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Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

Journal of Artificial Intelligence and technology 2021年第2期1卷 92-100页

作者： Jing Liu Yixin Liu Yingxu Lai Rongchen Li Siyu Wu Sami Mian College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Shaanxi Key Laboratory of Network and System Security Xi’an 710071China Science and Technology on Information Assurance Laboratory Beijing 100072China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Fan Gongxiu Honors College Beijing University of TechnologyBeijing 100124China University of Pittsburgh PittsburghPA 15260USA

With the rising popularity of the Internet and the development of big data technology,an increasing number of organizations are opting to cooperate across domains to maximize their *** organizations use public key infrastructure to ensure security in accessing their data and ***,with the continuous development of identity-based encryption(IBE)technology,small-and medium-sized enterprises are increasingly using IBE to deploy internal authentication *** solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process,we propose using blockchain technology to establish a reliable cross-domain authentication *** the distributed and tamper-resistant characteristics of the blockchain,we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on *** model does not change the internal trust structure of each authentication domain and is highly ***,on the premise of ensuring security,the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate,thereby improving the authentication *** developed prototype exhibits generality and simplicity compared to previous methods.

关键词： public key infrastructure identity-based encryption cross-domain authentication blockchain

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Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

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Bioresource technology 2025年 434卷 132773页

作者： Zhai, Mudi Fu, Bomin Wu, Zhaozhong Wang, Junsen Wang, Weijie Wang, Hongtao College of Environmental Science and Engineering Key Laboratory of Yangtze River Water Environment Ministry of Education Tongji University Siping Rd 1239 Shanghai200092 China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong999077 China School of Computer Science Northwestern Polytechnical University Xi'an710129 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments. © 2025 Elsevier Ltd

关键词： Generative adversarial networks

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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An Anomaly Event Detection Method Based on GNN Algorithm for Multi-data Sources 3

An Anomaly Event Detection Method Based on GNN Algorithm for...

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3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2021

作者： Ji, Yipeng Wang, Jingyi Li, Shaoning Li, Yangyang Lin, Shenwen Li, Xiong School of Computer Science and Engineering Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Beijing China Beijing China Beijing Biwei Network Technology Co. Ltd Beijing China Beijing Xindatek Technology Co. Ltd Beijing China

ISBN: (纸本)9781450384001

Anomaly event detection is crucial for critical infrastructure security(transportation system, social-ecological sector, insurance service, government sector etc.) due to its ability to reveal and address the potential cyber-Threats in advance by analysing the data(messages, microblogs, logs etc.) from digital systems and networks. However, the convenience and applicability of smart devices and the maturity of connected technology make the social anomaly events data multi-source and dynamic, which result in the inadaptability for multi-source data detection and thus affect the critical infrastructure security. To effectively address the proposed problems, in this paper, we design a novel anomaly detection method based on multi-source data. First, we leverage spectral clustering algorithm for feature extraction and fusion of multiple data sources. Second, by harnessing the power of deep graph neural network(Deep-GNN), we perform a fine-gained anomaly social event detection, revealing the threatening events and guarantee the critical infrastructure security. Experimental results demonstrate that our framework outperforms other baseline anomaly event detection methods and shows high tracking accuracy, strong robustness and stability. © 2021 ACM.

关键词： Critical infrastructures

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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A Novel Sparse Adaptive Filter for Suppressing Impulsive Disturbance in Audio Signals

SSRN

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SSRN 2024年

作者： Zhou, Lei Liu, Hongqing Gan, Lu Zhou, Yi Niedźwiecki, Maciej Truong, Trieu-Kien School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Lab of Mobile Communications Technology and Intelligent Speech and Audio Research Lab Chongqing University of Posts and Telecommunications Chongqing400065 China College of Engineering Design and Physical Science Brunel University LondonUB8 3PH United Kingdom Department of Automatic Control Faculty of Electronics Telecommunications and Computer Science Gdansk University of Technology Gdansk80-233 Poland Department of Information Engineering I-Shou University 84001 Taiwan

This work studies the sparse adaptive filter designs in the presence of impulsive disturbance for audio signal recovery. By using the sparse representation of desired signal and compressibility of impulsive disturbance, a joint sparse least mean p-norm (JSLMP) optimization, in which p-norm (1 ≤ p ≤ 2) measures the data fidelity and q-norm (0 ≤ q ≤ 1) enforces sparse solutions, is developed, termed as q-JSLMP. The filter weights update is derived based on the gradient descent, and the Adam and variable step size (VSS) are integrated to accelerate convergence and avoid potential local minima. In the special case of q = 1, namely 1-JSLMP, its convergence condition and mean square deviation (MSD) analysis are derived. Finally, the frame of application to corrupted audio signals is designed. The results from experiments demonstrate that the proposed approach outperforms other approaches in terms of objective measures on numerical simulation and speech datasets. © 2024, The Authors. All rights reserved.

关键词： Adaptive filters

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Sliding Mode control for Quadrotor-Slung Load Transportation System with State Constraints*

Sliding Mode Control for Quadrotor-Slung Load Transportation...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Feng Ding Chong Sun Yong Ai Jian Huang School of Computer Science South-Central Minzu University Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and automation Huazhong University of Science and Technology Wuhan Hubei China

Quadrotor-slung load transportation is an important way to transport goods, but the swing of the load bring a great deal of challenges to the stability of the system. In this paper, a barrier Lyapunov function based sliding mode controller is proposed to limit the load swing, which also satisfies the speed limitation of the quadrotor. An inner-outer loop control strategy is proposed, where the outer one is the virtual position controller which guarantees the stability of the quadrotor while eliminating the load swing, and the inner one is the attitude controller which driver the actual force to the position controller to ensure the effectiveness of the system. Specifically, the dynamics of quadrotor-slung load transportation system is reconstructed as a cascade form. Then, a barrier Lyapunov function based sliding mode controller is designed to ensure the trajectory tracking errors uniformly bounded with state constraints valid. Finally, comparative simulation results illustrate the effectiveness of the proposed method.

关键词： Attitude control Trajectory tracking Simulation Force Dynamics Transportation Stability analysis

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

关键词：

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Lines cluster approaching mode control for air-breathing hypersonic vehicle under disturbances and uncertainties

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International Journal of Modeling, Simulation, and Scientific Computing 2020年第1期11卷 152-175页

作者： Fei Ma Yunjie Wu Xiaocen Liu Xiaodong Liu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191P.R.China School of Automation Science and Electrical Engineering Beihang University Beijing 100191P.R.China Science and Technology on Aircraft Control Laboratory Beijing 100191 P.R.China Beijing Aerospace Automatic Control Institute Beijing 100854 P.R.China

This paper investigates the velocity and altitude tracking control problem for airbreathing hypersonic vehicle(AHV)in the presence of external disturbances and parameter uncertainties.A composite controller containing improved lines cluster approaching mode control(LCAMC)and nonlinear disturbance observer(NDO)is developed to guarantee the tracking errors converge to zero and enhance the robustness of control ***,considering the multiple uncertain parameters,a genetic algorithm(GA)based Pareto uncertainty estimation is employed to predict the parameter uncertainties of the AHV ***,the mathematical proofs of proposed method are analyzed by utilizing Lyapunov *** results demonstrate the effective tracking performance,excellent disturbance estimation and uncertainty estimation ability of the composite method.

关键词： Air-breathing hypersonic vehicle tracking control lines cluster approaching mode control nonlinear disturbance observer Pareto uncertainty estimation

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Maximal η-clique maintenance over uncertain graph streams

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Information sciences 2025年 717卷

作者： Ma, Ziyi Wang, Liqing Yang, Jianye Zhou, Xu Li, Kenli Gao, Cuiyun School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Chinacoal Beijing Coal Mining Machinery Co. Ltd. Beijing102400 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China Department of New Networks PengCheng Laboratory Shenzhen518055 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Shenzhen518055 China

Maximal clique enumeration is a critical task for analyzing graph data and has a wide range of applications, such as community detection, protein complex identification, and group recommendation. Although many efficient algorithms have been proposed, most of them are designed for static or deterministic graphs. However, in many real-life applications, graphs are often highly dynamic and nondeterministic. In this paper, we study the problem of maximal η-clique maintenance (shorted as MCM) over uncertain graph streams. Given an uncertain graph G and a probability threshold η, MCM reports all incremental η-cliques in G for each update edge of G. To the best of our knowledge, we are the first to systematically study this problem. We first develop a local pivot-based enumeration technique that utilizes the pivot priority of candidates to achieve effective pruning during the maximal η-clique enumeration procedure. Based on the technique, we propose a competitive baseline approach LPMCM, which finds newly generated maximal η-cliques and internal maximal η-cliques. Although LPMCM avoids repeated enumeration of the same maximal η-cliques, it still requires expensive costs in terms of η-clique maximality determination. To further enhance maintenance efficiency, we introduce an approach HLPMCM using the hash-based maximality determination technique and an improved internal maximal η-clique enumeration technique. Extensive experiments show that HLPMCM can achieve 1.1x-19.0x speedup over the competitors and consumes slightly more memory. © 2025 Elsevier Inc.

关键词： Maintenance

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