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arXiv 2023年

作者： Wu, Guowei Pan, Weike Ming, Zhong College of Computer Science and Software Engineering Shenzhen University China

Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items. However, these methods need to collect personal interaction data between a user and the corresponding items and then model them in a central server, which would break the privacy laws such as GDPR. So far, no existing work can construct a global graph without leaking each user's private interaction data (i.e., his or her subgraph). In this paper, we are the first to design a novel lossless federated recommendation framework based on GNN, which achieves full-graph training with complete high-order structure information, enabling the training process to be equivalent to the corresponding un-federated counterpart. In addition, we use LightGCN to instantiate an example of our framework and show its equivalence. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Dynamic Defense Method for Industrial Control Networks based on Shadow Honeynet 9

Dynamic Defense Method for Industrial Control Networks based...

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9th International Conference on computer and Communications, ICCC 2023

作者： Yang, Wei Li, XiaoLong Xu, Chao Yao, Yu Yu, Hai Jiao, XuanQi Shen, YiJia Wang, JiaXuan Liu, GeHao Software College Northeastern University Shenyang China Northeastern University College of Computer Science and Engineering Shenyang China State Grid Liaoning Information and Communication Company Department of Cybersecurity Monitoring Shenyang China

ISBN: (纸本)9798350317251

With the deep integration of Internet technology and industrial development, traditional closed industrial Internet faces an increasing array of dynamic attack vectors. To address these evolving threats, dynamic defense methods have become a crucial component of industrial control network security. This paper proposes a dynamic defense method based on a shadow honeynet, which involves a collaborative construction strategy with the industrial control network to share operational states and network structures. The method aims to collect targeted attack traffic and utilizes an efficient adaptive clustering technique to classify malicious traffic within the shadow honeynet. This enables real-time adjustments of defense strategies to detect new attacks. Furthermore, extensive experiments are conducted on a composite dataset comprising real-world data, SCADA data, and industrial control honeypot data. The results demonstrate significant performance improvements of the proposed method compared to existing approaches. © 2023 IEEE.

关键词： Network security

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Chang-Long Xu, Xin-Xin Chen, Chun-Hua Hong, Jun He, Zhenan Yu, Dengxiu Kwong, Sam Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Computer Science and Technology Ocean University of China Qingdao266100 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9781665410205

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations. © 2024 IEEE.

关键词： Charging stations

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Economic Analysis of Joint Mobile Edge Caching and Peer Content Sharing

Economic Analysis of Joint Mobile Edge Caching and Peer Cont...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Changkun Jiang College of Computer Science and Software Engineering Shenzhen University China

Mobile edge caching (MEC) allows edge devices to cache popular contents and deliver them to end-users directly, which can effectively alleviate increasing backbone loads and improve end-users’ quality of service. Peer content sharing (PCS) enables edge devices to share the cached contents with others, and can further increase the caching efficiency. While many efforts have been made to content caching or sharing technologies, it remains open to study the complicated technical and economic interplay between both technologies. In this paper, we propose a joint MEC-PCS framework, and focus on capturing the strategic interactions between different types of edge devices. Specifically, we model their interactions as a non-cooperative game, where each device (player) can choose to be an agent (who caches and shares contents with others) or a requester (who doesn’t cache but requests contents from others) of each content. We analyze the existence and uniqueness of the game equilibrium systematically under the generic usage-based pricing (for PCS). To address the incomplete information issue, we further design a behavior rule that allows players to achieve the equilibrium via a dynamic learning algorithm. Simulations show that the joint MEC-PCS framework can reduce the total system cost by 60%, compared with the benchmark pure caching system without PCS.

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Enhanced Fire Detection for Industrial and Environmental Safety using IoT-Integrated Vision Sensors and Residual Clutch Attention Network and Depthwise Distinguishable Convolutional Neural Networks 3

Enhanced Fire Detection for Industrial and Environmental Saf...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Christy Sujatha, D. Singh, Priyanshu Vallikannu, C. Varman, Ravi Skanda, M.G. Gavaskar, T. Department of Software Engineering Periyar Maniammai Institute of Science & Technology Tamil Nadu Thanjavur India Department of Computer Science and Engineering R.V college of engineering Karnataka Bangalore India Department of Humanities and Sciences Rajalakshmi Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Karpagam academy of Higher Education Tamil Nadu Coimbatore India Department of Industrial and Production Engineering SJCE JSS Science and Technology University Karnataka Mysore India Department of Mechanical Engineering St. Joseph's College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527549

Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensitivity, and low coverage when compared with our requirement in large industrial scenes and vast natural terrains vulnerable to fire occurrence. These systems are highly unsuitable for dynamic and high-risk situations as they require a higher accuracy and a faster reaction time for the alarms to be efficient as is now the case due to the aforementioned reasons. In an attempt to remedy these limitations, this research introduces a fire detection system using IoT and vision sensors that combines a RCAN with DDCNN. This architecture is tuned using the Crayfish Optimization Algorithm (COA), which seeks to improve model parameter to improve response time and accuracy. By incorporating feedbacks from sensors placed at its different parts and reading from the environment, the RCAN-DDCNN model can quickly identify the fire patterns and respond by activating the sprinkler systems as well as setting off alarms. The proposed system presents a superior option for fire detecting because it is scalable and adaptive and it provides high degree of precision, making it an optimal development in fire detection technology for industry and environment. The introduced approach attains higher accuracy as 99%. © 2025 IEEE.

关键词： Sprinkler systems (fire fighting)

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OptEx: expediting first-order optimization with approximately parallelized iterations 24

OptEx: expediting first-order optimization with approximatel...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yao Shu Jiongfeng Fang Ying Tiffany He Fei Richard Yu Guangdong Lab of AI and Digital Economy (SZ) China College of Computer Science and Software Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China and School of Information Technology Carleton University Canada

ISBN: (纸本)9798331314385

First-order optimization (FOO) algorithms are pivotal in numerous computational domains, such as reinforcement learning and deep learning. However, their application to complex tasks often entails significant optimization inefficiency due to their need of many sequential iterations for convergence. In response, we introduce first-order optimization expedited with approximately parallelized iterations (OptEx), the first general framework that enhances the optimization efficiency of FOO by leveraging parallel computing to directly mitigate its requirement of many sequential iterations for convergence. To achieve this, OptEx utilizes a kernelized gradient estimation that is based on the history of evaluated gradients to predict the gradients required by the next few sequential iterations in FOO, which helps to break the inherent iterative dependency and hence enables the approximate paral-lelization of iterations in FOO. We further establish theoretical guarantees for the estimation error of our kernelized gradient estimation and the iteration complexity of SGD-based OptEx, confirming that the estimation error diminishes to zero as the history of gradients accumulates and that our SGD-based OptEx enjoys an effective acceleration rate of θ(√N) over standard SGD given parallelism of N, in terms of the sequential iterations required for convergence. Finally, we provide extensive empirical studies, including synthetic functions, reinforcement learning tasks, and neural network training on various datasets, to underscore the substantial efficiency improvements achieved by OptEx in practice. Our implementation is available at https://***/youyve/OptEx.

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HSI-DETR: A DETR-based Transfer Learning from RGB to Hyperspectral Images for Object Detection of Live and Dead Cells: To achieve better results, convert models with the fewest changes from RGB to HSI. 22

HSI-DETR: A DETR-based Transfer Learning from RGB to Hypersp...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Songxin Ye Nanying Li Jiaqi Xue Yaqian Long Sen Jia College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450397056

Traditional cell viability judgment methods are invasive and damaging to cells. Moreover, even under a microscope, it is difficult to distinguish live cells from dead cells by the naked eye alone. With the development of optical imaging technology, hyperspectral imaging is more and more widely used in various fields. Hyperspectral imaging is a non-contact optical technique that provides both spectral and spatial information in a single measurement. It becomes a fast, non-invasive option to differentiate between live and dead cells. In recent years, the rapid development of deep learning has provided a better way to distinguish the difference between living and dead cells through a large amount of data. However, it is often necessary to acquire large amounts of labeled data at an expensive cost to train models. This is more difficult to achieve on medical hyperspectral images. Therefore, in this paper, a new model called HSI-DETR is proposed to solve the above problem on the target detection task of live and dead cells, which is based on the detection transformer (DETR) model. The HSI-DETR model suitable for hyperspectral images (HSI) is proposed with minimal modification. Then, some parameters of DETR trained on RGB images are transferred to HSI-DETR trained on hyperspectral images. Compared to the general method, this method can train a better model with a small number of labeled samples. And compared to the DETR-R50, the AP50 of HSI-DETR-R50 has increased by 5.15%.

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Automatic Adjustment of HPA Parameters and Attack Prevention in Kubernetes Using Random Forests 24

Automatic Adjustment of HPA Parameters and Attack Prevention...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Chan, Huah Yong Zhou, Hanlin Ni, Jingfei Wu, Mengchun Deng, Qing School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Xiamen Institute of Software Technology Xiamen China Department of Manzhouli Customs Manzhouli China College of Information Science and Engineering Jimei University Xiamen China

ISBN: (纸本)9798400717161

In this paper, HTTP status codes are used as custom metrics within the HPA as the experimental scenario. By integrating the Random Forest classification algorithm from machine learning, attacks are assessed and predicted, dynamically adjusting the maximum pod parameter in the HPA to manage attack traffic. This approach enables the adjustment of HPA parameters using machine learning scripts in targeted attack scenarios while effectively managing attack traffic. All access from attacking IPs is redirected to honeypot pods, achieving a lower incidence of 5XX status codes through HPA pod adjustments under high load conditions. This method also ensures effective isolation of attack traffic, preventing excessive HPA expansion due to attacks. Additionally, experiments conducted under various conditions demonstrate the importance of setting appropriate thresholds for HPA adjustments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Tour Spot Recommendation System via Content-Based Filtering 16

Tour Spot Recommendation System via Content-Based Filtering

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Muneer, Mishal Rasheed, Uzair Khalid, Sadia Ahmad, Muhammad University of Lahore Department of Computer Science and It Lahore Pakistan College of Electrical and Mechanical Engineering Nust Department of Computer and Software Engineering Islamabad Pakistan University of Engineering and Technology Department of Computer Engineering Lahore Pakistan

ISBN: (纸本)9781665464772

Online recommendation systems have gained prominence. The online recommendation systems provide a better and faster way to choose Tour Spots for traveling and transactions. Recommender systems are powerful emerging technologies that help consumers to find the best places for touring according to their interests. The recommendation system recommends the most appropriate destinations for the end users. Internet travel portals also compete with each other regularly by taking into consideration several characteristics. Recommendation systems are one of the best methods to raise income and attract consumers. Established systems retrieved irrelevant information that resulted in low customer satisfaction and disappointment of customers. In this research, the Tour Spot recommendation system through content-based filtering is proposed and designed. It recommends the best picnic spot according to the budget and interest of the user. The proposed system improves the tourist experience and recommends the best place according to the things they would like;most significantly it is budget friendly as it suggests different places according to the interests and needs of the user. Data collection, including user information, consolidated user engagement reports, and tour spot data, is the primary challenge of implementing a travel recommendation system. User information originates mainly from information entered by the user during the registration process. When the system has the data of user likes and dislikes, then the system compares the user preferences to the dataset that the author makes for the tour recommendation systems in the dataset first identifies all the features that must be considered at the time of selection of travel destination. This Article also considers user likes and dislikes and the previous history of users to recommend a similar tour spot. © 2022 IEEE.

关键词： Recommender systems

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