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

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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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection 39

GLCF: A Global-Local Multimodal Coherence Analysis Framework...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou 510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 Macao

ISBN: (纸本)157735897X

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Public risks

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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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A software upgrade security analysis method on network traffic classification using deep learning

A software upgrade security analysis method on network traff...

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2020 International Conference on Urban engineering and Management Science, ICUEMS 2020

作者： Zhang, Bing State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

ISBN: (纸本)9781728188324

Nowadays, the vulnerability in the software upgrade process are extremely harmful to network security. However, the detection of upgrade vulnerability is facing serious difficulties and problems. Aiming at the current software upgrade vulnerability analysis problem, we propose a neural network model for traffic classification based on two levels of data packets and network flows. This method does not need to manually extract features and can learn the dependency relationship between data packets and networks well. It can make full use of comprehensive flow features for traffic classification. In order to evaluate the model, we built a dataset of software upgrade vulnerabilities and conducted relevant experiments on the CICIDS 2017 dataset. The experimental results show that compared with other RNN models, our model has the best performance, reaching a precision of 99.86% and an F1 score of 99.44%. At the same time, based on the pre-trained model, we use the software upgrade vulnerability dataset to fine-tuning the model, with the F1 score reaching 99.78%. © 2020 IEEE.

关键词： Neural network 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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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System 21

Distributed Deep Learning System for Efficient Face Recognit...

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Proceedings of the 3rd International Conference on advanced Information Science and System

作者： Jinjin Liu Zhifeng Chen Xiaonan Li Tongxin Wei School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process.

关键词： Deep learning

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The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface

The Executors Scheduling Algorithm for the Web Server Based ...

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2020 IEEE International Conference on Advances in Electrical engineering and Computer Applications, AEECA 2020

作者： Nie, Guanglai Zhang, Zheng Zhao, Yufeng Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728165202

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm. © 2020 IEEE.

关键词： Scheduling algorithms

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Implementation of Multi-compartment Neuron Model with Synaptic Plasticity Using FPGA 21

Implementation of Multi-compartment Neuron Model with Synapt...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： DIng, Yiwen Fu, Wenlong Cao, Lihong Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432061

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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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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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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