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This book is a comprehensive presentation of embedded Java
security. It is compared with the security model of the Java 2
Standard Edition in order to view the impact of limited resources
on security. No other book specifically addresses the topic of
embedded Java security. Furthermore, the book provides hints and
suggestions as ways for hardening security, and offers researchers
and practitioners alike a broader and deeper understanding of the
issues involved in embedded Java security, and as a larger view -
mobile devices security. The author is a well-known authority and
expert in mobile computing and embedded devices.
Modelling real-life systems and phenomena using mathematical based
formalisms is ubiquitous in science and engineering. The reason is
that mathematics o er a suitable framework to carry out formal and
rigorous analysis of these systems. For instance, in software
engineering, formal methods are among the most e cient tools to
identify aws in software. The behavior of many real-life systems is
inherently stochastic which require stochastic models such as
labelled Markov processes (LMPs), Markov decision processes (MDPs),
predictive state representations (PSRs), etc. This thesis is about
quantifying the di erence between stochastic systems. The important
point of the thesis is that reinforcement learning (RL), a branch
of arti cial intelligence particularly e cient in presence of
uncertainty, can be used to quantify e ciently the divergence
between stochastic systems. The key idea is to de ne an MDP out of
the systems to be compared and then to interpret the optimal value
of the MDP as the divergence between them. The most appealing
feature of the proposed approach is that it does not rely on the
knowledge of the internal structure of the systems.
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