47,324 research outputs found
Statistical Model Checking : An Overview
Quantitative properties of stochastic systems are usually specified in logics
that allow one to compare the measure of executions satisfying certain temporal
properties with thresholds. The model checking problem for stochastic systems
with respect to such logics is typically solved by a numerical approach that
iteratively computes (or approximates) the exact measure of paths satisfying
relevant subformulas; the algorithms themselves depend on the class of systems
being analyzed as well as the logic used for specifying the properties. Another
approach to solve the model checking problem is to \emph{simulate} the system
for finitely many runs, and use \emph{hypothesis testing} to infer whether the
samples provide a \emph{statistical} evidence for the satisfaction or violation
of the specification. In this short paper, we survey the statistical approach,
and outline its main advantages in terms of efficiency, uniformity, and
simplicity.Comment: non
Analysis of the Security of BB84 by Model Checking
Quantum Cryptography or Quantum key distribution (QKD) is a technique that
allows the secure distribution of a bit string, used as key in cryptographic
protocols. When it was noted that quantum computers could break public key
cryptosystems based on number theory extensive studies have been undertaken on
QKD. Based on quantum mechanics, QKD offers unconditionally secure
communication. Now, the progress of research in this field allows the
anticipation of QKD to be available outside of laboratories within the next few
years. Efforts are made to improve the performance and reliability of the
implemented technologies. But several challenges remain despite this big
progress. The task of how to test the apparatuses of QKD For example did not
yet receive enough attention. These devises become complex and demand a big
verification effort. In this paper we are interested in an approach based on
the technique of probabilistic model checking for studying quantum information.
Precisely, we use the PRISM tool to analyze the security of BB84 protocol and
we are focused on the specific security property of eavesdropping detection. We
show that this property is affected by the parameters of quantum channel and
the power of eavesdropper.Comment: 12 Pages, IJNS
Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts
Most of the JavaScript code deployed in the wild has been minified, a process
in which identifier names are replaced with short, arbitrary and meaningless
names. Minified code occupies less space, but also makes the code extremely
difficult to manually inspect and understand. This paper presents Context2Name,
a deep learningbased technique that partially reverses the effect of
minification by predicting natural identifier names for minified names. The
core idea is to predict from the usage context of a variable a name that
captures the meaning of the variable. The approach combines a lightweight,
token-based static analysis with an auto-encoder neural network that summarizes
usage contexts and a recurrent neural network that predict natural names for a
given usage context. We evaluate Context2Name with a large corpus of real-world
JavaScript code and show that it successfully predicts 47.5% of all minified
identifiers while taking only 2.9 milliseconds on average to predict a name. A
comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
approach performs comparably in terms of accuracy while improving in terms of
efficiency. Moreover, Context2Name complements the state-of-the-art by
predicting 5.3% additional identifiers that are missed by both existing tools
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