A document processing system is configured to identify, for each accessed electronic document in a first set of multiple electronic documents, a set of identified multi-word phrases determined to be in ordered text information in the accessed electronic document, each multi-word phrase of the set of identified multi-word phrases including adjacent words in the ordered text information; and determine, for each accessed electronic document in the first set of multiple electronic documents, a selected document type from the first set of document types based at least on an analysis of the set of identified multi-word phrases with respect to multi-word-phrase characteristics identified by a first definition and associated with each document type in a first set of document types associated with a first document-set type.

The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements

Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

Systems and methods for determining neural network brittleness are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a modeling request comprising a preliminary model and a dataset. The operations may include determining a preliminary brittleness score of the preliminary model. The operations may include identifying a reference model and determining a reference brittleness score of the reference model. The operations may include comparing the preliminary brittleness score to the reference brittleness score and generating a preferred model based on the comparison. The operations may include providing the preferred model.

An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.

An event can be analyzed for association with a security violation. Characters or other values of event data (e.g., command-line text) associated with the event can be provided sequentially to a trained representation mapping to determine respective representation vectors. Respective indicators can be determined by applying the vectors to a trained classifer. A token in the event data can be located based on the indicators. The event's can be determined to be associated with a security violation based on the token satisfying a token-security criterion. The representation mapping can be trained by adjusting model parameters so the trained representation predicts, based on a character of training command-line text, an immediately following character in the training command-line text. The classifier can be determined based on the trained representation mapping and classification training data indicating whether respective portions of training event data are associated with security violations.

Provided are a feature transformation device and others enabling feature transformation with high precision. The feature transformation device includes receiving means for receiving training data and test data each including a plurality of samples, optimization means for optimizing weight and feature transformation parameter based on an objective function related to the weight and the feature transformation parameter, the optimization means including weight derivation means for deriving the weight assigned to each element included in the training data and feature transformation parameter derivation means for deriving the feature transformation parameter that transforms each of the samples included in the training data or the test data, objective function derivation means for deriving a value of the objective function, the objective function derivation means including a constraint determination means for determining whether the weight satisfies a prescribed constraint and regularization means for regularizing at least one of the weight or the feature transformation parameter, and transformation means for transforming an element included in at least one of the training data or the test data based on the feature transformation parameter.

The present disclosure provides a method for target detection, including: obtaining an image captured by a camera, and zoning the image based on a mounting angle of the camera to obtain at least one image block; determining target-detection algorithms for the at least one image block based on positions of the at least one image block in the image; and performing target detection on the at least one image block based on the target-detection algorithms.

Systems and methods for connecting datasets are disclosed. For example, a system may include a memory unit storing instructions and a processor configured to execute the instructions to perform operations. The operations may include receiving a plurality of datasets and a request to identify a cluster of connected datasets among the received plurality of datasets. The operations may include selecting a dataset. In some embodiments, the operations include identifying a data schema of the selected dataset and determining a statistical metric of the selected dataset. The operations may include identifying foreign key scores. The operations may include generating a plurality of edges between the datasets based on the foreign key scores, the data schema, and the statistical metric. The operations may include segmenting and returning datasets based on the plurality of edges.

An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.