Abstract: System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive a sentence having a labeled aspect term and context; convert the sentence into a dependency tree graph; calculate an attention matrix of the dependency tree graph based on one-hop attention between any two nodes of the graph; calculate multi-head attention diffusion for any two nodes from the attention matrix; obtain updated embedding of the graph using the multi-head diffusion attention; classify the aspect term based on the updated embedding of the graph to obtain predicted classification of the aspect term; calculate loss function based on the predicted classification and the ground truth label of the aspect term; and adjust parameters of models in the computer executable code based on the loss function.

Abstract: A system and a method for obtaining a photo-realistic video from a text. The method includes: providing the text and an image of a talking person; synthesizing a speech audio from the text; extracting an acoustic feature from the speech audio by an acoustic feature extractor; and generating the photo-realistic video from the acoustic feature and the image by a video generation neural network. The video generating neural network is pre-trained by: providing a training video and a training image; extracting a training acoustic feature from training audio of the training video by the acoustic feature extractor; generating video frames from the training image and the training acoustic feature by the video generation neural network; and comparing the generated video frames with ground truth video frames using generative adversarial network (GAN). The ground truth video frames correspond to the training video frames.

Abstract: System and method for prediction using a machine learning model. The system includes a coordinator, an active computing device and a passive computing device in communication with each other. The active computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: obtain parameters of the machine learning model; retrieve an instance from the local data; sample a random direction of the instance; compute a dot product of the random direction and the instance, and calculate a random feature; compute predicted values of the instance in the active and passive computing devices and summarize them to obtain a final predicted value; determine a model coefficient using the random feature, the final predicted value, and a target value of the instance; update the machine learning model using the model coefficient; and predict a value for a new instance.

Abstract: System and method for training a federated learning model asynchronously. The system includes a coordinator, an active computing device and a passive computing device in communication with each other. The active computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: train the federated learning model in the active computing device using dimensions of an instance in the active computing device; and instruct the at least one passive computing device to train the federated learning model in the at least one passive computing device using dimensions of the instance in the at least one passive computing device. The training instances in the active and the at least one passive computing devices do not correspond to each other at the same training time.

Abstract: A system and a method for cooperative maneuvering and cooperative risk warning of vehicles. The system includes monocular surveillance cameras, local computing devices, and a master server. Each local computing device receives video frames from the camera, detects and tracks vehicles from the video frames, and converts the video frames to bird-view. Each detected vehicle is represented by a detection vector having first dimensions representing two dimensional (2D) parameters of the vehicle and second dimensions representing three dimensional (3D) parameters of the vehicle. Tracking of the vehicles is performed by minimizing loss calculated based on the first dimensions and the second dimensions of the detection vectors. The master server receives bird-views from different computing devices and combines the bird-views into a global bird-view, and performs cooperative maneuvering and cooperative risk warning of the vehicles using the global bird-view.

Abstract: A method and a system for counting pigs in a pig house. The system includes a computing device and an imaging device. The computing device has a processor and a storage device storing computer executable code. The computer executable code is configured to: receive images captured from one end to the other end of the house; detect keypoints in the images using a neural network; identify pig skeletons by associating several related keypoints; track the skeletons in the images to obtain trajectories; divide each image into an activated zone and a deactivated zone; designate a spatial value of 0 for the skeletons in the activated zone and a spatial value of 1 for the skeletons in the deactivated zone; summating first order difference of the spatial values for each trajectory to obtain a trajectory count; and add the trajectory counts to obtain pig count.