Xishuang Dong
Biographic Data
| ID | 6505546 |
|---|---|
| NAME | Xishuang Dong |
| GIVEN NAMES | Xishuang |
| FAMILY NAME | Dong |
| SIGNATURE | DONG X |
| AFFILIATIONS | Texas A&M University System |
| ORCID | 0000-0002-3742-0071 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Urban Synthetic Data for Whom? Shifting the Focus from Objects to People
Urban synthetic data has emerged as a transformative tool for planning research, yet its full potential remains constrained by an overwhelming focus on physical spaces and object recognition. Current digital twin analysis prioritizes built environments over human interactions, while privacy concerns in social media-based behavioral studies limit reproducibility. This commentary argues for a paradigm shift—one that integrates human vibrancy into u…
Two-Path Deep Semisupervised Learning for Timely Fake News Detection
News in social media, such as Twitter, has been generated in high volume and speed. However, very few of them are labeled (as fake or true news) by professionals in near real time. In order to achieve timely detection of fake news in social media, a novel framework of two-path deep semisupervised learning (SSL) is proposed where one path is for supervised learning and the other is for unsupervised learning. The supervised learning path learns on …
No prominent works on this page.
Two-Path Deep Semisupervised Learning for Timely Fake News Detection
News in social media, such as Twitter, has been generated in high volume and speed. However, very few of them are labeled (as fake or true news) by professionals in near real time. In order to achieve timely detection of fake news in social media, a novel framework of two-path deep semisupervised learning (SSL) is proposed where one path is for supervised learning and the other is for unsupervised learning. The supervised learning path learns on …
Urban Synthetic Data for Whom? Shifting the Focus from Objects to People
Urban synthetic data has emerged as a transformative tool for planning research, yet its full potential remains constrained by an overwhelming focus on physical spaces and object recognition. Current digital twin analysis prioritizes built environments over human interactions, while privacy concerns in social media-based behavioral studies limit reproducibility. This commentary argues for a paradigm shift—one that integrates human vibrancy into u…
3D Modeling in Geospatial Applications (1 work) · Advanced Malware Detection Techniques (1 work) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Big data (1 work) · Bridging (networking (1 work) · Computer Science (1 work) · Convolutional neural network (1 work) · Data sharing (1 work) · Deep learning (1 work)