Abstract: Disclosed are methods and apparatuses for classifier evaluation. The evaluation involves constructing a ground truth refinement having a degree of error within specified bounds from a malware reference dataset as an approximate ground truth refinement. The evaluation further involves using the approximate ground truth refinement to determine at least one of: a lower bound on precision or an upper bound on recall and accuracy. The evaluation further involves evaluating a classifier by evaluating at least one of a classification method or clustering method by examining changes to the upper bound and/or the lower bound produced by the approximate ground truth refinement.

Abstract: Disclosed are methods and apparatuses for classifier evaluation. The evaluation involves constructing a ground truth refinement having a degree of error within specified bounds from a malware reference dataset as an approximate ground truth refinement. The evaluation further involves using the approximate ground truth refinement to determine at least one of: a lower bound on precision or an upper bound on recall and accuracy. The evaluation further involves evaluating a classifier by evaluating at least one of a classification method or clustering method by examining changes to the upper bound and/or the lower bound produced by the approximate ground truth refinement.

Abstract: A computer-implemented method for generating an interpretable kernel embedding for heterogeneous data. The method can include identifying a set of base kernels in the heterogeneous data; and creating multiple sets of transformed kernels by applying a unique composition rule or a unique combination of multiple composition rules to the set of base kernels. The method can include fitting the multiple sets into a stochastic process model to generate fitting scores that respectively indicate a degree of the fitting for each of the multiple sets; storing the fitting scores in a matrix; and standardizing the matrix to generate the interpretable kernel embedding for the heterogeneous data.

Abstract: Biometrics fingerprint matching has been done with a heavily hand-tuned and designed process of classical computer vision techniques for several decades. This approach has led to accurate solutions for solving crimes today and, as such, little effort has been devoted to using deep learning in this domain. Exemplary embodiments disclosed herein leverage synthetic data generators to train a neural fingerprint enhancer to improve matching accuracy on real fingerprint images.

Abstract: A computer-implemented method for generating an interpretable kernel embedding for heterogeneous data. The method can include identifying a set of base kernels in the heterogeneous data; and creating multiple sets of transformed kernels by applying a unique composition rule or a unique combination of multiple composition rules to the set of base kernels. The method can include fitting the multiple sets into a stochastic process model to generate fitting scores that respectively indicate a degree of the fitting for each of the multiple sets; storing the fitting scores in a matrix; and standardizing the matrix to generate the interpretable kernel embedding for the heterogeneous data.

Abstract: Biometrics fingerprint matching has been done with a heavily hand-tuned and designed process of classical computer vision techniques for several decades. This approach has led to accurate solutions for solving crimes today and, as such, little effort has been devoted to using deep learning in this domain. Exemplary embodiments disclosed herein leverage synthetic data generators to train a neural fingerprint enhancer to improve matching accuracy on real fingerprint images.