Demo Data from EDBT 2026 / reviewers in the wild / expert
Xiaomeng Tong
dblp:328/1011
· DBLP profile ↗
6ranked-venue papers
0as first author
5since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Security and privacy · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
2 papers |
Geometric modeling and processing · 54% Computational fabrication · 46% | |
| Network and information security
1 paper |
Systems and software security · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% | |
| Artificial intelligence
1 paper |
Representation and self-supervised learning · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 100% |
Topics — the 8 heaviest of 11, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing
shape analysis |
1.0 | 1 | 2026 | Unfolding 3D shape convolutional networks for aerodynamic drag prediction in vehicle design · Comput. Aided Des. 2026 |
Systems and software security › exploitation
code reuse attack |
0.3 | 1 | 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018 |
Systems and software security › memory safety
control-flow integrity |
0.3 | 1 | 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018 |
Systems and software security › memory safety › control-flow integrity
kernel control-flow integrity |
0.3 | 1 | 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018 |
Systems and software security
memory safety |
0.3 | 1 | 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018 |
Machine learning › Representation and self-supervised learning › representation learning › unsupervised representation learning
self-supervised representation learning |
0.3 | 1 | 2025 | Self-supervised representation of non-standard mechanical parts and fine-tuning method integrating macro process knowledge · Comput. Aided Des. 2025 |
Program analysis › static analysis › pointer analysis
context-sensitive pointer analysis |
0.1 | 1 | 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018 |
Program analysis › static analysis
pointer analysis |
0.1 | 1 | 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018 |
Methods — techniques the papers use, named apart from their topics
convolutional neural network · 2.03d shape convolutional networks · 2.0self-supervised learning · 1.7macro process knowledge integration · 1.7fine-tuning · 1.7fine-grained control-flow graph · 0.7field-sensitive pointer analysis · 0.7compiler-based instrumentation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Unfolding 3D shape convolutional networks for aerodynamic drag prediction in vehicle design
Yangchen Liu, Yubin Tang, Xiaomeng Tong, Qinglei Cao, Zichuan Fan |
Comput. Aided Des. | 3 |
| 2026 | Intelligent method combining models and process data for springback prediction in sheet metal stamping
Xuwen Zhao, Maolin Cai, Dongjiang Han, Xiaomeng Tong |
Eng. Appl. Artif. Intell. | 6 |
| 2025 | Self-supervised representation of non-standard mechanical parts and fine-tuning method integrating macro process knowledge
Maolin Cai, Fangwei Ning, Zhongliang Xie, Xiaomeng Tong |
Comput. Aided Des. | 6 |
| 2025 | Classification of non-standard mechanical parts with graph convolutional networks
Xiaomeng Tong, Maolin Cai |
Eng. Appl. Artif. Intell. | 2 |
| 2024 | Energy-saving scheduling strategy for variable-speed flexible job-shop problem considering operation-dependent energy consumption
Hongquan Qu, Xiaomeng Tong, Maolin Cai, Yan Shi 0003, Xing Lan |
Expert Syst. Appl. | 2 |
| 2018 | Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System KernelsabstractThe operating system kernel is often the security foundation for the whole system. To prevent attacks, control-flow integrity (CFI) has been proposed to ensure that any control transfer during the program's execution never deviates from its control-flow graph (CFG). Existing CFI solutions either work in user space or are coarse-grained; thus they cannot be readily deployed in kernels or are vulnerable to state-of-the-art attacks. In this paper, we present Fine-CFI, a system that enforces fine-grained CFI for operating system kernels. Unlike previous systems, Fine-CFI constructs the kernel's fine-grained CFG with a retrofitted context-sensitive and field-sensitive pointer analysis, then enforces CFI with this CFG. At the same time, Fine-CFI provides comprehensive protection to the control data in the kernel's interrupt context. Combining the above two kinds of protection, we can thus defeat those formidable ret2usr and kernel code-reuse attacks. We have developed a compiler-based prototype and implemented this technique in Linux 3.14 kernel. Our evaluation indicates that Fine-CFI prevents all the gadgets found by an open-source gadget-finding tool from being misused, as well as all the attacks from the RIPE benchmark and malicious attempts to modify control data in the interrupt context; and it also reduces the number of indirect control-flow targets by 99.998%, thus largely raising the bar for attackers. Our evaluation also shows that the performance overhead introduced by Fine-CFI is less than 10% on average. Jinku Li, Xiaomeng Tong, Fengwei Zhang, Jianfeng Ma 0001 |
IEEE Trans. Inf. Forensics Secur. | 2 |