Peng Ren

Shuyu Chang, Rui Wang, Peng Ren et al.

Modeling topics effectively in short texts, such as tweets and news snippets, is crucial to capturing rapidly evolving social trends. Existing topic models often struggle to accurately capture the underlying semantic patterns of short texts, primarily due to the sparse nature of such data. This nature of texts leads to an unavoidable lack of co-occurrence information, which hinders the coherence and granularity of mined topics. This paper introduces a novel model-agnostic mechanism, termed Topic Refinement, which leverages the advanced text comprehension capabilities of Large Language Models (LLMs) for short-text topic modeling. Unlike traditional methods, this post-processing mechanism enhances the quality of topics extracted by various topic modeling methods through prompt engineering. We guide LLMs in identifying semantically intruder words within the extracted topics and suggesting coherent alternatives to replace these words. This process mimics human-like identification, evaluation, and refinement of the extracted topics. Extensive experiments on four diverse datasets demonstrate that Topic Refinement boosts the topic quality and improves the performance in topic-related text classification tasks.

Shuaiyu Chen, Fu Wang, Peng Ren et al.

Semantic segmentation is commonly used for Oil Spill Detection (OSD) in remote sensing images. However, the limited availability of labelled oil spill samples and class imbalance present significant challenges that can reduce detection accuracy. Furthermore, most existing methods, which rely on convolutional neural networks (CNNs), struggle to detect small oil spill areas due to their limited receptive fields and inability to effectively capture global contextual information. This study explores the potential of State-Space Models (SSMs), particularly Mamba, to overcome these limitations, building on their recent success in vision applications. We propose OSDMamba, the first Mamba-based architecture specifically designed for oil spill detection. OSDMamba leverages Mamba's selective scanning mechanism to effectively expand the model's receptive field while preserving critical details. Moreover, we designed an asymmetric decoder incorporating ConvSSM and deep supervision to strengthen multi-scale feature fusion, thereby enhancing the model's sensitivity to minority class samples. Experimental results show that the proposed OSDMamba achieves state-of-the-art performance, yielding improvements of 8.9% and 11.8% in OSD across two publicly available datasets.

He Zhang, Xingrui Yu, Peng Ren et al.

Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both \emph{data scarcity} and \emph{data imbalance}. The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation. Given a small set of network intrusion samples, it first derives a Poisson-Gamma joint probabilistic generative model to generate synthesised intrusion data using Monte Carlo methods. Those synthesised data are then augmented by deep generative neural networks through adversarial learning. Finally, it adopts the augmented intrusion data to train supervised models for detecting network intrusions. Comprehensive experimental validations on KDD Cup 99 dataset show that the proposed framework outperforms the existing learning based IDSs in terms of improved accuracy, precision, recall, and F1-score.