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Container cluster placement in edge computing based on reinforcement learning incorporating graph convolutional networks scheme

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Digital Communications and Networks 2025年第1期11卷 60-70页

作者： Zhuo Chen Bowen Zhu Chuan Zhou College of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China

Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.

关键词： Edge computing Network virtualization Container cluster Deep reinforcement learning Graph convolutional network

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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TRE-DSP: A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and Telecommunications College of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Department of Computer Science and Engineering University College of Engineering Department of Computer Engineering National Institute of Technology Kurukshetra

With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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Temporal fusion transformer-based strategy for efficient multi-cloud content replication

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PeerJ computer science 2025年 11卷 1-22页

作者： Naganandhini, S. Shanthi, D. Computer Science and Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul India

In cloud computing, ensuring the high availability and reliability of data is dominant for efficient content delivery. Content replication across multiple clouds has emerged as a solution to achieve the above. However, managing optimal replication while considering dynamic changes in data popularity and cloud resource availability remains a formidable challenge. In order to address these challenges, this article employs TFT-based Dynamic Data Replication Strategy (TD2RS), leveraging the Temporal Fusion Transformer (TFT), a deep learning temporal forecasting model. This proposed system collects historical data on content popularity and resource availability from multiple cloud sources, which are then used as input to TFT. Then TFT is used to capture temporal patterns and forecasts future data demands. An intelligent replication is performed to optimize content replication across multiple cloud environments based on these forecasts. The framework’s performance was validated through extensive experiments using synthetic time-series data simulating with varied cloud resource characteristics. Some of the findings include that the proposed TFT approach improves the availability of data by 20% when compared to traditional replication techniques and also cuts down the latency level by 15%. These outcomes indicate that the TFT-based replication strategy targets to improve content delivery efficiency in the dynamic cloud computing environment, thus providing effective solution to dynamically address the availability, reliability, and performance challenges. © Copyright 2025 S. and D.

关键词： Spatio-temporal data

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Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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A verifiable variable threshold visual image secret sharing scheme

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Multimedia Systems 2025年第3期31卷 1-18页

作者： Yang, Yu-Guang Cheng, Wen Xu, Guang-Bao Jiang, Dong-Huan Zhou, Yi-Hua Shi, Wei-Min Jiang, Dong-Hua College of Computer Science and Technology Beijing University of Technology Beijing100124 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao266590 China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou511400 China

This paper proposes a visual image encryption algorithm based on 2D compressive sensing and variable threshold secret sharing, aiming to achieve efficient, secure, and flexible image encryption and decryption. Firstly, the plain image is compressed and encrypted using the 2D compressive sensing technique, while the measurement matrix and disordered sequence are generated through an improved chaotic system. Secondly, the shadow image is produced using the variable threshold secret sharing scheme, and a corresponding authentication signature is generated to verify the integrity and authenticity of the shadow image. Finally, the shadow image and its signature are embedded into a carrier image to generate a cipher image with visual steganography. Experimental results demonstrate that the proposed algorithm achieves excellent performance in terms of operational efficiency, visual encryption quality, and decryption performance. The encryption execution time is 0.694 s, the PSNR of decrypted images is approximately 32 dB, and the PSNR of cipher images is around 42 dB, ensuring high visual concealment and reconstruction quality while maintaining security. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image compression

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Coarse-to-fine lightweight meta-embedding for ID-based recommendations

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science China(Information sciences) 2025年第4期68卷 82-97页

作者： Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG School of Computer Science and Information Engineering Hefei University of Technology College of Information and Intelligence Hunan Agricultural University

State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.

关键词： lightweight meta-embedding coarse-to-fine learning ID-based recommendations

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