Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to preserve privacy in a user dataset including interface circuitry, machine readable instructions, and programmable circuitry to determine a data usage type for each one of a plurality of user data features in a first dataset, classify the data usage type associated with each user data feature of the plurality of user data feature into a feature category, apply at least one feature engineering mechanism to feature categories of the data usage types of the plurality of user data features, select, based on application of feature engineering, a subset of the plurality of user data features for a feature selection training model, and output a second dataset based on the subset of the plurality of user data for the feature selection training model, the second dataset to include fewer user data features than the first dataset.

Abstract: Systems and methods herein describe a multi-modal interaction system. The multi-modal interaction system, receives a selection of an augmented reality (AR) experience within an application on a computer device, displays a set of AR objects associated with the AR experience on a graphical user interface (GUI) of the computer device, display textual cues associated with the set of augmented reality objects on the GUI, receives a hand gesture and a voice command, modifies a subset of augmented reality objects of the set of augmented reality objects based on the hand gesture and the voice command, and displays the modified subset of augmented reality objects on the GUI.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed that include interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to associate first datapoints of a first feature with a first node, associate second datapoints of a second feature with a second node, construct a graph from the first datapoints and the second datapoints, and perform a comparison of a graph accuracy with a baseline accuracy.

Abstract: Systems and methods herein describe a multi-modal interaction system. The multi-modal interaction system, receives a selection of an augmented reality (AR) experience within an application on a computer device, displays a set of AR objects associated with the AR experience on a graphical user interface (GUI) of the computer device, display textual cues associated with the set of augmented reality objects on the GUI, receives a hand gesture and a voice command, modifies a subset of augmented reality objects of the set of augmented reality objects based on the hand gesture and the voice command, and displays the modified subset of augmented reality objects on the GUI.

Abstract: A method and a system for detecting rough handling. The system has a camera and a computing device. The computing device is configured to: receive a video stream captured by the camera; detect persons and packages from video frames of the video stream; construct person and package trajectories based on the detected persons and packages; recognize an action between one person trajectory and one package trajectory, where the action includes the one person picks up, holds, and drops off the one package; and determine existence of a rough handling when, within a predetermined frames after drop-off of the one package, a motion distance of the one package is greater than a threshold distance, and a motion speed of the one package is greater than a threshold speed.

Abstract: Provided are a processing method, a terminal device, and a medium. The method includes the steps described below. Basic information of a target video is determined; attribute information of the target video is determined based on the basic information; in a case where the attribute information indicates that the target video is a video capable of being structured, chapter division is performed on the target video based on the basic information to obtain at least two video clips; and chapter description information of the at least two video clips, a key frame of the at least two video clips and video description information of the target video are determined.

Abstract: Provided are a video processing method and apparatus, an electronic device, and a medium. The method includes that a candidate slice information set of an original video is determined based on video information of the original video, the original video is segmented into a plurality of video segments based on each piece of candidate slice information in the candidate slice information set, the plurality of video segments is input into an evaluation model to determine a piece of probability information of each piece of candidate slice information, and target slice information of the original video is determined based on the piece of probability information corresponding to each piece of candidate slice information.

Abstract: An image authenticity detection method includes: obtaining a target image; inputting the target image into a generator of a generative adversarial network, and outputting an artifact image corresponding to the target image through the generator, where the artifact image is used for representing a difference between the target image and a real image; the generator is configured to output a prediction artifact image corresponding to a sample image and generate a fitting image based on the prediction artifact image; and the generative adversarial network further comprises a discriminator in a training stage, and the discriminator is configured to discriminate the authenticity of the fitting image, to assist the generator to learn a difference feature between a false image and a real image; and determining an authenticity detection result of the target image based on the artifact image.

Abstract: A method and a system for detecting rough handling. The system has a camera and a computing device. The computing device is configured to: receive a video stream captured by the camera; detect persons and packages from video frames of the video stream; construct person and package trajectories based on the detected persons and packages; recognize an action between one person trajectory and one package trajectory, where the action includes the one person picks up, holds, and drops off the one package; and determine existence of a rough handling when, within a predetermined frames after drop-off of the one package, a motion distance of the one package is greater than a threshold distance, and a motion speed of the one package is greater than a threshold speed.

Abstract: In one embodiment, a method includes identifying a facial image from an image of a scene. The method then determines a context associated with the facial image based on a context model. The method then identifies a person from a database based on the context and a facial recognition model associated with the facial image.

Abstract: A method of living body detection includes generating a detection interface in response to a receipt of a living body detection request for a verification of a detection object. The detection interface includes a first region with a viewing target for the detection object to track, a position of the first region in the detection interface changes during a detection time according to a first sequence of a position change of the first region. The method also includes receiving a video stream that is captured during the detection time, determining a second sequence of a sight line change of the detection object based on the video stream, and determining that the detection object is a living body at least partially based on the second sequence of the sight line change of the detection object matching the first sequence of the position change of the first region.

Abstract: A method and a system for self-checkout. The system includes a scanner, an imaging device, and a computing device. The computing device is configured to: initiate a self-checkout event; instruct the imaging device to capture video frames of a region of interest (ROI); track the product; record scanning status and location status of the product; in response to receive a scanning signal from the scanner, record scanning status of the product as scanned; calculate a shoplifting risk score based on a number of the product having the scanning status of unscanned and disappears from the table region or ROI; and provide a shoplifting warning when the shoplifting score is large.

Abstract: A method and a system for self-checkout. The system includes a scanner, an imaging device, and a computing device. The computing device is configured to: initiate a self-checkout event; instruct the imaging device to capture video frames of a region of interest (ROI); track the product; record scanning status and location status of the product; in response to receive a scanning signal from the scanner, record scanning status of the product as scanned; calculate a shoplifting risk score based on a number of the product having the scanning status of unscanned and disappears from the table region or ROI; and provide a shoplifting warning when the shoplifting score is large.

Abstract: In one embodiment, a method includes identifying a facial image from an image of a scene. The method then determines a context associated with the facial image based on a context model. The method then identifies a person from a database based on the context and a facial recognition model associated with the facial image.