IEEE

A Proposal of Code Typing Problem for Basic Java Programming Learning

Abstract: Java is an object-oriented programming language that helps programmers to run programs easily. A web-based Java Programming Learning Assistant system (JPLAS) has been developed to help ...
IEEE

Learning-Based Reflection-Aware Virtual Point Removal for Large-Scale 3D Point Clouds

Abstract: 3D point clouds are widely used for robot perception and navigation. LiDAR sensors can provide large scale 3D point clouds (LS3DPC) with a certain level of accuracy in common environment.
IEEE

Efficient Sampling and Grouping Acceleration for Point Cloud Deep Learning via Single Coordinate Comparison

Abstract: With the focus on three-dimensional (3D) applications, the importance of applying deep learning to point clouds have been growing recently. It is known that mapping operations including ...
IEEE

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

Abstract: Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated ...
IEEE

PointWavelet: Learning in Spectral Domain for 3-D Point Cloud Analysis

Abstract: With recent success of deep learning in 2-D visual recognition, deep-learning-based 3-D point cloud analysis has received increasing attention from the community, especially due to the rapid ...
IEEE

A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission

Abstract: Fault diagnosis of railway assets has drawn the interest of both the scholarly and engineering communities. Federated learning (FL) enables training models across distributed assets to ...
IEEE

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

Abstract: Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, ...
IEEE

Rethinking Masked Representation Learning for 3D Point Cloud Understanding

Abstract: Self-supervised point cloud representation learning aims to acquire robust and general feature representations from unlabeled data. Recently, masked point modeling-based methods have shown ...
IEEE

Enhanced Local Feature Learning With Simple Offset Attention for Semantic Segmentation of Large-Scale Point Clouds

Abstract: The semantic segmentation network performance of large-scale outdoor point clouds is usually limited by the number of input point clouds. In the application of most methods, the point cloud ...
IEEE

GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning

Abstract: Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face ...