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Spin-orbit torque effect in silicon-based sputtered Mn_(3)Sn film

Chinese Physics B 2024年第10期33卷 394-399页

作者： Sha Lu Dequan Meng Adnan Khan Ziao Wang Shiwei Chen Shiheng Liang School of Physics Hubei UniversityWuhan 430062China Key Laboratory for Intelligent Sensing System and Security of Ministry of Education Wuhan 430062China

Noncollinear antiferromagnet Mn_(3)Sn has shown remarkable efficiency in charge-spin conversion,a novel magnetic spin Hall effect,and a stable topological antiferromagnetic state,which has resulted in great interest from researchers in the field of spin-orbit *** research has primarily focused on the spin-orbit torque effect of epitaxially grown noncollinear antiferromagnet Mn_(3)Sn ***,this method is not suitable for large-scale industrial *** this study,amorphous Mn_(3)Sn films and Mn_(3)Sn/Py heterostructures were prepared using magnetron sputtering on silicon *** spin-torque ferromagnetic resonance measurement demonstrated that only the conventional spin-orbit torque effect generated by in-plane polarized spin currents existed in the Mn_(3)Sn/Py heterostructure,with a spin-orbit torque efficiency of ***,we prepared the perpendicular magnetized Mn_(3)Sn/CoTb heterostructure based on amorphous Mn_(3)Sn film,where the spin-orbit torque driven perpendicular magnetization switching was achieved with a lower critical switching current density(3.9×10^(7)A/cm^(2))compared to Ta/CoTb *** research reveals the spin-orbit torque effect of amorphous Mn_(3)Sn films and establishes a foundation for further advancement in the practical application of Mn_(3)Sn materials in spintronic devices.

关键词： spintronics noncollinear antiferromagnetism spin-orbit torque

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Tuning Second Chern Number in a Four-Dimensional Topological Insulator by High-Frequency Time-Periodic Driving

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Chinese Physics Letters 2024年第4期41卷 77-89页

作者：刘峥嵘陈锐周斌 Department of Physics Hubei UniversityWuhan 430062China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei UniversityWuhan 430062China

Floquet engineering has attracted considerable attention as a promising approach for tuning topological phase *** investigate the effects of high-frequency time-periodic driving in a four-dimensional(4D)topological insulator,focusing on topological phase transitions at the off-resonant quasienergy *** 4D topological insulator hosts gapless three-dimensional boundary states,characterized by the second Chern number C_(2).We demonstrate that the second Chern number of 4D topological insulators can be modulated by tuning the amplitude of time-periodic *** includes transitions from a topological phase with C_(2)=±3 to another topological phase with C_(2)=±1,or to a topological phase with an even second Chern number C_(2)=±2,which is absent in the 4D static ***,the approximation theory in the high-frequency limit further confirms the numerical conclusions.

关键词： topological transitions Periodic

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Higher-order topological Anderson insulator on the Sierpiński lattice

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Chinese Physics B 2024年第1期33卷 218-222页

作者：陈焕刘峥嵘陈锐周斌 Department of Physics Hubei UniversityWuhan 430062China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei UniversityWuhan 430062China

Disorder effects on topological materials in integer dimensions have been extensively explored in recent years. However, its influence on topological systems in fractional dimensions remains unclear. Here, we investigate the disorder effects on a fractal system constructed on the Sierpiński lattice in fractional dimensions. The system supports the second-order topological insulator phase characterized by a quantized quadrupole moment and the normal insulator phase. We find that the second-order topological insulator phase on the Sierpiński lattice is robust against weak disorder but suppressed by strong disorder. Most interestingly, we find that disorder can transform the normal insulator phase to the second-order topological insulator phase with an emergent quantized quadrupole moment. Finally, the disorder-induced phase is further confirmed by calculating the energy spectrum and the corresponding probability distributions.

关键词： fractal system topological insulator

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Integrating Structural Semantic Knowledge for Enhanced Information Extraction Pre-training

Integrating Structural Semantic Knowledge for Enhanced Infor...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yi, Xiaoyang Bao, Yuru Zhang, Jian Qin, Yifang Lin, Faxin College of Cyber Science Nankai University China College of Computer Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education China

ISBN: (纸本)9798891761643

Information Extraction (IE), aiming to extract structured information from unstructured natural language texts, can significantly benefit from pre-trained language models. However, existing pre-training methods solely focus on exploiting the textual knowledge, relying extensively on annotated large-scale datasets, which is labor-intensive and thus limits the scalability and versatility of the resulting models. To address these issues, we propose SKIE, a novel pre-training framework tailored for IE that integrates structural semantic knowledge via contrastive learning, effectively alleviating the annotation burden. Specifically, SKIE utilizes Abstract Meaning Representation (AMR) as a low-cost supervision source to boost model performance without human intervention. By enhancing the topology of AMR graphs, SKIE derives high-quality cohesive subgraphs as additional training samples, providing diverse multi-level structural semantic knowledge. Furthermore, SKIE refines the graph encoder to better capture cohesive information and edge relation information, thereby improving the pre-training efficacy. Extensive experimental results demonstrate that SKIE outperforms state-of-the-art baselines across multiple IE tasks and showcases exceptional performance in few-shot and zero-shot settings. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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Computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field data

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7452-7462页

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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MPGStack: Membership Privacy Protection on Graph data via Model Stacking

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Chenyang Zhang, Xiaoyu Lin, Shen Chen, Xiaofeng Shaanxi Xi'an710071 China Key Laboratory of Data and Intelligent System Security Ministry of Education China

Graph neural networks (GNNs) can retain the structural information of graph data when processing graph data via message passing mechanism. Recently, GNNs have been widely used in recommendation systems, social networks, finance, etc. However, GNNs are also vulnerable when encountering severe security and data privacy challenges. Membership inference attacks (MIA) on graph data can make the trained GNN model leak the training data, which causes serious privacy problems. However, all the studies reviewed so far suffer from the fact that there are few types of research on GNNs to defend against MIA. In this paper, we propose MPGStack: a framework that utilizes ensemble learning of GNNs to safeguard against MIA. More concretely, we propose three ensemble strategies in MPGStack: KFold, GP-KFold, and LPA-KFold, which partition the graph at different degrees of granularity based on random uniform partition, class information, and label propagation, respectively. MPGStack can enhance the target model's generalization performance, mitigate MIA, and boost classification accuracy. The experimental results conducted on seven benchmark datasets demonstrate that MPGStack can effectively defend against MIA and significantly reduce the attack model's success rate to the level of random guessing. MPGStack also improves the classification accuracy of the target model up to 99% or more. Extensive experimental results demonstrate that MPGStack is more effective, stable, and applicable to different datasets and scenarios than other methods, achieving random guessing attack models while improving the model's classification accuracy on different datasets. © 2004-2012 IEEE.

关键词： Differential privacy

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Secure Multi-way Join Query and Its Bench-Marking with Trusted Execution Environments 2nd

Secure Multi-way Join Query and Its Bench-Marking with Tru...

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2nd International Conference on data security and Privacy Protection, DSPP 2024

作者： Zhao, Yi Zhao, Sen Lv, Siyi School of Cyber Engineering Xidian University Xi’an China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China

ISBN: (纸本)9789819785452

Because of server-untrustable model in outsourcing encrypted database system, it’s fatal for data owners to keep their data confidential under complex query operations performed by database servers. Trusted Execution Environments (TEEs) provide a secure space for privacy computation via hardware-based methods to increase the efficiency of database system, and it ensures the availability of computing directly on sensitive data in untrusted outsourcing database servers. However, while various query schemes are designed under the combination of encrypted database and TEEs, designing an available and secure scheme for join queries on multiple data tables with TEEs remains an open problem. Besides, previous works also devote less to bench-marking multi-way joins on TEEs. In our work, we extend binary join query scheme into multi-way forms in encrypted database system based on TEEs. It combines various join algorithms based on hash functions (hash-based, radix-based, etc.) with parallelized optimizing mechanism. Besides, we do bench-marking with TEEBench framework on those multi-way join schemes in three-way forms using both cache-fit and cache-exceed datasets and do different evaluation on two TEEs hardware platforms (Intel SGX and AMD SEV) to show its availability. After that, we also propose a multi-way join scheme with access pattern protection through the combination between our previous scheme and oblivious I/O methods. With the evaluation of its efficiency and memory leakage, we can also verify the feasibility of our access-pattern-protected multi-way join scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Outsourcing

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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NCCMF:Non-Collaborative Continuous Monitoring Framework for Container-Based Cloud Runtime Status

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Computers, Materials & Continua 2024年第10期81卷 1687-1701页

作者： Tao Zheng Wenyi Tang Xingshu Chen Changxiang Shen School of Cyber Science and Engineering Sichuan UniversityChengdu610065China Intelligent Policing and National Security Risk Management Laboratory Luzhou646000China Cyber Science Research Institute Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China

The security performance of cloud services is a key factor influencing users’selection of Cloud Service Providers(CSPs).Continuous monitoring of the security status of cloud services is ***,existing research lacks a practical framework for such ongoing *** address this gap,this paper proposes the first NonCollaborative Container-Based Cloud Service Operation State Continuous Monitoring Framework(NCCMF),based on relevant *** operates without the CSP’s collaboration by:1)establishing a scalable supervisory index system through the identification of security responsibilities for each role,and 2)designing a Continuous Metrics Supervision Protocol(CMA)to automate the negotiation of supervisory *** framework also outlines the supervision process for cloud services across different deployment *** results demonstrate that NCCMF effectively monitors the operational state of two real-world IoT(Internet of Things)cloud services,with an average supervision error of less than 15%.

关键词： Container-based cloud non-collaborative continuous monitor runtime status

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