Abstract: A global partitioning-based method for anonymizing a dataset of biometric data may include an anonymization computer program: (1) receiving a value k representing a number of records to hide a biometric datum among, a value t that represents a t-closeness parameter for a t-close distribution, a weight parameter, and a first number of features to retain for determining an attribute of interest; (2) receiving the attribute of interest; (3) calculating a distribution of the attribute of interest in a biometric dataset; (4) splitting the biometric dataset into a plurality of k-sized clusters that satisfy the t-close distribution; (5) anonymizing each biometric datum in the plurality of k-sized clusters using a weighted average of landmarks for the biometric datums in k-sized clusters using the weight parameter; (6) adding each anonymized biometric datum into an anonymized biometric dataset; and (7) persisting the anonymized biometric dataset.

Abstract: Systems and methods for weakly supervised unit test quality scoring are disclosed. According to one embodiment, a method may include: parsing, by a generative model computer program, a plurality of code snippets in a repository using an abstract syntax tree; receiving, by the generative model computer program, a plurality of binary labelling functions from a labelling function repository; creating, by the generative model computer program, a labelling matrix by applying the binary labelling functions to the parsed code snippets; training, by the generative model computer program, a probabilistic generative model using the labelling matrix resulting in a vector of pseudo-labels; building, by a unit test scoring computer program, a discriminative model, wherein the discriminative model receives an array of real value inputs; and training, by the unit test scoring computer program, the discriminative model using the parsed code snippets, the vector of pseudo labels, and the abstract syntax tree.

Abstract: Systems and methods for automated code analysis and tagging are disclosed. In one embodiment, a method for automated code analysis and tagging may include: (1) receiving, by a code annotation computer program executed by a computer processor, a training code snippet from a training codebase; (2) parsing, by the code annotation computer program, the training code snippet into a data structure; (3) quantifying, by the code annotation computer program, the data structure, (4) parsing, by the code annotation computer program, a docstring associated with the training code snippet into a plurality of keywords; (5) quantifying, by the code annotation computer program, the plurality of keywords; and (6) training, by the code annotation computer program, a code annotation model based on a similarity between the quantified data structure and a smoothing parameter for a Dirichlet prior smoothing estimate.

Abstract: Systems and methods for weak supervision classification with probabilistic generative latent variable models are disclosed. A method for weak supervision classification with probabilistic generative latent variable models may include: (1) receiving, by a generative model computer program, a plurality of records from a database; (2) receiving, by the generative model computer program, a plurality of user-defined label functions; (3) labeling, by the generative model computer program, each of the plurality of records with each of the plurality of user-defined label functions; (4) representing, by the generative model computer program, the plurality of records that are labeled with the user-defined label functions in a matrix; (5) performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model; and (6) outputting, by the generative model computer program, a labeled dataset for the plurality of records.

Abstract: Systems and methods for generating a fusion score between electronic documents. The method includes receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document. The method further includes generating a fusion score based on a comparison of the first set of features and the second set of features.

Abstract: Systems and methods for federated secure vocabulary learning. The method may include communicating, by a first client network and a second client network and to a global federation terminal, a first set of encrypted tokens and a second set of encrypted tokens. The method may further include generating, by the global federation terminal, a consolidated vocabulary based on the first set of encrypted tokens and the second set of encrypted tokens. The method may include determining, a first set of weights and a second set of weights for the consolidated vocabulary. The method may include receiving by the global federation terminal the first set of weights and the second set of weights. The method may further include aggregating the first set of weights and the second set of weights. The method may further include distributing, to the first client network and the second client network, an aggregated set of weights.

Abstract: Systems and methods for generating a fusion score between electronic documents. The method includes receiving a first electronic document by a document management system. The method further includes extracting a first set of features from the first electronic document including at least one feature type indicating the hierarchical structure of the first electronic document. The method also includes receiving a second electronic document by the document management server. The method further includes extracting a second set of features from the second electronic document including at least one feature type indicating the hierarchical structure of the second electronic document. The method further includes generating a fusion score based on a comparison of the first set of features and the second set of features.

Abstract: A global partitioning-based method for anonymizing a dataset of biometric data may include an anonymization computer program: (1) receiving a value k representing a number of records to hide a biometric datum among, a value t that represents a t-closeness parameter for a t-close distribution, a weight parameter, and a first number of features to retain for determining an attribute of interest; (2) receiving the attribute of interest; (3) calculating a distribution of the attribute of interest in a biometric dataset; (4) splitting the biometric dataset into a plurality of k-sized clusters that satisfy the t-close distribution; (5) anonymizing each biometric datum in the plurality of k-sized clusters using a weighted average of landmarks for the biometric datums in k-sized clusters using the weight parameter; (6) adding each anonymized biometric datum into an anonymized biometric dataset; and (7) persisting the anonymized biometric dataset.

Abstract: Systems and methods for counteracting data-skewness for locality sensitive hashing via feature selection and pruning are disclosed. In one embodiment, a method for feature selection for counteracting data skewness on locality sensitive hashing (LSH)-based search may include: (1) ingesting, by an ingestion computer program and from a plurality of data sources, data; (2) extracting, by the ingestion computer program, a plurality of features from the ingested data; (3) transforming, by the ingestion computer program, each of the plurality of features into a feature vector; (4) selecting, by the ingestion computer program, a subset of the plurality of features; and (5) for each selected feature vector: computing, by the ingestion computer program, a random hash function for the selected feature; and inserting, by the ingestion computer program, an output of the random hash function into a hash table with the selected feature.