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2nd International Conference on Algorithm, Image processing and Machine Vision, AIPMV 2024

作者： Wang, Chuangchuang Jia, Zhenhong Huang, Xiaohui Song, Sensen Wang, Jiajia Zhou, Gang Shi, Fei Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350390254

Video captured by surveillance equipment will jitter due to the shaking of the equipment, this jitter will affect the detection results of moving target detection algorithms that rely on stable video frames. This paper proposes an improved ORB algorithm to solve the video jitter problem, so that the moving target detection algorithm can accurately detect moving targets in jittery videos. First, wavelet transform is used to locate the high-frequency area of the image, and then feature extraction is performed on the area, which improves the efficiency of ORB feature extraction. And use Boosted Efficient Binary Local Image Descriptor (BEBLID) to replace the descriptors of directional FAST and rotation BRIEF (ORB) to improve matching accuracy. In the feature matching stage, a neighborhood query method is proposed to replace the global search of the traditional BFMatcher, which improves the matching speed. Finally, Progressive Sample Consistency (PROSAC) is employed to ensure accurate matching of point pairs, resulting in a motion matrix for video stabilization. Finally, a Gaussian mixture model with adaptive distribution numbers is combined to quickly detect moving targets. Comparative experiments with scale-invariant feature transform(SIFT), Accelerated-KAZE(AKAZE), ORB, Qtree _ ORB and SIRB prove the superior accuracy and speed of this algorithm. © 2024 IEEE.

关键词： Wavelet transforms

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Improved Self-Consistency Training with Selective Feature Fusion for Sound Event detection 6

Improved Self-Consistency Training with Selective Feature Fu...

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6th International Conference on Information Communication and signal processing, ICICSP 2023

作者： Wang, Mingyu Li, Yunlong Hu, Ying Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350339994

Sound event detection (SED) is a joint task of identifying the categories and time boundaries of sound events within an audio clip. In this paper, we propose an improved self-consistency training (ISCT) strategy for semi-supervised SED based on Mean Teacher (MT) method. For teacher and student models, each adopts two branches with the same CRNN structure, the two branches help training the model by means of consistency regularization. ISCT strategy incorporates self-consistency loss on the basis of MT loss to improve the generalization performance of the model. A selective feature fusion (SFF) module is designed for applying in the shallow layers of the feature extraction part to selectively fuse the features with different scales. A parallel attention (PA) module is designed for applying in the deep layers of the feature extraction part to obtain much richer high-level features by the channel and spatial-wise attention. Ablation experiments verify the effectiveness of our proposed ISCT strategy, SFF and PA modules. In addition, compared with four methods, our proposed method achieves competitive performance on the DCASE 2020 task4 dataset. © 2023 IEEE.

关键词： attention selective feature fusion self-consistency training sound event detection

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AMIMC: Based on Improved Meta-Learning Contrast Network with Attention Mechanism for Few Shot Text Classification 3

AMIMC: Based on Improved Meta-Learning Contrast Network with...

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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Li, Senyan Dong, Ye Qin, Xizhong Xinjiang University Xinjiang Signal Detection and Processing Key Laboratory Urumqi830049 China

ISBN: (纸本)9798331506582

Currently, meta-learning is the mainstream approach to solving the problem of scarce data in few-shot text classification. Still, challenges remain, such as embedding vectors not being compact enough, suboptimal meta-task sampling strategies, and the rigidity of convolution operations in traditional Text CNNs. To address this, we propose the Attention Mechanism-based Improved Meta-learning Contrast Network (AMIMC), which enhances intra-class aggregation, increases inter-class separation and improves embedding quality. Additionally, Double Dynamic Similar Sampling (DDSS) generates more challenging meta-tasks, and the attention mechanism enhances the flexibility of Text CNNs, significantly boosting accuracy on five few-shot text classification datasets. © 2024 IEEE.

关键词： Contrastive Learning

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DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy detection

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Computers, Materials & Continua 2023年第10期77卷 893-909页

作者： Chengfei Hua Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Software Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China

With the development of social media and the prevalence of mobile devices,an increasing number of people tend to use social media platforms to express their opinions and attitudes,leading to many online *** online controversies can severely threaten social stability,making automatic detection of controversies particularly *** controversy detection methods currently focus on mining features from text semantics and propagation ***,these methods have two drawbacks:1)limited ability to capture structural features and failure to learn deeper structural features,and 2)neglecting the influence of topic information and ineffective utilization of topic *** light of these phenomena,this paper proposes a social media controversy detection method called Dual Feature Enhanced Graph Convolutional Network(DFE-GCN).This method explores structural information at different scales from global and local perspectives to capture deeper structural features,enhancing the expressive power of structural ***,to strengthen the influence of topic information,this paper utilizes attention mechanisms to enhance topic features after each graph convolutional layer,effectively using topic *** validated our method on two different public datasets,and the experimental results demonstrate that our method achieves state-of-the-art performance compared to baseline *** the Weibo and Reddit datasets,the accuracy is improved by 5.92%and 3.32%,respectively,and the F1 score is improved by 1.99%and 2.17%,demonstrating the positive impact of enhanced structural features and topic features on controversy detection.

关键词： Controversy detection graph convolutional network feature enhancement social media

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The detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

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Computers, Materials & Continua 2023年第12期77卷 4073-4087页

作者： Xuanchen Zhou Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Software Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Urumqi830000China

With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing *** some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing *** are three main issues with current ***,there exists a severe data imbalance between fraudulent and non-fraudulent ***,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent ***,most prior studies have used contract source code as raw features,but many smart contracts only exist in *** address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data *** method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data *** then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB *** proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart *** results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract *** research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

关键词： Fraud detection smart contract ECA-EfficientNet Ethereum

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Research on cancer rapid diagnosis technology based on adaptive channel attention mechanism and two-dimensional spectral fusion

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Microchemical Journal 2025年 213卷

作者： Wei Liu Chenjie Chang Jiahe Li Xiaoyi Lv Yang Liu Chen Chen Cheng Chen College of Software Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang Cloud Computing Engineering Technology Research Center Karamay 834000 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi 830046 China

With the widespread use of Artificial Intelligence (AI) in biomedicine, integrating infrared and Raman spectra with biological information offers new prospects for enhanced disease diagnosis. For these two types of data, more information can be extracted using multimodal data fusion compared to single modality, but existing studies tend to only fuse one-dimensional spectral data, ignoring the richer information of the spectra when they are converted to two-dimensional images. In this study, we propose a rapid diagnostic method for cancer, which first converts one-dimensional (1D) Raman and Infrared spectra into two-dimensional (2D) spectral images using Short-Time Fourier Transform (STFT), respectively. Then the features are fused by our designed Adaptive Graph Channel Attention Spacial Cross-Attention Fusion Module (ASCFM). This fusion module innovatively uses the adaptive graph channel attention mechanism to strengthen the model’s ability to capture the key information of different spectral features, and utilizes the spatial attention fusion module and the cross-attention module for spatial information extraction and fusion. The output fused features effectively avoid feature information loss through this multimodal feature extraction and fusion, which greatly improves the performance of the model. In this study, Gradient-weighted Class Activation Mapping is also used to make the decision-making process of the neural network more interpretable. The results show that the accuracy of 2D Raman spectral images fused with 2D infrared spectral images by ASCFM followed by ResidualNetwork (ResNet) for diagnosis of lung cancer and glioma datasets is 98.88 % and 96.38 %, which suggests that it is a highly promising adjunctive technique for cancer diagnosis.

关键词： Auxiliary diagnostic technology Deep learning Infrared spectroscopy Lung cancer Multimodal fusion Raman spectroscopy

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GRATDet:Smart Contract Vulnerability Detector Based on Graph Representation and Transformer

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Computers, Materials & Continua 2023年第8期76卷 1439-1462页

作者： Peng Gong Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Information Science and Engineering Xinjiang UniversityUrumqi830000China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Xinjiang UniversityUrumqi830000China

Smart contracts have led to more efficient development in finance and healthcare,but vulnerabilities in contracts pose high risks to their future *** current vulnerability detection methods for contracts are either based on fixed expert rules,which are inefficient,or rely on simplistic deep learning techniques that do not fully leverage contract semantic ***,there is ample room for improvement in terms of detection *** solve these problems,this paper proposes a vulnerability detector based on deep learning techniques,graph representation,and Transformer,called *** method first performs swapping,insertion,and symbolization operations for contract functions,increasing the amount of small sample *** line of code is then treated as a basic semantic element,and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph(LG),which shows more structural features that differ from the serialized presentation of the ***,the node information and edge information of the graph are jointly learned using an improved Transformer-GP model to extract information globally and locally,and the fused features are used for vulnerability *** effectiveness of the method in reentrancy vulnerability detection is verified in experiments,where the F1 score reaches 95.16%,exceeding stateof-the-art methods.

关键词： Vulnerability detection smart contract graph representation deep learning source code

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End-To-End Residual Stage Network for COVID-19 detection 4

End-To-End Residual Stage Network for COVID-19 Detection

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Chen, Hongwei Huang, Zhihua Lu, Kexin Liu, Hanzheng Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Currently, the COVID-19 virus is in a low-level epidemic state but still spreading in society. To timely cut off the transmission chain, early screening for COVID-19 is crucial. However, adding extra network modules during training may lead to module stacking errors and increased complexity. In this paper, we propose an optimization method based on residual stage networks (ResStage) and polarized self-Attention mechanism to improve the detection ability of the model. This method provides better paths for information propagation across different layers of the network and extraction more refined and high-quality features. Results show that the fusion model based on ResStage and polarized self-Attention can improve the detection accuracy of COVID-19 without increasing excessive parameters, achieving an AUC score of 86.53%. Moreover, this paper also subdivides the training tasks, including COVID-19 positive patients, COVID-19 positive patients with coughing, healthy individuals with coughing, asthma patients with coughing, and healthy asymptomatic individuals. These different tasks help to evaluate the performance of model and provide more references for practical applications. © 2023 IEEE.

关键词： COVID-19

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MetricGAN+ Speech Enhancement Model Combining Multi-scale and Multi-resolution Feature Network and Joint Perception Loss 4

MetricGAN+ Speech Enhancement Model Combining Multi-scale an...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Guo, Chuangjian Huang, Zhihua Li, Hui Song, Jingyi Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Urumchi China

ISBN: (纸本)9798350324303

Recently, generative adversarial networks have been widely used in speech enhancement. However, some existing speech enhancement methods only aim to optimize a single perceptual metric, which may cause other perceptual metrics to be suboptimal. In this paper, we propose a joint perceptual loss function to guide the training of speech enhancement models. In addition, we use a multi-scale and multi-resolution feature extraction network in the discriminator of MetriGAN+model, so that the model can make full use of high-level context information and low-level time-frequency features, so as to improve the prediction performance of the discriminator and guide the training of the generator better. The evaluation results on the VoiceBank-DEMAND dataset show that the proposed model can effectively improve speech quality and intelligibility, and can improve the PESQ score by 0.19 compared to the previous MetricGAN+. © 2023 IEEE.

关键词： Speech enhancement

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Change detection based on optimized guided filtering in wide-field video image under low illumination

Change detection based on optimized guided filtering in wide...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Xie, Chengyou Jia, Zhenhong Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China

ISBN: (纸本)9781510681514

To effectively and accurately detect regions with subtle changes in wide-field videos captured by eagle-eye equipment in low-light environments. A two-level change detection (CD) algorithm based on guided filter fusion is proposed to achieve better separation in complicated scenarios. First, the difference images (DIs) is obtained through the difference operator and the extreme minimum ratio operator. At the same time, using Principal Component Analysis (PCA) fuse the global brightness component and the V component are extracted from images to obtain the optimized brightness map. Then, the saliency map is obtained from the optimized brightness map based on the maximum salient pixel and spatial continuity characteristics as a guide for DIs fusion. Finally, a two-level method is proposed to combine the threshold method and clustering algorithm to further suppress the noise and DI normalization. Then, using the K-means algorithm to to generate the final binary change map. Experimental results show that the proposed algorithm can accurately and effectively detect changes in Eagle Eye surveillance video images in low-light environments. Compared with other algorithms, this algorithm reduces the running time and effectively minimizes the interference of light sources and dark spot noise in low-light environments. © 2024 SPIE.

关键词： Luminance

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