40,904 research outputs found
Parametric Verification of Weighted Systems
This paper addresses the problem of parametric model checking for weighted transition systems. We consider transition systems labelled with linear equations over a set of parameters and we use them to provide semantics for a parametric version of weighted CTL where the until and next operators are themselves indexed with linear equations. The parameters change the model-checking problem into a problem of computing a linear system of inequalities that characterizes the parameters that guarantee the satisfiability. To address this problem, we use parametric dependency graphs (PDGs) and we propose a global update function that yields an assignment
to each node in a PDG. For an iterative application of the function, we prove that a fixed point assignment to PDG nodes exists and the set of assignments constitutes a well-quasi ordering, thus ensuring that the fixed point assignment can be found after finitely many iterations. To demonstrate the utility of our technique, we have implemented a prototype tool that computes the constraints on parameters for model checking problems
Architectures in parametric component-based systems: Qualitative and quantitative modelling
One of the key aspects in component-based design is specifying the software
architecture that characterizes the topology and the permissible interactions
of the components of a system. To achieve well-founded design there is need to
address both the qualitative and non-functional aspects of architectures. In
this paper we study the qualitative and quantitative formal modelling of
architectures applied on parametric component-based systems, that consist of an
unknown number of instances of each component. Specifically, we introduce an
extended propositional interaction logic and investigate its first-order level
which serves as a formal language for the interactions of parametric systems.
Our logics achieve to encode the execution order of interactions, which is a
main feature in several important architectures, as well as to model recursive
interactions. Moreover, we prove the decidability of equivalence,
satisfiability, and validity of first-order extended interaction logic
formulas, and provide several examples of formulas describing well-known
architectures. We show the robustness of our theory by effectively extending
our results for parametric weighted architectures. For this, we study the
weighted counterparts of our logics over a commutative semiring, and we apply
them for modelling the quantitative aspects of concrete architectures. Finally,
we prove that the equivalence problem of weighted first-order extended
interaction logic formulas is decidable in a large class of semirings, namely
the class (of subsemirings) of skew fields.Comment: 53 pages, 11 figure
Weighted Branching Simulation Distance for Parametric Weighted Kripke Structures
This paper concerns branching simulation for weighted Kripke structures with
parametric weights. Concretely, we consider a weighted extension of branching
simulation where a single transitions can be matched by a sequence of
transitions while preserving the branching behavior. We relax this notion to
allow for a small degree of deviation in the matching of weights, inducing a
directed distance on states. The distance between two states can be used
directly to relate properties of the states within a sub-fragment of weighted
CTL. The problem of relating systems thus changes to minimizing the distance
which, in the general parametric case, corresponds to finding suitable
parameter valuations such that one system can approximately simulate another.
Although the distance considers a potentially infinite set of transition
sequences we demonstrate that there exists an upper bound on the length of
relevant sequences, thereby establishing the computability of the distance.Comment: In Proceedings Cassting'16/SynCoP'16, arXiv:1608.0017
Fast computation of the performance evaluation of biometric systems: application to multibiometric
The performance evaluation of biometric systems is a crucial step when
designing and evaluating such systems. The evaluation process uses the Equal
Error Rate (EER) metric proposed by the International Organization for
Standardization (ISO/IEC). The EER metric is a powerful metric which allows
easily comparing and evaluating biometric systems. However, the computation
time of the EER is, most of the time, very intensive. In this paper, we propose
a fast method which computes an approximated value of the EER. We illustrate
the benefit of the proposed method on two applications: the computing of non
parametric confidence intervals and the use of genetic algorithms to compute
the parameters of fusion functions. Experimental results show the superiority
of the proposed EER approximation method in term of computing time, and the
interest of its use to reduce the learning of parameters with genetic
algorithms. The proposed method opens new perspectives for the development of
secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012
Statistical post-processing of hydrological forecasts using Bayesian model averaging
Accurate and reliable probabilistic forecasts of hydrological quantities like
runoff or water level are beneficial to various areas of society. Probabilistic
state-of-the-art hydrological ensemble prediction models are usually driven
with meteorological ensemble forecasts. Hence, biases and dispersion errors of
the meteorological forecasts cascade down to the hydrological predictions and
add to the errors of the hydrological models. The systematic parts of these
errors can be reduced by applying statistical post-processing. For a sound
estimation of predictive uncertainty and an optimal correction of systematic
errors, statistical post-processing methods should be tailored to the
particular forecast variable at hand. Former studies have shown that it can
make sense to treat hydrological quantities as bounded variables. In this
paper, a doubly truncated Bayesian model averaging (BMA) method, which allows
for flexible post-processing of (multi-model) ensemble forecasts of water
level, is introduced. A case study based on water level for a gauge of river
Rhine, reveals a good predictive skill of doubly truncated BMA compared both to
the raw ensemble and the reference ensemble model output statistics approach.Comment: 19 pages, 6 figure
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