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ITA-AITES World Tunnel Congress, WTC 2024

作者： Tao, Lianjin Deng, Lijia Zhang, Yu Jia, Zhibo The Key Laboratory of Urban Security and Disaster Engineering Ministry of Education Beijing University of Technology Beijing China Beijing University of Technology Beijing China Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781032800424

With the rapid development of urban transportation construction, numerous engineering projects involving the construction of new roads crossing established underground railway lines have emerged. The vibratory compaction of road surfaces has a significant impact on the structural integrity of subway stations to some extent. In the context of the crossing of Road 5 with the established shallow-buried subway station, numerical simulation methods were employed to study the dynamic response characteristics of the shallow-buried subway station under the action of surface vibration loads. Furthermore, sensitivity analysis was conducted on factors such as vibration frequency and excitation force. The research results indicate that under the influence of surface vibration loads, the acceleration response of the station’s structure exhibits a trend of attenuation from top to bottom and from the center towards both sides, specifically: top plate > middle plate > side walls > bottom plate. As the vibration frequency increases, the acceleration response of the station’s structure gradually intensifies. Based on the aforementioned research findings, it is recommended that the optimal construction parameters for vibratory compaction of the roadbed above this shallow-buried subway entail a vibration frequency of 28-33Hz and an excitation force ranging from 150-300kN. © 2024 The Author(s).

关键词： Vibration analysis

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A quantum federated learning framework for classical clients

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Science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan Li Qiaoyan Wen Sujuan Qin Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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Variational quantum semi-supervised classifier based on label propagation

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Chinese Physics B 2023年第7期32卷 279-289页

作者：侯艳艳李剑陈秀波叶崇强 College of Information Science and Engineering ZaoZhuang UniversityZaozhuang 277160China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China Information Security Center State Key Laboratory Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China

Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data *** paper proposes a quantum semi-supervised classifier based on label *** the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)*** this method,a locally parameterized quantum circuit is created to reduce the parameters required in the ***,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum *** demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test *** work opens a new path to quantum machine learning based on graphs.

关键词： semi-supervised learning variational quantum algorithm parameterized quantum circuit

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Analytical solutions of limit support pressure and vertical earth pressure on cutting face for tunnels

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Underground Space 2023年第5期12卷 65-78页

作者： Pengfei Li Xiaopu Cui Junwei Xia Xinyu Wang Key Laboratory of Urban Security and Disaster Engineering Ministry of EducationBeijing University of TechnologyBeijing 100124China China Railway 19 Bureau Group Rail Traffic Engineering Co.LTD Beijing 101399China School of Civil Engineering Henan Polytechnic UniversityJiaozuo 454000China

This paper focuses on theoretical analytical models to calculate the limit support pressure and vertical earth pressure on the cutting face for *** failure zone is divided into two parts:a sliding failure zone and an upper loosen zone,and the limit support pressure calculation equation is *** verify the rationality of the theoretical model,it was compared with the existing theory,numerical simulation,and centrifugal test,and then the parameter analysis was carried *** results show that the results of this paper agree well with the existing theory,numerical simulation,and centrifugal *** inclination angle of the proposed mechanism is determined based on the results of the existing centrifuge test,and the recommended inclination angle is between 52°+φ/2 and 54°+φ/*** method is proven to be safe and *** can provide a theoretical basis for similar projects.

关键词： Instability and failure Excavation face stability Global method Soil arch effect Limit support pressure

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Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental Learning 24

Generative Replay and Multi-steps Knowledge Distillation in ...

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3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Meng, Yicheng Ping, Jinming Shi, Jingye Zhi, Ruicong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400707308

Data-free generative modeling (GM) methods have gained much attention because pseudo data avoids security and privacy issues, and no amount limitation for storing real data in Class-Incremental Learning (CIL). However, GM-based methods have three drawbacks: un stable quality of generated image, semantic sifting, and domain bias for knowledge and distillation. To tackle with these problems, we propose an effective two-stage training method based on generative replay distillation, namely GA-RCT in this paper. It mainly consists of three modules. The Generative Adaptive Replay (GAR) module in stage one obtainsa well-trained generator for every task, then freezes generator for generating stable images, and trains feature extractor with mixture of real data and generated data. The Global Average Class (GAC) module is designed to reserve class prototypes to reduce semantic drift in the training stage. And the Generative Auxiliary Teaching (GAT) module in stage two performs generative distillation in dual-teacher to student way, for solving domain bias. Experiments on four datasets have shown that GA-RCT performs better than other generative replay methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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FirmPorter: Porting RTOSes at the Binary Level for Firmware Re-hosting 26th

FirmPorter: Porting RTOSes at the Binary Level for Firmwa...

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26th International Conference on Information and Communications security, ICICS 2024

作者： Xin, Mingfeng Wen, Hui Deng, Liting Li, Hong Li, Qiang Sun, Limin Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819788002

The rapid growth of the Industrial Internet of Things (IIoT) has brought real-time operating system (RTOS) into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded RTOS devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, which greatly limits the ability of analysis. In this work, we explore the problem of firmware re-hosting related to the RTOS. A device driver is developed by developers so that RTOS can be run on their platform. By providing alternative implementations for device drivers, we can make minimal modifications to the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach is capable of offering the ability to emulate various RTOS firmware in an automated manner without modifying existing emulators. Our approach, called static binary-level driver porting, first locates device driver initialization function and identify driver types in the target firmware, then adapts pre-built drivers to the existing emulator hardware. Finally, it replaces the drivers in the firmware with ours by utilizing binary rewriting technique. We demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The results show that the approach is flexible enough to emulate firmware for vulnerability assessment and exploit development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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Dual Prompt-Based Few-Shot Learning for Automated Vulnerability Patch Localization 31

Dual Prompt-Based Few-Shot Learning for Automated Vulnerabil...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Zhang, Junwei Hu, Xing Bao, Lingfeng Xia, Xin Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Software Engineering Application Technology Lab Huawei China

ISBN: (纸本)9798350330663

Vulnerabilities are disclosed with corresponding patches so that users can remediate them in time. However, there are instances where patches are not released with the disclosed vulnerabilities, causing hidden dangers, especially if dependent software remains uninformed about the affected code repository. Hence, it is crucial to automatically locate security patches for disclosed vulnerabilities among a multitude of commits. Despite the promising performance of existing learning-based localization approaches, they still suffer from the following limitations: (1) They cannot perform well in data scarcity scenarios. Most neural models require extensive datasets to capture the semantic correlations between the vulnerability description and code commits, while the number of disclosed vulnerabilities with patches is limited. (2) They struggle to capture the deep semantic correlations between the vulnerability description and code commits due to inherent differences in semantics and characters between code changes and commit messages. It is difficult to use one model to capture the semantic correlations between vulnerability descriptions and code commits. To mitigate these two limitations, in this paper, we propose a novel security patch localization approach named Prom VPat, which utilizes the dual prompt tuning channel to capture the semantic correlation between vulnerability descriptions and commits, especially in data scarcity (i.e., few-shot) scenarios. We first input the commit message and code changes with the vulnerability description into the prompt generator to generate two new inputs with prompt templates. Then, we adopt a pre-trained language model (i.e., PLM) as the encoder, utilize the prompt tuning method to fine-tune the encoder, and generate two correlation probabilities as the semantic features. In addition, we extract 26 handcrafted features from the vulnerability descriptions and the code commits. Finally, we utilize the attention mechanism to fuse the

关键词： Semantics

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Efficient Certificateless Authenticated key Agreement for Blockchain-Enabled Internet of Medical Things

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Computers, Materials & Continua 2023年第4期75卷 2043-2059页

作者： Chaoyang Li Yanbu Guo Mianxiong Dong Gang Xu Xiu-Bo Chen Jian Li Kaoru Ota College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450002China Department of Sciences and Informatics Muroran Institution of TechnologyMuroran050-8585Japan School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing100876China

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical *** recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical ***,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy *** paper first designs a cross-device key agreement model for blockchain-enabled *** model can establish a key agreement mechanism for secure medical data ***,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement *** proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution ***,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange *** blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow ***,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

关键词： Certificateless key agreement authentication blockchain internet of medical things

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Cancer classification with data augmentation based on generative adversarial networks

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Frontiers of Computer Science 2022年第2期16卷 69-79页

作者： Kaimin WEI Tianqi LI Feiran HUANG Jinpeng CHEN Zefan HE College of Information Science and Technology Jinan UniversityGuangzhou 510632China Guangdong Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Accurate diagnosis is a significant step in cancer *** learning can support doctors in prognosis decision-making,and its performance is always weakened by the high dimension and small quantity of genetic ***,deep learning can effectively process the high dimensional data with ***,the problem of inadequate data remains unsolved and has lowered the performance of deep *** end it,we propose a generative adversarial model that uses non target cancer data to help target generator *** use the reconstruction loss to further stabilize model training and improve the quality of generated *** also present a cancer classification model to optimize classification *** results prove that mean absolute error of cancer gene made by our model is 19.3%lower than DC-GAN,and the classification accuracy rate of our produced data is higher than the data created by *** for the classification model,the classification accuracy of our model reaches 92.6%,which is 7.6%higher than the model without any generated data.

关键词： data mining cancer data analysis deep learning generative adversarial networks

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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