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A 3–8 decoder of terahertz metamaterials and its sensing application

Diamond and Related Materials 2025年 156卷

作者： Zhu, Aijun Wei, Wenrui Hou, Weigang Cheng, Lei Hu, Cong Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400000 China Jincheng Research Institute of Opto-mechatronics Industry Shanxi Jincheng048000 China Shanxi Key laboratory of Advanced semiconductor Optoelectronic Devices and Integrated Systems Shanxi Jincheng048000 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin541004 China

A 3–8 decoder based on terahertz metamaterial was proposed for the first time, which can achieve both 3–8 decoding function and dual band selectable 2–4 decoding function. The function of this device mainly relies on VO2 acting as a conductor or insulator. The finite integration in time domain (FITD) method was used to simulate and optimize the performance of the metamaterial absorber (MMA), and its working mechanism was analyzed through electric field distribution diagram and equivalent circuit model (ECM). When the device is used as a 3–8 decoder, the maximum modulation depth (MD) is 99.60 %, the minimum extinction ratio (ER) is 17.7 dB, the maximum insertion loss (IL) is 0.76 dB, and it has the property of being insensitive to the incident angle, with stable decoding ability. In addition, the device can also be used as a sensor with a refractive index sensitivity (S) of 0.55 THz/RIU, a quality factor (Q) of 24.714, and a FOM of 3.93RIU−1, demonstrating excellent sensing capabilities. This work provides new ideas for improving the decoding capability of terahertz devices and designing dynamic devices. © 2025

关键词： Refractive index

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A wolf in sheep's clothing: practical black-box adversarial attacks for evading learning-based windows malware detection in the wild 24

A wolf in sheep's clothing: practical black-box adversarial ...

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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Xiang Ling Zhiyu Wu Bin Wang Wei Deng Jingzheng Wu Shouling Ji Tianyue Luo Yanjun Wu Intelligent Software Research Center Institute of Software Chinese Academy of Sciences and Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Zhejiang University Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security and Hangzhou Research Institute Xidian University Intelligent Software Research Center Institute of Software Chinese Academy of Sciences

ISBN: (纸本)9781939133441

Given the remarkable achievements of existing learning-based malware detection in both academia and industry, this paper presents MalGuise, a practical black-box adversarial attack framework that evaluates the security risks of existing learning-based Windows malware detection systems under the black-box setting. MalGuise first employs a novel semantics-preserving transformation of call-based redividing to concurrently manipulate both nodes and edges of malware's control-flow graph, making it less noticeable. By employing a Monte-Carlo-tree-search-based optimization, MalGuise then searches for an optimized sequence of call-based redividing transformations to apply to the input Windows malware for evasions. Finally, it reconstructs the adversarial malware file based on the optimized transformation sequence while adhering to Windows executable format constraints, thereby maintaining the same semantics as the original. MalGuise is systematically evaluated against three state-of-the-art learning-based Windows malware detection systems under the black-box setting. Evaluation results demonstrate that MalGuise achieves a remarkably high attack success rate, mostly exceeding 95%, with over 91% of the generated adversarial malware files maintaining the same semantics. Furthermore, MalGuise achieves up to a 74.97% attack success rate against five anti-virus products, highlighting potential tangible security concerns to real-world users.

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The Lee weight distributions of several classes of linear codes over Z4

arXiv

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arXiv 2025年

作者： Wang, Zhexin Li, Nian Zeng, Xiangyong Tang, Xiaohu Ministry of Education The Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Ministry of Education The Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Hubei University Wuhan430062 China State Key Laboratory of Integrated Services Networks Xi’an710071 China Southwest Jiaotong University Chengdu610031 China

Let Z4 denote the ring of integers modulo 4. The Galois ring GR(4, m), which consists of 4m elements, represents the Galois extension of degree m over Z4. The constructions of codes over Z4 have garnered significant interest in recent years. In this paper, building upon previous research, we utilize the defining-set approach to construct several classes of linear codes over Z4 by effectively using the properties of the trace function from GR(4, m) to Z4. As a result, we have been able to obtain new linear codes over Z4 with good parameters and determine their Lee weight distributions. Upon comparison with the existing database of Z4 codes, our construction can yield novel linear codes, as well as linear codes that possess the best known minimum Lee distance. Copyright © 2025, The Authors. All rights reserved.

关键词： Turbo codes

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Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneity

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Tian, Zhuojun Zhang, Zhaoyang Li, Yiwei Bennis, Mehdi Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China University of Oulu Center for Wireless Communications Oulu90014 Finland State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing Hangzhou310027 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China

To jointly tackle the challenges of data and node heterogeneity in decentralized learning, we propose a distributed strong lottery ticket hypothesis (DSLTH), based on which a communication-efficient personalized learning algorithm is developed. In the proposed method, each local model is represented as the ‌Hadamard product of global real-valued parameters and a personalized binary mask for pruning. The local model is learned by updating and fusing the personalized binary masks while the real-valued parameters are fixed among different agents. To further reduce the complexity of hardware implementation, we incorporate a group sparse regularization term in the loss function, enabling the learned local model to achieve structured sparsity. Then, a binary mask aggregation algorithm is designed by introducing an intermediate aggregation tensor and adding a personalized fine-tuning step in each iteration, which constrains model updates towards the local data distribution. The proposed method effectively leverages the relativity among agents while meeting personalized requirements in heterogeneous node conditions. We also provide a theoretical proof for the DSLTH, establishing it as the foundation of the proposed method. Numerical simulations confirm the validity of the DSLTH and demonstrate the effectiveness of the proposed algorithm. © 2015 IEEE.

关键词： Iterative methods

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Economic and resilient planning of hydrogen-enriched power distribution network with mobile hydrogen energy resources

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International Journal of Hydrogen Energy 2025年

作者： Wang, Tengbo Dong, Yuchen Cao, Xiaoyu Xu, Zhanbo Gao, Feng Guan, Xiaohong Systems Engineering Institute School of Automation Science and Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'an710049 China School of Management Xi'an Jiaotong University Shaanxi Xi'an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper presents a risk-averse stochastic mixed-integer programming method to support the economic and resilient planning of hydrogen-enriched power distribution network with mobile hydrogen energy resources (MHERs). Our planning formulation holds a two-stage problem structure. The first-stage problem is to minimize the investment cost for optimally siting and configuring stationary distributed energy resources (DERs) and MHERs. In the second-stage, the operational feasibility of planning alternatives is verified subject to a finite support of stochastic scenarios. Besides of the economic assessment, an additional set of recourse problems based on contingency scenarios is involved to reflect the impact of extreme events (e.g., natural disasters or malicious attacks). To evaluate the risk-hedging capability of long-run system operation, a special objective function based on conditional value-at-risk (CVaR) is introduced. Moreover, various on-emergency corrective measures, e.g., energy storage dispatching, MHERs’ dynamic re-routing, and distribution feeders reconfiguration, are considered to enhance the system resilience. Case studies on a 33-bus test distribution network have validated the economic and resilience benefits of the proposed planning method. Through the proper configuration of stationary and mobile DERs, the load curtailment risks can be reduced by more than 75% given similar levels of system expenses. Besides, the inclusion of CVaR objective provides a flexible and quantitative measure for risk controls by tuning key model parameters. © 2025 Hydrogen Energy Publications LLC

关键词： Power distribution planning

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

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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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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment: Advancing Big Data Intelligence in Control Systems

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Computers, Materials and Continua 2025年第3期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu Chong Lv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Library of Shanghai Lixin University of Accounting and Finance Shanghai 201209 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing 100081 China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPU Taicang 215400 China Key Laboratory of Education Informatization for Nationalities (Yunnan Normal University) Ministry of Education Kunming 650092 China

As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment. Specifically, the enhanced PBFT consensus mechanism (VRPBFT) significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function (VRF) and a node reputation grading model. Experimental results demonstrate that compared to traditional PBFT, the proposed VRPBFT algorithm reduces consensus latency by approximately 30% and decreases the proportion of Byzantine nodes by 40% after 100 rounds of consensus. Furthermore, the DRL-based SFC deployment algorithm (SDRL) exhibits rapid convergence during training, with improvements in long-term average revenue, request acceptance rate, and revenue/cost ratio of 17%, 14.49%, and 20.35%, respectively, over existing algorithms. Additionally, the CPU resource utilization of the SDRL algorithm reaches up to 42%, which is 27.96% higher than other algorithms. These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency, service quality, and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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Hierarchical Alignment-enhanced Adaptive Grounding network for Generalized Referring Expression Comprehension

arXiv

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arXiv 2025年

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing notarget and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, The Authors. All rights reserved.

关键词： Electric grounding

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A Study on Influential User Identification in Online Social networks

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Chinese Journal of Electronics 2016年第3期25卷 467-473页

作者： WANG Nan SUN Qindong ZHOU Yadong SHEN Si Shaanxi Key Laboratory of Network Computing and Security Xi'an University of Technology Ministry of Education Key Laboratory For Intelligent Network and Network Security Xi'an Jiaotong University

Influential user evaluation is great importance in many application areas of online social *** order to identify influential users in a more adequate and practical way, we propose a Dynamic regional interaction model(DRI) to evaluate user influence in online social networks. Influential users can be identified by the influence effect on different distance users based on dynamic regional interaction model. We have applied the influential user identification method to Sina Weibo and the experimental results show that compared with the existing methods the proposed method can identify the influence users in a more accuracy and efficiency way.

关键词： Online social networks Influential user identification Dynamic regional interaction model(DRI) User influence evaluation

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Provably Secure Authenticated key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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