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A Systematic Review and Performance Evaluation of Open-Source Tools for Smart Contract Vulnerability Detection

computers, Materials & Continua 2024年第7期80卷 995-1032页

作者： Yaqiong He Jinlin Fan Huaiguang Wu School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450001China

With the rise of blockchain technology,the security issues of smart contracts have become increasingly *** the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation *** challenges hinder the adoption and practicality of these *** paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of science(WOS)and Google *** systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further *** a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation *** on this,we propose an Ethereum smart contract vulnerability detection *** framework enables developers to easily utilize various detection tools and accurately analyze contract security *** validate the framework’s stability,over 1700 h of testing were ***,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection *** results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection *** study represents the first performance evaluation of testing tools in this domain,providing significant reference value.

关键词： Blockchain security ethereum smart contracts detection tools evaluation

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Enhance the Performance of Directional Feature-based Palmprint Recognition by Directional Response Stability Measurement

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Machine Intelligence Research 2024年第3期21卷 597-614页

作者： Haitao Wang Wei Jia School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China

Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.

关键词： Biometrics palmprint recognition directional response stability directional coding-based methods directional feature

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DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s

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computers, Materials & Continua 2025年第5期83卷 1929-1949页

作者： Honglin Wang Yaolong Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border ***,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV *** address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object *** leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small *** the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere *** components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference ***,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object *** results on the VisDrone 2019 dataset demonstrate the effectiveness *** to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter *** results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关键词： YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function

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YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

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computers, Materials & Continua 2024年第11期81卷 3261-3280页

作者： Chenghai Yu Zhilong Lu School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Railway turnouts often develop defects such as chipping,cracks,and wear during *** not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger *** advances in defect detection technologies,research specifically targeting railway turnout defects remains *** address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex *** enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex *** the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection ***,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection *** to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and *** on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.

关键词： YOLO railway turnouts defect detection mamba FPN(Feature Pyramid Network)

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Research on News Text Classification Based on Graph Convolutional Neural Networks 17

Research on News Text Classification Based on Graph Convolut...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Luo, Wen-Xuan Ma, Li-Ping Zhang, Hai-Yan School of Computer Science and Technology Southwest University of Science and Technology China Wuhan Sanjiang Yuanfan Technology co. China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507398

This paper proposes a new approach for classifying news text. It utilizes Graph Convolutional Networks (GCNs) as the foundation for this method. The approach starts with building a document-word graph. This graph is crafted to capture the content of the text. It also captures the structural information inherent in the text. Then, it integrates the features of nodes along with their neighboring nodes to improve the representation of each node. Furthermore, contrastive learning is utilized to boost the GCN's ability to represent data, thereby improving the modeling of graph structures. The contrastive loss, derived from the contrastive learning process, is combined with the classification loss from the Softmax classifier. Together, these losses jointly optimize the overall learning objective, enhancing model performance through both representation learning and label prediction. The experimental results have been analyzed. They show that the proposed method surpasses traditional sequence-based deep learning models. Additionally, it outperforms other graph neural network models in news text classification tasks across several public datasets. This confirms the efficacy of the suggested approach in capturing and utilizing the intricate relationships present in textual data for classification purposes. © 2024 IEEE.

关键词： Contrastive Learning

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A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

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computer Modeling in Engineering & sciences 2024年第7期140卷 77-108页

作者： Huaiguang Wu Yibo Peng Yaqiong He Jinlin Fan School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450001China

In recent years,the number of smart contracts deployed on blockchain has ***,the issue of vulnerability has caused incalculable *** to the irreversible and immutability of smart contracts,vulnerability detection has become particularly *** the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart *** paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart ***,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract *** tools are categorized based on their open-source status,the data format and the type of feature extraction they *** we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and ***,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection ***,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

关键词： Smart contract vulnerability detection deep learning

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Entity and Relationship Extraction with High-quality Spans and Enhanced Marker Strategies

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Machine Intelligence Research 2025年第2期22卷 289-303页

作者： Qibin Li Nianmin Yao Nai Zhou School of Computer Science Dalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 116026China

Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate spans,which hinders the effective extraction of information from high-quality candidate ***,the information encoded by existing marker strategies is often too simple to fully capture the nuances of the span,resulting in the loss of potentially valuable *** address these issues,we propose an enhancing entity and relation extraction with high-quality spans and enhanced marker(HSEM)strategies,it assigns adaptive weights to different spans in order to make the model more focused on high quality ***,the HSEM model enriches marker representation to incorporate more span information and enhance entity ***,we design a span scoring framework that assesses span quality based on the fusion of internal information and focuses the model on training high-quality samples to improve *** results on six benchmark datasets demonstrate that our model achieves state-of-the-art results after discriminating span quality.

关键词： High-quality spans enhanced marker span scoring framework span representations entity and relation extraction

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CA-DBMNet: a channel attention based dual branch multi-scale network for depth map super-resolution

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Multimedia Tools and Applications 2025年第4期84卷 1577-1596页

作者： Miao, Yongwei Zhang, Xinjie Sun, Yuliang Wang, Jinrong School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China School of Information Science and Technology Zhejiang Shuren University Hangzhou310015 China

Scene depth information plays a fundamental role and can be beneficial to various computer vision or visual robotics applications. The scene color image acquired by consumer depth sensors usually has a high resolution, whilst its depth map counterpart often performs low resolution or man-made artifacts. Due to its strong similarity in terms of scene structures between RGB-D pairs, taking the color image as prior information, this paper proposes a Dual Branch Multi-scale Network (CA-DBMNet) based on the channel attention mechanism which can effectively guide the task of depth map super-resolution (SR). The network consists of two branches–color image feature extraction branch and depth map super-resolution branch. The first branch adopts the feature pyramid structure to extract the color image features, capturing image features and structures at different scales. The second branch is composed of three modules: 1) A dense residual feature fusion (DRFF) module to integrate the extracted features from two branches with dense connection and residual learning;2) A channel multi-scale (CMS) module to exploit multi-scale features from depth feature maps;3) A channel attention (CA) module to effectively enhance the channel proportion of high-frequency components in the depth feature maps. Extensive experiments demonstrate that CA-DBMNet can effectively reconstruct the high-resolution depth map with complete scene structures and sharp edges. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Color

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Microfluidic preparation of surfactant-free ultrafine DAAF with tunable particle size for insensitive initiator explosives

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Defence technology（防务技术） 2024年第9期39卷 42-52页

作者： Bo Yang Rui Li Wei Cao Si-min He Jincan Zhu Qi Wu Heng Ding Jin Chen Weimiao Wang Zhiqiang Qiao Xiaodong Li Guangcheng Yang Institute of Chemical Materials China Academy of Engineering PhysicsMianyang 621900China Southwest University of Science and Technology Mianyang 621010China School of Chemical Engineering Nanjing University of Science and TechnologyNanjing 210094China

High purity and ultrafine DAAF(u-DAAF)is an emerging insensitive charge in *** there are many ways to obtain u-DAAF,developing a preparation method with stable operation,accurate control,good quality consistency,equipment miniaturization,and minimum manpower is an inevitable requirement to adapt to the current social technology development *** reported is the microfluidic preparation of u-DAAF with tunable particle size by a passive swirling *** the guidance of recrystallization growth kinetics and mixing behavior of fluids in the swirling microreactor,the key parameters(liquid flow rate,explosive concentration and crystallization temperature)were screened and optimized through screening *** the condition that no surfactant is added and only experimental parameters are controlled,the particle size of recrystallized DAAF can be adjusted from 98 nm to 785 nm,and the corresponding specific surface area is 8.45 m^(2)·g^(-1)to 1.33 m^(2)·g^(-1).In addition,the preparation method has good batch stability,high yield(90.8%-92.6%)and high purity(99.0%-99.4%),indicating a high practical application *** explosion derived flyer initiation tests demonstrate that the u-DAAF shows an initiation sensitivity much lower than that of the raw DAAF,and comparable to that of the refined DAAF by conventional spraying crystallization *** study provides an efficient method to fabricate u-DAAF with narrow particle size distribution and high reproducibility as well as a theoretical reference for fabrication of other ultrafine explosives.

关键词： Microfluidic preparation Screening crystallization conditions Narrow particle size distribution Low initiation sensitivity Ultrafine DAAF

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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