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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Xueting Huang Linshu Chen Xiaoyan Chen Mo Wang Yuxin Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Nowadays, the research on wireless communication is hot, and it has achieved leapfrog development in the past few decades and has made astonishing breakthroughs in emerging applications such as 5G technology, Internet of Things (lOT), and smart cities. Channel estimation and equalization in wireless communication are very important technologies, which are cru-cial for ensuring reliable data transmission and improving system performance. However, with the increasing demand for wireless high-speed data communication, traditional channel estimation and equalization technologies are facing enormous challenges. Nowadays, with the development of artificial intelligence, deep learning has been successfully applied to wireless communication systems. Compared to traditional channel estimation and equalization techniques, deep learning-based channel estimation and equalization techniques have many advantages. For example, traditional channel estimation and equalization methods typically require modeling of channel models and are only applicable to specific channel conditions, making it difficult to achieve optimal performance under different channel conditions. Deep learning technology can improve the performance of channel estimation and equalization by learning a large amount of data and training under different channel conditions, especially in nonlinear and complex channel conditions. Therefore, this article will provide a detailed description of the research on channel estimation and equalization based on deep learning, in order to demonstrate its ultra-high performance and development prospects.

关键词： Wireless communication Deep learning Training Smart cities System performance Channel estimation Data communication

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Deep Learning Object Detection 7th

Deep Learning Object Detection

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Liu, Jingnian Huang, Weihong Xiao, Lijun Huo, Yingzi Xiong, Huixuan Li, Xiong Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Xiangtan411201 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

Object detection techniques are a major part of computer vision research, with large-scale applications in industrial, scientific and other scenarios. Technologies such as face detection, medical image detection, autonomous driving, and traffic detection have played a significant role in people’s lives. With the rapid development of deep learning, many application areas, such as image classification, text classification, machine translation, etc., have achieved breakthrough success in combination with deep learning. R-CNN brings object detection into the era of deep learning, and its advantage compared with traditional methods is that the former requires personnel to extract features manually, while the latter uses deep learning to extract features automatically, which greatly improves efficiency, simplifies operation, and opens a new era of object detection research. First, this paper provides an overview of deep learning-based object detection backbone networks, reviews and analyzes milestone object detection algorithms, compares commonly used datasets, summarizes applications, and finally concludes the paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Security Analysis for a VANET Privacy Protection Scheme

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Journal of Cyber Security 2022年第1期4卷 57-64页

作者： Yuzhen Liu Xiaoliang Wang Zhoulei Cao Frank Jiang School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201China School of Engineering and IT University of New South WalesNSWAustralia

Vehicular ad hoc network(VANET)is a self-organizing wireless sensor network model,which is extensively used in the existing *** to the openness of wireless channel and the sensitivity of traffic information,data transmission process in VANET is vulnerable to leakage and *** of vehicle identitywhile protecting vehicle privacy information is an advantageous way to improve the security of *** propose a scheme based on fair blind signature and secret sharing *** this paper,we prove that the scheme is feasible through security analysis.

关键词： Vehicular ad hoc network anonymous authentication fair blind signature secret sharing algorithm security analysis BAN logic ideology

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A novel Privacy-Preserving Shared Charging Pile for Online Electric Vehicle Matching System

SSRN

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SSRN 2022年

作者： Wang, Xiaoliang Huang, Zhiyu Xiong, Neal Naixue Zeng, Peng School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Northeastern State University United States

With the increase of electric vehicles, the demand of electric vehicles for charging piles is increasing, but the number of charging piles is far less than that of electric vehicles, especially in rural areas and underdeveloped areas. We can effectively solve the problem of electric vehicle power supply demand by sharing private charging piles by using the characteristics of V2V technology. Ordinary online matching charging piles need to upload data to the server. In the process of matching, the data may be attacked, which may expose users' privacy information, and privacy protection is also a concern of most users now, users often worry that the leakage of location information will lead to their privacy invasion. To solve this problem, this paper proposes an anonymous matching scheme for shared charging piles. The system uses homomorphic encryption technology to encrypt the privacy information of users, and completes the matching between electric vehicles and charging piles on the premise of protecting users' privacy. In the proxy server, the data is encrypted, and in the cloud server, the data is the result of interference, so the privacy of users can be effectively protected. We also set up a new set of matching rules. © 2022, The Authors. All rights reserved.

关键词： Piles

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A Survey of Recommender Systems Based on Hypergraph Neural Networks 1

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Liu, Canwei He, Tingqin Zhu, Hangyu Li, Yanlu Xie, Songyou Hosam, Osama School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Guangdong Financial High-tech Zone "Blockchain +" Fintech Research Institute Guangzhou510623 China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Honglong Chen Mindong Liu Jing Huang Zhiling Zheng Weihong Huang Yufeng Xiao School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Information Center Hunan Industry Polytechnic Changsha China

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization.

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Zhi Zhang Shiwen Zhang Shuang Chen Wei Liang Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology and Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients’ potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning.

关键词： Training Privacy Data privacy Accuracy Federated learning Data integrity Predictive models Data models Long short term memory Convergence

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Vision Transformer (ViT)-based Applications in Image Classification

Vision Transformer (ViT)-based Applications in Image Classif...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Yingzi Huo Kai Jin Jiahong Cai Huixuan Xiong Jiacheng Pang School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China

In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introduces the image classification imple- mentation process and the basic architecture of the ViT model, then analyzes and summarizes the image classification methods, including traditional image classification methods, CNN-based image classification methods, and ViT-based image classification methods, and provides a comparative analysis of CNN and ViT. Subsequently, this paper outlines the application prospects of ViT in image classification and its future development and also outlines some shortcomings of ViT and its solutions.

关键词： Representation learning Analytical models Visualization Computational modeling Transformers Reservoirs Data models

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model

Emergency Transportation and Structural Prediction and Optim...

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Applied Intelligence and Sustainable computing (ICAISC), International Conference on

作者： Yiyang Nie Xiang Chen Xuanli Wu Zihao Deng School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China Shcool of Mathematics and Computational Science Hunan University of Science and Technology Xiangtan China

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model; second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed; finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes.

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A Survey on Task Partitioning and Scheduling for Vehicular Edge computing

A Survey on Task Partitioning and Scheduling for Vehicular E...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Jing Huang Wenyu Wu Weihong Huang Yufeng Xiao Li Lisi Jinxi Sun School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China The Bilingual Experimental School Attached to HNSDFZ Changsha China

Vehicle edge computing (VEC) has become an important research field in recent years. In VEC, computation offloading moves computationally intensive tasks from resource-constrained devices to the network edge, it provides service closer to the end-users. By processing tasks with abundant idle resources at the network edge, low-latency demands for some tasks can be met. However, the mobility and uncertainty of vehicles pose significant challenges to vehicle computation offloading. This paper focuses on the decision-making process of vehicle computation offloading, specifically task partitioning and scheduling decisions. This paper summarizes some hot problems and solutions, including latency optimization, reliability optimization, energy efficiency optimization, cost optimization, and mobility support. This study will help researchers discover important features of vehicle computation offloading and find the most suitable scheme to solve the vehicle offloading problem in different scenarios.

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