A system to quantify aggregate alignment of segmented text with an evaluator population, with a data processing system comprising memory and one or more processors, can segment a first extended text object into one or more evaluation text objects associated with a population reference, identify one or more text frame objects corresponding to the evaluation text objects, the text frame objects being associated with a second extended text object, generate, based on the text frame objects, one or more context identifier objects corresponding to the evaluation text objects, and generate a condensed text object including one or more of the evaluation text objects, the evaluation text objects being positioned in the condensed text object in response to output of a first machine learning model trained with input including at least one of the first extended text objects, the evaluation text objects, the context identifier objects, and the text frame objects.

Methods, systems, and computer-readable media are disclosed herein that provide for a machine learning classification model that is trained with historical data and which, by ingesting minimal test data from a particular instance of the application, predictively determines an existing text block that is most relevant for that particular instance of the application. When determined by the model, a particular template is auto-populated into a free text input box within the application for presentation in a graphical user interface.

Methods, systems, and computer-readable media are disclosed herein that provide for a machine learning classification model that is trained with historical data and which, by ingesting minimal test data from a particular instance of the application, predictively determines an existing text block that is most relevant for that particular instance of the application. When determined by the model, a particular template is auto-populated into a free text input box within the application for presentation in a graphical user interface.

In an example embodiment, a combination of machine learning and rule-based techniques are used to automatically detect social engineering attacks in a computer system. More particularly, three phases of detection are utilized on communications in a thread or stream of communications: attack contextualization, intention classification, and security policy violation detection. Each phase of detection causes a score to be generated that is reflective of the degree of danger in the thread or stream of communications, and these scores may then be combined into a single global social engineering attack score, which then may be used to determined appropriate actions to deal with the attack if it transgresses a threshold.

In an example embodiment, a combination of machine learning and rule-based techniques are used to automatically detect social engineering attacks in a computer system. More particularly, three phases of detection are utilized on communications in a thread or stream of communications: attack contextualization, intention classification, and security policy violation detection. Each phase of detection causes a score to be generated that is reflective of the degree of danger in the thread or stream of communications, and these scores may then be combined into a single global social engineering attack score, which then may be used to determined appropriate actions to deal with the attack if it transgresses a threshold.

A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

A method and system are provided which facilitate construction of an ensemble of neural network kernel classifiers. The system divides a training set into partitions. The system trains, based on the training set, a first neural network encoder to output a first set of features, and trains, based on each respective partition of the training set, a second neural network encoder to output a second set of features. The system generates, for each respective partition, based on the first and second set of features, kernel models which output a third set of features. The system classifies, by a classification model, the training set based on the third set of features. The generated kernel models for each respective partition and the classification model comprise the ensemble of neural network kernel classifiers. The system predicts a result for a testing data object based on the ensemble of neural network kernel classifiers.

There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.

There are provided systems and methods for a compute platform for machine leaning model roll-out. A service provider, such as an electronic transaction processor for digital transactions, may provide intelligent decision-making through decision services that execute machine learning models. When deploying or updating machine learning models in these engines and decision services, a model package may include multiple models, each of which may have an execution graph required for model execution. When models are tested from proper execution, the models may have non-performant compute items, such as model variables, that lead to improper execution and/or decision-making. A model deployer may determine and flag these compute items as non-performant and may cause these compute items to be skipped or excluded from execution. Further, the model deployer may utilize a pre-production computing environment to generate the execution graphs for the models prior to deployment or upgrading.

In various examples, a deep neural network(s) (e.g., a convolutional neural network) may be trained to detect moving and stationary obstacles from RADAR data of a three dimensional (3D) space. In some embodiments, ground truth training data for the neural network(s) may be generated from LIDAR data. More specifically, a scene may be observed with RADAR and LIDAR sensors to collect RADAR data and LIDAR data for a particular time slice. The RADAR data may be used for input training data, and the LIDAR data associated with the same or closest time slice as the RADAR data may be annotated with ground truth labels identifying objects to be detected. The LIDAR labels may be propagated to the RADAR data, and LIDAR labels containing less than some threshold number of RADAR detections may be omitted. The (remaining) LIDAR labels may be used to generate ground truth data.