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A Multi-Scale Infrared and Visible Image Fusion network Based on Context Perception

A Multi-Scale Infrared and Visible Image Fusion Network Base...

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International Conference on Computer Supported Cooperative Work in Design

作者： Huixuan Zhao Jinyong Cheng Rundong Du Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Existing image fusion methods often fail to fully utilize the contextual information of the images, resulting in blurry and distorted fusion results. In addition, although the existing multi-scale feature extraction methods can consider the information of the source image from multiple perspectives, they tend to ignore the local information. These limitations degrade the performance and effectiveness of image fusion techniques in practical applications. To address above issues, we propose a fusion network architecture called MIA-Nest, consisting of encoder network, fusion network, and decoder network. Specifically, we introduce the Squeeze-and-Excitation Blocks(SEBs) into the encoder network, so that it can take local information into account while extracting multi-scale features. At the same time, in order to allow the fusion network to integrate the context information of the extracted features, we introduce Global Context Blocks (GCBs), so as to enrich the details in the fusion result and enable it to contain more information. Finally, the decoder network reconstructs the fusion image from the fused features obtained by the fusion network. Experimental results demonstrate that our proposed method significantly improves the quality and detail preservation capability of the fused images.

关键词： Federated learning Computer architecture network architecture Feature extraction Decoding Data mining Image fusion

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Android Malware Detection Method Based on Graph Convolutional networks

Android Malware Detection Method Based on Graph Convolutiona...

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Artificial Intelligence and power Systems (AIPS), International Conference on

作者： Qingling Xu Shumian Yang Lijuan Xu Dawei Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Facing the serious security threats posed by Android malware amidst the rapid development of mobile internet, there is an urgent need to develop more accurate and efficient detection technologies. This paper introduces an innovative Android malware detection solution based on graph neural networks(GCN). The solution centers around function call graphs as core features, incorporating sensitive API identification and node importance analysis to streamline redundant information and reinforce key feature representation. Leveraging graph neural network models, complex call graphs are efficiently encoded into low-dimensional features, revealing the deep structure of malicious code. This method not only significantly improves detection accuracy to 99% but also drastically reduces detection time while ensuring performance, demonstrating its strong advantage in Android malware detection and classification tasks.

关键词： Training Accuracy Costs Graph convolutional networks Biological system modeling Feature extraction Malware power systems Complexity theory security

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BTP-CAResNet: An Encrypted Traffic Classification Method Based on Byte Transfer Probability and Coordinate Attention Mechanism

BTP-CAResNet: An Encrypted Traffic Classification Method Bas...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Junhao Li Huiling Shi Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks.

关键词： Measurement Attention mechanisms Accuracy Image synthesis Redundancy Telecommunication traffic Gray-scale Robustness Convolutional neural networks Residual neural networks

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Lightweight Fusion Model with Time-Frequency Features for Speech Emotion Recognition

Lightweight Fusion Model with Time-Frequency Features for Sp...

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International Conference on Computer Supported Cooperative Work in Design

作者： Peng Zhang Meijuan Li Hui Zhao Yida Chen Fuqiang Wang Ye Li Wei Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Automatic speech emotion recognition (SER) is the task to predict the emotional state from speech, and has become a vital part of human-machine interaction. Recent studies focus on learning from multiple features via feature fusion. This paper proposes a lightweight fusion model with time-frequency features for SER. First, time-frequency features are extracted from the Mel frequency cepstral coefficients (MFCC) of speech by using parallel convolutional neural networks, and enhanced by using a dimension-specific weighting method. Then, a two-stage feature fusion model is proposed to fully capture the complementary information between time and frequency features and to reduce the redundancy. It consists of an adaptive fusion module to redistribute the features for a local fusion, and a global fusion module to enhance the cross-dimension feature communication. Experimental results show that our model achieves competitive performance on the datasets of IEMOCAP and RAVDESS, with recognition accuracies of 74.62% and 86.11%, respectively. The model contains only 0.82M and 0.85M parameters for the two datasets, which is lightweight for SER tasks.

关键词： Adaptation models Time-frequency analysis Emotion recognition Accuracy Computational modeling Redundancy Speech recognition

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Context-Guided and Syntactic Augmented Dual Graph Convolutional network for Aspect-Based Sentiment Analysis

Context-Guided and Syntactic Augmented Dual Graph Convolutio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jia Yi Xiaoming Wu Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Predicting the sentiment polarity of aspect terms in sentences is the goal of Aspect-Based Sentiment Analysis(ABSA) task. Graph Convolutional network(GCN) is used in majority of the ABSA task due to its ability to effectively capture the dependencies among words or entities within sentences. However, it may not work as expected when some sentences have no obvious syntactic structure. To alleviate this issue, we propose a Context-guided and Syntactic Augmented Dual Graph Convolutional network(CSADGCN) model for the ABSA task. Specifically, we propose a context-guided attention mechanism that captures both global and local information by combining self-attention and aspect-level attention, even though some sentences have no obvious syntactic structure. In addition,we augment the GCN with multiple linguistic features and utilize a biaffine attention module to capture the relationship between words. On three datasets, extensive experimental modifications reveal that our CSADGCN model performs better than the most recent baseline approach.

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An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ying Zhao Xiaoming Wu Xiangzhi Liu Hao Chen Bei Qi Huomin Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models.

关键词： Location awareness Adaptation models Accuracy Convolution Shape Computational modeling Gaussian noise Lighting Feature extraction Personnel

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression

Lightweight Malicious Encrypted Traffic Detection Method Bas...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuecheng Wen Xiaotong Li Haiqing Lv Menglu Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab’s self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic.

关键词： Image coding Accuracy Computational modeling Transforms Telecommunication traffic Gray-scale Data models Mobile handsets Cryptography Convolutional neural networks

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

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Data Science and information Technology (DSIT), International Conference on

作者： Shuai Ren Yahui Liu Yaping Zhu Wanlong Bing Hongliang Ma Wei Wang School of Information Science and Technology Shihezi University Shihezi China Key Laboratory of Computing Intelligence and Network Information Security Xinjiang Production and Construction Corps Shihezi China School of Computer Science and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Short video platforms have become an essential part of people’s daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods.

关键词： Visualization Adaptation models Attention mechanisms Quantization (signal) Computational modeling Feature extraction Rendering (computer graphics) Encoding Real-time systems Fake news

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Semantic Reconstruction Guided Missing Cross-modal Hashing

Semantic Reconstruction Guided Missing Cross-modal Hashing

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International Joint Conference on Neural networks (IJCNN)

作者： Yafang Li Chaoqun Zheng Ruifan Zuo Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cross-modal hashing is favored in the field of large-scale cross-modal retrieval for its fast query and low storage cost. Existing cross-modal hashing methods are typically based on an idealized assumption that data from different modalities are fully and completely paired. However, in practical scenarios, the occurrence of missing modal data is common due to technical difficulties, hardware failures, and challenges in data collection, contradicting the aforementioned assumption. In this paper, we propose an innovative unsupervised cross-modal hashing frame-work, named Semantic Reconstruction Guided Missing Hashing (SRGMH). Specifically, we utilize Dual-Variational Autoencoders (D-VAEs) to map features of different modalities into a shared low-dimensional latent representation, better bridging the gaps between modalities. Notably, we generate pseudo-representations of other modalities corresponding to the missing modality, effectively solving the problem of missing modal data. Moreover, we construct a refined adjacency similarity matrix to align the latent representations of different modalities, enhancing the quality of completed data and ensuring semantic consistency across modalities. Finally, we embed the adjacency similarity matrices in a shared latent representation space to jointly learn hash functions and hash codes, which enriches the semantic content and maintains structural similarity between hash functions and codes. Experiments on three real-world datasets demonstrate the superior performance of the proposed method on both retrieval accuracy and efficiency.

关键词： Hash functions Codes Costs Accuracy Semantics Neural networks Data collection

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DTBNet: medical image segmentation model based on dual transformer bridge

DTBNet: medical image segmentation model based on dual trans...

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International Joint Conference on Neural networks (IJCNN)

作者： Kequan Chen Yuli Wang Jinyong Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Accurate medical image segmentation [1] techniques help doctors make better diagnoses. The traditional U-Net [2] model usually uses skip connections to directly connect the corresponding layers of the encoder and decoder, which can induce the model to retain more spatial information. Therefore, a series of ways to optimise skip connections, such as the introduction of residual connections, have appeared to optimise the convergence speed of the model and to improve the model’s performance. Although skip connections have achieved remarkable success in the field of medical image segmentation, since the structure of medical images is more complex than that of general images, skip connections may cause the model to learn some irrelevant features when performing image segmentation, and the transfer of the bottom layer information to the top layer will also cause some interference, in order to better achieve the multi-scale fusion of the bottom layer features and the top layer features and to reduce the interference between them, we proposed DTBNet for medical image segmentation, which is a U-shaped network with a bridge structure over the skip connections, and is characterised by the fact that the features output from the different layers of the encoder are first processed by the bridge structure before being transmitted to the corresponding layers of the decoder. Next, we design a SE_ASPP module in the encoder that can capture information using different scales of receptive fields and adaptively adjusts the weights for each channel. We also propose a neighbour fusion module(NFM) in the encoder for fusing features from adjacent layers of the encoder to enhance the representation of features at different scales. In addition, we construct a perceptual loss function [3] to comprehensively supervise the task-aware features from the bottom to the top layer, which helps the model to better capture the structures and textures in medical images. Our method achieves sota performan

关键词： Bridges Image segmentation Semantics Neural networks Interference Medical services Transformers

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