Abstract: A data protection system is provided to detect data and execute security actions on the detected data using multiple tiers of parallel processing and incremental processing. For example, the data protection system can employ parallel job-submission and parallel-job execution to cataloging, scanning, searching, and other processes. Only source data that has not already been processed or has modified may be loaded to a cataloging data queue and a scanning data queue to reduce processing time. Scan results can include different data groups and can be used to search for specific data sets.

Abstract: The computing device obtains a training data set related to a plurality of historic user inputs associated with preferences of one or more services or items from an entity. For each of the one or more services or items, the computing device executes operations to train a plurality of models using the training data set to generate a plurality of recommended models, apply a validation data set to generate a plurality of predictions from the plurality of recommended models, obtain a weight of each metric of a plurality of metrics from the entity, obtain user inputs associated with user preferences, and determine a relevancy score for each metric. The computing device selects a recommended model based on the relevancy score of the selected metric or a combination of selected metrics, generates one or more recommendations for the users, and outputs the one or more generated recommendations to the users.

Abstract: An apparatus includes at least one node device to host a computing cluster, and at least one processor to: use at least one of a level of resource observed to be consumed by operation of the computing cluster or a level of performance observed to be provided by operation of the computing cluster as an input to a pre-existing cluster model to derive a predicted level; compare the predicted level to a corresponding observed level of resource consumed or performance provided; and in response to the predicted level not matching the observed level to within a pre-selected degree, derive a new cluster model from observations of the operation of the computing cluster, and generate a prompt to perform repeat the configuration of the computing cluster using the new cluster model in place of the pre-existing cluster model to generate a new set of configuration settings for the computing cluster.

Abstract: A flexible computer architecture for performing digital image analysis is described herein. In some examples, the computer architecture can include a distributed messaging platform (DMP) for receiving images from cameras and storing the images in a first queue. The computer architecture can also include a first container for receiving the images from the first queue, applying an image analysis model to the images, and transmitting the image analysis result to the DMP for storage in a second queue. Additionally, the computer architecture can include a second container for receiving the image analysis result from the second queue, performing a post-processing operation on the image analysis result, and transmitting the post-processing result to the DMP for storage in a third queue. The computer architecture can further include an output container for receiving the post-processing result from the third queue and generating an alert notification based on the post-processing result.

Abstract: A computer system can automatically generate a directed graph interface for use in detecting and mitigating anomalies in entity interactions. For example, the system can receive interaction data describing a set of interactions at two entities. The system can then generate a directed network graph based on the interaction data. To do so, the system can identify pairs of interactions associated with the two entities in the set of interactions. The system can classify the pairs of interactions as outbound and/or inbound interaction pairs. The system can then generate one or more directed links in the directed network graph to represent the outbound and/or inbound interaction pairs. The system can further determine a characteristic of the outbound and/or inbound interaction pairs, automatically detect an anomaly that may be suggestive of malicious activity by one or both entities based on the characteristic, and output an indicator of the detected anomaly.

Abstract: An apparatus includes a processor to: receive a request to perform a job flow; within a performance container, based on the data dependencies among a set of tasks of the job flow, derive an order of performance of the set of tasks that includes a subset able to be performed in parallel, and derive a quantity of task containers to enable the parallel performance of the subset; based on the derived quantity of task containers, derive a quantity of virtual machines (VMs) to enable the parallel performance of the subset; provide, to a VM allocation routine, an indication of a need for provision of the quantity of VMs; and store, within a task queue, multiple task routine execution request messages to enable parallel execution of task routines within the quantity of task containers to cause the parallel performance of the subset.

Abstract: A computer monitors a state of a system. A time branch is defined for each valid value of each discrete variable. A system model is executed with observed values to update each time branch and determine a probability associated with each time branch. A discrete variable is selected, and a sequence duration value is incremented. When the incremented sequence duration value is greater than a predefined minimum sequence duration value, a probability change value is computed for the discrete variable, and, when the computed probability change value is less than or equal to a synchronization probability change value, a continuous value for each continuous variable for each time branch of the discrete variable is synchronized, and the sequence duration value for the selected discrete variable is reinitialized. The continuous value for at least one non-observed continuous variable is output.

Abstract: A computer trains a neural network. A neural network is executed with a weight vector to compute a gradient vector using a batch of observation vectors. Eigenvalues are computed from a Hessian approximation matrix, a regularization parameter value is computed using the gradient vector, the eigenvalues, and a step-size value, a search direction vector is computed using the eigenvalues, the gradient vector, the Hessian approximation matrix, and the regularization parameter value, a reduction ratio value is computed, an updated weight vector is computed from the weight vector, a learning rate value, and the search direction vector or the gradient vector based on the computed reduction ratio value, and an updated Hessian approximation matrix is computed from the Hessian approximation matrix, the predefined learning rate value, and the search direction vector or the gradient vector based on the reduction ratio value. The step-size value is updated using the search direction vector.

Abstract: A computing device determines a disaggregated solution vector of a plurality of variables. A first value is computed for a known variable using a predefined density distribution function, and a second value is computed for an unknown variable using the computed first value, a predefined correlation value, and a predefined aggregate value. The predefined correlation value indicates a correlation between the known variable and the unknown variable. A predefined number of solution vectors is computed by repeating the first value and the second value computations. A solution vector is the computed first value and the computed second value. A centroid vector is computed from solution vectors computed by repeating the computations. A predefined number of closest solution vectors to the computed centroid vector are determined from the solution vectors. The determined closest solution vectors are output.

Abstract: In one example, a system can receive a set of text samples and generate a set of summaries based on the set of text samples. The system can then generate a training dataset by iteratively executing a training-sample generation process. Each iteration can involve selecting multiple text samples from the set of text samples, combining the multiple text samples together into a training sample, determining a text category and a summary corresponding to a selected one of the multiple text samples, and including the text category and the summary in the training sample. After generating the training dataset, the system can use it to train a model. The trained model can then receive a target textual dataset and a target category as input, identify a portion of the target textual dataset corresponding to the target category, and generate a summarization of the portion of that target textual dataset.

Abstract: Embodiments are directed to techniques for image content extraction. Some embodiments include extracting contextually structured data from document images, such as by automatically identifying document layout, document data, document metadata, and/or correlations therebetween in a document image, for instance. Some embodiments utilize breakpoints to enable the system to match different documents with internal variations to a common template. Several embodiments include extracting contextually structured data from table images, such as gridded and non-gridded tables. Many embodiments are directed to generating and utilizing a document template database for automatically extracting document image contents into a contextually structured format. Several embodiments are directed to automatically identifying and associating document metadata with corresponding document data in a document image to generate a machine-facilitated annotation of the document image.

Abstract: An apparatus including a processor to: within a kill container, in response to a set of error messages indicative of errors in executing multiple instances of a task routine to perform a task of a job flow with multiple data object blocks of a data object, and in response to the quantity of error messages reaching a threshold, output a kill tasks request message that identifies the job flow; within a task container, in response to the kill tasks request message, cease execution of the task routine and output a task cancelation message that identifies the task and the job flow; and within a performance container, in response to he task cancelation message, output a job cancelation message to cause the transmission of an indication of cancelation of the job flow, via a network, and to a requesting device that requested the performance of the job flow.

Abstract: A computing system establishes a hierarchy for monitoring model(s). The hierarchy comprises an association between each of multiple measures of a measure level of the hierarchy and intermediate level(s) of the hierarchy. An intermediate level comprises one or more of a measurement category or analysis type. The hierarchy comprises an association between the intermediate level(s) and at least one model. The system monitors the model(s) by generating health measurements. Each of the health measurements corresponds to one of the multiple measures. Each of the health measurements indicates a performance of a monitored model according to a measurement category or analysis type associated in the hierarchy with the respective measure of the multiple measures. The system generates a visualization in a graphical user interface. The visualization comprises a graphical representation of an indication of a health measurement for each of measure(s), and associations in the hierarchy.

Abstract: An apparatus includes a processor to: output a request message to cause a first task to be performed; within a task container, in response to the request message and a data object not being divided, divide the data object into a set of data object blocks based on at least the sizes of the data object and the atomic unit of organization of data therein, as well as the storage resources allocated to task containers, and output a task completion message indicating that the first task has been performed, and including a set of data block identifiers indicating the location of the set of data object blocks within at least one federated area; and in response to the task completion message, output a set of request messages to cause a second task to be performed by executing multiple instances of a task routine within multiple task containers.

Abstract: A computing device selects a piecewise linear regression model for multivariable data. A hyperplane is fit to observation vectors using a linear multivariable regression. A baseline fit quality measure is computed for the fit hyperplane. For each independent variable, the observation vectors are sorted, contiguous segments to evaluate are defined, for each contiguous segment, a segment hyperplane is fit to the sorted observation vectors using a multivariable linear regression, path distances are computed between a first observation of the and a last observation of the sorted observation vectors based on a predefined number of segments, a shortest path associated with a smallest value of the computed path distances is selected, and a fit quality measure is computed for the selected shortest path. A best independent variable is selected from the independent variables based on having an extremum value for the computed fit quality measure.

Abstract: An apparatus including a processor to: output a first request message onto a group sub-queue shared by multiple task containers to request execution of a first task routine; within a task container, respond to the first request message, by outputting a first task in-progress message onto an individual sub-queue not shared with other task containers to accede to executing the first task routine, followed by a task completion message; and respond to the task completion message by allowing the task completion message to remain on the individual sub-queue to keep the task container from executing another task routine from another request message on the group sub-queue, outputting a second request message onto the individual sub-queue to cause execution of a second task routine within the same task container to perform a second task, and responding to the second task in-progress message by de-queuing the task completion message.

Abstract: A computing device determines an optimal number of threads for a computer task. Execution of a computing task is controlled in a computing environment based on each task configuration included in a plurality of task configurations to determine an execution runtime value for each task configuration. An optimal number of threads value is determined for each set of task configurations having common values for a task parameter value, a dataset indicator, and a hardware indicator. The optimal number of threads value is an extremum value of an execution parameter value as a function of a number of threads value. A dataset parameter value is determined for a dataset. A hardware parameter value is determined as a characteristic of each distinct executing computing device in the computing environment. The optimal number of threads value for each set of task configurations is stored in a performance dataset in association with the common values.

Abstract: A computing device accesses a machine learning model trained on training data of first bonding operations (e.g., a ball and/or stitch bond). The first bonding operations comprise operations to bond a first set of multiple wires to a first set of surfaces. The machine learning model is trained by supervised learning. The device receives input data indicating process data generated from measurements of second bonding operations. The second bonding operations comprise operations to bond a second set of multiple wires to a second set of surfaces. The device weights the input data according to the machine learning model. The device generates an anomaly predictor indicating a risk for an anomaly occurrence in the second bonding operations based on weighting the input data according to the machine learning model. The device outputs the anomaly predictor to control the second bonding operations.

Abstract: An apparatus includes a processor to: receive, from a requesting device, a request to perform speech-to-text conversion of a speech data set; within a first thread of a thread pool, perform a first pause detection technique to identify a first set of likely sentence pauses; within a second thread of the thread pool, perform a second pause detection technique to identify a second set of likely sentence pauses; perform a speaker diarization technique to identify a set of likely speaker changes; divide the speech data set into data segments representing speech segments based on a combination of at least the first set of likely sentence pauses, the second set of likely sentence pauses, and the set of likely speaker changes; use at least an acoustic model with each data segment to identify likely speech sounds; and generate a transcript based, at least in part, on the identified likely speech sounds.

Abstract: An apparatus includes a processor to: receive a request to perform speech-to-text conversion of a speech data set; perform pause detection to identify a set of likely sentence pauses and/or speaker diarization technique to identify a set of likely speaker changes; based the set of likely sentence pauses and/or the set of likely speaker changes, divide the speech data set into data segments representing speech segments; use an acoustic model with the data segments to derive sets of probabilities of speech sounds uttered; store the sets of probabilities in temporal order within a buffer queue; distribute the sets of probabilities from the buffer queue in temporal order among threads of a thread pool; and within each thread, and based on set(s) of probabilities, derive one candidate word and select either the candidate word or an alternate candidate word derived from a language model as the next word most likely spoken.