Activity data of a set of tasks as a training set is obtained from a list of communication platforms associated with the tasks. For each of the tasks in the training set, a set of activity metrics is compiled according to a set of predetermined activity categories based on the activity data of each task. The activity metrics of all of the tasks in the training set are aggregated based on the activity categories to generate a data matrix. A principal component analysis is performed on the metrics of its covariance matrix to derive an activity dimension vector, where the activity dimension vector represents a distribution pattern of the activity metrics of the tasks. The activity dimension vector can be utilized to determine an activity score of a particular task, where the activity score of a task can be utilized to estimate a probability of completeness of the task.

A digital model repair method includes: providing a point cloud digital model of a target object as input to a generative network of a trained generative adversarial network ‘GAN’, the input point cloud comprising a plurality of points erroneously perturbed by one or more causes, and generating, by the generative network of the GAN, an output point cloud in which the erroneous perturbation of some or all of the plurality of points has been reduced; where the generative network of the GAN was trained using input point clouds comprising a plurality of points erroneously perturbed by said one or more causes, and a discriminator of the GAN was trained to distinguish point clouds comprising a plurality of points erroneously perturbed by said one or more causes and point clouds substantially without such perturbations.

A system, method and apparatus for detecting facial expressions according to EMG signals.

A method includes receiving, by a computing device, concepts of a domain; determining, by the computing device, objects relevant to the concepts; generating, by the computing device, a new image by stitching the relevant objects together; determining, by the computing device, whether the new image is accurate or inaccurate; and in response to determining the new image is inaccurate, propagating, by the computing device, the inaccurate new image back to a convolutional neural network (CNN).

The present invention discloses system and method for processing an image. The invention processes the image by segmenting a human portrait region of the image. The invention uses ahierarchical hybrid loss module for masking the portrait region generating masked portrait region. The invention also uses data learning the masked portrait region.

The present invention discloses system and method for processing an image. The invention processes the image by segmenting a human portrait region of the image. The invention uses ahierarchical hybrid loss module for masking the portrait region generating masked portrait region. The invention also uses data learning the masked portrait region.

A system, method and apparatus for detecting facial expressions according to EMG signals.

A system, method and apparatus for detecting facial expressions according to EMG signals.

A method includes: obtaining a plurality of clinical red blood cell images, dividing red blood cells of different shapes at different positions in each of the red blood cell images into a plurality of submasks, and synthesizing the submasks corresponding to each of the red blood cell images to generate one mask to obtain a plurality of masks corresponding to the red blood cell images; collecting shape data of a plurality of red blood cells from the masks to obtain a training data set, calculating a segmentation boundary of each red blood cell in the training data set, and establishing a red blood cell shape data set based on the segmentation boundary of each red blood cell; collecting distribution data of each red blood cell in the red blood cell shape data set; and synthesizing the red blood cell shape data set into a plurality of red blood cell images.

A method includes: obtaining a plurality of clinical red blood cell images, dividing red blood cells of different shapes at different positions in each of the red blood cell images into a plurality of submasks, and synthesizing the submasks corresponding to each of the red blood cell images to generate one mask to obtain a plurality of masks corresponding to the red blood cell images; collecting shape data of a plurality of red blood cells from the masks to obtain a training data set, calculating a segmentation boundary of each red blood cell in the training data set, and establishing a red blood cell shape data set based on the segmentation boundary of each red blood cell; collecting distribution data of each red blood cell in the red blood cell shape data set; and synthesizing the red blood cell shape data set into a plurality of red blood cell images.