Abstract: Examples for the baggage weight prediction system are provided. The system may create a baggage prediction model in response to a carrier baggage weight prediction requirement requesting a total baggage weight to be carried in a future transportation operation by a carrier. The baggage prediction model may be used to determine a predicted baggage to be carried by a plurality of future passengers associated with a future transportation operation. The system may determine a predicted weight for the predicted baggage. The system may sort the predicted baggage into checked baggage and a carry-on baggage. The system may determine a carry-on baggage weight associated with the carry-on baggage based on the baggage prediction model. The system may determine a baggage weight ratio associated with the predicted baggage. The system may generate a baggage prediction result for performing a baggage management action.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated account maintenance and fraud mitigation for secure accounts such as vendor master accounts or client master accounts. To illustrate, a system receives a request from a user to update an account. The system extracts request data from the request, for example using natural language processing and optical character recognition. The system performs validation operation(s) (e.g., entry validation, location validation, domain validation, etc.), in some implementations using one or more machine learning models. Upon successful validation, if the user is an authorized contact for the account, the system authenticates the request (e.g., via request of an authorization code) and updates the account.

Abstract: A system for providing cognition assistance including a contextual memory trainer, which receives preprocessed data including facial data, scene data, and activity data related to a video in association with temporal data and geographical location data of the camera that captured the video, where the scene data, the activity data, the geographical location data, and the temporal data collectively define spatiotemporal data. The trainer identifies an unknown aspect in the preprocessed data based on historical data and determines a predefined priority factor therefor. The priority factor includes one of a frequency of occurrence within a set period and relative proximity of the unknown aspect to the camera, a known face, place, or scene. The unknown aspect is prioritized for annotation based on a value of the priority factor exceeding a predefined threshold value, based on which facial data is associated with the spatiotemporal data to provide contextual annotated data.

Abstract: A system for providing cognition assistance including a contextual memory trainer, which receives preprocessed data including facial data, scene data, and activity data related to a video in association with temporal data and geographical location data of the camera that captured the video, where the scene data, the activity data, the geographical location data, and the temporal data collectively define spatiotemporal data. The trainer identifies an unknown aspect in the preprocessed data based on historical data and determines a predefined priority factor therefor. The priority factor includes one of a frequency of occurrence within a set period and relative proximity of the unknown aspect to the camera, a known face, place, or scene. The unknown aspect is prioritized for annotation based on a value of the priority factor exceeding a predefined threshold value, based on which facial data is associated with the spatiotemporal data to provide contextual annotated data.

Abstract: A device may receive video data associated with a video, and may identify image data associated with the video data and that corresponds to a frame of the video. The device may process the image data, with an image analysis model, to determine an image compliance score associated with a policy and with context of the image data, and may identify text data associated with the video data. The device may process the text data, with a text analysis model, to determine a text compliance score associated with the policy and with context of the text data, and may determine, based on the image compliance score and the text compliance score, an aggregate compliance score associated with the frame. The device may determine whether the aggregate compliance score satisfies a threshold associated with the policy, and may perform actions based on whether the aggregate compliance score satisfies the threshold.

Abstract: Examples for the baggage weight prediction system are provided. The system may create a baggage prediction model in response to a carrier baggage weight prediction requirement requesting a total baggage weight to be carried in a future transportation operation by a carrier. The baggage prediction model may be used to determine a predicted baggage to be carried by a plurality of future passengers associated with a future transportation operation. The system may determine a predicted weight for the predicted baggage. The system may sort the predicted baggage into checked baggage and a carry-on baggage. The system may determine a carry-on baggage weight associated with the carry-on baggage based on the baggage prediction model. The system may determine a baggage weight ratio associated with the predicted baggage. The system may generate a baggage prediction result for performing a baggage management action.

Abstract: A method for predicting conditions associated with a coal stock pile is described. The method includes collecting aerial data for a site including one or more coal stock piles. Using the aerial data, the method includes performing localization of the site to identify boundaries of the coal stock piles and extracting multi-spectral features. The method also includes obtaining additional data associated with the coal stock piles from at least one data source and merging the aerial data with the additional data. Using the merged data and the extracted multi-spectral features, the method includes analyzing a status of the coal stock piles by a prediction module to predict at least one of an impending combustion event or a severe condition associated with the coal stock piles. In response to the predicted at least one impending combustion event or severe condition, the method includes implementing a response.

Abstract: A device may receive video data associated with a video, and may identify image data associated with the video data and that corresponds to a frame of the video. The device may process the image data, with an image analysis model, to determine an image compliance score associated with a policy and with context of the image data, and may identify text data associated with the video data. The device may process the text data, with a text analysis model, to determine a text compliance score associated with the policy and with context of the text data, and may determine, based on the image compliance score and the text compliance score, an aggregate compliance score associated with the frame. The device may determine whether the aggregate compliance score satisfies a threshold associated with the policy, and may perform actions based on whether the aggregate compliance score satisfies the threshold.

Abstract: Embodiments of the present disclosure provide systems and methods for recognizing a masked face. According to the present disclosure, the disclosed systems and methods include features that provide augmentation of existing face recognition databases, real-time mask detection, and real-time masked face recognition. In embodiments, masked face recognition includes a multi-layered approach, which includes finding matching simulated masked faces in the database that match the masked face being analyzed, comparing the unmasked portion of the masked face to stored unmasked faces in a database to identify any matches, and executing face restoration algorithms in which the masked portion is reconstructed to generate an unmasked representation which may then be matched against unmasked faces in the database.

Abstract: A method for predicting conditions associated with a coal stock pile is described. The method includes collecting aerial data for a site including one or more coal stock piles. Using the aerial data, the method includes performing localization of the site to identify boundaries of the coal stock piles and extracting multi-spectral features. The method also includes obtaining additional data associated with the coal stock piles from at least one data source and merging the aerial data with the additional data. Using the merged data and the extracted multi-spectral features, the method includes analyzing a status of the coal stock piles by a prediction module to predict at least one of an impending combustion event or a severe condition associated with the coal stock piles. In response to the predicted at least one impending combustion event or severe condition, the method includes implementing a response.