Abstract: A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

Abstract: Systems and methods for predicting differentiating features from tabular data for two different populations. In some aspects, the systems and methods provide for receiving first data entries and second data entries and generating a first graph based on a data entry from the first data entries and a second graph based on a data entry from the second data entries. A first node in the first graph is determined to correspond to a second node in the second graph. A first set of graph embeddings is generated based on the first graph. A second set of graph embeddings is generated based on the second graph. Using a machine learning model, the first set of graph embeddings and the second set of graph embeddings are processed to identify at least one feature indicative of a difference between the first data entries and the second data entries.

Abstract: Systems and methods for adjusting data processing components. In some aspects, the systems and methods include training a first machine learning model using a similarity graph generated based on training entries to predict whether a target system related to a node in the similarity graph will be non-operational within a future period of time, processing using the trained first machine learning model an updated similarity graph generated based on training and inference entries to predict for each node for the inference entries whether a target system related to the node will be non-operational within the future period of time, processing using a second machine learning model predictions and associated inference entries to predict that a target system related to a node for an entry will be non-operational within the future period of time, and adjusting data processing components related to the target system.

Abstract: Systems and methods for predicting change points in tabular data. In some aspects, the systems and methods provide for generating time-stamped graphs based on data entries and corresponding time stamps. Each graph of the time-stamped graphs corresponds to a data entry and is representative of one or more events associated with a time stamp corresponding to the data entry. The graph is independent of any events before or after the time stamp. For each graph of the time-stamped graphs, a set of graph embeddings is generated based on the graph and processed using a machine learning model to predict an occurrence of a change point in the data entries.

Abstract: In some aspects, a computing system may create different node embeddings (e.g., different instances of the same graph) and aggregate the node embeddings to form “multipurpose” node embeddings. As an example, the different node embeddings may include node embeddings generated using unsupervised machine learning and node embeddings generated using supervised machine learning. In this way, for example, a variety of machine learning models may use the aggregated node embeddings without the need for each machine learning model to generate separate node embeddings each time a machine learning task is performed. A machine learning model may use all or a portion of the features in the aggregated node embeddings as appropriate for the task the model is performing.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for enhancing a vehicle identification with preprocessing. The system may comprise memory and processor devices to execute instructions for receiving an image depicting a vehicle. The image may be analyzed and first predicted identity and first confidence value may be determined. The first confidence value may be compared to a predetermined threshold. The processors may further select a processing technique for modifying the image and further analyze the modified image determining a second predicted identity of the vehicle. And a second confidence value may be determined. And the system may further compare the second confidence value to the predetermined threshold to select the first or second predicted identity for transmission to a user.

Abstract: Disclosed embodiments provide systems and user devices for enhancing vehicle identification with preprocessing. The systems or user devices may comprise at least one memory device, an augmentation tool, and at least one processor. The at least one processor may be configured to execute instructions to receive an image depicting a vehicle, analyze the image, and determine a first predicted identity of the vehicle and a first confidence value distribution. The at least one processor may further select a processing technique for modifying the image and analyze the modified image to determine a second predicted identity of the vehicle and a second confidence value distribution. The system may further compare the second confidence value distribution to a predetermined threshold or to the first confidence value distribution to select the first or second predicted identity for transmission to a user.

Abstract: A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

Abstract: Disclosed embodiments provide systems and user devices for enhancing vehicle identification with preprocessing. The systems or user devices may comprise at least one memory device, an augmentation tool, and at least one processor. The at least one processor may be configured to execute instructions to receive an image depicting a vehicle, analyze the image, and determine a first predicted identity of the vehicle and a first confidence value distribution. The at least one processor may further select a processing technique for modifying the image and analyze the modified image to determine a second predicted identity of the vehicle and a second confidence value distribution. The system may further compare the second confidence value distribution to a predetermined threshold or to the first confidence value distribution to select the first or second predicted identity for transmission to a user.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for enhancing a vehicle identification with preprocessing. The system may comprise memory and processor devices to execute instructions for receiving an image depicting a vehicle. The image may be analyzed and first predicted identity and first confidence value may be determined. The first confidence value may be compared to a predetermined threshold. The processors may further select a processing technique for modifying the image and further analyze the modified image determining a second predicted identity of the vehicle. And a second confidence value may be determined. And the system may further compare the second confidence value to the predetermined threshold to select the first or second predicted identity for transmission to a user.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for stress testing a vehicle identification model by determining image quality parameters optimizing model confidence values. The embodiments comprise one or more memory devices storing instructions and one or more processors configured to execute instructions. The one or more processors perform operations comprising receiving a first set of images of the vehicle, wherein the images contain preset values for parameters comprising at least one of a rotation, a focus, a contrast, and a color. The system further analyzes the first set of images with an identification model and determining an initial identification confidence of the vehicle. The system further modifies the first set of images, compares the modified images to the first set of images, and determines whether the model should be modified based on at least the comparison.

Abstract: A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for enhancing a vehicle identification with preprocessing. The system may comprise memory and processor devices to execute instructions for receiving an image depicting a vehicle. The image may be analyzed and first predicted identity and first confidence value may be determined. The first confidence value may be compared to a predetermined threshold. The processors may further select a processing technique for modifying the image and further analyze the modified image determining a second predicted identity of the vehicle. And a second confidence value may be determined. And the system may further compare the second confidence value to the predetermined threshold to select the first or second predicted identity for transmission to a user.

Abstract: A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for stress testing a vehicle identification model by determining image quality parameters optimizing model confidence values. The embodiments comprise one or more memory devices storing instructions and one or more processors configured to execute instructions. The one or more processors perform operations comprising receiving a first set of images of the vehicle, wherein the images contain preset values for parameters comprising at least one of a rotation, a focus, a contrast, and a color. The system further analyzes the first set of images with an identification model and determining an initial identification confidence of the vehicle. The system further modifies the first set of images, compares the modified images to the first set of images, and determines whether the model should be modified based on at least the comparison.

Abstract: A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for enhancing a vehicle identification with preprocessing. The system may comprise memory and processor devices to execute instructions for receiving an image depicting a vehicle. The image may be analyzed and first predicted identity and first confidence value may be determined. The first confidence value may be compared to a predetermined threshold. The processors may further select a processing technique for modifying the image and further analyze the modified image determining a second predicted identity of the vehicle. And a second confidence value may be determined. And the system may further compare the second confidence value to the predetermined threshold to select the first or second predicted identity for transmission to a user.

Abstract: The present disclosure relates to systems and methods for training image recognition models. The system may include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including storing, in a database, a plurality of images depicting vehicles; determining, for a first subset of the images, that additional images of the vehicles depicted in the first subset are desired; determining, for a second subset of the images, that additional images of the vehicles depicted in the second subset are not desired; identifying, within the first subset, images suitable for training an image recognition model; assigning a classification to the suitable images; determining, based on the classification, whether a threshold number of images exist in the database; and initiating an update of the image recognition model, based on the determination of whether the threshold number of images exists.

Abstract: Disclosed embodiments provide systems, methods, and computer-readable storage media for stress testing a vehicle identification model by determining image quality parameters optimizing model confidence values. The embodiments comprise one or more memory devices storing instructions and one or more processors configured to execute instructions. The one or more processors perform operations comprising receiving a first set of images of the vehicle, wherein the images contain preset values for parameters comprising at least one of a rotation, a focus, a contrast, and a color. The system further analyzes the first set of images with an identification model and determining an initial identification confidence of the vehicle. The system further modifies the first set of images, compares the modified images to the first set of images, and determines whether the model should be modified based on at least the comparison.

Abstract: Computer-readable media include computer-readable instructions. The computer readable instructions include a class definition for a first object and a class definition for a second object. The first object includes a buffer for storing first information that identifies fields; a first function for storing the first information in the buffer; and a second function for extracting values of the fields, identified by the first information stored in the buffer, from a portion of a log. The second object includes a third function for obtaining configuration information from a configuration file, wherein the configuration information includes the first information; storing the configuration information at a first memory location; and performing a process.