Information Fusion最新文献_第3页

Polyp-Mamba: A Hybrid Multi-Frequency Perception Gated Selection Network for polyp segmentation 息肉-曼巴：用于息肉分割的混合多频感知门控选择网络

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102759

Xingguo Zhu , Wei Wang , Chen Zhang , Haifeng Wang

Accurate segmentation of polyps in the colorectal region is crucial for medical diagnosis and the localization of polyp areas. However, challenges arise from blurred boundaries due to the similarity between polyp edges and surrounding tissues, variable polyp morphology, and speckle noise. To address these challenges, we propose a Hybrid Multi-Frequency Perception Gated Selection Network (Polyp-Mamba) for precise polyp segmentation. First, we design a dual multi-frequency fusion encoder that employs Mamba and ResNet to quickly and effectively learn global and local features in polyp images. Specifically, we incorporate a novel Hybrid Multi-Frequency Fusion Module (HMFM) within the encoder, using discrete cosine transform to analyze features from multiple spectral perspectives. This approach mitigates the issue of blurred polyp boundaries caused by their similarity to surrounding tissues, effectively integrating local and global features. Additionally, we construct a Gated Selection Decoder to suppress irrelevant feature regions in the encoder and introduce deep supervision to guide decoder features to align closely with the labels. We conduct extensive experiments using five commonly used polyp test datasets. Comparisons with 14 state-of-the-art segmentation methods demonstrate that our approach surpasses traditional methods in sensitivity to different polyp images, robustness to variations in polyp size and shape, speckle noise, and distribution similarity between surrounding tissues and polyps. Overall, our method achieves superior mDice scores on five polyp test datasets compared to state-of-the-art methods, indicating better performance in polyp segmentation.

对结肠直肠区域的息肉进行精确分割对于医学诊断和息肉区域定位至关重要。然而，由于息肉边缘与周围组织的相似性、多变的息肉形态和斑点噪声，导致边界模糊不清，这给我们带来了挑战。为了应对这些挑战，我们提出了一种用于精确息肉分割的混合多频感知门控选择网络（Polyp-Mamba）。首先，我们设计了一个双多频融合编码器，利用 Mamba 和 ResNet 快速有效地学习息肉图像中的全局和局部特征。具体来说，我们在编码器中加入了新颖的混合多频融合模块（HMFM），利用离散余弦变换从多个光谱角度分析特征。这种方法能有效整合局部和全局特征，从而缓解息肉边界因与周围组织相似而模糊不清的问题。此外，我们还构建了一个门控选择解码器来抑制编码器中的无关特征区域，并引入深度监督来引导解码器特征与标签紧密一致。我们使用五个常用的息肉测试数据集进行了广泛的实验。与 14 种最先进的分割方法相比，我们的方法在对不同息肉图像的敏感性、对息肉大小和形状变化的鲁棒性、斑点噪声以及周围组织和息肉之间的分布相似性等方面都优于传统方法。总体而言，与最先进的方法相比，我们的方法在五个息肉测试数据集上获得了更高的 mDice 分数，这表明我们的方法在息肉分割方面有更好的表现。

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引用次数: 0

Divide and augment: Supervised domain adaptation via sample-wise feature fusion 分而治之：通过样本特征融合实现有监督的领域适应

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-28 DOI: 10.1016/j.inffus.2024.102757

Zhuangzhuang Chen , Bin Pu , Lei Zhao , Jie He , Pengchen Liang

The training of deep models relies on appropriate regularization from a copious amount of labeled data. And yet, obtaining a large and well-annotated dataset is costly. Thus, supervised domain adaptation (SDA) becomes attractive, especially when it aims to regularize these networks for a data-scarce target domain by exploiting an available data-rich source domain. Different from previous methods focusing on an cumbersome adversarial learning manner, we assume that a source or target sample in the feature space can be regarded as a combination of (1) domain-oriented features (i.e., those reflecting the difference among domains) and (2) class-specific features (i.e., those inherently defining a specific class). By exploiting this, we present Divide and Augment (DivAug), a feature fusion-based data augmentation framework that performs target domain augmentation by transforming source samples into the target domain in an energy-efficient manner. Specifically, with a novel semantic inconsistency loss based on a multi-task ensemble learning scheme, DivAug enforces two encoders to learn the decomposed domain-oriented and class-specific features, respectively. Furthermore, we propose a simple sample-wise feature fusion rule that transforms source samples into target domain by combining class-specific features from a source sample and domain-oriented features from a target sample. Extensive experiments demonstrate that our method outperforms the current state-of-the-art methods across various datasets in SDA settings.

深度模型的训练依赖于大量标注数据的适当正则化。然而，获得一个庞大且标注齐全的数据集成本高昂。因此，有监督的领域适应（SDA）变得很有吸引力，尤其是当它旨在通过利用可用的数据丰富的源领域，为数据稀缺的目标领域正则化这些网络时。与以往侧重于繁琐的对抗学习方式的方法不同，我们假定特征空间中的源样本或目标样本可被视为（1）面向领域的特征（即反映领域间差异的特征）和（2）特定类别的特征（即定义特定类别的特征）的组合。利用这一点，我们提出了基于特征融合的数据增强框架 Divide and Augment (DivAug)，该框架通过将源样本转化为目标域，以节能的方式执行目标域增强。具体来说，DivAug 采用基于多任务集合学习方案的新型语义不一致损失，强制使用两个编码器分别学习面向领域的分解特征和特定类别的特征。此外，我们还提出了一种简单的样本特征融合规则，通过结合源样本中的特定类别特征和目标样本中的面向领域特征，将源样本转化为目标领域样本。广泛的实验证明，在 SDA 设置的各种数据集上，我们的方法优于目前最先进的方法。

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引用次数: 0

IFNet: Data-driven multisensor estimate fusion with unknown correlation in sensor measurement noises IFNet：数据驱动的多传感器估计融合与传感器测量噪声中的未知相关性

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-24 DOI: 10.1016/j.inffus.2024.102750

Ming Wang, Haiqi Liu, Hanning Tang, Mei Zhang, Xiaojing Shen

In recent years, multisensor fusion for state estimation has gained considerable attention. The effectiveness of the optimal fusion estimation method heavily relies on the correlation among sensor measurement noises. To enhance estimate fusion performance by mining unknown correlation in the data, this paper introduces a novel multisensor fusion approach using an information filtering neural network (IFNet) for discrete-time nonlinear state space models with cross-correlated measurement noises. The method presents three notable advantages: First, it offers a data-driven perspective to tackle uncertain correlation in multisensor estimate fusion while preserving the interpretability of the information filtering. Second, by harnessing the RNN’s capability to manage data streams, it can dynamically update the fusion weights between sensors to improve fusion accuracy. Third, this method has a lower complexity than the state-of-the-art KalmanNet measurement fusion method when dealing with the fusion problem involving a large number of sensors. Numerical simulations demonstrate that IFNet exhibits better fusion accuracy than traditional filtering methods and KalmanNet fusion filtering when correlation among measurement noises is unknown.

近年来，用于状态估计的多传感器融合受到了广泛关注。最佳融合估计方法的有效性在很大程度上取决于传感器测量噪声之间的相关性。为了通过挖掘数据中未知的相关性来提高估计融合性能，本文针对具有交叉相关测量噪声的离散时间非线性状态空间模型，介绍了一种使用信息过滤神经网络（IFNet）的新型多传感器融合方法。该方法具有三个显著优势：首先，它提供了一个数据驱动的视角来解决多传感器估计融合中的不确定相关性问题，同时保留了信息过滤的可解释性。其次，通过利用 RNN 管理数据流的能力，它可以动态更新传感器之间的融合权重，从而提高融合精度。第三，在处理涉及大量传感器的融合问题时，该方法的复杂度低于最先进的卡尔曼网络测量融合方法。数值模拟证明，当测量噪声之间的相关性未知时，IFNet 比传统滤波方法和 KalmanNet 融合滤波方法表现出更高的融合精度。

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引用次数: 0

Efficient audio–visual information fusion using encoding pace synchronization for Audio–Visual Speech Separation 利用编码同步实现高效视听信息融合，实现视听语音分离

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102749

Xinmeng Xu , Weiping Tu , Yuhong Yang

Contemporary audio–visual speech separation (AVSS) models typically use encoders that merge audio and visual representations by concatenating them at a specific layer. This approach assumes that both modalities progress at the same pace and that information is adequately encoded at the chosen fusion layer. However, this assumption is often flawed due to inherent differences between the audio and visual modalities. In particular, the audio modality, being more directly tied to the final output (i.e., denoised speech), tends to converge faster than the visual modality. This discrepancy creates a persistent challenge in selecting the appropriate layer for fusion. To address this, we propose the Encoding Pace Synchronization Network (EPS-Net) for AVSS. EPS-Net allows for the independent encoding of the two modalities, enabling each to be processed at its own pace. At the same time, it establishes communication between the audio and visual modalities at corresponding encoding layers, progressively synchronizing their encoding speeds. This approach facilitates the gradual fusion of information while preserving the unique characteristics of each modality. The effectiveness of the proposed method has been validated through extensive experiments on the LRS2, LRS3, and VoxCeleb2 datasets, demonstrating superior performance over state-of-the-art methods.

当代的视听语音分离（AVSS）模型通常使用编码器，通过在特定层上串联来融合视听表征。这种方法假设两种模态以相同的速度发展，并且所选的融合层已对信息进行了充分编码。然而，由于音频和视觉模式之间的固有差异，这一假设往往存在缺陷。尤其是音频模式，由于与最终输出（即去噪语音）有更直接的联系，其融合速度往往快于视觉模式。这种差异给选择合适的融合层带来了持续的挑战。为了解决这个问题，我们提出了用于 AVSS 的编码同步网络（EPS-Net）。EPS-Net 允许对两种模式进行独立编码，使每种模式都能以自己的速度进行处理。同时，它在相应的编码层建立音频和视觉模式之间的通信，逐步同步它们的编码速度。这种方法既能促进信息的逐步融合，又能保持每种模式的独特性。通过在 LRS2、LRS3 和 VoxCeleb2 数据集上进行大量实验，验证了所提方法的有效性，证明其性能优于最先进的方法。

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引用次数: 0

DSEM-NeRF: Multimodal feature fusion and global–local attention for enhanced 3D scene reconstruction DSEM-NeRF：多模态特征融合和全局-局部注意力用于增强型三维场景重建

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102752

Dong Liu , Zhiyong Wang , Peiyuan Chen

3D scene understanding often faces the problems of insufficient detail capture and poor adaptability to multi-view changes. To this end, we proposed a NeRF-based 3D scene understanding model DSEM-NeRF, which effectively improves the reconstruction quality of complex scenes through multimodal feature fusion and global–local attention mechanism. DSEM-NeRF extracts multimodal features such as color, depth, and semantics from multi-view 2D images, and accurately captures key areas by dynamically adjusting the importance of features. Experimental results show that DSEM-NeRF outperforms many existing models on the LLFF and DTU datasets, with PSNR reaching 20.01, 23.56, and 24.58 respectively, and SSIM reaching 0.834. In particular, it shows strong robustness in complex scenes and multi-view changes, verifying the effectiveness and reliability of the model.

三维场景理解往往面临细节捕捉不足、多视角变化适应性差等问题。为此，我们提出了一种基于 NeRF 的三维场景理解模型 DSEM-NeRF，通过多模态特征融合和全局-局部关注机制，有效提高了复杂场景的重建质量。DSEM-NeRF 从多视角二维图像中提取颜色、深度和语义等多模态特征，并通过动态调整特征的重要性来准确捕捉关键区域。实验结果表明，DSEM-NeRF 在 LLFF 和 DTU 数据集上的表现优于许多现有模型，PSNR 分别达到 20.01、23.56 和 24.58，SSIM 达到 0.834。特别是，它在复杂场景和多视角变化中表现出很强的鲁棒性，验证了该模型的有效性和可靠性。

引用次数: 0

Euclidean and Poincaré space ensemble Xgboost 欧几里得和庞加莱空间集合 Xgboost

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-23 DOI: 10.1016/j.inffus.2024.102746

Ponnuthurai Nagaratnam Suganthan , Lingping Kong , Václav Snášel , Varun Ojha , Hussein Ahmed Hussein Zaky Aly

The Hyperbolic space has garnered attention for its unique properties and efficient representation of hierarchical structures. Recent studies have explored hyperbolic alternatives to hyperplane-based classifiers, such as logistic regression and support vector machines. Hyperbolic methods have even been fused into random forests by constructing data splits with horosphere, which proved effective for hyperbolic datasets. However, the existing incorporation of the horosphere leads to substantial computation time, diverting attention from its application on most datasets. Against this backdrop, we introduce an extension of Xgboost, a renowned machine learning (ML) algorithm to hyperbolic space, denoted as PXgboost. This extension involves a redefinition of the node split concept using the Riemannian gradient and Riemannian Hessian. Our findings unveil the promising performance of PXgboost compared to the algorithms in the literature through comprehensive experiments conducted on 64 datasets from the UCI ML repository and 8 datasets from WordNet by fusing both their Euclidean and hyperbolic-transformed (hyperbolic UCI) representations. Furthermore, our findings suggest that the Euclidean metric-based classifier performs well even on hyperbolic data. Building upon the above finding, we propose a space fusion classifier called, EPboost. It harmonizes data processing across various spaces and integrates probability outcomes for predictive analysis. In our comparative analysis involving 19 algorithms on the UCI dataset, our EPboost outperforms others in most cases, underscoring its efficacy and potential significance in diverse ML applications. This research marks a step forward in harnessing hyperbolic geometry for ML tasks and showcases its potential to enhance algorithmic efficacy.

双曲空间因其独特的特性和对层次结构的高效表示而备受关注。最近的研究探索了基于双曲的分类器，如逻辑回归和支持向量机。双曲方法甚至被融合到随机森林中，方法是用角圈构建数据分割，这被证明对双曲数据集很有效。然而，现有的双曲法加入水平层会导致大量的计算时间，从而分散了人们对其在大多数数据集上应用的关注。在此背景下，我们将著名的机器学习（ML）算法 Xgboost 扩展到双曲空间，称为 PXgboost。这一扩展涉及使用黎曼梯度和黎曼赫塞斯重新定义节点分割概念。通过对 UCI ML 数据库中的 64 个数据集和 WordNet 中的 8 个数据集进行融合欧几里得和双曲变换（双曲 UCI）表示，我们的研究结果揭示了 PXgboost 与文献中的算法相比具有良好的性能。此外，我们的研究结果表明，基于欧氏度量的分类器即使在双曲数据上也表现良好。基于上述发现，我们提出了一种名为 EPboost 的空间融合分类器。它协调了不同空间的数据处理，并整合了用于预测分析的概率结果。我们在 UCI 数据集上对 19 种算法进行了比较分析，在大多数情况下，我们的 EPboost 都优于其他算法，这突出表明了它在各种 ML 应用中的功效和潜在意义。这项研究标志着在利用双曲几何完成 ML 任务方面向前迈进了一步，并展示了其提高算法效率的潜力。

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引用次数: 0

Hypergraph convolutional networks with multi-ordering relations for cross-document event coreference resolution 具有多排序关系的超图卷积网络，用于跨文档事件核心参照解析

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-22 DOI: 10.1016/j.inffus.2024.102769

Wenbin Zhao , Yuhang Zhang , Di Wu , Feng Wu , Neha Jain

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of documents. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

识别文本中不同事件提及之间的核心参照关系（即事件核心参照解析）是自然语言处理中的一项重要任务。它有助于理解文本中各种事件之间的关联，在信息提取、问题解答系统和阅读理解中发挥着重要作用。现有研究在提高事件核心参照解析性能方面取得了进展，但也存在一些不足。例如，现有方法大多以串行处理模式分析文档中的事件数据，没有考虑事件之间的复杂关系，难以挖掘事件的深层语义。为了解决这些问题，本文提出了一种基于超图卷积神经网络的跨文档事件共参照解析方法（HGCN-ECR）。首先，使用 BiLSTM-CRF 模型对从大量文档中提取的事件进行语义角色标注。根据标注结果，确定事件的触发词和非触发词，并围绕事件触发词构建多文档事件超图。然后，利用超图卷积神经网络学习多文档事件超图中的高阶语义信息，并引入多头注意机制，将每种事件关系视为一组独立的注意机制，从而理解不同事件关系类型的隐藏特征。最后，利用前馈神经网络和平均链接聚类方法计算事件的核心关联分值，完成核心关联事件聚类，实现跨文档事件核心关联解析。实验结果表明，跨文档事件同源解析方法优于基线模型。

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引用次数: 0

Multimodal dual perception fusion framework for multimodal affective analysis 用于多模态情感分析的多模态双重感知融合框架

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-22 DOI: 10.1016/j.inffus.2024.102747

Qiang Lu , Xia Sun , Yunfei Long , Xiaodi Zhao , Wang Zou , Jun Feng , Xuxin Wang

The misuse of social platforms and the difficulty in regulating post contents have culminated in a surge of negative sentiments, sarcasms, and the rampant spread of fake news. In response, Multimodal sentiment analysis, sarcasm detection and fake news detection based on image and text have attracted considerable attention recently. Due to that these areas share semantic and sentiment features and confront related fusion challenges in deciphering complex human expressions across different modalities, integrating these multimodal classification tasks that share commonalities across different scenarios into a unified framework is expected to simplify research in sentiment analysis, and enhance the effectiveness of classification tasks involving both semantic and sentiment modeling. Therefore, we consider integral components of a broader spectrum of research known as multimodal affective analysis towards semantics and sentiment, and propose a novel multimodal dual perception fusion framework (MDPF). Specifically, MDPF contains three core procedures: (1) Generating bootstrapping language-image Knowledge to enrich origin modality space, and utilizing cross-modal contrastive learning for aligning text and image modalities to understand underlying semantics and interactions. (2) Designing dynamic connective mechanism to adaptively match image-text pairs and jointly employing gaussian-weighted distribution to intensify semantic sequences. (3) Constructing a cross-modal graph to preserve the structured information of both image and text data and share information between modalities, while introducing sentiment knowledge to refine the edge weights of the graph to capture cross-modal sentiment interaction. We evaluate MDPF on three publicly available datasets across three tasks, and the empirical results demonstrate the superiority of our proposed model.

社交平台的滥用和对帖子内容监管的困难导致负面情绪、讽刺和假新闻的肆意传播。为此，基于图像和文本的多模态情感分析、讽刺检测和假新闻检测最近引起了广泛关注。由于这些领域共享语义和情感特征，并且在解读不同模态的复杂人类表达时面临相关的融合挑战，因此将这些在不同场景中具有共性的多模态分类任务整合到一个统一的框架中有望简化情感分析研究，并提高涉及语义和情感建模的分类任务的有效性。因此，我们考虑了面向语义和情感的多模态情感分析这一更广泛研究的组成部分，并提出了一种新颖的多模态双感知融合框架（MDPF）。具体来说，MDPF 包含三个核心程序：(1) 生成引导性语言图像知识以丰富原初模态空间，并利用跨模态对比学习对齐文本和图像模态以理解潜在语义和交互。(2) 设计动态连接机制来自适应性地匹配图像-文本对，并联合使用高斯加权分布来强化语义序列。(3) 构建一个跨模态图，以保留图像和文本数据的结构信息，并在模态之间共享信息，同时引入情感知识来完善图的边缘权重，以捕捉跨模态情感交互。我们在三个任务的三个公开数据集上对 MDPF 进行了评估，实证结果证明了我们提出的模型的优越性。

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引用次数: 0

Vul-LMGNNs: Fusing language models and online-distilled graph neural networks for code vulnerability detection Vul-LMGNNs：融合语言模型和在线蒸馏图神经网络进行代码漏洞检测

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-21 DOI: 10.1016/j.inffus.2024.102748

Ruitong Liu , Yanbin Wang , Haitao Xu , Jianguo Sun , Fan Zhang , Peiyue Li , Zhenhao Guo

Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection. However, a critical yet often overlooked issue is that GNNs primarily rely on aggregating information from adjacent nodes, limiting structural information transfer to single-layer updates. In code graphs, nodes and relationships typically require cross-layer information propagation to fully capture complex program logic and potential vulnerability patterns. Furthermore, while some studies utilize codeLMs to supplement GNNs with code semantic information, existing integration methods have not fully explored the potential of their collaborative effects.

To address these challenges, we introduce Vul-LMGNNs that integrates pre-trained CodeLMs with GNNs, leveraging knowledge distillation to facilitate cross-layer propagation of both code semantic knowledge and structural information. Specifically, Vul-LMGNNs utilizes Code Property Graphs (CPGs) to incorporate code syntax, control flow, and data dependencies, while employing gated GNNs to extract structural information in the CPG. To achieve cross-layer information transmission, we implement an online knowledge distillation (KD) program that enables a single student GNN to acquire structural information extracted from a simultaneously trained counterpart through an alternating training procedure. Additionally, we leverage pre-trained CodeLMs to extract semantic features from code sequences. Finally, we propose an ”implicit-explicit” joint training framework to better leverage the strengths of both CodeLMs and GNNs. In the implicit phase, we utilize CodeLMs to initialize the node embeddings of each student GNN. Through online knowledge distillation, we facilitate the propagation of both code semantics and structural information across layers. In the explicit phase, we perform linear interpolation between the CodeLM and the distilled GNN to learn a late fusion model. The proposed method, evaluated across four real-world vulnerability datasets, demonstrated superior performance compared to 17 state-of-the-art approaches. Our source code can be accessed via GitHub: https://github.com/Vul-LMGNN/vul-LMGGNN.

代码语言模型（codeLMs）和图神经网络（GNNs）被广泛应用于代码漏洞检测。然而，一个关键但经常被忽视的问题是，图神经网络主要依赖于聚合相邻节点的信息，从而将结构信息传输限制在单层更新。而在代码图中，节点和关系通常需要跨层信息传播才能完全捕捉到复杂的程序逻辑和潜在的漏洞模式。为了应对这些挑战，我们引入了 Vul-LMGNN，它将预先训练好的 CodeLM 与 GNN 集成在一起，利用知识提炼来促进代码语义知识和结构信息的跨层传播。具体来说，Vul-LMGNNs 利用代码属性图（CPG）来整合代码语法、控制流和数据依赖关系，同时采用门控 GNNs 来提取 CPG 中的结构信息。为了实现跨层信息传输，我们实施了一个在线知识蒸馏（KD）程序，使单个学生 GNN 能够通过交替训练程序，从同时训练的对等 GNN 中获取结构信息。此外，我们还利用预先训练好的 CodeLM 从代码序列中提取语义特征。最后，我们提出了一个 "隐式-显式 "联合训练框架，以更好地发挥 CodeLM 和 GNN 的优势。在隐式阶段，我们利用 CodeLMs 来初始化每个学生 GNN 的节点嵌入。通过在线知识提炼，我们促进了代码语义和结构信息的跨层传播。在显式阶段，我们在 CodeLM 和经过提炼的 GNN 之间执行线性插值，以学习后期融合模型。通过对四个真实世界的漏洞数据集进行评估，与 17 种最先进的方法相比，所提出的方法表现出了卓越的性能。我们的源代码可通过 GitHub 访问：https://github.com/Vul-LMGNN/vul-LMGGNN。

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引用次数: 0

Dynamic clustering-based consensus model for large-scale group decision-making considering overlapping communities 基于聚类的大规模群体决策动态共识模型（考虑重叠群体

IF 14.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Information Fusion

Pub Date : 2024-10-20 DOI: 10.1016/j.inffus.2024.102743

Zhen Hua , Xiangjie Gou , Luis Martínez

Consensus-reaching strategy is crucial in large-scale group decision-making (LSGDM) as it serves as an effective approach to reducing group conflicts. Meanwhile, the common social network relationships in large groups can affect information exchange, thereby influencing the consensus-reaching process (CRP) and decision results. Therefore, how to leverage social network information in LSGDM to obtain an agreed solution has received widespread attention. However, most existing research assumes relative independence between communities in the dimension reduction process of LSGDM and neglects the possibility of different overlaps between them. Moreover, the impact of overlapping communities on CRP has not been adequately explored. Besides, the dynamic variations in clusters and their weights caused by evaluation updates need to be further studied. To address these issues, this paper proposes a dynamic clustering-based consensus-reaching method for LSGDM considering the impact of overlapping communities. First, the LINE-based label propagation algorithm is designed to cluster decision makers (DMs) and detect overlapping communities with social network information. An overlapping community-driven feedback mechanism is then developed to enhance group consensus by utilizing the bridging role of overlapping DMs. During CRP, clusters and their weights are dynamically updated with trust evolution due to the evaluation iteration. Finally, a case study using the Film Trust dataset is conducted to verify the effectiveness of the proposed method. Simulation experiments and comparative analysis demonstrate the capability of our method in modeling practical scenarios and addressing LSGDM problems under social network contexts.

达成共识的策略在大规模群体决策（LSGDM）中至关重要，因为它是减少群体冲突的有效方法。同时，大型群体中常见的社会网络关系会影响信息交流，从而影响达成共识的过程（CRP）和决策结果。因此，如何在 LSGDM 中利用社会网络信息获得一致同意的解决方案受到了广泛关注。然而，现有研究大多假定 LSGDM 降维过程中社群之间具有相对独立性，而忽视了社群之间可能存在的不同重叠。此外，重叠群落对 CRP 的影响也未得到充分探讨。此外，评价更新引起的聚类及其权重的动态变化也有待进一步研究。针对这些问题，本文提出了一种考虑到重叠群落影响的基于动态聚类的 LSGDM 达成共识方法。首先，设计了基于 LINE 的标签传播算法对决策者（DM）进行聚类，并利用社交网络信息检测重叠社区。然后，开发了一种重叠社区驱动的反馈机制，利用重叠 DM 的桥梁作用来增强群体共识。在 CRP 期间，群组及其权重会随着评估迭代带来的信任演变而动态更新。最后，使用电影信任数据集进行了案例研究，以验证所提方法的有效性。仿真实验和对比分析证明了我们的方法在社交网络环境下模拟实际场景和解决 LSGDM 问题的能力。

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