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Zhou Su

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3 papers

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IJCAI Conference 2025 Conference Paper

MutationGuard: A Graph and Temporal-Spatial Neural Method for Detecting Mutation Telecommunication Fraud

Haitao Bai
Pinghui Wang
Ruofei Zhang
Ziyang Zhou
Juxiang Zeng
Yulou Su
Li Xing
Zhou Su

Telecommunication fraud refers to deceptive activities in the field of communication services. This research focuses on a category of fraud identified as ''mutation telecommunication fraud". There is currently a lack of research on mutation telecommunication fraud detection, allowing this type of fraud to persist uncaught. We identify that detecting mutation fraud requires capturing multi-source patterns, including user communication graphs and temporal-spatial Voice of Call (VOC) features. Specifically, we introduce MutationGuard, which leverages Graph Neural Networks (GNN) to capture changes in user communication graphs. For VOC records, we map call start times onto a 3D cylindrical surface, thereby representing each VOC record in spatial coordinates and utilizing proposed LFFE and TCFE modules to capture local fraud behaviors and temporal behavior changes. The proposed neural modeling approach that facilitates multi-source information fusion constitutes a significant advancement in detecting mutation fraud. Experiment results reveal a significant improvement in the AUC score by 1. 52% and the F1 score by 1. 36% on the proposed telecommunication fraud dataset. Particularly, our method shows a significant improvement of 13. 93% in accuracy on mutation fraud data. We also validate the effectiveness of our method on the publicly available Sichuan Telecommunication Fraud dataset.

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AAAI Conference 2023 Conference Paper

Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference

Yabin Wang
Zhiheng Ma
Zhiwu Huang
Yaowei Wang
Zhou Su
Xiaopeng Hong

This paper focuses on the prevalent stage interference and stage performance imbalance of incremental learning. To avoid obvious stage learning bottlenecks, we propose a new incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task at each stage, without interference from others. To be concrete, to aggregate multiple stage classifiers as a uniform one impartially, we first introduce a temperature-controlled energy metric for indicating the confidence score levels of the stage classifiers. We then propose an anchor-based energy self-normalization strategy to ensure the stage classifiers work at the same energy level. Finally, we design a voting-based inference augmentation strategy for robust inference. The proposed method is rehearsal-free and can work for almost all incremental learning scenarios. We evaluate the proposed method on four large datasets. Extensive results demonstrate the superiority of the proposed method in setting up new state-of-the-art overall performance. Code is available at https://github.com/iamwangyabin/ESN.

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AAAI Conference 2023 Conference Paper

SegFormer: A Topic Segmentation Model with Controllable Range of Attention

Haitao Bai
Pinghui Wang
Ruofei Zhang
Zhou Su

Topic segmentation aims to reveal the latent structure of a document and divide it into multiple parts. However, current neural solutions are limited in the context modeling of sentences and feature representation of candidate boundaries. This causes the model to suffer from inefficient sentence context encoding and noise information interference. In this paper, we design a new text segmentation model SegFormer with unidirectional attention blocks to better model sentence representations. To alleviate the problem of noise information interference, SegFormer uses a novel additional context aggregator and a topic classification loss to guide the model to aggregate the information within the appropriate range. In addition, SegFormer applies an iterative prediction algorithm to search for optimal boundaries progressively. We evaluate SegFormer's generalization ability, multilingual ability, and application ability on multiple challenging real-world datasets. Experiments show that our model significantly improves the performance by 7.5% on the benchmark WIKI-SECTION compared to several strong baselines. The application of SegFormer to a real-world dataset to separate normal and advertisement segments in product marketing essays also achieves superior performance in the evaluation with other cutting-edge models.

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