Abstract: Provided are a method and apparatus for displaying active friend information, an electronic device, and a storage medium. The method for displaying the active friend information includes receiving an activity information checking instruction, where the activity information checking instruction is generated when a user triggers an entry control on a message page; and displaying an active friend list, where the active friend list displays activity information of an active friend of the user, and the active friend is a friend who is online in a recent preset time period. In this manner, a process of checking the active friend information is simplified and the time spent on checking the active friend information is shortened.

Abstract: Various embodiments of a fall-detection system for detecting personal fall while preserving the privacy of a detected person are disclosed. This disclosed fall-detection system can begin by receiving a sequence of video images comprising a person being monitored. The disclosed fall-detection system then processes each video image in the sequence of video images by: detecting the person in the video image; and extracting a skeletal figure of the detected person by identifying a set of human keypoints from the detected person. Next, the disclosed fall-detection system processes the sequence of skeletal figures corresponding to the sequence of video images by labeling each skeletal figure in the sequence of skeletal figures with an action among a set of predetermined actions. The disclosed fall-detection system subsequently generates a fall/non-fall decision for the detected person based on the set of action labels corresponding to the sequence of video images.

Abstract: Techniques for providing improved online communication are provided herein. In one example, a model may be used to predict network parameters of a network connection of a computing device. One or more attributes specifying characteristics of the network connection for the computing device may be received and used by the model to predict the network parameters. The predicted network parameters may be provided to a computing device for initiation of a media session.

Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.

Abstract: Various embodiments of a fall-detection system for detecting personal fall while preserving the privacy of a detected person are disclosed. This disclosed fall-detection system can begin by receiving a sequence of video images comprising a person being monitored. The disclosed fall-detection system then processes each video image in the sequence of video images by: detecting the person in the video image; and extracting a skeletal figure of the detected person by identifying a set of human keypoints from the detected person. Next, the disclosed fall-detection system processes the sequence of skeletal figures corresponding to the sequence of video images by labeling each skeletal figure in the sequence of skeletal figures with an action among a set of predetermined actions. The disclosed fall-detection system subsequently generates a fall/non-fall decision for the detected person based on the set of action labels corresponding to the sequence of video images.

Abstract: Various embodiments of a health-monitoring system are disclosed. In one aspect, this health-monitoring system includes a server and a set of health-monitoring sensors communicatively coupled to the server. In some embodiments, the server is configured to establish a new profile for a person to be monitored by: receiving a new profile request for establishing the new profile; generating a unique person-ID for the received new profile request; creating a new entry including the unique person-ID for the person in a profile database; and transmitting the unique person-ID along with the profile photos of the person to the set of health-monitoring sensors. Moreover, each health-monitoring sensor in the set of sensors is configured to establish the new profile by adding a new entry for the person in a person-ID dictionary of the health-monitoring sensor based on the received person-ID and the profile photos of the person.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.

Abstract: Techniques for providing improved online communication are provided herein. In one example, a model may be used to predict network parameters of a network connection of a computing device. One or more attributes specifying characteristics of the network connection for the computing device may be received and used by the model to predict the network parameters. The predicted network parameters may be provided to a computing device for initiation of a media session.

Abstract: Techniques for providing improved online communication are provided herein. In one example, a model may be used to predict network parameters of a network connection of a computing device. One or more attributes specifying characteristics of the network connection for the computing device may be received and used by the model to predict the network parameters. The predicted network parameters may be provided to a computing device for initiation of a media session.

Abstract: Techniques for providing improved online communication are provided herein. In one example, a model may be used to predict network parameters of a network connection of a computing device. One or more attributes specifying characteristics of the network connection for the computing device may be received and used by the model to predict the network parameters. The predicted network parameters may be provided to a computing device for initiation of a media session.

Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.

Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.

Abstract: Various embodiments of a video-based fall risk assessment system are disclosed. During operation, this fall risk assessment system can receives a sequence of video frames including a person being monitored for fall risk assessment. The system next generates a sequence of action labels for the sequence of video frames by, for each video frame in the sequence of video frames: estimating a pose of the person within the video frame; and classifying the estimated pose as a given action among a set of predetermined actions. Next, the system identifies a subset of action labels within the sequence of action labels. The system next extracts a set of gait features for the person from a subset of video frames within the sequence of video frames corresponding to the subset of action labels. Subsequently, the system analyzes the set of extracted gait features to generate a fall risk assessment for the person.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.

Abstract: Embodiments described herein provide various examples of an age and gender estimation system capable of performing age and gender classifications on face images having sizes greater than the maximum number of input pixels supported by a given small-scale hardware convolutional neural network (CNN) module. In some embodiments, the proposed age and gender estimation system can first divide a high-resolution input face image into a set of image patches with judiciously designed overlaps among neighbouring patches. Each of the image patches can then be processed with a small-scale CNN module, such as the built-in CNN module in Hi3519 SoC. The outputs corresponding to the set of image patches can be subsequently merged to obtain the output corresponding to the input face image, and the merged output can be further processed by subsequent layers in the age and gender estimation system to generate age and gender classifications for the input face image.

Abstract: Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded video system. In one aspect, a process for performing real-time face-pose-estimation and best-pose selection for a detected person captured in a video is disclosed. This process includes the steps of: receiving a video image among a sequence of video frames of a video; performing a face detection operation on the video image to detect a set of faces in the video image; detecting a new person appears in the video based on the set of detected faces; tracking the new person through subsequent video images in the video by detecting a sequence of face images of the new person in the subsequent video images; and for each of the subsequent video images which contains a detected face of the new person being tracked: estimating a pose associated with the detected face and updating a best pose for the new person based on the estimated pose.

Abstract: Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded video system. In one aspect, a process for performing real-time face-pose-estimation and best-pose selection for a detected person captured in a video is disclosed. This process includes the steps of: receiving a video image among a sequence of video frames of a video; performing a face detection operation on the video image to detect a set of faces in the video image; detecting a new person appears in the video based on the set of detected faces; tracking the new person through subsequent video images in the video by detecting a sequence of face images of the new person in the subsequent video images; and for each of the subsequent video images which contains a detected face of the new person being tracked: estimating a pose associated with the detected face and updating a best pose for the new person based on the estimated pose.

Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.