Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Xiaomeng Tong

dblp:328/1011 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Geometric modeling and processing
shape analysis
1.012026
Unfolding 3D shape convolutional networks for aerodynamic drag prediction in vehicle design · Comput. Aided Des. 2026
Systems and software security › exploitation
code reuse attack
0.312018
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
0.312018
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
0.312018
Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018
Systems and software security
memory safety
0.312018
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
0.312025
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
0.112018
Fine-CFI: Fine-Grained Control-Flow Integrity for Operating System Kernels · IEEE Trans. Inf. Forensics Secur. 2018
Program analysis › static analysis
pointer analysis
0.112018
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
YearPublicationVenuePosition
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 Kernels
abstract
The 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