31,341 research outputs found
Taming Horizontal Instability in Merge Trees: On the Computation of a Comprehensive Deformation-based Edit Distance
Comparative analysis of scalar fields in scientific visualization often
involves distance functions on topological abstractions. This paper focuses on
the merge tree abstraction (representing the nesting of sub- or superlevel
sets) and proposes the application of the unconstrained deformation-based edit
distance. Previous approaches on merge trees often suffer from instability:
small perturbations in the data can lead to large distances of the
abstractions. While some existing methods can handle so-called vertical
instability, the unconstrained deformation-based edit distance addresses both
vertical and horizontal instabilities, also called saddle swaps. We establish
the computational complexity as NP-complete, and provide an integer linear
program formulation for computation. Experimental results on the TOSCA shape
matching ensemble provide evidence for the stability of the proposed distance.
We thereby showcase the potential of handling saddle swaps for comparison of
scalar fields through merge trees
Measuring the accuracy of page-reading systems
Given a bitmapped image of a page from any document, a page-reading system identifies the characters on the page and stores them in a text file. This OCR-generated text is represented by a string and compared with the correct string to determine the accuracy of this process. The string editing problem is applied to find an optimal correspondence of these strings using an appropriate cost function. The ISRI annual test of page-reading systems utilizes the following performance measures, which are defined in terms of this correspondence and the string edit distance: character accuracy, throughput, accuracy by character class, marked character efficiency, word accuracy, non-stopword accuracy, and phrase accuracy. It is shown that the universe of cost functions is divided into equivalence classes, and the cost functions related to the longest common subsequence (LCS) are identified. The computation of a LCS can be made faster by a linear-time preprocessing step
Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity
Consider two parties who want to compare their strings, e.g., genomes, but do
not want to reveal them to each other. We present a system for
privacy-preserving matching of strings, which differs from existing systems by
providing a deterministic approximation instead of an exact distance. It is
efficient (linear complexity), non-interactive and does not involve a third
party which makes it particularly suitable for cloud computing. We extend our
protocol, such that it mitigates iterated differential attacks proposed by
Goodrich. Further an implementation of the system is evaluated and compared
against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure
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