulating different privacy settings with a public social network due to the
unavailability of privacy-concerned networks. Our study examines the influences
of privacy concerns on the local and global network features, and their impacts
on the performance of people search. Our results show that: 1) the privacy
concerns of different people in the networks have different influences. People
with higher association (i.e. higher degree in a network) have much greater
impacts on the performance of people search; 2) local network features are more
sensitive to the privacy concerns, especially when such concerns come from high
association peoples in the network who are also related to the querying user.
As the first study on this topic, we hope to generate further discussions on
these issues.Comment: 4 pages, 5 figure
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model. Our project page is publicly available at:
https://github.com/zhen-he/tracking-by-animationComment: CVPR 201
Mutual Information-Maximizing Quantized Belief Propagation Decoding of Regular LDPC Codes
In mutual information-maximizing lookup table (MIM-LUT) decoding of
low-density parity-check (LDPC) codes, table lookup operations are used to
replace arithmetic operations. In practice, large tables need to be decomposed
into small tables to save the memory consumption, at the cost of degraded error
performance. In this paper, we propose a method, called mutual
information-maximizing quantized belief propagation (MIM-QBP) decoding, to
remove the lookup tables used for MIM-LUT decoding. Our method leads to a very
efficient decoder, namely the MIM-QBP decoder, which can be implemented based
only on simple mappings and fixed-point additions. Simulation results show that
the MIM-QBP decoder can always considerably outperform the state-of-the-art
MIM-LUT decoder, mainly because it can avoid the performance loss due to table
decomposition. Furthermore, the MIM-QBP decoder with only 3 bits per message
can outperform the floating-point belief propagation (BP) decoder at high
signal-to-noise ratio (SNR) regions when testing on high-rate codes with a
maximum of 10-30 iterations
A New Method Of Distinguishing Models For The High- Events At HERA
Many explanations for the excess high-Q^2 events from H1 and
ZEUS at HERA have been proposed each with criticisms. We propose a new method
to distinguish different models by looking at a new distribution which is
insensitive to parton distribution function but sensitive to new physics.Comment: 11 pages in revtex plus 3 figures in postscrip
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