92,406 research outputs found
The Decidability Frontier for Probabilistic Automata on Infinite Words
We consider probabilistic automata on infinite words with acceptance defined
by safety, reachability, B\"uchi, coB\"uchi, and limit-average conditions. We
consider quantitative and qualitative decision problems. We present extensions
and adaptations of proofs for probabilistic finite automata and present a
complete characterization of the decidability and undecidability frontier of
the quantitative and qualitative decision problems for probabilistic automata
on infinite words
Demonstrating adequate safety for a concrete column exposed to fire, using probabilistic methods
Demonstrating adequate safety for exceptional designs and new design applications requires an explicit evaluation of the safety level, considering the uncertainties associated with the design. The recently published PD 7974-7:2019 provides five routes to demonstrating adequate safety through probabilistic methods but does not include worked examples. The case study in this paper presents three state-of-the-art approaches for demonstrating achievement of an absolute safety target (acceptance concept âAC3â in PD 7974-7:2019) for a concrete column in an office building with stringent reliability requirements. The case study shows how fragility curves listed by, for example, industry organizations can support probabilistic approaches and a more comprehensive understanding of design performance
p-probabilistic k-anonymous microaggregation for the anonymization of surveys with uncertain participation
We develop a probabilistic variant of k-anonymous microaggregation which we term p-probabilistic resorting to a statistical model of respondent participation in order to aggregate quasi-identifiers in such a manner that k-anonymity is concordantly enforced with a parametric probabilistic guarantee. Succinctly owing the possibility that some respondents may not finally participate, sufficiently larger cells are created striving to satisfy k-anonymity with probability at least p. The microaggregation function is designed before the respondents submit their confidential data. More precisely, a specification of the function is sent to them which they may verify and apply to their quasi-identifying demographic variables prior to submitting the microaggregated data along with the confidential attributes to an authorized repository.
We propose a number of metrics to assess the performance of our probabilistic approach in terms of anonymity and distortion which we proceed to investigate theoretically in depth and empirically with synthetic and standardized data. We stress that in addition to constituting a functional extension of traditional microaggregation, thereby broadening its applicability to the anonymization of statistical databases in a wide variety of contexts, the relaxation of trust assumptions is arguably expected to have a considerable impact on user acceptance and ultimately on data utility through mere availability.Peer ReviewedPostprint (author's final draft
A Simple Computational Model for Acceptance/Rejection of Binary Sequence Generators
A simple binary model to compute the degree of balancedness in the output
sequence of LFSR-combinational generators has been developed. The computational
method is based exclusively on the handling of binary strings by means of logic
operations. The proposed model can serve as a deterministic alternative to
existing probabilistic methods for checking balancedness in binary sequence
generators. The procedure here described can be devised as a first selective
criterium for acceptance/rejection of this type of generators.Comment: 16 pages, 0 figure
A Labelling Framework for Probabilistic Argumentation
The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature
Fair Simulation for Nondeterministic and Probabilistic Buechi Automata: a Coalgebraic Perspective
Notions of simulation, among other uses, provide a computationally tractable
and sound (but not necessarily complete) proof method for language inclusion.
They have been comprehensively studied by Lynch and Vaandrager for
nondeterministic and timed systems; for B\"{u}chi automata the notion of fair
simulation has been introduced by Henzinger, Kupferman and Rajamani. We
contribute to a generalization of fair simulation in two different directions:
one for nondeterministic tree automata previously studied by Bomhard; and the
other for probabilistic word automata with finite state spaces, both under the
B\"{u}chi acceptance condition. The former nondeterministic definition is
formulated in terms of systems of fixed-point equations, hence is readily
translated to parity games and is then amenable to Jurdzi\'{n}ski's algorithm;
the latter probabilistic definition bears a strong ranking-function flavor.
These two different-looking definitions are derived from one source, namely our
coalgebraic modeling of B\"{u}chi automata. Based on these coalgebraic
observations, we also prove their soundness: a simulation indeed witnesses
language inclusion
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