Interactions between users and videos are the major data source of performing video recommendation.
In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds.
The key idea behind our system is a new type of motion capture suit which contains a special pattern with checkerboard-like corners and two-letter codes.
State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.
Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics.
In this paper, we depart from the multi-person 3D pose estimation formulation, and instead reformulate it as crowd pose estimation.
We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC).
Information Theory Networking and Internet Architecture Signal Processing Information Theory
This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as "message-based tag embedding".
In this paper, a novel signature of human action recognition, namely the curvature of a video sequence, is introduced.
This paper presents a unified definition for point cloud normals of feature and non-feature points, which allows feature points to possess multiple normals.