Author name cluster

Fan Mo

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Connectivity-Guided Sparsification of 2-FWL GNNs: Preserving Full Expressivity with Improved Efficiency

Rongqin Chen
Fan Mo
Pak Lon Ip
Shenghui Zhang
Dan Wu
Ye Li
Leong Hou U

Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2-node and 3-node interactions, but incur cubic computational cost. Existing efficiency methods typically reduce this burden at the expense of expressivity. We propose Co-Sparsify, a connectivity-aware sparsification framework that eliminates provably redundant computations while preserving full 2-FWL expressive power. Our key insight is that 3-node interactions are expressively necessary only within biconnected components, namely, maximal subgraphs where every node pair lies on a cycle. Outside these components, structural relationships are fully captured via 2-node message passing and graph readouts, rendering higher-order modeling unnecessary. Co-Sparsify restricts 2-node message passing to connected components and 3-node interactions to biconnected components, eliminating redundant computation without approximation or sampling. We prove that Co-Sparsified GNNs match the expressivity of the 2-FWL test. Empirically, when applied to PPGN, Co-Sparsify matches or exceeds accuracy on synthetic substructure counting tasks and achieves state-of-the-art performance on real-world benchmarks (ZINC, QM9 and TUD). This study demonstrates that high expressivity and scalability are not mutually exclusive: principled, topology-guided sparsification enables powerful, efficient GNNs with theoretical guarantees.

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NeurIPS Conference 2025 Conference Paper

DataSIR: A Benchmark Dataset for Sensitive Information Recognition

Fan Mo
Bo Liu
Yuan Fan
Kun Qin
Yizhou Zhao
Jinhe Zhou
Jia Sun
Jinfei Liu

With the rapid development of artificial intelligence technologies, the demand for training data has surged, exacerbating risks of data leakage. Despite increasing incidents and costs associated with such leaks, data leakage prevention (DLP) technologies lag behind evolving evasion techniques that bypass existing sensitive information recognition (SIR) models. Current datasets lack comprehensive coverage of these adversarial transformations, limiting the evaluation of robust SIR systems. To address this gap, we introduce DataSIR, a benchmark dataset specifically designed to evaluate SIR models on sensitive data subjected to diverse format transformations. We curate 26 sensitive data categories based on multiple international regulations, and collect 131, 890 original samples correspondingly. Through empirical analysis of real-world evasion tactics, we implement 21 format transformation methods, which are applied to the original samples, expanding the dataset to 1, 647, 501 samples to simulate adversarial scenarios. We evaluated DataSIR using four traditional NLP models and four large language models (LLMs). For LLMs, we design structured prompts with varying degrees of contextual hints to assess the impact of prior knowledge on recognition accuracy. These evaluations demonstrate that our dataset effectively differentiates the performance of various SIR algorithms. Combined with its rich category and format diversity, the dataset can serve as a benchmark for evaluating related models and help develop future more advanced SIR models. Our dataset and experimental code are publicly available at https: //www. kaggle. com/datasets/fanmo1/datasir and https: //github. com/Fan-Mo-ZJU/DataSIR.

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AAAI Conference 2025 Conference Paper

Multi-Turn Jailbreaking Large Language Models via Attention Shifting

Xiaohu Du
Fan Mo
Ming Wen
Tu Gu
Huadi Zheng
Hai Jin
Jie Shi

Large Language Models (LLMs) have achieved significant performance in various natural language processing tasks but also pose safety and ethical threats, thus requiring red teaming and alignment processes to bolster their safety. To effectively exploit these aligned LLMs, recent studies have introduced jailbreak attacks based on multi-turn dialogues. These attacks aim to prompt LLMs to generate harmful or biased content by guiding them through contextual content. However, the underlying reasons for the effectiveness of multi-turn jailbreaks remain unclear. Existing attacks often focus on optimizing queries and escalating toxicity to construct dialogues, lacking a thorough analysis of the inherent vulnerabilities of LLMs. In this paper, we first conduct an in-depth analysis of the differences between single-turn and multi-turn jailbreaks and find that successful multi-turn jailbreaks can effectively disperse the attention of LLMs on keywords associated with harmful behaviors, especially in historical responses. Based on this, we propose ASJA, a new multi-turn jailbreak approach by shifting the attention of LLMs, specifically by iteratively fabricating the dialogue history through a genetic algorithm to induce LLMs to generate harmful content. Extensive experiments on three LLMs and two datasets show that our approach surpasses existing approaches in jailbreak effectiveness, the stealth of jailbreak prompts, and attack efficiency. Our work emphasizes the importance of enhancing the robustness of LLMs' attention mechanism in multi-turn dialogue scenarios for a better defense strategy.

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EAAI Journal 2025 Journal Article

Omni-scale spatio-temporal attention network for impact localization of sandwich composite panels

Yang Zhang
Bo Yang
Shilong Wang
Fan Mo
Fengyang Bi
Yan He

Sandwich composite panels (SCPs) have been widely used in aerospace, shipbuilding, and other fields due to their excellent flexural rigidity, designability, and other advantages. However, SCPs are susceptible to damage from external impacts during service, resulting in internal damage that is difficult to detect, and seriously affecting the equipment's structural performance and service life. Accurate impact localization is a prerequisite for condition monitoring and damage detection of SCPs. However, existing methods cannot achieve accurate impact localization of SCPs since the core material of SCPs absorbs impact energy and suppresses signal transmission. Hence, this paper investigates the utilization of deep learning technology to analyze impact signals, aiming to achieve accurate localization. Firstly, an impact signal acquisition scheme is designed. Then, this paper designs an omni-scale spatio-temporal attention network (OSTNet). It has the capability to extract all temporal scale features and spatio-temporal correlations of impact signals, thereby effectively handling SCPs impact signals with concentrated energy and low discriminability. Moreover, OSTNet also processes the impact signals in frequency domain to further improve accuracy. Finally, we set up the experimental platform and established an impact localization dataset of SCPs to test the localization effect of OSTNet. Experimental results show that the maximum, minimum, and average location errors of OSTNet are 7. 410 mm, 1. 145 mm, and 5. 157 mm, respectively, which are 11. 93 mm, 1. 776 mm, and 4. 372 mm lower than the optimal values of classical comparison models SFNet, PZTNet, LSTM, and RNN, respectively. And, OSTNet outperforms the recently published MNQGN and DAGNN methods, with 2. 378 mm, 5. 070 mm, and 1. 070 mm decreasing, respectively. Moreover, the statistical tests also strongly confirm that OSTNet exhibits significant differences compared to the 6 comparative networks. Furthermore, the effectiveness of OSTNet is validated by employing impact signal data with varying energy levels, resulting in satisfactory localization performances being achieved.

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EAAI Journal 2022 Journal Article

A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates

Bo Yang
Yang Zhang
Shilong Wang
Weichun Xu
Meng Xiao
Yan He
Fan Mo

Carbon fiber reinforced plastic (CFRP) has become one of the main structural materials for aerospace vehicles. However, some internal defects are prone to occur and have potential to cause significant losses of life and property. Currently, the detection of internal defects for CFRP mainly relies on ultrasonic, and other technologies, while they have disadvantages of low efficiency, and poor adaptability. Therefore, this paper explores a novel method to locate internal defects of CFRP laminates by analyzing vibration signals. Firstly, a signal acquisition scheme is designed. Then, a global interactive attention-based lightweight denoising network (GIALDN) is designed to analyze vibration signals and locate internal defects of CFRP laminates. In GIALDN, the threshold denoising method is used to eliminate noise-related features and improve feature discrimination; a global interactive attention module is designed, which makes the network pay more attention to the valid features while realizing the global interactive connection and obtains the rich contextual features; combining with the convolution layer of de-pooling strategy and multi-layer convolution using the residual connection, the backbone of the network is formed. Finally, an experimental platform is established to test the performance of GIALDN. Results show that the location accuracy of GIALDN can reach 98. 68%, which is more than 15% higher than those of VGGnet11 and FaultNet, and is also superior to those of LSTM, RNN, Rsenet18, SEresnet18 and Densenet121. Lastly, the location accuracies of GIALDN on CFRP laminates with the same thickness and different stacking sequences are investigated and a good model applicability can be observed.

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ICRA Conference 2022 Conference Paper

A User-customized Automatic Music Composition System

Fan Mo
Xiaoqiang Ji 0001
Huihuan Qian
Yangsheng Xu

This paper introduces an intelligent system which composes music following the users' instructions. Current auto-matic music generation models are lack of stability. Meanwhile, they cannot satisfy the preference of different people. To overcome these challenges, we train a Transformer-based neural network to generate short music segments using a dataset. A user can compose music pieces by interacting with a well-trained generator. Our system collects the user's feedback during the interactions, and fine-tunes the neural network to optimize the generator. After a large number of interactions, our system can learn the musical taste of the user and customize a personal automatic music composer for him or her. Our work enhances the application value of generative models significantly, which enables people to compose music with the assistance of artificial intelligence.

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