The Inclusion Problem for Regular Expressions

Hovland, Dag

doi:10.1007/978-3-642-13089-2_26

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Furthermore, we notice that disproving times for invalid properties are consistently lower than those for proved properties, regardless of program complexity. This finding echos the insights from prior TRS-based works [14,[19][20][21][22], which suggest that TRS is a better average-case algorithm than those based on the comparison of automata.…”

Section: Implementation and Evaluationsupporting

confidence: 81%

Automated Temporal Verification for Algebraic Effects

Song

Foo

Chin

2022

Lecture Notes in Computer Science

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Although effect handlers offer a versatile abstraction for userdefined effects, they produce complex and less restricted execution traces due to the composable non-local control flow mechanisms. This paper is interested in the temporal behaviors of effect sequences, such as unhandled effects, termination of the communication, safety, fairness, etc. Specifically, we propose a novel effects logic ContEffs, to write precise and modular specifications for programs in the presence of user-defined effect handlers and primitive effects. As a second contribution, we devise a forward verifier together with a fixpoint calculator to infer the behaviors of such programs. Lastly, our automated verification framework provides a purely algebraic term-rewriting system (TRS) as the back-end solver, efficiently checking the entailments between ContEffs assertions. To demonstrate the feasibility of our proposals, we prototype a verification system where zero-shot, one-shot, and multi-shot continuations coexist; prove its correctness; present experimental results; and report on case studies.

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Section: Implementation and Evaluationsupporting

confidence: 81%

Automated Temporal Verification for Algebraic Effects

Song

Foo

Chin

2022

Lecture Notes in Computer Science

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“…23 For subsets of regular languages, there are polynomial time DFA constructions 24 and polynomial time comparison approaches for direct regular expression comparisons. 25 However, the structural differences between regular expressions and chemical patterns are too large to transfer these findings.…”

Section: ■ Introductionmentioning

confidence: 99%

Comparing Molecular Patterns Using the Example of SMARTS: Theory and Algorithms

Schmidt¹,

Ehmki²,

Ohm³

et al. 2019

J. Chem. Inf. Model.

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Molecular patterns are widely used for compound filtering in molecular design endeavors. They describe structural properties that are connected with unwanted physical or chemical properties like reactivity or toxicity. With filter sets comprising hundreds of structural filters, an analytic approach to compare those patterns is needed. Here we present a novel approach to solve the generic pattern comparison problem. We introduce chemically inspired fingerprints for pattern nodes and edges to derive an easy-to-compare pattern representation. On two annotated pattern graphs we apply a maximum common subgraph algorithm enabling the calculation of pattern inclusion and similarity. The resulting algorithm can be used in many different ways. We can automatically derive pattern hierarchies or search in large pattern collections for more general or more specific patterns. To the best of our knowledge, the presented algorithm is the first of its kind enabling these types of chemical pattern analytics. Our new tool named SMARTScompare is an implementation of the approach for the SMARTS language, which is the quasi-standard for structural filters. We demonstrate the capabilities of SMARTScompare on a large collection of SMARTS patterns from real applications.

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“…Such kinds of applications motivate us to investigate the problem of learning RE(&) from positive and negative examples. Most researchers have studied subclasses of REs, which are expressive enough to cover the vast majority of real-world applications [6,7,22] and perform better on several decision problems than general ones [6,7,19,20,25,27]. Bex et al [3] proposed learning algorithms for two subclasses of REs: SOREs and CHAREs, which capture many practical DTDs/XSDs and are both single occurrence REs.…”

Section: Introductionmentioning

confidence: 99%

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

Chen

Zhang

et al. 2020

Advances in Knowledge Discovery and Data Mining

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The presence of a schema for XML documents has numerous advantages. Unfortunately, many XML documents in practice are not accompanied by a schema or a valid schema. Therefore, it is essential to devise algorithms to infer schemas. The fundamental task in XML schema inference is to learn regular expressions. In this paper, we focus on learning the subclass of RE(&) called SIREs (the subclass of regular expressions with interleaving). Previous work in this direction lacks inference algorithms that support inference from positive and negative examples. We provide an algorithm to learn SIREs from positive and negative examples based on genetic algorithms and parallel techniques. Our algorithm also has better expansibility, which means that our algorithm not only supports learning with positive and negative examples, but also supports learning with positive or negative examples only. Experimental results demonstrate the effectiveness of our algorithm.

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The Inclusion Problem for Regular Expressions

Cited by 4 publications

References 15 publications

Automated Temporal Verification for Algebraic Effects

Automated Temporal Verification for Algebraic Effects

Comparing Molecular Patterns Using the Example of SMARTS: Theory and Algorithms

Inferring Restricted Regular Expressions with Interleaving from Positive and Negative Samples

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