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Directed incremental symbolic execution
TLDR
In this paper, we present Directed Incremental Symbolic Execution (DiSE), a novel technique for detecting and characterizing the effects of program changes. Expand
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Regression model checking
TLDR
We have developed a new technique for regression model checking (RMC), that applies model checking incrementally to new versions of systems. Expand
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Memoized symbolic execution
TLDR
This paper introduces memoized symbolic execution (Memoise), a new approach for more efficient application of forward symbolic execution, which is a well-studied technique for systematic exploration of program behaviors based on bounded execution paths. Expand
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Employing quaternion wavelet transform for banknote classification
TLDR
In order to improve the performance of the banknote classification, this paper presents a feature extraction method based on quaternion wavelet transform (QWT). Expand
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Exponential stability of impulsive stochastic fuzzy reaction-diffusion Cohen-Grossberg neural networks with mixed delays
TLDR
The problem of mean square exponential stability for a class of impulsive stochastic fuzzy Cohen-Grossberg networks with mixed delays and reaction-diffusion terms is investigated in this paper. Expand
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Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix
TLDR
This paper introduces a new feature extraction method for texture classification application that can achieve higher texture classification accuracy rate than conventional methods. Expand
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Property differencing for incremental checking
TLDR
This paper introduces iProperty, a novel approach that facilitates incremental checking of programs based on a property differencing technique. Expand
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Compositional Symbolic Execution with Memoized Replay
TLDR
This paper introduces a new approach for compositional symbolic execution that uses memoization trees to efficiently replay the symbolic execution of the corresponding methods with respect to their calling contexts. Expand
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Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation
TLDR
Multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve classification accuracy for hyperspectral images. Expand
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Multiscale texture classification using reduced quaternion wavelet transform
Abstract This article proposes a study of the reduced quaternion wavelet transform (RQWT) which has one shift-invariant magnitude and three angle phases at each scale from digital image analysisExpand
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