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

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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Fake News detection Based on Cross-modal Co-attention 4

Fake News Detection Based on Cross-modal Co-attention

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

作者： Gu, Yadong Ablimit, Mijit Hamdulla, Askar Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350324303

The spread of rumors has brought many negative impacts to society. Nowadays, rumors on social media platforms often exist in the form of both images and text. In response to this, many methods for multimodal rumor detection have been proposed, but most of these methods only utilize extracted text features and image features for simple fusion, without considering the connections between modalities. In this study, we employ ConvNet to extract image features, BERT to extract text features, and take into account the complementarity between modalities. We use a co-attention module to enhance the extracted features of individual modalities, aiming to achieve the interaction of modality features and the complementary nature between modality information. The obtained features are then concatenated to achieve the fusion of image and text features. The multimodal fusion model used in this study was experimented on Twitter and Weibo, achieving accuracy rates of 81.20% and 88.15% respectively. © 2023 IEEE.

关键词： Feature extraction

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Heavy snow removal in video based on tensor dimensionality reduction and simultaneous sparse coding

Heavy snow removal in video based on tensor dimensionality r...

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

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

ISBN: (纸本)9781510681514

We propose a novel snow removal algorithm. First, we make full use of the high-dimensional character- istics of video data and propose a tensor dimensionality reduction-based robust principal component analysis (TDR-RPCA) method to reconstruct clean background, which improves the traditional RPCA’s ability to handle four-dimensional data and solves the problems with artifacts residue. Then a penalty regularization term-based outlier detection method is used to detect moving objects and glob- al large snowflakes. A binary mask containing moving object and snowflakes is obtained. According to the mask, we design an adaptive thresholding strategy to remove snowflakes. Our scheme can effectively remove global snowflakes. Considering the sparse characteristic of snowflakes, tinny snowflakes in front of moving objects can be regarded as noise;hence, we deal with these tiny snowflakes using Gaussian-scale mixture-based simultaneous sparse coding. Experiments show that our method can remove snowflakes in videos captured during heavy snow conditions and eliminate artifact residue, which outperforms other state-of-the-art methods. © 2024 SPIE.

关键词： Tensors

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Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction 27

Pipeline Chain-of-Thought: A Prompt Method for Large Languag...

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27th International Conference on Asian Language Processing, IALP 2023

作者： Zhao, Hangtian Yilahun, Hakiz Hamdulla, Askar School of Computer Science and Technology XinJiang University Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China

ISBN: (纸本)9798350330786

The development of language models has been influencing approaches to relation extraction (RE) problems. Although large language models (LLMs) have demonstrated breakthrough potential in certain aspects, they are still in the exploratory stage for RE tasks. Currently, the mainstream approach to improving the RE performance of LLMs is through prompt fine-tuning, but most methods require providing entity information in the prompt, which effectively only allows the LLMs to perform relationship classification tasks. We propose the Pipeline Chain-of-Thought (Pipeline-COT), which breaks down the RE task into steps and transforms it into reasoning tasks that have flat scaling curves, thereby enabling the use of Chain-of-Thought (COT) to enhance model inference. In addition, our method utilizes n-shot samples to provide signals for the Bayesian inference of the model by prompting the LLMs to focus on specific concepts to generate answers. We evaluated pipeline-COT on the Chinese dataset DuIE2.0, and compared with baseline methods that require including entity information in the prompt, our method still shows competitive performance. © 2023 IEEE.

关键词： Pipelines

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Simulation analysis of ground-penetrating radar in seasonal freezing soil area with temperature-varying media 20

Simulation analysis of ground-penetrating radar in seasonal ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Lu, Guojiang Chen, Qinghua Zhang, Xianwu Key Laboratory of Signal Detection and Processing College of Information Science and Engineering Xinjiang University Urumqi830000 China

Affected by the seasonal freezing area climate, the shallow engineering geological electromagnetic environment exhibits seasonal variations, affecting the propagation process of electromagnetic waves in shallow geological soil. This brings excellent difficulties in accurately identifying the detection target. In this paper, Kaba-he, the seasonal freezing area of Xinjiang, is taken as an example to build a model of the dielectric constant of the temperature variable soil medium in different seasons, and utilizing the high-order O(2,4) FDTD algorithm implemented with MATLAB software. This experiment provides the propagation velocity of electromagnetic waves in shallow subgrade soil of Kaba-he during different seasons and, based on numerical simulation results, gives the calculated depth of the underground targets buried in other seasons. This research can provide theoretical guidance for the application of GPR in Kaba-he. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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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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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： Liu, Wei Chang, Chenjie Li, Jiahe Lv, Xiaoyi Liu, Yang Chen, Chen Chen, Cheng College of Software Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi 830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang Cloud Computing Engineering Technology Research Center Karamay 834000 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. © 2025

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

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Medical image segmentation based on adaptive threshold prediction and feature fusion

Medical image segmentation based on adaptive threshold predi...

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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Liu, Wei Mo, Jiaqing Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510677685

In the realm of computer-aided diagnosis, image segmentation techniques have garnered significant attention over the years. However, medical image data present distinctive characteristics, such as confidentiality, class imbalance, and challenges in acquiring accurate labels. Of certain rare medical conditions, it is arduous to obtain an adequate number of data samples within a limited scope for training models. Moreover, complex medical conditions may lead to diagnostic omissions concerning the locations of minor lesions. Consequently, our primary objective in this article is to address the challenge of enhancing the segmentation accuracy of lesion locations in complex medical conditions when working with limited data. Additionally, we believe that threshold selection and feature choice are particularly crucial for medical image segmentation tasks. However, existing medical image segmentation methods often prioritizes model complexity while overlooking the impact of threshold variations on segmentation performance. In this paper, we propose a multi-feature fusion network with adaptive threshold prediction and introduce a novel paradigm of semi-supervised pseudo-label generation to facilitate supervised learning. The multi-feature fusion module ensures the comprehensive utilization of dimension-sensitive features, while the adaptive threshold predictor enables the model to generate distinct segmentation thresholds based on varying inputs. We employed publicly available medical image datasets, namely Kvasir-SEG, Rim-one r2, and ISIC 2018, to train and validate our model and approach. In comparison to other supervised and semi-supervised methods, the model and the approach we proposed demonstrate superior segmentation performance. © 2024 SPIE.

关键词： Computer aided diagnosis

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Classification of X-ray images of COVID-19 based on CNN and improved swin transformer model 3

Classification of X-ray images of COVID-19 based on CNN and ...

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3rd International Conference on Advanced Algorithms and signal Image Processing, AASIP 2023

作者： Song, Fubao Mo, Jiaqing Zhang, Jiangwei Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510668522

Faced with the rapid spread of COVID-19, nucleic acid testing methods can detect positive cases relatively quickly. Still, the time-consuming detection and frequent false-negative issues have led to a sharp increase in the demand for alternative diagnostic tools for COVID-19. In this paper, using medical imaging technology and deep learning technology, a model combining a convolutional neural network and an improved Swin Transformer network is designed to detect chest X-ray images of COVID-19. The image is input into the convolutional layer to extract the local details of the image. Then, to solve the problem that some heads do not play a role in calculating multi-head self-Attention due to too small a weight, a learnable bias parameter is added to each individual computing head to enhance the specific weight of each head. Experiments show that this method has a recognition rate of 98.25% for chest X-ray images of COVID-19. Indicators such as recall rate and F1 score have been improved compared with some current methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： COVID-19

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Semi-supervised Medical Image Classification Based on DenseNet and Capsule Network Combination Model

Semi-supervised Medical Image Classification Based on DenseN...

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Shen, Hao Mo, Jiaqing Ren, Jinxun Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510680012

Medical image classification plays a crucial role in clinical treatment and medical education. However, traditional classification methods have reached performance limits and require substantial time and effort for feature extraction and selection. In this study, we introduce a novel deep learning model that combines the feature extraction part of DenseNet with the dynamic routing mechanism of capsule networks. DenseNet, known for its dense connections, aids in gradient propagation and feature reuse. Capsule networks introduce capsule layers with sensitivity to spatial relationships. By merging these two structures, our aim is to enhance the model's ability to model object hierarchy and pose variations. Our approach initially incorporates the dynamic routing mechanism of capsule networks on top of DenseNet, achieving an effective fusion of the two structures. Experimental results on the Chest X-Ray14 dataset demonstrate a significant improvement in accuracy and robustness compared to traditional DenseNet and capsule networks. To address the issue of limited annotation in medical image data, we introduce a semi-supervised learning approach and improve training balance through anti-curriculum pseudo-labeling. Furthermore, to obtain more accurate pseudo-labels, we adopt a mixed model for pseudo-label generation. This method not only effectively balances the pseudo-label predictions generated by two models but also mitigates the challenges associated with threshold-based confidence intervals in the context of multi-class, multi-label scenarios. In summary, the combination of deep convolutional neural networks and capsule networks, along with semi-supervised learning and anti-curriculum pseudo-labeling, holds great promise for medical image classification. It has significant application value in clinical practice and presents a wide-ranging prospect for the future. © 2024 SPIE.

关键词： Feature extraction

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