检索结果-内蒙古大学图书馆

Research progress of quantum artificial intelligence in smart city

Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

来源：评论

学校读者我要写书评

暂无评论

Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection 8

Real-to-Sim Grasp: Rethinking the Gap between Simulation and...

引用

8th Conference on Robot Learning, CoRL 2024

作者： Cai, Jia-Feng Chen, Zibo Wu, Xiao-Ming Jiang, Jian-Jian Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way1. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data when training grasp detectors, where the positional drift and structural distortion within the camera noise will harm the grasp learning. In this work, we propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way2, which directly bypasses the camera noise in grasp detector training through an inference-time real-to-sim adaption. To achieve this real-to-sim adaptation, our R2SGrasp designs the Real-to-Sim Data Repairer (R2SRepairer) to mitigate the camera noise of real depth maps in data-level, and the Real-to-Sim Feature Enhancer (R2SEnhancer) to enhance real features with precise simulated geometric primitives in feature-level. To endow our framework with the generalization ability, we construct a large-scale simulated dataset cost-efficiently to train our grasp detector, which includes 64,000 RGB-D images with 14.4 million grasp annotations. Sufficient experiments show that R2SGrasp is powerful and our real-to-sim perspective is effective. The real-world experiments further show great generalization ability of R2SGrasp. Project page is available on https://***/R2SGrasp. © 2024 Proceedings of Machine Learning Research.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data 38

Revealing Distribution Discrepancy by Sampling Transfer in U...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Zhilin Cao, Longbing Fan, Xuhui Zheng, Wei-Shi School of Computing Macquarie University Australia School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

There are increasing cases where the class labels of test samples are unavailable, creating a significant need and challenge in measuring the discrepancy between training and test distributions. This distribution discrepancy complicates the assessment of whether the hypothesis selected by an algorithm on training samples remains applicable to test samples. We present a novel approach called Importance Divergence (I-Div) to address the challenge of test label unavailability, enabling distribution discrepancy evaluation using only training samples. I-Div transfers the sampling patterns from the test distribution to the training distribution by estimating density and likelihood ratios. Specifically, the density ratio, informed by the selected hypothesis, is obtained by minimizing the Kullback-Leibler divergence between the actual and estimated input distributions. Simultaneously, the likelihood ratio is adjusted according to the density ratio by reducing the generalization error of the distribution discrepancy as transformed through the two ratios. Experimentally, I-Div accurately quantifies the distribution discrepancy, as evidenced by a wide range of complex data scenarios and tasks. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Highly Secure C2C-PAKE Cross-domain Protocol

A Highly Secure C2C-PAKE Cross-domain Protocol

引用

2021 International Conference on Network Communication and Information Security, ICNCIS 2021

作者： Wang, Xiangyang Gu, Chunxiang Lu, Siqi Chen, Xi Henan Key Laboratory of Network Cryptography Technology Henan Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China

ISBN: (数字)9781510653283

ISBN: (纸本)9781510653276

This paper studies the password-based strong authentication key exchange protocol in the cross-domain scenario, and gives an end-to-end highly secure C2C-PAKE protocol, in which the server shares a password with the client and also has a pair of private/public keys. In contrast, the client has the shared password and the server's public-key. Specifically, we combine the ECQV implicit certificate scheme with the ECDH key exchange protocol to give a highly secure cross-domain protocol. Considering the application of the ECQV implicit certificate scheme in the Internet of Things (IoT), we give an analysis the possibility of the proposed protocol applied in the cross-domain scenario of the IoT. Finally, based on some common attacks, we show a security analysis of our protocol, which achieves perfect forward security and resists dictionary attacks, KCI attacks, replay attacks, Insider-Assisted attacks, and so on. © 2022 SPIE

关键词： Computer security Network security Cryptography

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

引用

IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

来源：评论

学校读者我要写书评

暂无评论

Adversarial Example Attacks Against Intelligent Malware Detection: A Survey 4

Adversarial Example Attacks Against Intelligent Malware Dete...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Qi, Xuyan Tang, Yonghe Wang, Huanwei Liu, Tieming Jing, Jing State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665462655

With the advancement of information technology and the increasing prevalence of the internet, the number of malware has experienced an exponential, and the security threat to users' property and privacy is becoming more serious. With the great success of artificial intelligence especially deep learning technology in fields such as computer vision and speech processing, deep learning are gradually introduced into the field of malware detection, to process large amounts of malware more efficiently. However, in recent years, researchers have discovered that the deep learning model itself also has security risks. While malware detection based on deep learning brings convenience and technical progress, it also introduces some new threats, such as adversarial examples. This paper investigates adversarial example attacks against deep learning-based malware detectors. First, We reviewed research on malware detection techniques. Then we analyze the works related to adversarial example attacks against intelligent malware detectors. Finally, we discuss the challenges and prospects in future research. © 2022 IEEE.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

MDFA: A Malware Detection Framework Based on Attention Mechanism with Bi-LSTM 4

MDFA: A Malware Detection Framework Based on Attention Mecha...

引用

4th International Conference on Applied Machine Learning, ICAML 2022

作者： Qi, Xuyan Wang, Huanwei Tang, Yonghe Liu, Wei Xie, Jiangtao State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665462655

As the existing malware intelligent detection methods have shortcomings and low accuracy of manual feature extraction and feature processing, a Malware Detection Framework with Attention mechanism based on Bi-directional Long Short-Term Memory (MDFA) was proposed. To training this method, We have collected 15, 893 malware and 10, 959 benign program samples and constructed a dataset. Then, the sample data was labeled and sanitized by determining the results based on VirusTotal analysis. Finally, the Bi-directional Long Short-Term Memory model injected into the attention mechanism was built and trained to complete the malware detection. Experimental results show that the accuracy of MDFA was improved by at least 2. 3%, and the recall rate was improved significantly compared with the existing methods, verifying the effectiveness of MDFA. © 2022 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

A malware detection method using satisfiability modulo theory model checking for the programmable logic controller system

A malware detection method using satisfiability modulo theor...

引用

作者： Xie, Yaobin Chang, Rui Jiang, Liehui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Nowadays programmable logic controllers (PLCs) are suffering increasing cyberattacks. Attackers could reprogram PLCs to inject malware that would cause physical damages and economic losses. These PLC malwares are highly customized for the target which makes it difficult to extract a general pattern to detect them. In this article, we propose a PLC malware detection method based on model checking. Firstly, we improve the existing modeling method for PLC system by using the Satisfiability Modulo Theory (SMT) constraints to model the PLC system. We also present an algorithm that can transform the PLC program to the model. Our SMT-based model can deal with the features of the PLC system such as undetermined input signals, edge detection and so on. Secondly, we focus on malware detection and propose two methods, invariant extraction and rule design pattern, to generate detection rules. The former can extract the invariants from an original program, and the latter can lower the bar for user to design detection rules. Finally, we implement a prototype and evaluate it on three representative ICS scenarios. The evaluation result shows that our proposed method can successfully detect the malwares using four attack patterns. © 2020 John Wiley & Sons, Ltd.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

Improvement and optimization of consensus algorithm based on PBFT 4

Improvement and optimization of consensus algorithm based on...

引用

4th International Conference on Communications, Information System and Computer engineering, CISCE 2022

作者： Zhao, Liang Li, Bin Zhou, Qinglei Chen, Xiaojie Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

This paper proposes an improved consensus algorithm based on PBFT(EBCR-PBFT). Firstly, The Modified Random Select(MRS) function is used to perform preliminary screening of network nodes, so as to solve the problem of multiple and redundant network nodes. Secondly, based on the idea of clustering, logical stratification of blockchain network nodes can reduce communication times and improve network efficiency. Thirdly, the core module of MRS is optimized by FPGA to further improve the efficiency of the algorithm. At the same time, the improved dynamic reputation evaluation model is introduced to evaluate and feedback the node behavior in time to ensure the smooth operation of the system. Experiments show that the proposed and optimized consensus algorithm can effectively improve the consensus efficiency, timely punish malicious nodes, increase the reliability of nodes, communication overhead, throughput, delay and other high performance, has a certain application value. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

Misleading Image Classification with Multi-shearing and Random Padding 22

Misleading Image Classification with Multi-shearing and Rand...

引用

Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Chenwei Li Zheming Li Bo Yang Hengwei Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Key Laboratory of Information Security China

ISBN: (纸本)9781450397148

Neural networks are vulnerable when input data is applied with human-imperceptible perturbations, which is called adversarial examples. When used in image classification models, adversarial examples mislead neural netwoks to classify images with wrong labels, posing great threat to network security. White-box attack has achieved considerable success rate, for the model structure is already known. But black-box attack remains to be improved, so as to the transferability. We refer to the model augmentation method in network training process, and apply to generating adversarial examples to reduce overfitting. Consulting fundamental methods in adversarial examples, we propose a multi-cropping transformation method to alleviate overfitting and enhance transferability. Firstly, referring to data augmentation, we multi-crop original images in every iteration with random possibilities in adversarial exaples generating process. Secondly, the gradient of model loss function is calculated, and the perturbations are added to original images. Finally, we generate adversarial examples with iterative perturbations. The validation of our method is verified on single models and ensemble models, and the transferability is improved, compared to other fundamental methods.

关键词： Transferability Image shearing Neural networks Adversarial examples Image padding

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：