Abstract: A method includes obtaining driver characterization data based on sensor data from one or more sensors onboard a vehicle. The sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle. The method also includes providing the driver characterization data as input to a trained model to generate a model output. The model output includes a classification that associates the driver characterization data with a driver type profile. The method also includes generating an alert responsive to a charge of the battery deviating from an expected charge of the battery. The expected charge is based on the classification.

Abstract: A method of detecting deviation from an operational state of a rotational device includes receiving, from one or more sensor devices coupled to the rotational device, frequency domain data indicative of vibration data sensed during a sensing period. The method also includes processing the frequency domain data using a trained anomaly detection model to generate an anomaly score for the sensing period and processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A method of generating a searchable representation of an electronic document includes obtaining an electronic document specifying a graphical layout of content items including text. The method also includes determining pixel data representing the graphical layout of the content items and providing input data based, at least in part, on the pixel data to a document parsing model. The document parsing model is trained to detect functional regions within the graphical layout based on the input data, assign boundaries to the functional regions based on the input data, and assign a category label to each functional region that is detected. The method also includes matching portions of the text to corresponding functional regions based on the boundaries assigned to the functional regions and locations associated with the portions of the text and storing data representing the content items, the functional regions, and the category labels in a searchable data structure.

Abstract: Automated malware detection for application file packages using machine learning (e.g., trained neural network-based classifiers) is described. A particular method includes generating, at a first device, a first feature vector based on occurrences of character n-grams corresponding to a first subset of files of multiple files of an application file package. The method includes generating, at the first device, a second feature vector based on occurrences of attributes in a second subset of files of the multiple files. The method includes sending the first feature vector and the second feature vector from the first device to a second device as inputs to a file classifier. The method includes receiving, at the first device from the second device, classification data associated with the application file package based on the first feature vector and the second feature vector. The classification data indicates whether the application file package includes malware.

Abstract: A method includes receiving input that identifies one or more data sources and determining, based on the input, a machine learning problem type of a plurality of machine learning problem types supported by an automated model building (AMB) engine. The method also includes generating an input data set of the AMB engine based on application of one or more rules to the one or more data sources. The method further includes, based on the input data set and the machine learning problem type, initiating execution of the AMB engine to generate a neural network configured to model at least a portion of the input data set.

Abstract: Standardized model packaging and deployment, including: generating a model package comprising: model definition data for a model; function code facilitating execution of the model; and at least one interface for at least one operating system.

Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.

Abstract: A method includes receiving, at a mobile hub device, communications including location-specific risk data and a task assignment. The method also includes generating an output indicating dispatch coordinates. The dispatch coordinates identifying a dispatch location from which to dispatch, from the mobile hub device, one or more unmanned vehicles to perform a task of the task assignment.

Abstract: A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

Abstract: A method includes obtaining sensor data associated with operation of one or more devices and providing input data based on the sensor data to a dimensional-reduction model that includes a first layer having a first count of nodes, a second layer having a second count of nodes, and a third layer having a third count of nodes. The second layer is disposed between the first layer and the third layer, and the second count of nodes is greater than the first count of nodes and the third count of nodes. The method also includes determining a reconstruction error indicating a difference between the input data and the output data of the dimensional-reduction model. The method also includes performing a comparison of the reconstruction error to an anomaly detection criterion and generating an anomaly detection output for the one or more devices based on a result of the comparison.

Abstract: A particular method includes automatically generating, at a processor of a computing device, annotation data indicating that a column of a data table corresponds to a particular class of an ontology. The method also includes storing the annotation data. The method further includes receiving a natural language query. The method also includes generating a second query based on detecting a match between at least one term of the natural language query and the annotation data. The method further includes determining a response to the second query. The method also includes outputting the response to the second query as a response to the natural language query.

Abstract: A method of detecting deviation from an operational state of a device includes obtaining preprocessed data corresponding to data sensed by one or more sensor devices coupled to the device. The method also includes processing the preprocessed data using a trained anomaly detection model to generate an anomaly score. The method also includes processing the anomaly score using an alert generation model to determine whether to generate an alert.

Abstract: A processor-readable storage device storing instructions that cause a processor to perform operations including, subsequent to determining, at a first device based on a first file attribute associated with a file, that a classification for the file is unavailable at the first device, sending the first file attribute from the first device to a second device to determine whether the classification for the file is available at the second device. The operations include receiving a notification at the first device from the second device that the classification for the file is unavailable at the second device. The operations include, determining the classification for the file by performing, at the first device, an analysis of a second file attribute based on a trained file classification model. The operations include sending the classification from the first device to the second device and to a third device.

Abstract: Translating text encodings of machine learning models to executable code, the method comprising: receiving a text encoding of a machine learning model; generating, based on the text encoding of the machine learning model, compilable code encoding the machine learning model; and generating, based on the compilable code, executable code encoding the machine learning model.

Abstract: A method includes generating, by a processor of a computing device, an output set of models corresponding to a first epoch of a genetic algorithm and based on an input set of models of the first epoch. The input set and the output set includes data representative of a neural network. The method includes determining a particular model of the output set based on a fitness function. A first topological parameter of a first model of the input set is modified to generate the particular model of the output set. The method includes modifying a probability that the first topological parameter is to be changed by a genetic operation during a second epoch of the genetic algorithm that is subsequent to the first epoch. The method includes generating a second output set of models corresponding to the second epoch and based on the output set and the modified probability.

Abstract: A method includes determining a trainable model to provide to a trainer, the trainable model determined based on modification of one or more models of a plurality of models. The plurality of models is generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method also includes providing the trainable model to the trainer. The method further includes adding a trained model, output by the trainer based on the trainable model, as input to a second epoch of the genetic algorithm, the second epoch subsequent to the first epoch.

Abstract: A method includes generating, by a processor of a computing device, a first plurality of models (including a first number of models) based on a genetic algorithm and corresponding to a first epoch of the genetic algorithm. The method includes determining whether to modify an epoch size for the genetic algorithm during a second epoch of the genetic algorithm based on a convergence metric associated with at least one epoch that is prior to the second epoch. The second epoch is subsequent to the first epoch. The method further includes, based on determining to modify the epoch size, generating a second plurality of models (including a second number of models that is different than the first number) based on the genetic algorithm and corresponding to the second epoch. Each model of the first plurality of models and the second plurality of models includes data representative of neural networks.

Abstract: Automated malware detection for application file packages using machine learning (e.g., trained neural network-based classifiers) is described. A particular method includes generating, at a first device, a first feature vector based on occurrences of character n-grams corresponding to a first subset of files of multiple files of an application file package. The method includes generating, at the first device, a second feature vector based on occurrences of attributes in a second subset of files of the multiple files. The method includes sending the first feature vector and the second feature vector from the first device to a second device as inputs to a file classifier. The method includes receiving, at the first device from the second device, classification data associated with the application file package based on the first feature vector and the second feature vector. The classification data indicates whether the application file package includes malware.