189,123 research outputs found
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
Identification of a reversible quantum gate: assessing the resources
We assess the resources needed to identify a reversible quantum gate among a
finite set of alternatives, including in our analysis both deterministic and
probabilistic strategies. Among the probabilistic strategies we consider
unambiguous gate discrimination, where errors are not tolerated but
inconclusive outcomes are allowed, and we prove that parallel strategies are
sufficient to unambiguously identify the unknown gate with minimum number of
queries. This result is used to provide upper and lower bounds on the query
complexity and on the minimum ancilla dimension. In addition, we introduce the
notion of generalized t-designs, which includes unitary t-designs and group
representations as special cases. For gates forming a generalized t-design we
give an explicit expression for the maximum probability of correct gate
identification and we prove that there is no gap between the performances of
deterministic strategies an those of probabilistic strategies. Hence,
evaluating of the query complexity of perfect deterministic discrimination is
reduced to the easier problem of evaluating the query complexity of unambiguous
discrimination. Finally, we consider discrimination strategies where the use of
ancillas is forbidden, providing upper bounds on the number of additional
queries needed to make up for the lack of entanglement with the ancillas.Comment: 24 + 8 pages, published versio
Group Testing with Probabilistic Tests: Theory, Design and Application
Identification of defective members of large populations has been widely
studied in the statistics community under the name of group testing. It
involves grouping subsets of items into different pools and detecting defective
members based on the set of test results obtained for each pool.
In a classical noiseless group testing setup, it is assumed that the sampling
procedure is fully known to the reconstruction algorithm, in the sense that the
existence of a defective member in a pool results in the test outcome of that
pool to be positive. However, this may not be always a valid assumption in some
cases of interest. In particular, we consider the case where the defective
items in a pool can become independently inactive with a certain probability.
Hence, one may obtain a negative test result in a pool despite containing some
defective items. As a result, any sampling and reconstruction method should be
able to cope with two different types of uncertainty, i.e., the unknown set of
defective items and the partially unknown, probabilistic testing procedure.
In this work, motivated by the application of detecting infected people in
viral epidemics, we design non-adaptive sampling procedures that allow
successful identification of the defective items through a set of probabilistic
tests. Our design requires only a small number of tests to single out the
defective items. In particular, for a population of size and at most
defective items with activation probability , our results show that tests is sufficient if the sampling procedure should
work for all possible sets of defective items, while
tests is enough to be successful for any single set of defective items.
Moreover, we show that the defective members can be recovered using a simple
reconstruction algorithm with complexity of .Comment: Full version of the conference paper "Compressed Sensing with
Probabilistic Measurements: A Group Testing Solution" appearing in
proceedings of the 47th Annual Allerton Conference on Communication, Control,
and Computing, 2009 (arXiv:0909.3508). To appear in IEEE Transactions on
Information Theor
Efficiently Decodable Non-Adaptive Threshold Group Testing
We consider non-adaptive threshold group testing for identification of up to
defective items in a set of items, where a test is positive if it
contains at least defective items, and negative otherwise.
The defective items can be identified using tests with
probability at least for any or tests with probability 1. The decoding time is
. This result significantly improves the
best known results for decoding non-adaptive threshold group testing:
for probabilistic decoding, where
, and for deterministic decoding
The supersingular Endomorphism Ring and One Endomorphism problems are equivalent
The supersingular Endomorphism Ring problem is the following: given a
supersingular elliptic curve, compute all of its endomorphisms. The presumed
hardness of this problem is foundational for isogeny-based cryptography. The
One Endomorphism problem only asks to find a single non-scalar endomorphism. We
prove that these two problems are equivalent, under probabilistic polynomial
time reductions. We prove a number of consequences. First, assuming the
hardness of the endomorphism ring problem, the Charles--Goren--Lauter hash
function is collision resistant, and the SQIsign identification protocol is
sound. Second, the endomorphism ring problem is equivalent to the problem of
computing arbitrary isogenies between supersingular elliptic curves, a result
previously known only for isogenies of smooth degree. Third, there exists an
unconditional probabilistic algorithm to solve the endomorphism ring problem in
time O~(sqrt(p)), a result that previously required to assume the generalized
Riemann hypothesis. To prove our main result, we introduce a flexible framework
for the study of isogeny graphs with additional information. We prove a general
and easy-to-use rapid mixing theorem
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