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Optimal Linear Codes With Few Weights From Simplicial Complexes

IEEE Transactions on Information Theory 2025年第5期71卷 3531-3543页

作者： Chen, Bing Xu, Yunge Hu, Zhao Li, Nian Zeng, Xiangyong Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Wuhan430062 China Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei Provincial Engineering Research Center of Intelligent Connected Vehicle Network Security School of Cyber Science and Technology Wuhan430062 China State Key Laboratory of Integrated Service Networks Xi’an710071 China Hubei University Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan430062 China

Recently, constructions of optimal linear codes from simplicial complexes have attracted much attention and some related nice works were presented. Let q be a prime power. In this paper, by using the simplicial complexes of Fmq with one single maximal element, we construct four families of linear codes over the ring Fq+uFq}( u2=0 ), which generalizes the results of Wu et al. (2020). The parameters and Lee weight distributions of these four families of codes are completely determined. Most notably, via the Gray map, we obtain several classes of optimal linear codes over Fq, including (near) Griesmer codes and distance-optimal codes. Moreover, it is shown that most of the Gray images are minimal or self-orthogonal codes which are useful in applications. © 1963-2012 IEEE.

关键词： Turbo codes

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UARC:Unsupervised Anomalous Traffic Detection with Improved U-Shaped Autoencoder and RetNet Based Multi-clustering 26th

UARC:Unsupervised Anomalous Traffic Detection with Improved ...

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

作者： Xie, Yunyang Chen, Kai Li, Shenghui Li, Bingqian Zhang, Ning Hubei Key Laboratory of Distributed System Security Hubei Engin eering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University Technology Wuhan430074 China

ISBN: (纸本)9789819787975

With the ongoing advancement of deep learning, modern network intrusion detection systems increasingly favor utilizing deep learning networks to improve their ability to learn traffic characteristics. To address the challenge of obtaining a substantial amount of labeled training data, many intrusion detection systems now focus on unsupervised anomaly detection methods. Despite this shift, researchers still face the daunting task of distinguishing a significant volume of anomalous traffic and dealing with data imbalance. To address these real-world challenges, we introduce UARC, a system capable of achieving unsupervised anomaly traffic detection through multi-clustering. UARC utilizes an enhanced U-shaped autoencoder and a feature fusion method incorporating Masked Retnet to effectively extract spatiotemporal features from network traffic. It combines these techniques with the HDBSCAN algorithm for multi-clustering of traffic, providing a form of reverse guidance for network learning. Experimental results on multiple datasets demonstrate that UARC can cluster various types of traffic with an impressive accuracy rate of up to 97.96%, while achieving a 99.70% AUC value for anomaly detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Area Coverage Reliability Evaluation for Collaborative Intelligence and Meta-Computing of Decentralized Industrial Internet of Things

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IEEE Internet of Things Journal 2025年第10期12卷 13734-13745页

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao Yin, Duan Gao, Hanjun Yang, Laurence T. Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Mobile Communications Group Terminal Co Beijing China CNNC Wuhan Nuclear Power Operation Technology Co Wuhan China

Industrial Internet of Things (IIoT) is evolving towards decentralization and autonomous operation. Nodes in decentralized IIoT collaboratively sense data and communicate to participate in meta-computing and provide diverse intelligent services. Area coverage reliability evaluates the information collaborative intelligence sensing and communication capabilities of nodes in decentralized IIoT for a target area. It serves as a crucial performance evaluation metric for the meta-computing and intelligent services of decentralized IIoT. Existing area coverage reliability evaluation algorithms do not consider the dynamic node states in decentralized IIoT, and overlook the capability of nodes to collaborate in meta-computing. To address these issues, this paper proposes a novel Confident Information Coverage (CIC)-based area coverage reliability metric (CACREL), which comprehensively considers node multistate, node collaboration, network coverage rate, and network connectivity. To effectively evaluate CACREL, a CIC-based coverage reliability evaluation algorithm (CACR) is proposed. Specifically, CACR transforms complex networks into grid networks and utilizes a Coverage Table (CT) to describe different coverage states of each grid, which reduces the complexity of the area coverage reliability evaluation. Additionally, CACR employs a reliable path algorithm to converge the CT of each grid to the sink node based on grid connectivity. Simulation results demonstrate that the proposed CACR can accurately evaluate the area coverage reliability of decentralized IIoT and enhance its service performance. © 2014 IEEE.

关键词： Decentralized finance

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RIGIDITY RESULTS OF SPACE-LIKE SUBMANIFOLDS WITH PARALLEL GAUSSIAN MEAN CURVATURE VECTOR

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Tohoku Mathematical Journal 2025年第1期77卷 105-118页

作者： Wang, Huijuan FACULTY OF MATHEMATICS AND STATISTICS KEY LABORATORY OF APPLIED MATHEMATICS OF HUBEI PROVINCE KEY LABORATORY OF INTELLIGENT SENSING SYSTEM AND SECURITY MINISTRY OF EDUCATION HUBEI UNIVERSITY WUHAN China

In this paper, we study the space-like submanifolds with parallel Gaussian mean curvature vector in pseudo-Euclidean space. We establish a relevant Omori-Yau maximum principle for these space-like submanifolds which is closed with respect to the Euclidean topology. Then, we prove a rigidity theorem for these space-like submanifolds with ⃦ ε ⃦ <^√_n² under a growth condition on the mean curvature. Finally, we use a Liouville type result to obtain a further rigidity theorem for space-like submanifolds with parallel Gaussian mean curvature vector in pseudo-Euclidean space. © 2025 Tohoku University, Mathematical Institute. All rights reserved.

关键词： Omori-Yau maximum principle parallel Gaussian mean curvature vector Rigidity theorems space-like submanifolds

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Low-temperature direct ethanol solid oxide fuel cells based on LiNi0.8Co0.15Al0.05O2-δ electrodes

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International Journal of Hydrogen Energy 2025年 130卷 242-248页

作者： Lei, Yaoyi Fang, Jingtong Xu, Yifan Wang, Xunying Xia, Chen Wang, Baoyuan Dong, Wenjing Key Laboratory of Intelligent Sensing System and Security of the Ministry of Education Hubei Key Laboratory of Micro-Nanoelectronic Materials and Devices School of Microelectronics Hubei University Wuhan430062 China

Solid oxide fuel cells (SOFCs) are environmentally friendly energy conversion devices that can be operated by using hydrogen and various hydrocarbons as fuels. Among those hydrocarbon fuels, ethanol has garnered widespread attention due to its high energy density, low transportation cost, and easy of storage, making it one of the most promising fuels for SOFCs. However, the ethanol-based SOFCs often require high operating temperature, and have the issue of performance degradation due to carbon deposition at the anode. Searching for high performance anode material is of significant importance for low-temperature direct ethanol SOFCs. This study investigates the application of LiNi0.8Co0.15Al0.05O2-δ (NCAL) as the electrode for direct ethanol SOFCs and aims to improve fuel cell performance while enhancing resistance to carbon deposition. The NCAL anode is pretreated to optimize its surface microstructure and catalytic properties. In addition, from a thermodynamic perspective, the C–H–O ratio is adjusted by using different carrier gases for ethanol to mitigate carbon deposition on the anode surface. The result shows that using CO2 as the carrier gas facilitates the dry reforming reaction, which significantly reduces carbon deposition on the anode support and improves cell stability. This study provides insights into enhancing the performance of low-temperature direct ethanol SOFCs based on NCAL electrodes. © 2025

关键词： Ethanol fuels

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AC-Resistance Evaluation and Reduction Based on Nonuniformity Current Distribution of Large-Strand-Count Litz Wire

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IEEE Transactions on Power Electronics 2025年第7期40卷 10111-10122页

作者： Deng, Qijun Huang, Qiming Li, Zhifan Deng, Haozhe Li, Shuaiqi Hu, Wenshan Wuhan University Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan430072 China Wuhan Britain-China School Wuhan430033 China

Large-strand-count Litz wire (LLW) coils are commonly utilized in high-power wireless power transfer systems. An accurate ac-resistance prediction method of LLW coil is necessary to select the proper operation frequency dedicated for the highest coil quality factor and system efficiency. However, most present literatures ignore the interstrands nonuniform current distribution (NCD) of LLW caused by the soldering tins at terminals, which leads to a high ac-resistance prediction error. This article develops a method extracting the NCD among strands based on the two-dimension finite element analysis and strands probability transposition. And an improved ac-resistance prediction method considering the NCD is proposed subsequently, which is primarily applicable to LLW containing over 500 strands. Moreover, a novel strand edge-soldering connector (SEC) is designed to mitigate NCD and thereby reducing ac-resistance. Finally, the satisfactory accuracy of improved ac-resistance prediction for LLW circular coils up to 1000 strands is verified experimentally. Then, the proposed SEC prototype is measured with a maximum resistance reduction of 33.28%, which demonstrates the effectiveness of the proposed NCD mitigation method. © 2025 IEEE.

关键词： Resistance Tin Tail Coils Vectors Magnetic heads Wire Impedance Current distribution Inductance

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An FPGA-based key-Switching Accelerator with Ultra-High Throughput for FHE 24

An FPGA-based Key-Switching Accelerator with Ultra-High Thro...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Lu, Zhaojun Xu, Peng Wang, Yijie Yang, Yifan Chen, Qidong Yu, Weizong Qu, Gang Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Wuhan JinYinHu Laboratory China University of Maryland College Park United States

ISBN: (纸本)9798400710773

Fully Homomorphic Encryption (FHE) enables computations directly on encrypted numbers, thereby preserving the privacy of sensitive information even in untrusted environments. However, the substantial computational overhead associated with homomorphic evaluations restricts the practical application of FHE schemes. To deal with the performance challenges, this paper proposes a hardware/software pipeline framework with a three-level cache architecture to accelerate the costly key-Switching operation in FHE. This framework supports the dynamically reconfigurable processing mode, two parallelism strategies, and flexible control flow, effectively breaking the compute-bound and the memory-bound limitations. A no-stall and conflict-free memory mapping algorithm is implemented on the Xilinx U55C FPGA platform that boosts the throughput of ciphertext-ciphertext multiplication to 395 operations per second, which 1.4× and 2.5× speeds up the FPGA-based (HPCA’23) and GPU-based (HPCA’23) schemes with the same parameter set and precision. © 2024 Copyright is held by the owner/author(s).

关键词： Sensitive data

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Mining User–Item Interactions via Knowledge Graph for Recommendation

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ACM Transactions on Recommender systems 2025年第3期3卷 1-19页

作者： Shenghao Liu Lingyun Lu Bang Wang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology (HUST) Wuhan China

Introducing a Knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KGs also may contribute to better user representation learning. To solve this issue, we propose a novel algorithm, the KIGR (Knowledge-aware Interaction Graph for Recommendation), to mine user–item interactions via KGs for assisting user representation learning. Specifically, a user–item interaction is encoded by attentively summing up the relation embedding about the item in the KG. Then, an unsupervised learning method is used to group the user–item interactions into different latent types. Further, a user–item interaction graph is divided into several subgraphs, which is referred to as a Knowledge-aware Interaction Graph, making each subgraph only contain one latent type of interaction. Finally, user representation is the fusion of user interest embedding, which is learned on the knowledge-aware interaction graph, whereas item representation is learned on the KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the state-of-the-art algorithms.

关键词： Recommendation system knowledge graph graph neural network

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Blockchain-Enhanced Federated Learning for Secure and Intelligent Consumer Electronics : An Overview

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IEEE Consumer Electronics Magazine 2025年

作者： Zhu, Chenlu Wang, Shuilong Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao He, Yuanyuan Wu, Celimuge Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China The University of Electro-Communications China

With the rapid advancement of Artificial Intelligence (AI) and Internet of Things (IoT), consumer electronics have become essential in daily life. However, these devices generate and store vast amounts of personal data, posing significant security threats. Federated learning, a distributed machine learning method, protects user privacy by training models collaboratively without sharing raw data. However, it still faces numerous challenges. To further enhance data security, the integration of blockchain technology is crucial. Blockchain's decentralized and immutable characteristics enable secure data sharing and privacy protection in consumer electronics. This paper analyzes the application of federated learning in consumer electronics, examines how blockchain can enhance federated learning, and discusses future research directions. The primary contributions of this paper include a detailed application of blockchain-enhanced federated learning in consumer electronics, intended to serve as a reference for future research. © 2012 IEEE.

关键词： Federated learning

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WBSan: WebAssembly Bug Detection for Sanitization and Binary-Only Fuzzing 25

WBSan: WebAssembly Bug Detection for Sanitization and Binary...

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Proceedings of the ACM on Web Conference 2025

作者： Xiao Wu Junzhou He Liyan Huang Cai Fu Weihang Wang Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan China University of Southern California Los Angeles CA USA

ISBN: (纸本)9798400712746

With the advancement of WebAssembly, abbreviated as Wasm, various memory bugs and undefined behaviors have emerged, leading to security issues that affect usability and portability. Existing methods struggle to detect these problems in Wasm binaries due to challenges associated with binary instrumentation and the difficulty of defining legal memory *** sanitizers combined with fuzzing are recognized as effective means for identifying bugs, current Wasm sanitizers necessitate compile-time instrumentation, rendering them unsuitable for practical scenarios where only binaries are accessible. In this paper, we propose WBSan, the first Wasm binary sanitizer employing static analysis and Wasm binary instrumentation to detect memory bugs and undefined behaviors. We develop distinct instrumentation patterns tailored for each type of bug and introduce Wasm shadow memory to address complex memory bugs. Our results reveal that WBSan achieves a 16.8% false detection rate, outperforming current Wasm binary checkers and native sanitizers in detecting memory bugs and undefined behaviors. Furthermore, when compared with the binary-only fuzzer, WBSan uncovers more crashes and achieves greater code coverage.

关键词： WebAssembly

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