37,447 research outputs found
A Trust Model Based on Service Classification in Mobile Services
Internet of Things (IoT) and B3G/4G communication are promoting the pervasive
mobile services with its advanced features. However, security problems are also
baffled the development. This paper proposes a trust model to protect the
user's security. The billing or trust operator works as an agent to provide a
trust authentication for all the service providers. The services are classified
by sensitive value calculation. With the value, the user's trustiness for
corresponding service can be obtained. For decision, three trust regions are
divided, which is referred to three ranks: high, medium and low. The trust
region tells the customer, with his calculated trust value, which rank he has
got and which authentication methods should be used for access. Authentication
history and penalty are also involved with reasons.Comment: IEEE/ACM Internet of Things Symposium (IOTS), in conjunction with
GreenCom 2010, IEEE, Hangzhou, China, December 18-20, 201
An Alarm System For Segmentation Algorithm Based On Shape Model
It is usually hard for a learning system to predict correctly on rare events
that never occur in the training data, and there is no exception for
segmentation algorithms. Meanwhile, manual inspection of each case to locate
the failures becomes infeasible due to the trend of large data scale and
limited human resource. Therefore, we build an alarm system that will set off
alerts when the segmentation result is possibly unsatisfactory, assuming no
corresponding ground truth mask is provided. One plausible solution is to
project the segmentation results into a low dimensional feature space; then
learn classifiers/regressors to predict their qualities. Motivated by this, in
this paper, we learn a feature space using the shape information which is a
strong prior shared among different datasets and robust to the appearance
variation of input data.The shape feature is captured using a Variational
Auto-Encoder (VAE) network that trained with only the ground truth masks.
During testing, the segmentation results with bad shapes shall not fit the
shape prior well, resulting in large loss values. Thus, the VAE is able to
evaluate the quality of segmentation result on unseen data, without using
ground truth. Finally, we learn a regressor in the one-dimensional feature
space to predict the qualities of segmentation results. Our alarm system is
evaluated on several recent state-of-art segmentation algorithms for 3D medical
segmentation tasks. Compared with other standard quality assessment methods,
our system consistently provides more reliable prediction on the qualities of
segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures
CNOT-count optimized quantum circuit of the Shor's algorithm
We present improved quantum circuit for modular exponentiation of a constant,
which is the most expensive operation in Shor's algorithm for integer
factorization. While previous work mostly focuses on minimizing the number of
qubits or the depth of circuit, we try to minimize the number of CNOT gate
which primarily determines the running time on a ion trap quantum computer.
First, we give the implementation of basic arithmetic with known lowest number
of CNOT gate and the construction of improved modular exponentiation of a
constant by accumulating intermediate date and windowing technique. Then, we
precisely estimate the number of improved quantum circuit to perform Shor's
algorithm for factoring a -bit integer, which is
. According to the number of CNOT gates, we
analyze the running time and feasibility of Shor's algorithm on a ion trap
quantum computer. Finally, we discuss the lower bound of CNOT numbers needed to
implement Shor's algorithm
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