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Shaowen Yao

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12 papers

1 author row

EAAI Journal 2026 Journal Article

A privacy-preserving and byzantine-robust consensus for blockchain Federated Learning

Libo Feng
Mengzhuang Liu
Zhiyu Jing
Shaowen Yao
Hui Li
Yimin Yu

Federated Learning (FL) has emerged as a crucial distributed machine learning paradigm for privacy-preserving artificial intelligence. By exchanging model parameters rather than raw data. However, it still faces critical challenges including centralized server risks, poisoning attacks, and low communication efficiency. This paper proposes a novel framework combining Blockchain technology with Federated Learning, named BDFL-VM, aimed at addressing these issues. First, we introduce a decentralized model validation mechanism to verify the authenticity and effectiveness of model updates. Second, we employ local differential privacy and ring signature technologies to support data privacy and identity anonymity Third, we assign participants reputation scores based on a multidimensional comprehensive evaluation, where those with higher scores can receive more substantial rewards in the distribution of benefits, effectively incentivizing active participation. Finally, we design a Proof of Reputation (PoR) consensus mechanism based on reputation scores, allocating mining rights according to these scores to reduce the waste of computational resources and improve communication efficiency. Our simulation results on classification confirm that compared to different FL frameworks, our proposed BDFL-VM framework excels in resisting attacks from malicious devices, maintaining system robustness and security, and shows significant advantages in block generation time and resource consumption.

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EAAI Journal 2026 Journal Article

Attention-guided network for infrared unmanned aerial vehicle target detection

Qian Jiang
Hao Yu
Xin Jin
Puming Wang
Shin-Jye Lee
Shaowen Yao
Huan Jiang
Wangming Lan

Infrared unmanned aerial vehicle target detection is of great value in protecting national and personal security due to the inherent strong anti-interference capability of infrared sensors. Therefore, it has become an important area of research in remote sensing and computer vision. Influenced by long shooting distances and complex backgrounds, infrared unmanned aerial vehicle images often contain significant background noise and weak features, which pose significant challenges for infrared unmanned aerial vehicle target detection. In this work, we propose an attention-guided network for infrared unmanned aerial vehicle target detection. We first extract frames from videos in the Anti-unmanned aerial vehicle dataset and corrects incorrect labels, so that we can obtain the dataset used for model training. Then, we enhanced asymptotic feature pyramid network for the neck portion of the model, reducing the loss of small target features during network propagation. Next, we introduce efficient spatial coordinate attention to highlight the features of infrared unmanned aerial vehicle targets and enable the network to quickly focus on the regions of interest. Finally, to account for the varying aspect ratios of small targets, we employ the shape intersection over the union function as the bounding box loss function to improve the accuracy of target localization. The experimental results show that our network has achieved better performance than the state-of-the-art one-stage detection frameworks.

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AAAI Conference 2026 Conference Paper

FACTGUARD: Event-Centric and Commonsense-Guided Fake News Detection

Jing He
Han Zhang
Yuanhui Xiao
Wei Guo
Shaowen Yao
Renyang Liu

Fake news detection methods based on writing style have achieved remarkable progress. However, as adversaries increasingly imitate the style of authentic news, the effectiveness of such approaches is gradually diminishing. Recent research has explored incorporating large language models (LLMs) to enhance fake news detection. Yet, despite their transformative potential, LLMs remain an untapped goldmine for fake news detection, with their real-world adoption hampered by shallow functionality exploration, ambiguous usability, and prohibitive inference costs. In this paper, we propose a novel fake news detection framework, dubbed FACTGUARD, that leverages LLMs to extract event-centric content, thereby reducing the impact of writing style on detection performance. Furthermore, our approach introduces a dynamic usability mechanism that identifies contradictions and ambiguous cases in factual reasoning, adaptively incorporating LLM advice to improve decision reliability. To ensure efficiency and practical deployment, we employ knowledge distillation to derive FACTGUARD-D, enabling the framework to operate effectively in cold-start and resource-constrained scenarios. Comprehensive experiments on two benchmark datasets demonstrate that our approach consistently outperforms existing methods in both robustness and accuracy, effectively addressing the challenges of style sensitivity and LLM usability in fake news detection.

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AAAI Conference 2026 Conference Paper

MFmamba: A Multi-function Network for Panchromatic Image Resolution Restoration Based on State-Space Model

Qian Jiang
Qianqian Wang
Xin Jin
Michał Woźniak
Shaowen Yao
Wei Zhou

Remote sensing images are becoming increasingly widespread in military, earth resource exploration. Because of the limitation of a single sensor, we can obtain high spatial resolution grayscale panchromatic (PAN) images and low spatial resolution color multispectral (MS) images. Therefore, an important issue is to obtain a color image with high spatial resolution when there is only a PAN image at the input. The existing methods improve spatial resolution using super-resolution (SR) technology and spectral recovery using colorization technology. However, the SR technique cannot improve the spectral resolution, and the colorization technique cannot improve the spatial resolution. Moreover, the pansharpening method needs two registered inputs and can not achieve SR. As a result, an integrated approach is expected. We designed a novel multi-function model (MFmamba) to realize the tasks of SR, spectral recovery, joint SR and spectral recovery through three different inputs. Firstly, MFmamba utilizes UNet++ as the backbone, and a Mamba Upsample Block (MUB) is combined with UNet++. Secondly, a Dual Pool Attention (DPA) is designed to replace the skip connection in UNet++. Finally, a Multi-scale Hybrid Cross Block (MHCB) is proposed for initial feature extraction. Many experiments show that MFmamba is competitive in evaluation metrics and visual results and performs well in the three tasks when only the input PAN image is used.

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AAAI Conference 2026 Conference Paper

MPA: Multimodal Prototype Augmentation for Few-Shot Learning

Liwen Wu
Wei Wang
Lei Zhao
Zhan Gao
Qika Lin
Shaowen Yao
Zuozhu Liu
Bin Pu

Recently, Few-shot Learning (FSL) has become a popular task that aims to recognize new classes from only a few labeled examples and has been widely applied in fields such as natural science, remote sensing, and medical images. However, most existing methods focus only on the visual modality and compute prototypes directly from raw support images, which lack comprehensive and rich multimodal information. To address these limitations, we propose a novel Multimodal Prototype Augmentation FSL framework called MPA, including LLM-based Multi-Variant Semantic Enhancement (LMSE), Hierarchical Multi-View Augmentation (HMA), and an Adaptive Uncertain Class Absorber (AUCA). LMSE leverages large language models to generate diverse paraphrased category descriptions, enriching the support set with additional semantic cues. HMA exploits both natural and multi-view augmentations to enhance feature diversity (e.g., changes in viewing distance, camera angles, and lighting conditions). AUCA models uncertainty by introducing uncertain classes via interpolation and Gaussian sampling, effectively absorbing uncertain samples. Extensive experiments on four single-domain and six cross-domain FSL benchmarks demonstrate that MPA achieves superior performance compared to existing state-of-the-art methods across most settings. Notably, MPA surpasses the second-best method by 12.29% and 24.56% in the single-domain and cross-domain setting, respectively, in the 5-way 1-shot setting.

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EAAI Journal 2025 Journal Article

Polyhedral representations with high-frequency for three-dimensional point cloud classification

Xiaoxin Mao
Xue Li
Puming Wang
Xin Jin
Shengfa Miao
Peng Liu
Shaowen Yao
Siwang Yang

Point clouds have become increasingly important due to their wide applications in fields such as autonomous driving and robotic navigation. However, the disordered and irregular nature of point clouds poses a challenge to traditional coordinate-based processing methods. These methods often struggle to accurately capture the geometric features of point clouds obtained in real environments, and the high-frequency structural information inherent in point clouds is often overlooked. In this study, a novel feature extraction method called Polyhedral Representations with High-Frequency (HF-Poly) is developed for point cloud classification. The HF-Poly method constructs tangent plane features for each point and creates polyhedral representations by neighboring tangent plane features. This approach enhances the capacity of the model to capture local features. Secondly, we design a novel high-frequency coding function that enhances the edge structure features using a high-frequency mapping strategy. This approach effectively delays the decay of these features in the deep network. In addition, we propose a general Local Channel Attention module (LCA), which assigns varying attention weights to different channels of the fused high-frequency polyhedral features to extract global information from the point cloud more efficiently. Extensive experiments on the ModelNet40 and ScanObjectNN datasets demonstrate that HF-Poly maintains superior classification accuracy while reducing computational costs compared to traditional methods, particularly achieving an impressive 88. 7 % accuracy on the ScanObjectNN dataset, with a 10. 8 % increase compared to PointNet++.

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EAAI Journal 2025 Journal Article

Pseudo-label attention-based multiple instance learning for whole slide image classification

Jing He
Ping Wang
Jingwen Cai
Dan Tang
Shaowen Yao
Renyang Liu

Automating disease classification in whole slide images (WSIs) is crucial for improving clinical diagnostic efficiency. However, existing multiple instance learning (MIL) approaches for this task often struggle with challenges such as insufficient focus on positive regions and data imbalance between positive and negative regions. These issues can lead to suboptimal performance in practical applications. To address these problems, in this paper, we propose a novel embedding-based MIL technique called pseudo-label attention-based multiple instance learning (PAMIL). PAMIL aggregates each instance’s features regarding their contributions to improving downstream classification performance. The key insight of PAMIL involves training the model in a supervised manner by introducing pseudo-labels to emphasize positive regions. Additionally, we propose a fine-tuning strategy to effectively refine the dataset, eliminating the interference of false-positive data and alleviating data imbalance. The effectiveness of PAMIL was demonstrated through comparisons with six state-of-the-art MIL techniques across two large-scale, real-world datasets. Empirical results show that the proposed method outperforms other methods, achieving up to a 2. 15% improvement in accuracy and a 1. 61% increase in area under the curve (AUC) on the Cancer Genome Atlas Non-Small Cell Lung Cancer (TCGA-NSCLC) dataset, highlighting the superiority of our method in practical applications, such as helping clinicians diagnose quickly.

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EAAI Journal 2024 Journal Article

A block-based heuristic search algorithm for the two-dimensional guillotine strip packing problem

Hao Zhang
Shaowen Yao
Shenghui Zhang
Jiewu Leng
Lijun Wei
Qiang Liu

Introduction: This paper addresses the two-dimensional strip-packing (2DSP) problem of placing a set of rectangular pieces onto a fixed-width rectangular sheet to minimize the total length used. Methodology: We propose a Block-Based Heuristic Search Algorithm (BBHSA) to solve 2DSP problems with guillotine cut constraints. Initially, it converts the 2DSP problem into a series of 2D rectangular packing problems (2DRP), where the size of the sheet is fixed. In the BBHSA, rectangular pieces are aggregated into blocks, which are partial solutions without residual space. These blocks provide the ingredients for a good layout and are basic components in a tree-based constructive search process. Two basic operations, called placing & splitting and approximate binary search are used in the search process. Furthermore, several block-based placement rules are explored to speed up the search process and improve solution quality. Result discussion: To verify the performance of our proposed algorithm, we conducted extensive experiments using the zero-waste benchmark and non-zero-waste benchmark instances. Final results: The results show that BBHSA demonstrates computational effectiveness, particularly in zero-waste cases, achieving optimal solutions for almost all zero-waste benchmark instances reported in the existing literature.

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EAAI Journal 2023 Journal Article

A theoretical analysis of continuous firing condition for pulse-coupled neural networks with its applications

Xin Jin
Pingfan Zhang
Youwei He
Qian Jiang
Puming Wang
Jingyu Hou
Wei Zhou
Shaowen Yao

The pulse-coupled neural network (PCNN) has become a popular biology-inspired model because of its remarkable performance, such as image fusion, segmentation, and recognition. Although the PCNN is an unsupervised neural network model, its parameters are often manually adjusted, which leads to the shortcomings of being time-consuming, verbose, and with bad consistency. Researchers have conducted work on the PCNN's neurodynamic analysis and parameter settings. However, most of these works were proposed for specific application areas, and there are no widely recognized and accepted methods or theories for PCNN parameter setting at present. Thus, how to reduce the difficulty of parameter setting based on neurodynamic analysis is a very important research area for applying PCNN. In this work, we proposed a general formulae of a continuous firing condition based on the dynamic analysis of PCNN neurons to avoid constructing an invalid model and to learn the neurons' firing characteristics. The results obtained from the proposed formulae were used to explore the usability of the PCNN parameter setting theory. We first utilized the pre-existing theory of the PCNN firing period to examine the rationality of our theory, and then we also introduce an image fusion method using our theory and whale optimization algorithm to verify our method and theory, besides, numerical tests and experiments on common images were also performed to verify our theory. Experimental results show that our method and theory is effective.

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EAAI Journal 2022 Journal Article

CASR-Net: A color-aware super-resolution network for panchromatic image

Ling Liu
Qian Jiang
Xin Jin
Jianan Feng
Ruxin Wang
Hangying Liao
Shin-Jye Lee
Shaowen Yao

Spatial resolution is the ability to distinguish the spatial details of remote sensing images, and high spatial resolution images are conducive to object recognition and visual interpretation. Spectral resolution is the ability to distinguish the spectral details of the ground objects in remote sensing images, and high spectral resolution images are of great significance to the classification and recognition of objects in remote sensing images. The image super-resolution model is used to enhance the spatial resolution of remote sensing image, but it cannot enhance the spectral resolution, while the image colorization model can increase the number of channels by predicting chromatic channels for the input image, thereby improving spectral resolution. In this paper, a color-aware super-resolution network that combines image colorization and super-resolution ideas is designed to improve the spectral and spatial resolution of panchromatic images. The color-aware super-resolution network mainly contains color-aware block and spatial-aware block, color-aware block is presented to predict color information for panchromatic images to improve the spectral resolution, meanwhile, spatial-aware block is used to restore the texture details for panchromatic images to improve the spatial resolution. The trained color-aware super-resolution network only needs to input panchromatic images to generate images with more spectral information and higher spatial resolution than input images. Extensive experiments demonstrate that our color-aware super-resolution network has a good performance in image colorization and super-resolution, and experimental results show that compare with some existing excellent image colorization methods and super-resolution methods, our method is excellent in objective indicators and visual effects.

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IJCAI Conference 2021 Conference Paper

A New Upper Bound Based on Vertex Partitioning for the Maximum K-plex Problem

Hua Jiang
Dongming Zhu
Zhichao Xie
Shaowen Yao
Zhang-Hua Fu

Given an undirected graph, the Maximum k-plex Problem (MKP) is to find a largest induced subgraph in which each vertex has at most k−1 non-adjacent vertices. The problem arises in social network analysis and has found applications in many important areas employing graph-based data mining. Existing exact algorithms usually implement a branch-and-bound approach that requires a tight upper bound to reduce the search space. In this paper, we propose a new upper bound for MKP, which is a partitioning of the candidate vertex set with respect to the constructing solution. We implement a new branch-and-bound algorithm that employs the upper bound to reduce the number of branches. Experimental results show that the upper bound is very effective in reducing the search space. The new algorithm outperforms the state-of-the-art algorithms significantly on real-world massive graphs, DIMACS graphs and random graphs.

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TCS Journal 2017 Journal Article

Group Rekeying in the Exclusive Subset-Cover Framework

Jing Liu
Minmin Liu
Changji Wang
Shaowen Yao

Group Rekeying deals with the problem about how to efficiently and securely distribute a new group key GK to remaining legitimate users when there are changes in group membership (join/leave). Given a universe U of n users, an exclusive key K S for an arbitrary subset S ⊂ U is a long-term key shared by all users in U ∖ S. Hence we can distribute a new group key GK encrypted under K S such that all users in U except those in S can decrypt it during group rekeying. This method allows us to exclude S from the group with a rekey message whose length is just one single encrypted key. In this paper, we use this idea to extend the famous Subset-Cover Framework to obtain its exclusive version — Exclusive Subset-Cover Framework. We provide sufficient conditions that guarantee the security of any stateless group rekeying protocol in this framework. We propose a concrete exclusive subset-cover protocol called exclusive complete subtree protocol. Compared with existing 1-resilient stateless group rekeying protocols, this protocol achieves not only constant communication overhead but also better computational efficiency as well as better collusion resistance. From this protocol, it is easy to obtain a 1-resilient stateful group rekeying protocol which also outperforms the existing 1-resilient stateful protocols. Recent researches have proved some lower bounds on the communication complexity of group rekeying protocols. These bounds suggest that it is impossible to achieve a lower communication overhead without trading off some degree of collusion resistance. However, there are application scenarios which require communication overhead below these bounds. We show that any 1-resilient stateless group rekeying protocol with constant communication overhead can be used in tandem with a Subset-Cover based protocol to construct a hybrid protocol with tunable collusion-bandwidth tradeoffs.

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