检索结果-内蒙古大学图书馆

Smart World Congress (SWC), IEEE

作者： Shuai Wang Shenghao Liu Xiaoxuan Fan Haijun Wang Yinxin Zhou Hanjun Gao Hongwei Lu Xianjun Deng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hunan Technical College of Railway High-speed China Nanjing University of Posts and Telecommunications China China National Nuclear Power Co Ltd China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

intelligent Cyber-Physical systems typically utilize sensors to gather a significant amount of raw data which often possesses temporal and high-dimensional characteristics. Ensuring the data reliability is crucial for the normal operation of the system. While most existing methods are capable of identifying anomalies in high-dimensional time series data from sensors, they often lack adequate accuracy and fail to adequately consider the correlation between sensor nodes. To address these limitations, we propose an unsupervised framework which is a combination of variational autoencoder and graph neural network(VG-Net). By jointly optimizing the reconstruction module and the prediction module, VG-Net effectively models the correlation between sensor nodes while simultaneously detecting anomalies. The reconstruction module is responsible for modeling the latent representation space and reconstructing the time series data through variational autoencoder. The prediction module leverages bidirectional long short term memory networks and neural tensor network to learn the relationships between sensor nodes, and uses graph neural network to predict the future behavior of the data. Experiments on eight real and synthetic datasets demonstrate that, compared with existing methods, VG-Net can accurately model the relationship between sensor nodes and enhance the anomaly detection performance of high-dimensional time series data.

关键词： Time series analysis Autoencoders Sensor phenomena and characterization Predictive models data models Graph neural networks Reliability Anomaly detection intelligent sensors Synthetic data

来源：评论

学校读者我要写书评

暂无评论

On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

引用

International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

来源：评论

学校读者我要写书评

暂无评论

HRPGAN: A GAN-based Model to Generate High-resolution Remote Sensing Images 7

HRPGAN: A GAN-based Model to Generate High-resolution Remote...

引用

7th Annual International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2019

作者： Sun, Hai Wang, Ping Chang, Yifan Qi, Li Wang, Hailei Xiao, Dan Zhong, Cheng Wu, Xuelian Li, Wenbo Sun, Bingyu Co.Ltd Shanghai China Department of Airborne Remote Sensing National Disaster Reduction Center of China Beijing China Institute of Technology Innovation Hefei Institute of Physical Science Chinese Academy of Sciences Hefei China Third Research Institute of the Ministry of Public Security Shanghai China Guizhou Key Laboratory of Economics System Simulation Guizhou University of Finance and Economics Guiyang China Beijing Aerospace Flight Control Center Beijing China Institute of Intelligent Machines Chinese Academy of Sciences Hefei China

Generative adversarial networks (GAN) has been mainly used in the generation of natural images such as MNIST, CIFAR10 as well as Imagenet datasets and achieves satisfying generation results. However, GAN always fails in generating high quality high-resolution remote sensing images because remote sensing images are large in size and have various ground objects. To address this issue, a novel framework called High-Resolution PatchGAN (HRPGAN) is introduced in this paper. The structure of HRPGAN follows PatchGAN, but the batch normalization layers are removed and the ReLU activation is replaced by the SELU activation. In addition, a new loss function consisting of the adversarial loss, perceptual reconstruction loss and regularization loss is used in HRPGAN. Experiment results show that the proposed HRPGAN model generates the more diverse and lifelike images in HR remote sensing generation than Bicubic method and TGAN model. © Published under licence by IOP Publishing Ltd.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain system

arXiv

引用

arXiv 2024年

作者： Liu, Chao Hou, Cankun Jiang, Tianyu Ning, Jianting Qiao, Hui Wu, Yusen Fujian Provincial Key Laboratory of Data Intensive Computing the Key Laboratory of Intelligent Computing and Information Processing Fujian China The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Department of Computer Science University of Miami MiamiFL33146 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China The Faculty of Data Science City University of Macau China

data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system1 offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

关键词： Access control

来源：评论

学校读者我要写书评

暂无评论

Optimizing Location Quality in Privacy Preserving Crowdsensing

Optimizing Location Quality in Privacy Preserving Crowdsensi...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yuhui Zhang Ming Li Dejun Yang Jian Tang Guoliang Xue Colorado School of Mines Golden CO Colorado School of Mines Golden CO USA Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Syracuse University Syracuse NY Arizona State University Tempe AZ

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Crowdsensing enables a wide range of data collection, where the data are usually tagged with private locations. Protecting users' location privacy has been a central issue. The study of various location perturbation techniques for protecting users' location privacy has received widespread attention. Despite the huge promise and considerable attention, the location perturbation operation causes inevitable location errors, which can diminish the location quality of the crowdsensing results. Provable good algorithms that consider location quality in privacy preserving crowdsensing from optimization perspectives are still lacking in the literature. In this paper, we investigate the problem of location quality optimization in privacy preserving crowdsensing, which is to minimize the location quality desegregation, while protecting all users' location privacy. We present an optimal algorithm OLQDM for this problem. Extensive simulations demonstrate that OLQDM significantly outperforms an existing algorithm in terms of the location quality and SSE.

关键词： Privacy Degradation Sensors Perturbation methods Optimization Minimization Mobile handsets

来源：评论

学校读者我要写书评

暂无评论

Fast Estimation of Relative Transformation Based on Fusion of Odometry and UWB Ranging data

arXiv

引用

arXiv 2024年

作者： Fu, Yuan Zhang, Zheng Zeng, Guangyang Liu, Chun Wu, Junfeng Ren, Xiaoqiang School of Mechatronic Engineering and Automation Shanghai University China Shanghai Artificial Intelligence Laboratory China Institute of Artificial Intelligence Shanghai University China School of Data Science The Chinese University of HongKong Shenzhen China Key Laboratory of Marine Intelligent Unmanned Swarm Technology and System Ministry of Education China

MSC Codes 93J08In this paper, we investigate the problem of estimating the 4-DOF (three-dimensional position and orientation) robot-robot relative frame transformation using odometers and distance measurements between robots. Firstly, we apply a two-step estimation method based on maximum likelihood estimation. Specifically, a good initial value is obtained through unconstrained least squares and projection, followed by a more accurate estimate achieved through one-step Gauss-Newton iteration. Additionally, the optimal installation positions of Ultra-Wideband (UWB) are provided, and the minimum operating time under different quantities of UWB devices is determined. Simulation demonstrates that the two-step approach offers faster computation with guaranteed accuracy while effectively addressing the relative transformation estimation problem within limited space constraints. Furthermore, this method can be applied to real-time relative transformation estimation when a specific number of UWB devices are installed. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

TomoSAR-ALISTA: Efficient TomoSAR imaging via deep unfolded network

TomoSAR-ALISTA: Efficient TomoSAR imaging via deep unfolded ...

引用

International Conference on Radar systems (RADAR 2022)

作者： M. Wang Z. Zhang Y. Wang S. Gao X. Qiu Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 People's Republic of China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou 215123 People's Republic of China Electrical and Computer Engineering Department George Mason University Fairfax VA 22030 USA

ISBN: (数字)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving threedimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning from pseudo lesion: A self-supervised framework for covid-19 diagnosis

arXiv

引用

arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

arXiv

引用

arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring the Spatial Correlation of Shadowing in RF-based Device-Free Localization by Block Sparse Bayesian Learning

Exploring the Spatial Correlation of Shadowing in RF-based D...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Jiaju Tan Xin Zhao Xuemei Guo Guoli Wang School of Mathematics and Computational Science Wuyi University Jiangmen China Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781665405362

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the target’s position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the target’s position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the target’s shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the target’s shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse target’s shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

关键词： Performance evaluation Location awareness Radio frequency Correlation Robot sensing systems Bayes methods Sensors

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：