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2023 International Conference on Cryptography, Network Security, and Communication technology, CNSCT 2023

作者： Song, Yuxin School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (数字)9781510664951

ISBN: (纸本)9781510664944

With the development of science and technology, all kinds of data are generated in our daily life. While data analysis brings convenience, it also poses threat to users' privacy. This paper proposes a data security privacy protection model with blockchain in IoT. First, encrypt data before it is stored in the cloud. Secondly, in order to verify user identity and ciphertext safely, an efficient certificateless signature scheme is proposed. The signature scheme is security under random oracle model and time performance is efficient. © 2023 SPIE.

关键词： Blockchain

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Multi-Hop D2D Cluster Formation and Resource Allocation for Scalable Video Multicast 22

Multi-Hop D2D Cluster Formation and Resource Allocation for ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Nie, Hao Wang, Wei Dai, Rui Liu, Chenyan Wang, Zhixing Xu, Peng Huazhong University of Science and Technology Wuhan China University of Cincinnati Department of Electrical Engineering and Computer Science Cincinnati United States Centrin Data Vally Technology Co. Ltd Wuhan China

ISBN: (纸本)9798331509712

Scalable video coding (SVC) associated with adaptive modulation and coding (AMC) has provided an excellent solution for transmitting real-time videos. However, some users have bad channel conditions with the base station (BS) and need help getting a good quality of service (QoS). With the fifth generation (5G) of the cellular network, these users can also receive most of the data by device-to-device (D2D) transmissions. In this paper, we focus on live video delivery scenarios. We adopt the multicast Multi-Hop D2D and reuse spectrum spatially to help broadcast the scalable videos. We aim to maximize all users' average quality of service (QoS) by jointly optimizing D2D cluster heads (CHs) selection and channel reuse. The problem is an integer non-linear programming problem, and we decompose the problem into two subproblems: the D2D cluster formation problem and the resource allocation problem. To solve the first subproblem, we propose a greedy algorithm to form D2D clusters that can broadcast more efficiently. We convert the second subproblem to a combination of two partial graph coloring problems in two conflict graphs. An algorithm similar to the recursive largest first (RLF) algorithm but considering the conflicts in both graphs is provided for the second subproblem. We execute numerical simulations to validate the performance of our proposal. © 2024 IEEE.

关键词： Scalable video coding

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Expressive Voice Conversion Using Perturbation and Autoencoder Loss 12

Expressive Voice Conversion Using Perturbation and Autoencod...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Chen, Zhe Liu, Yining Liu, Gao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350355802

Expressive voice conversion is to transform voice speech with emotional expression. Modeling emotional styles in expressive voice conversion is challenging. It is essential to eliminate the interference of factors such as the identity in emotional style modeling. This paper addresses this issue using an information perturbation method. During the feature extraction phase, perturbation functions are applied to factors such as formants and pitch in the speech signal to eliminate the interference of speaker information. This allows for separate modeling and extraction of content information and emotional information. We propose a general framework for expressive voice conversion, which aims to disentangle content information, speaker information, and emotional style information from the voice signal. Experimental results demonstrate the effectiveness of our proposed framework in both subjective and objective evaluations. It can extract highly expressive and understandable features from the source speech while maintaining similarity to the target speaker. © 2024 IEEE.

关键词： Signal modulation

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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Metaheuristic-Driven Abnormal Traffic Detection Model for SDN Based on Improved Tyrannosaurus Optimization Algorithm

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computers, Materials and Continua 2025年第3期83卷 4495-4513页

作者： Hui Xu Jiahui Chen Zhonghao Hu School of Computer Science Hubei University of Technology Wuhan 430068 China

Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in local optimums, and has poor search accuracy. Then, this paper tries to improve TROA, namely the Improved Tyrannosaurus Optimization Algorithm (ITROA). It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA. Finally, the validity of the ITROA is verified by the benchmark function and the UCI dataset, and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection. The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN, achieving an accuracy of 99.37% on binary classification and 96.73% on multiclassification.

关键词： Software-defined networking abnormal traffic detection feature selection metaheuristic algorithm tyrannosaurus optimization algorithm

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Network Performance Optimization Based on Blockchain technology 4

Network Performance Optimization Based on Blockchain Technol...

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4th International Conference on computers and Artificial Intelligence technology, CAIT 2023

作者： Meng, Zirou Chen, Jing Yang, Lei Guangdong University of Science and Technology School of Computer Science Dongguan China

ISBN: (纸本)9798350326710

In view of the limitations of the traditional network performance optimization methods in terms of centralized architecture and trust problems, this study proposes a new idea of network performance optimization based on blockchain technology. As a distributed and decentralized technology, blockchain technology has a high degree of security and scalability, which can provide higher security and avoid the risk of single point of failure and centralized control. At the same time, its decentralized characteristics can also improve the scalability of the network, allowing more nodes to participate in the operation and maintenance of the network. Therefore, this paper studies the network performance optimization based on blockchain technology, and proposes three network performance optimization schemes based on blockchain technology by analyzing the existing network performance bottleneck and the application of blockchain technology. The three schemes are verified and the results show that the network performance optimization scheme based on blockchain can significantly improve the network performance. © 2023 IEEE.

关键词： Blockchain

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Mid- and High-frequency Resonance Characteristics and Suppression Strategies of VSC-UHVDC for Large-scale Renewable Energy Transmission

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 2058-2070页

作者： Junjie Feng Wang Xiang Jinyu Wen Chuang Fu Qingming Xin Xiaobin Zhao Changyue Zou Biyue Huang Zhiyong Yuan School of Electrical and Electronic Engineering Huazhong University of Science and TechnologyWuhanChina Electric Power Research Institute China Southern Power Grid Co.Ltd.GuangzhouChina

Mid-and high-frequency resonance(MHFR)is highly likely to occur at the sending end of voltage source con-verter-based ultra-high voltage direct current(VSC-UHVDC)for large-scale renewable energy *** is of great im-portance to investigate the resonance characteristics and the corresponding suppression ***,this paper intro-duces the overall'control scheme of VSC-UHVDC for large-scale renewable energy ***,the impedance mod-els of VSC under grid-forming control with AC voltage coordi-nated control are *** mid-and high-frequency im-pedance characteristics of VSC-UHVDC are *** key factors affecting the impedance characteristics have been re-vealed,including the AC voltage control,the voltage feedfor-ward,the inner current loop,the positive-sequence and nega-tive-sequence independent control(PSNSIC),and the control *** MHFR characteristics at the sending-end system are analyzed in the whole operation process,including the black start and the normal power transmission *** integrat-ed control scheme is proposed to address the MHFR ***,extensive case studies are conducted on a planned VSC-UHVDC project to verify the theoretical analysis.

关键词： Large-scale renewable energy voltage source converter-based ultra-high voltage direct current(UHVDC) mid-and high-frequency resonance suppression measure

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Implicit Learning of Rotational Decoupling Detection Heads Applied to Remote Sensing Object Detection

Implicit Learning of Rotational Decoupling Detection Heads A...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Song, Chengkun Ding, Sheng Chen, Yingying School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Kagoshima University Kagoshima Japan

ISBN: (纸本)9781665457033

Remote sensing image features complicated background, big image size, small object pixels, and variable object direction due to the arbitrary shooting angle. Conventional object detector has poor detection effect on remote sensing image, which is prone to missed detection and false detection. In view of this, an improved YOLOv5 object detection algorithm was proposed for remote sensing image. First, the C3 module was improved. The output feature information of each convolutional module was transmitted to the deep network, so as to preserve more fine-grained feature information and enhance the small object detection capability. The improved downsampling mechanism integrates convolution downsampling with maximum pooling downsampling mechanism in downs amp ling to separate foreground and background information while retaining feature information. A lightweight rotatory decoupling detection head with implicit learning was proposed to address the conflict between regression and classification, thus improving the network detection capability for objects in any direction. In the meantime, an implicit learning matrix was added to guarantee the lightweight detection head and improve the network detection accuracy for multiple tasks. According to experimental results, under the same model size, compared with the original YOLOv5 detection method, the detection accuracy and detection speed of the proposed method were improved by 8% and 15 frames, or 14.7%, respectively on DOTA-v1.5 data set, effectively improving the detection accuracy and speed in remote sensing image detection. The detection accuracy was increased by 3.5% on coco data set in natural scenario. The method herein can still effectively improve the detection accuracy in natural scenario, proving effectiveness of the modified YOLOv5 method. © 2023 IEEE.

关键词： Object detection

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A Composite Decomposition Method for Large-Scale Global Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6304-6319页

作者： Tian, Maojiang Chen, Minyang Du, Wei Tang, Yang Jin, Yaochu Yen, Gary G. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education Shanghai200237 China Westlake University School of Engineering Hangzhou310030 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74048 United States

Cooperative coevolution (CC) algorithms, based on the divide-and-conquer strategy, have emerged as the predominant approach to solving large-scale global optimization (LSGO) problems. The efficiency and accuracy of the grouping stage significantly impact the performance of the optimization process. While the general separability grouping (GSG) method has overcome the limitation of previous differential grouping (DG) methods by enabling the decomposition of nonadditively separable functions, it suffers from high computational complexity. To address this challenge, this article proposes a composite separability grouping (CSG) method, seamlessly integrating DG and GSG into a problem decomposition framework to utilize the strengths of both approaches. CSG introduces a step-by-step decomposition framework that accurately decomposes various problem types using fewer computational resources. By sequentially identifying additively, multiplicatively and generally separable variables, CSG progressively groups nonseparable variables by recursively considering the interactions between each nonseparable variable and the formed nonseparable groups. Furthermore, to enhance the efficiency and accuracy of CSG, we introduce two innovative methods: a multiplicatively separable variable detection method and a nonseparable variable grouping method. These two methods are designed to effectively detect multiplicatively separable variables and efficiently group nonseparable variables, respectively. Extensive experimental results demonstrate that CSG achieves more accurate variable grouping with lower computational complexity compared to GSG and state-of-the-art DG-series designs. © 2024 IEEE.

关键词： Computational efficiency

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Achieving Privacy-Preserving Optimizer Architecture for Intent Execution on EVM Blockchain 6

Achieving Privacy-Preserving Optimizer Architecture for Inte...

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6th ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2024

作者： Wang, Weijie Deng, Haotian Liang, Jinwen Zhang, Chuan School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798400706387

Intention adoption represents a migration from an imperative to a declarative paradigm, which is expected to significantly improve the user experience in blockchain. While the development of account abstraction has expanded the range of possibilities for intent expression, the architecture for intent-centric collaboration and coordination remains underdeveloped. Existing works depend on trusted centralized entities and employ broadcasts to the network to disseminate user intent, risking single points of failure and privacy leakage. In addition, no standard intent architecture has been proposed. In this paper, we propose a novel decentralized privacy-preserving optimizer architecture for executing intents on the EVM blockchain. We first propose standard intent structures and zero-knowledge intent structures. Our architecture allows users to transform raw intent structures into zero-knowledge intent structures and send them to a set of trusted optimizers. The optimizers can optimize and execute the intents without revealing the content of the intents. We use zero-knowledge proof and public-key encryption to ensure the privacy and correctness of the intents, and smart contracts to ensure the security and verifiability of the execution. We also design a quantization function to evaluate and compare solutions from different optimizers. We analyze the security and performance of our architecture and show that it can achieve privacy, efficiency, and scalability of intent execution on the EVM blockchain. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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