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High-precision end-to-end adaptive optics aided orbital angular momentum wireless communication with XFTC-Net

Optics Letters 2025年第10期50卷 3265-3268页

作者： Panpan Xu Huan Chang Fei Wang Ran Gao Dong Guo Lei Zhu Zhipei Li Sitong Zhou Qi Xu Jing Chen Xiangjun Xin School of Information and Electronics Beijing Institute of Technology (BIT) Beijing 100081 China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing 100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology 250353 China

In this Letter, we demonstrate high-precision end-to-end adaptive optics (AO) technique based on the X-Shape Fusion Transformer-convolutional neural network (XFTC-Net) without an additional probe path to compensate for distortions caused by atmospheric turbulence (AT) in orbital angular momentum (OAM) optical wireless communication links. The experimental results show that the XFTC-Net effectively extracts the intensity distribution features of distorted OAM beams, predicting AT phase screens and identifying OAM modes. After XFTC-Net-aided AO compensation, the bit error rate of the OAM optical communication system remains below 3.8 × 10 −3 , staying within $C_{n}^2=1 \times 10^{-15}$ –1 × 10 −13 m −2/3 . Moreover, the optical power with l = 3 increases from 5.08 dBm to 8.23 dBm at a transmission distance of 800 m. These experimental results indicate that the proposed XFTC-Net AO technique effectively compensates for OAM beam distortion in both single- and multiplexed-channel OAM links, which effectively balances the system complexity and transmission performance of OAM-based free space optical (FSO) communication systems, opening up new avenues for enhancing the performances of high-capacity optical communication.

关键词： Adaptive optics Bit error rate Neural networks Optical wireless communication Polarization division multiplexing Space division multiplexing

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A Novel Trustworthy Video Summarization Algorithm Through a Mixture of LoRA Experts

arXiv

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arXiv 2025年

作者： Du, Wenzhuo Wang, Gerun Chen, Guancheng Zhao, Hang Li, Xin Gao, Jian School of Information and Network Security People’s Public Security University of China Beijing102206 China Key Laboratory of Security Prevention Technology and Risk Assessment Ministry of Public Security Beijing100038 China School of Criminal Investigation People’s Public Security University of China Beijing102206 China

With the exponential growth of user-generated content on video-sharing platforms, the challenge of facilitating efficient searching and browsing of videos has garnered significant attention. To enhance users’ ability to swiftly locate and review pertinent videos, the creation of concise and informative video summaries has become increasingly important. Video-llama is an effective tool for generating video summarization, but it cannot effectively unify and optimize the modeling of temporal and spatial features and requires a lot of computational resources and time. Therefore, we propose MiLoRA-ViSum to more efficiently capture complex temporal dynamics and spatial relationships inherent in video data and to control the number of parameters for training. By extending traditional Low-Rank Adaptation (LoRA) into a sophisticated mixture-of-experts paradigm, MiLoRA-ViSum incorporates a dual temporal–spatial adaptation mechanism tailored specifically for video summarization tasks. This approach dynamically integrates specialized LoRA experts, each fine-tuned to address distinct temporal or spatial dimensions. Extensive evaluations of the VideoXum and ActivityNet datasets demonstrate that MiLoRA-ViSum achieves the best summarization performance compared to state-of-the-art models, while maintaining significantly lower computational costs. The proposed mixture-of-experts strategy, combined with the dual adaptation mechanism, highlights the model’s potential to enhance video summarization capabilities, particularly in large-scale applications requiring both efficiency and precision. © 2025, CC BY.

关键词： Video analysis

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TFLAG: Towards Practical APT Detection via Deviation-Aware Learning on Temporal Provenance Graph

arXiv

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arXiv 2025年

作者： Jiang, Wenhan Chai, Tingting Liu, Hongri Wang, Kai Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Advanced Persistent Threat (APT) have grown increasingly complex and concealed, posing formidable challenges to existing Intrusion Detection Systems in identifying and mitigating these attacks. Recent studies have incorporated graph learning techniques to extract detailed information from provenance graphs, enabling the detection of attacks with greater granularity. Nevertheless, existing studies have largely overlooked the continuous yet subtle temporal variations in the structure of provenance graphs, which may correspond to surreptitious perturbation anomalies in ongoing APT attacks. Therefore, we introduce TFLAG, an advanced anomaly detection framework that for the first time integrates the structural dynamic extraction capabilities of temporal graph model with the anomaly delineation abilities of deviation networks to pinpoint covert attack activities in provenance graphs. This self-supervised integration framework leverages the graph model to extract neighbor interaction data under continuous temporal changes from historical benign behaviors within provenance graphs, while simultaneously utilizing deviation networks to accurately distinguish authentic attack activities from false positive deviations due to unexpected subtle perturbations. The experimental results indicate that, through a comprehensive design that utilizes both attribute and temporal information, it can accurately identify the time windows associated with APT attack behaviors without prior knowledge (e.g., labeled data samples), demonstrating superior accuracy compared to current state-of-the-art methods in differentiating between attack events and system false positive events. © 2025, CC BY.

关键词： Contrastive Learning

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Protective performance of sacrificial cladding with foamed geopolymer core under impact loading

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International Journal of Impact Engineering 2025年 204卷

作者： Zhou, Hongyuan Liu, Hao Wang, Xiaojuan Lv, Yang Zhang, Hong Song, Tianyi Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education Beijing University of Technology Beijing100124 China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing100081 China Tianjin Key Laboratory of Civil Structure Protection and Reinforcement Tianjin Chengjian University Tianjin300384 China

In the present study, a novel sacrificial cladding with a foamed geopolymer (FG) energy-absorbing core was proposed for structural protection to resist impact loading. First, the mechanical and energy absorption performance of FG was investigated through quasi-static and dynamic compressive tests. The test results demonstrated that an increase in density or strain rate would significantly enhance the energy absorption capacity of FG. Based on the test results, a phenomenological constitutive model of FG, considering the effects of density and strain rate, was proposed. Subsequently, the drop hammer impact tests were conducted on the monolithic steel panel (MSP, regarded as the protected structure) and the cladding-structure system (CSS) to evaluate the protective performance of the proposed cladding. The impact test results indicated that MSP exhibited a significant local indentation in its central region, while the monolithic steel panel in CSS primarily demonstrated a global bending deformation. In addition, with the numerical model validated by the impact test results, the effects of the impact loading area, initial impact momentum, and the FG core density on the protective performance of the proposed cladding were further numerically investigated. The numerical results demonstrated that, although the monolithic steel panel in CSS exhibited a slightly larger residual deflection compared to MSP in some cases, the sacrificial cladding still provided effective protection in terms of damage extent, energy absorption, and load transfer, especially under small impact loading areas or low initial impact momentum. It was noted that, due to the premature densification or limited deformation of FG, the sacrificial cladding with either low- or high-density FG cores led to more impact energy and a higher peak load transferred to the protected structure, demonstrating unfavorable protective performance. The results in the present study provided valuable references for the des

关键词： Compression testing

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RobustPFL: Robust Personalized Federated Learning

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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Advanced critical temperature method of concrete-filled steel tubular columns for early warning of fire-induced failure

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Structures 2025年 77卷

作者： Song, Tian-Yi Wang, Yi-Xuan Yu, Wen-Chuan Zhou, Hongyuan Xiang, Kai Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education Beijing University of Technology Beijing100124 China Tianjin Fire Science and Technology Research Institute of MEM Tianjin300381 China Key Laboratory of Fire Protection Technology for Industry and Public Building Ministry of Emergency Management Tianjin300381 China

In recent years, building collapses due to fires have become more frequent. Early warning of fire-induced building collapses, along with prompt evacuation, can significantly reduce casualties, particularly among firefighters. This paper develops an advanced critical temperature (ACT) method for widely used concrete-filled steel tubular (CFST) columns under center-column and corner-column fire exposures, extending the traditional critical temperature approach. Refined critical temperatures (RCTs) are defined based on multiple characteristic temperature points (CTPs), providing a reliable tool for early fire failure warnings. Finite element analysis models were used to examine the fire performance of CFST columns under various fire exposures, revealing that columns exposed to fire along their full length have the lowest fire resistance. Guidelines for determining RCTs under different fire scenarios were proposed, and the impact of fire protection layers on RCTs was found to be minimal. A parametric analysis showed that the column load ratio, outside dimension, and slenderness ratio significantly affect RCT values. Based on these findings, a design table for the ACT method is proposed, offering a practical approach for the early warning of fire-induced failure in CFST columns. © 2025 Institution of Structural Engineers

关键词： Tubular steel structures

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Burst image super-resolution based on dual branch fusion and adaptive frame selection

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Displays 2025年 89卷

作者： Duan, Lijuan Zuo, Wendi Gu, Ke Gong, Zhi School of Computer Science Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing University of Technology Beijing100124 China School of Information Science and Technology Beijing University of Technology Beijing100124 China Chongqing Research Institute of Beijing University of Technology China

The modern handheld camera is capable of rapidly capturing multiple images and subsequently merging them into a single image. The extant methodologies typically select the first frame as the reference frame, utilising the information from the remaining frames and the information from the reference frame to calculate the high-resolution image. However, for complex scenes and unstable shooting situations, this fixed frame selection is not the optimum solution. In this direction, we propose an adaptive frame selection method which, by calculating the frame channel weight information, selects the best frame image as the reference frame to perform the subsequent computation. Moreover, to enhance the visual quality of high-resolution images, we propose a dual-branch fusion module in the feature fusion phase for the sequence attributes of the input frame images. This allows the network to concentrate on the temporal global features of the input sequence and the spatial local detail features of the frame images. Subsequently, the feature map is obtained through residual computation utilising the adaptive selected frame image and the image features obtained through fusion. The image is then reconstructed through up-sampling to obtain a high-resolution image. The experimental results on the BurstSR and RealBSR datasets demonstrate that our approach not only outperforms existing techniques in terms of evaluation metrics but also exhibits superior visual effects. © 2025

关键词： Photointerpretation

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Poisoning Attacks and Defenses to Learned Bloom Filters for Malicious URL Detection

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Fangming Wang, Pinghui Li, Rundong Cui, Xueyao Zhao, Junzhou Tao, Jing Zhang, Chen Guan, Xiaohong Zhejiang CreateLink Technology Co. Ltd Guangdong Guangzhou China Xi'an Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi'an710049 China Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing China

Approximate membership query (AMQ) structures represented by the Bloom Filter and its variants have been popularly researched in recent years. Researchers have recently combined machine learning with this type of structure to reduce space consumption and computation overhead further and make remarkable progress. However, with the booming performance in space or other metrics, researchers tend to ignore the security of the trained model. The machine learning model is vulnerable to poisoning attacks, and naturally, we infer that the learning-based filters also have the same deficiencies. Hence, in this paper, to confirm the inference mentioned above, experiments on the real-world datasets of URLs are conducted and prove that it is necessary to consider the security issue when using learning-based filters. We show that by data poisoning, the attacker can deflect learned Bloom Filters to make a false identification, which can lead to a significant loss in some cases. Aiming to solve this issue, we put forward a method named Defensive Learned Bloom Filter (DLBF) to diminish the influence of data poisoning and achieve a better performance compared to types of learned Bloom Filters. © 2004-2012 IEEE.

关键词： Wiener filtering

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Splicer+: Secure Hub Placement and Deadlock-Free Routing for Payment Channel network Scalability

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wang, Wei Gao, Sheng Qu, Qiang Tian, Wensheng Shen, Yulong The School of Computer Science and Technology Xidian University Xi’an710071 China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China The Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China The School of Information Central University of Finance and Economics Beijing100081 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Huawei Blockchain Lab Huawei Cloud Tech Co. Ltd. Shenzhen518055 China The Nanhu Lab Jiaxing314001 China The Cryptology and Computer Security Laboratory Shanghai Jiao Tong University Shanghai200240 China

Payment channel hub (PCH) is a promising approach for payment channel networks (PCNs) to improve efficiency by deploying robust hubs to steadily process off-chain transactions. However, existing PCHs, often preplaced without considering payment request distribution across PCNs, can lead to load imbalance. PCNs' reliance on source routing, which makes decisions based solely on individual sender requests, can degrade performance by overlooking other requests, thus further impairing scalability. In this paper, we introduce Splicer+, a highly scalable multi-PCH solution based on the trusted execution environment (TEE). We study tradeoffs in communication overhead between participants, transform the original NP-hard PCH placement problem by mixed-integer linear programming, and propose optimal/approximate solutions with load balancing for different PCN scales using supermodular techniques. Considering global PCN states and local directly connected sender requests, we design a deadlock-free routing protocol for PCHs. It dynamically adjusts the payment processing rate across multiple channels and, combined with TEE, ensures high-performance routing with confidential computation. We provide a formal security proof for the Splicer+ protocol in the UC-framework. Extensive evaluations demonstrate the effectiveness of Splicer+, with transaction success ratio (↑51.1%), throughput (↑181.5%), and latency outperforming state-of-the-art PCNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Scalability

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Efficiency Boosting of Secure Cross-Platform Recommender Systems Over Sparse Data

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2146-2160页

作者： Ren, Hao Xu, Guowen Zhang, Tianwei Ning, Jianting Huang, Xinyi Li, Hongwei Lu, Rongxing School of Cyber Science and Engineering Sichuan University Chengdu610017 China State Key Laboratory of Cryptology Beijing100878 China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education Chengdu610017 China Cyber Science Research Institute Sichuan University Chengdu610017 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Cyber Science and Engineering Wuhan University Wuhan430072 China Faculty of Data Science City University of Macau 999078 China Colledge of Cyber Security Jinan University Guangzhou510632 China School of Computer Science University of New Brunswick New BrunswickE3B 5A3 Canada

Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when data are shared among different platforms. In this paper, we follow the tune of existing works to investigate the problem of secure sparse matrix multiplication for cross-platform RSs. Two fundamental and critical issues are addressed: preserving the training data privacy and breaking the data silo problem. Specifically, we propose two concrete constructions with significantly boosted efficiency. They are designed for the sparse location insensitive case and location sensitive case, respectively. State-of-the-art cryptography building blocks including homomorphic encryption (HE) and private information retrieval (PIR) are fused into our protocols with non-trivial optimizations. As a result, our schemes can enjoy the HE acceleration technique without privacy trade-offs. We give formal security proofs for the proposed schemes and conduct extensive experiments on both real and large-scale simulated datasets. Compared with state-of-the-art works, our two schemes compress the running time roughly by 10×10× and 2.8×2.8×. They also attain up to 15×15× and 2.3×2.3× communication reduction without accuracy loss. © 2024 IEEE.

关键词： Differential privacy

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