检索结果-内蒙古大学图书馆

Programming Logic Modeling and Cross-Program Defect Detection Method for Object-Oriented Code

Computers, Materials & Continua 2020年第7期64卷 273-295页

作者： Yan Liu Wenyuan Fang Qiang Wei Yuan Zhao Liang Wang State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China The School of Computing Newcastle UniversityNewcastle upon TyneNE45TGUK

Code defects can lead to software vulnerability and even produce vulnerability *** research shows that the code detection technology with text analysis can judge whether object-oriented code files are defective to some ***,these detection techniques are mainly based on text features and have weak detection capabilities across *** with the uncertainty of the code and text caused by the developer’s personalization,the programming language has a stricter logical specification,which reflects the rules and requirements of the language itself and the developer’s potential way of *** article replaces text analysis with programming logic modeling,breaks through the limitation of code text analysis solely relying on the probability of sentence/word occurrence in the code,and proposes an object-oriented language programming logic construction method based on method constraint relationships,selecting features through hypothesis testing ideas,and construct support vector machine classifier to detect class files with defects and reduce the impact of personalized programming on detection *** the experiment,some representative Android applications were selected to test and compare the proposed *** terms of the accuracy of code defect detection,through cross validation,the proposed method and the existing leading methods all reach an average of more than 90%.In the aspect of cross program detection,the method proposed in this paper is superior to the other two leading methods in accuracy,recall and F1 value.

关键词： Method constraint relationship programming logic code defect hypothesis test

来源：评论

学校读者我要写书评

暂无评论

Shields for Digital Images: A Watermarking Method with KAN Block and Simulation-Enhanced Noise Pool to Resist Screen-Camera Attacks

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Guo, Daidou Qin, Chuan Luo, Xiangyang Feng, Guorui Zhang, Xinpeng University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai 200093 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450000 China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001 China Shanghai University School of Communication and Information Engineering Shanghai 200444 China

To address the issues of privacy leakage and copyright infringement in screen-camera scenarios, we propose a robust image watermarking method, which incorporates kolmogorov-arnold network (KAN) blocks and a simulation-enhanced noise pool to resist screen-camera noise attacks. Specifically, we first modify the traditional convolutional blocks for processing high-dimensional features in the U-Net-based encoder to KAN blocks. This operation enhances the ability of encoder to model nonlinear relationships between complex features, while preserving global structure of the original image and minimizing the damage to local details caused by watermark embedding, thereby improving visual quality of the watermarked image. Additionally, to enhance the robustness of the proposed method against complex screen-camera noise attacks, a simulation-enhanced noise pool containing mathematical models and a deep noise simulation network, called NSim-Net, is designed. Especially, in NSim-Net, adversarial training between the simulator based on the improved U-Net and the discriminator based on PatchGAN effectively improves the ability to simulate complex noise. Experimental results demonstrate that, compared to typical screen-camera resilient watermarking methods, the watermarked image generated by the proposed method achieves a maximum peak signal-to-noise ratio (PSNR) improvement of 4.78 dB. Furthermore, based on our simulation-enhanced noise pool, the watermark extraction accuracy of the proposed method exceeds 98% under various screen-camera noise attacks. © 1991-2012 IEEE.

关键词： Imperceptibility Robustness Screen-Camera Resilient Watermarking Simulation-Enhanced Noise Pool

来源：评论

学校读者我要写书评

暂无评论

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

引用

42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

来源：评论

学校读者我要写书评

暂无评论

Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Complex dynamics of downwind floating offshore wind turbines due to tower shadow and platform motions

引用

Energy 2025年 328卷

作者： Leng, Jun Li, Gen Duan, Lei State Key Laboratory of Ocean Engineering School of Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Center for Mathematical Science and Advanced Technology Japan Agency for Marine-Earth Science and Technology Yokohama236-0001 Japan

As the wind power industry expands into deeper waters and adopts floating wind turbines, downwind floating offshore wind turbines are becoming increasingly attractive due to their elimination of tip clearance requirements and improved yaw stability. However, the aerodynamic and structural complexities introduced by tower shadow effects, platform motions, and aeroelasticity present significant challenges. This study conducts a detailed simulation and analysis of a 5 MW downwind floating wind turbine using flexible multibody dynamics, actuator line method (ALM), and large eddy simulation (LES). The coupling effects of tower shadow, surge motion, and blade flexibility on the turbine's aerodynamic performance and structural responses are investigated. Results indicate that tower shadow causes notable decreases in angles of attack (AOAs) and aerodynamic loads, leading to intensified blade vibrations and higher-order frequency oscillations. Blade vibrations result in high-frequency fluctuations in AOAs, particularly near the blade tip, which jeopardize power generation stability. Additionally, simulations reveal that platform surge motions induce periodic blade oscillations, increasing the amplitude of bending vibrations and further destabilizing aerodynamic loads. The combined effects of aeroelasticity, tower motion, and platform movement produce unique aerodynamic performance patterns, with periodic variations in thrust and torque synchronized with surge motion frequency. © 2025

关键词： Aerodynamic loads

来源：评论

学校读者我要写书评

暂无评论

Network security assessment based on full host-based attack graph 2020

Network security assessment based on full host-based attack ...

引用

Proceedings of the 2020 International Conference on Cyberspace Innovation of advanced Technologies

作者： Xiao-fan Wang Tian-yang Zhou Jun-hu Zhu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (纸本)9781450387828

Network security assessment is an important method of evaluating network security. The existing researches of network security assessment have the problem that the method is not comprehensive and accurate. In order to solve this problem, we proposed a method of network security assessment based on a full host-based attack graph from the "point" and "plane" perspective. This method constructed a full host-based attack graph model, and proposed a splitting algorithm for weakly connected components of the full host-based attack graph. The algorithm is used to evaluate the network security from the "plane" perspective. Meanwhile, based on the full host-based attack graph and the theory of degree centrality and betweenness centrality, the key nodes in the network are evaluated. Therefore, this method accomplished the comprehensive and accurate security measurement of the network. The experimental results showed the effectiveness of the method.

关键词： Full Host-based Attack Graph

来源：评论

学校读者我要写书评

暂无评论

Cross-Network User Matching Based on Association Strength

Cross-Network User Matching Based on Association Strength

引用

作者： Qiuyan Jiang Daofu Gong Fenlin Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Qingdao Agricultural University

Cross-network user matching is the one of the fundamental problems in social network fusion and analysis. This paper proposes an unsupervised algorithm based on association strength to address this problem. Specifically, the algorithm firstly computes the association strength between nodes based on user behavior characteristics in the social network graph. Then, the node sequence is obtained by the biased walk method based on the association strength, and word2vec method is utilized to learn the node feature vector. Finally, cross-network user matching is achieved based on the distance of feature vectors. The highlight of this paper is that the bias walking based on the association strength can select nodes as the context to form a specific sequence, and then do benefits on feature vectors learning. The experimental results show that the proposed algorithm can effectively match user without any prior knowledge. Furthermore, the matching precision is improved compared to the baseline method.

关键词： Cross-network user matching Association strength Representation learning User behavior analysis

来源：评论

学校读者我要写书评

暂无评论

A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition 42

A Multi-Head Convolution Network with Attention Consistency ...

引用

42nd Chinese Control Conference, CCC 2023

作者： Liu, Wenkang Sun, Mingyi Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Peking University Advanced Institute of Information Technology Hangzhou310000 China

ISBN: (纸本)9789887581543

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

LCJ-Seg: Tunnel Lining Construction Joint Segmentation via Global Perception 27

LCJ-Seg: Tunnel Lining Construction Joint Segmentation via G...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Tan, Lei Zhang, Xiaohan Zeng, Xinrui Hu, Xiaoxi Chen, Fei Liu, Zongyang Liu, Jin Tang, Tao Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Municipal Engineering Research Institute Beijing100037 China College of Information and Electronic Engineering Zhejiang University Hangzhou310015 China School of Mathematical Sciences Beihang University Beijing100191 China Wuhan University State Key Laboratory of Water Resources Engineering and Management Wuhan430072 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Institute for Transport Studies University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9798331505929

As railway facilities and equipment maintenance becomes a focal point of current railway research, tunnels, as fundamental components of railway infrastructure, rightly garner attention for intelligent railway maintenance. However, detecting tunnel lining joints poses challenges due to their potential confusion with other internal tunnel objects such as pipelines and cables. To address this issue, we propose an effective segmentation method, LCJ-Seg, leveraging Global Perception Module (GPM) and Detail Preservation and Feature Denoising Module (DPFDM). The GPM is incorporated into the backbone for accurately identifying tunnel lining joints, as these joints require global context to properly distinguish them from other internal tunnel objects. The DPFDM preserves fine details and reduces noise of low-level features to enhance feature fusion. It ensures that both high-level and low-level features retain important details crucial for the lining construction joint segmentation task. Comprehensive comparative experiments and ablation studies on a real collected dataset, demonstrating the superiority of our approach over existing segmentation methods. © 2024 IEEE.

关键词： Tunnel linings

来源：评论

学校读者我要写书评

暂无评论

DPA-2:a large atomic model as a multitask learner

引用

npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：