Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.

A system and method for determining a mood for a crowd is disclosed. In example embodiments, a method includes identifying an event that includes two or more attendees, receiving at least one indicator representing emotions of attendees, determining a numerical value for each of the indicators, and aggregating the numerical values to determine an aggregate mood of the attendees of the event.

The present disclosure relates to a battery test system for a vehicle that includes a camera configured to capture an image of a vehicle identification number located on the vehicle, the camera being coupled to a processor which determines characters of the vehicle identification number from the image of the camera and correlates the characters of the vehicle identification number to a vehicle identification number database to receive battery parameters for the vehicle, a battery tester that is removably connected to terminals of a battery of the vehicle and configured to receive battery test results, and a display which conveys information relating to the battery parameters and the battery test results.

An abnormality diagnosis method for diagnosing an abnormality in operational state of a diagnosis subject includes creating a unit space from normal operation data of the diagnosis subject, the unit space serving as a reference for determining the operational state of the diagnosis subject, acquiring data having state quantities of a plurality of evaluation items from the diagnosis subject, calculating a Mahalanobis distance of the data acquired, using the unit space created, and determining an abnormality in the operational state of the diagnosis subject based on the Mahalanobis distance calculated. The creating a unit space includes creating a plurality of unit spaces having mutually different data lengths The calculating a Mahalanobis distance includes calculating a plurality of Mahalanobis distances using the plurality of unit spaces created. The determining an abnormality includes determining an abnormality based on the plurality of Mahalanobis distances calculated.

A microscope system for scanning a bone marrow aspirate (BMA) sample may include a scanning apparatus for scanning the BMA sample and a processor coupled to the scanning apparatus and a memory. The microscope system may obtain, using the scanning apparatus, scan data of the BMA sample and detect cells in the BMA sample from the scan data. The microscope system may also classify the detected cells into a plurality of cell types, and store, in the memory, cell data of the classified cells. The microscope system may include a display for presenting the cell data. Various other systems and methods are provided.

A method and a device for acquiring character information in a picture, a non-transitory storage medium, a page processing method, and a knowledge graph construction method are disclosed. The method for acquiring character information in a picture includes: acquiring a picture and extracting at least one piece of character information in the picture; and checking-and-correcting the at least one piece of character information based on a knowledge graph.

A method for identifying devices in a computer network includes collecting data points including device related parameters/information from a device accessing network resources, determining a string distance between the set of collected data points with the data sets collected from previously known network devices and selecting the smallest string distance value and/or highest similarity score. If the determined string distance to the data set of the closest device exceeds a threshold value, the device accessing the network resources is new and its entry can be created. If the determined string distance is under a predetermined threshold value, it is determined that the device accessing the network resources is the device having the closest string distance value to the collected set of data points, and the values in a database and/or the network element of the previously known device can be updated based on the collected set of data points.

System and method for splitting a trained neural network into a first neural network for execution on a first device and a second neural network for execution on a second device. The splitting is performed to optimize, within an accuracy constraint, an overall latency of: the execution of the first neural network on the first device to generate a feature map output based on input data, transmission of the feature map output from the first device to the second device, and execution of the second neural network on the second device to generate an inference output based on the feature map output from the first device.

A non-transitory computer-readable storage medium storing a model generation program that causes at least one computer to execute a process that includes acquiring, on a first assumption that assumes each of individual data items included in a training data set is easy for a user to interpret, each of first values for each of the individual data items by optimizing an objective function that has a loss weight related to ease of interpretation of the data item by using the training data set; acquiring, on a second assumption that assumes each of the individual data items is not easy, each of second values; selecting a specific data item from the individual data items based on each of the first values and each of the second values for each of the individual data items; and generating a linear model using user evaluation for the specific data item.

A method of controlling a display device is provided. The method includes a transparent screen to recognize a position of a user in front of the transparent screen and an eye gaze of the user looking at the transparent screen, recognize a position of a background object behind the transparent screen, estimate, based on a distance between the transparent screen and the user, and a direction of the eye gaze of the user, a minimum angular change of the eye gaze by which the display device is able to detect a change in the eye gaze, and display, on the transparent screen, information related to the background object when the background object is at a position within the minimum angular change of the eye gaze from a current eye gaze of the user.