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International Conference on Automation and computing (ICAC)

作者： Yuanhao Liu Fengshou Gu Qiang Zeng State Key Laboratory of Mechanical Transmissions for Advanced Equipment Chongqing University Chongqing China School of Computing and Engineering University of Huddersfield Huddersfield UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Instantaneous Angular Speed (IAS) is closely related to the dynamic behavior of rotating machinery and plays a crucial role in diagnosing faults. However, the measurement of the IAS is subject to various errors inevitably, which directly affect the accuracy of IAS estimation and diminish the precision of fault diagnosis in rotating machinery. Currently, the estimation of IAS primarily relies on time counting methods, and there has been limited research conducted on the application of Frequency Domain Demodulation (FDD) as an IAS estimation method, despite its potential for providing lower noise levels. The study of IAS errors based on the FFD method has received even less attention. This study undertakes pertinent research and investigates the issue identified. Firstly, it develops a model for IAS estimation using FDD, considering the specific characteristics of the encoder disk signal. Secondly, it establishes IAS models for eccentric and inclined encoder disks, considering the spatial geometric characteristics associated with different types of encoder disk errors. In conclusion, the experimental results successfully validate the accuracy and validity of the proposed theoretical models. This study lays a theoretical foundation for the precise measurement of IAS, thereby enhancing the reliability of using IAS for condition monitoring in rotating machinery.

关键词： Fault diagnosis Accuracy Frequency-domain analysis Measurement uncertainty Estimation Machining Reliability theory Machinery Rotation measurement Noise level

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A Relation-Oriented Method for Joint Entity and Relation Extraction Based on Neural Network 21

A Relation-Oriented Method for Joint Entity and Relation Ext...

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5th International Conference on Electronic Information Technology and Computer engineering, EITCE 2021

作者： Hu, Hongwei Yin, Meijuan Liu, Xiaonan Key Laboratory of Cyberspace Situation Awareness of Henan Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781450384322

Entity and relation extraction is a basic task of information extraction in natural language processing. At present, Entity and relation extraction based on artificial intelligence has been widely studied, but most methods adopt the idea of identifying the entity first and then extracting the relation, which suffers from the problems of overlapping triple and entity redundancy. We propose a Relation-Oriented method for Joint Entity and Relation Extraction (ROJER), which first extracts the relation types contained in the text through the relation extraction module, then the pre-extracted relation types are integrated into the entity recognition module, reduce the focus on irrelevant entities and avoid extracting redundant entities. Then the entity pairs corresponding to the extracted relation types are identified to tackle the overlapping triple problem and finally extract all the relation triples. Experiments on the DuIE dataset show that the F1 score of ROJER reaches 78.4%, which is 1.2% higher than the CasRel model, confirming the validity of our method. © 2021 ACM.

关键词： Artificial intelligence

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An accurate identification method for network devices based on spatial attention mechanism

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Security and Safety 2023年第2期2卷 13-29页

作者： Xiuting Wang Ruixiang Li Shaoyong Du Xiangyang Luo Henan Polytechnic Institute Nanyang 473000China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001China Institute of Information Engineering State Key Laboratory of Information SecurityBeijing 100093China

With the metaverse being the development direction of the next generation Internet,the popularity of intelligent devices,and the maturity of various emerging technologies,more and more intelligent devices try to connect to the Internet,which poses a major threat to the management and security protection of network *** present,the mainstream method of network equipment identification in the metaverse is to obtain the network traffic data generated in the process of device communication,extract the device features through analysis and processing,and identify the device based on a variety of learning *** methods often require manual participation,and it is difficult to capture the small differences between similar devices,leading to identification ***,we propose a deep learning device recognition method based on a spatial attention ***,we extract the required feature fields from the acquired network traffic ***,we normalize the data and convert it into grayscale *** that,we add a spatial attention mechanism to CNN and MLP respectively to increase the difference between similar network devices and further improve the recognition ***,we identify devices based on the deep learning model.A large number of experiments were carried out on 31 types of network devices such as web cameras,wireless routers,and *** results show that the accuracy of the proposed recognition method based on the spatial attention mechanism is increased by 0.8%and 2.0%,respectively,compared with the recognition method based only on the deep learning model under the CNN and MLP *** method proposed in this paper is significantly superior to the existing method of device-type recognition based only on a deep learning model.

关键词： Metaverse Device identification Deep learning Spatial attention

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Mechanistic investigation of highly bendable magnesium alloy sheet fabricated by short-process manufacturing

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Journal of Magnesium and Alloys 2024年第10期12卷 4289-4310页

作者： Shiwei Xu Peijie Xiao Xiaofan Wu John Holmes Frédéric Mompiou Zhenyu Xiao Chen Jin Tianjiao Li Kuaishe Wang Zhuoran Zeng College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan UniversityChangsha 410082China Suzhou Research Institute of Hunan University Suzhou 215131China College of Engineering Computing and CyberneticAustralian National UniversityACT 2601Australia CEMES-CNRS Universitéde Toulouse29 rue J.MarvigToulouse 31055France Qinghai Salt Lake Teli Magnesium Co. Ltd.Xining 810000China International Joint Laboratory for Light Alloys(Ministry of Education) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China School of Metallurgical Engineering Xi’an University of Architecture&TechnologyXi’an 710055China College of Materials Science and Engineering Hunan UniversityChangsha 410082China

In this study,a commercial magnesium alloy AZ31(Mg-3Al-1Zn-0.3Mn)sheet through a short manufacturing process was found to be ductile and highly formable in *** possessing a strong basal texture,the short-processed sheet without any annealing can be bent at a small radius,only 0.2 times its thickness in the 90°bending ***,it could withstand direct deformation by repeated *** in-situ microstructural characterization reveals that extension twin bands with strain localization appear in the bending *** subsequent flattening,these twin bands underwent detwinning,reducing local strain concentrations and enabling further bending *** outstanding bend formability originated from the significant-type dislocation loops slipping on the prismatic crystal planes within dynamic-recrystallized *** grains underwent a uniform refinement to several microns in the short manufacturing process and exhibited low residual *** active prismatic dislocation slip within refined grains was due to its much lower relative activation stress to basal slip(CRSSprism/CRSSbasal of only∼1.6)owing to the effective grain boundary ***,the prismatic dislocation activity was further enhanced when bypassing Al-Mn nano-particles during motion,leaving debris and loops that facilitated easy multiplication.

关键词： Magnesium Deformation Texture In-situ characterization Dislocation slip.

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RDGFuzz: A directed greybox fuzzing optimization method based on Rich-Branch nodes 6

RDGFuzz: A directed greybox fuzzing optimization method base...

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Wu, Zejun Lu, Li Jia, Qiong Chen, Zhihao State Key Laboratory of Mathematical Engineering and advanced Computing Zheng zhou China National Local Joint Engineering Laboratory of Network Space Security Technology Zheng zhou China Beijing Institute of Computer Technology and Application BeiJing China

ISBN: (纸本)9781450397148

Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo. © 2022 Association for computing Machinery.

关键词： Software testing

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Adversarial Example Attacks Against Intelligent Malware Detection: A Survey

Adversarial Example Attacks Against Intelligent Malware Dete...

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International Conference on Applied Machine Learning (ICAML)

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

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.

关键词： Deep learning Computer vision Technological innovation Privacy Perturbation methods Detectors Predictive models

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Controllable Majorana vortex states in iron-based superconducting nanowires

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National Science Review 2022年第9期9卷 195-203页

作者： Chuang Li Xun-Jiang Luo Li Chen Dong E.Liu Fu-Chun Zhang Xin Liu School of Physics and Institute for Quantum Science and Engineering Huazhong University of Science and Technology Wuhan National High Magnetic Field Center and Hubei Key Laboratory of Gravitation and Quantum Physics State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Kavli Institute for Theoretical Sciences and CAS Center for Excellence in Topological Quantum Computing University of Chinese Academy of Sciences Collaborative Innovation Center of Advanced Microstructures Nanjing University

To reve al the non-Abelian braiding statistics of Major ana zero modes(MZMs),it is crucial to design a Majorana platform,in which MZMs can be easily manipulated in a broad topological nontrivial parameter *** is also an essential step to confirm their *** this study,we propose an iron-based superconducting nanowire system with Majorana vortex states to satisfy desirable *** system has a radius-induced topological phase transition,giving a lower bound for the nanowire *** the topological phase,the iron-based superconducting nanowires have only one pair of MZMs over a wide range of radii,chemical potential and external magnetic *** wave function of MZMs has a sizable distribution at the side edge of the *** property enables the control of the interaction of MZMs in neighboring vortex nanowires and paves the way for Majorana fusion and braiding.

关键词： iron-based superconductor topological phase transition superconducting vortex Majorana zero modes nanowire

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VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model for C/C++ Code

VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model ...

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International Conference on Communications, Information System and Computer engineering (CISCE)

作者： Zhe Xiong Weiyu Dong School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Recently, deep learning techniques have received widespread attention in code vulnerability and vulnerability detection. However, there are some problems with current vulnerability detection techniques, firstly, because of the non-homogeneity of vulnerability categories and quantities, there are omission false positives and overfitting problems for vulnerabilities with a small amount of data. Second, the existing detection methods have limited ability to characterize the code, there is insufficient depth of characterization, does not contain all of its vulnerability characteristics or is interspersed with useless information. Finally, the current research does not use the powerful feature mining capability of pre-trained models and does not employ model fusion to increase the effectiveness of model vulnerability detection. To solve the above problems, this paper proposes a new deep learning based pre-trained model for code vulnerability detection tool VulD-CodeBERT. By fine-tuning CodeBERT, a code adaptation extension for pre-trained models developed by Microsoft, the CodeBERT model can solve the problem of not being able to learn the vulnerability patterns due to insufficient data, thus resulting in missed false alarms. The sequence output from the CodeBERT model are added to a BiLSTM network with an attention mechanism to mine the local key features of the vulnerabilities combined with the global features of the pooler output from the CodeBERT model to deeply mine the dependencies between the vulnerability statements and derive the probability of the existence of the vulnerability. For vulnerability types that are difficult to uncover captured features or the number of samples is not enough, a polynomial plain Bayesian classifier is used for detection, and finally the two models are combined to deeply mine the vulnerability features, thus solving the problem of difficult and small samples, and reducing the leakage rate and false alarm rate. Experimental results sh

关键词： Deep learning Adaptation models Codes Attention mechanisms Computational modeling Feature extraction Polynomials

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

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

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International Conference on Applied Machine Learning (ICAML)

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

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.

关键词： Training Computer viruses Bidirectional control Manuals Machine learning Feature extraction Transformers

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Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection

arXiv

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arXiv 2024年

作者： Song, Chengyu Ma, Linru Zheng, Jianming Liao, Jinzhi Kuang, Hongyu Yang, Lin Systems Engineering Institute Academy of Military Sciences Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China

Log-based insider threat detection (ITD) detects malicious user activities by auditing log entries. Recently, Large Language Models (LLMs) with strong common sense knowledge are emerging in the domain of ITD. Nevertheless, diverse activity types and overlong log files pose a significant challenge for LLMs to directly discern malicious ones within myriads of normal activities. Furthermore, the faithfulness hallucination issue from LLMs aggravates its application difficulty in ITD, as the generated conclusion may not align with user commands and activity context. In response to these challenges, we introduce Audit-LLM, a multi-agent log-based insider threat detection framework comprising three collaborative agents: (i) the Decomposer agent, breaking down the complex ITD task into manageable sub-tasks using Chain-of-Thought (COT) reasoning;(ii) the Tool Builder agent, creating reusable tools for sub-tasks to overcome context length limitations in LLMs;and (iii) the Executor agent, generating the final detection conclusion by invoking constructed tools. To enhance conclusion accuracy, we propose a pair-wise Evidence-based Multi-agent Debate (EMAD) mechanism, where two independent Executors iteratively refine their conclusions through reasoning exchange to reach a consensus. Comprehensive experiments conducted on three publicly available ITD datasets—CERT r4.2, CERT r5.2, and PicoDomain—demonstrate the superiority of our method over existing baselines and show that the proposed EMAD significantly improves the faithfulness of explanations generated by LLMs. 1 2 Copyright © 2024, The Authors. All rights reserved.

关键词： Reusability

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