Topic
Edit distance
About: Edit distance is a research topic. Over the lifetime, 2887 publications have been published within this topic receiving 71491 citations.
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TL;DR: Experimental results over a variety of shape databases show that the proposed approach is suitable for shape recognition.
49 citations
27 Sep 2016
TL;DR: A novel procedure for measuring robustness between digitized CPS signals and Signal Temporal Logic (STL) specifications is proposed and a dynamic programming algorithm for computing the robustness degree is developed.
Abstract: In cyber-physical systems (CPS), physical behaviors are typically controlled by digital hardware. As a consequence, continuous behaviors are discretized by sampling and quantization prior to their processing. Quantifying the similarity between CPS behaviors and their specification is an important ingredient in evaluating correctness and quality of such systems. We propose a novel procedure for measuring robustness between digitized CPS signals and Signal Temporal Logic (STL) specifications. We first equip STL with quantitative semantics based on the weighted edit distance (WED), a metric that quantifies both space and time mismatches between digitized CPS behaviors. We then develop a dynamic programming algorithm for computing the robustness degree between digitized signals and STL specifications. We implemented our approach and evaluated it on an automotive case study.
49 citations
TL;DR: A thorough analysis of the alignment hierarchy is provided, including a new polynomial-time algorithm and an NP-completeness proof, which gives rise to edit models that have not been studied yet.
Abstract: We describe a theoretical unifying framework to express the comparison of RNA structures, which we call alignment hierarchy. This framework relies on the definition of common supersequences for arc-annotated sequences and encompasses the main existing models for RNA structure comparison based on trees and arc-annotated sequences with a variety of edit operations. It also gives rise to edit models that have not been studied yet. We provide a thorough analysis of the alignment hierarchy, including a new polynomial-time algorithm and an NP-completeness proof. The polynomial-time algorithm involves biologically relevant edit operations such as pairing or unpairing nucleotides. It has been implemented in a software, called gardenia, which is available at the Web server http://bioinfo.lifl.fr/RNA/gardenia.
48 citations
TL;DR: Using dynamic programming principles, an algorithm is presented which yields X+ without computing individually the distances between every word of H and Y, and it can be shown that it is, in general, computationally less complex than all other existing algorithms which perform the same task.
48 citations
TL;DR: The performance of the Levenshtein distance for classifying languages by subsampling three language subsets from a large database of Austronesian languages shows poor performance, suggesting the need for more linguistically nuanced methods for automated language classification tasks.
Abstract: The Levenshtein distance is a simple distance metric derived from the number of edit operations needed to transform one string into another. This metric has received recent attention as a means of automatically classifying languages into genealogical subgroups. In this article I test the performance of the Levenshtein distance for classifying languages by subsampling three language subsets from a large database of Austronesian languages. Comparing the classification proposed by the Levenshtein distance to that of the comparative method shows that the Levenshtein classification is correct only 40% of time. Standardizing the orthography increases the performance, but only to a maximum of 65% accuracy within language subgroups. The accuracy of the Levenshtein classification decreases rapidly with phylogenetic distance, failing to discriminate homology and chance similarity across distantly related languages.This poor performance suggests the need for more linguistically nuanced methods for automated language classification tasks.
48 citations