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How to Securely Outsource the Multiple Kernel Fuzzy Clustering Task in Edge Computing

IEEE Internet of Things Journal 2025年第11期12卷 16451-16466页

作者： Sun, Xinrong Tao, Yunting Kong, Fanyu Yu, Qian Ge, Chunpeng Xu, Qiuliang Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China Singapore Singapore Qingdao University College of Computer Science and Technology Qingdao266071 China

For the huge amount of data from the Internet of Things (IoT) devices, multiple kernel learning is a widely concerned issue in data analyzing, among which the multiple kernel fuzzy clustering (MKFC) algorithm is an effective approach for extracting linear features in high-dimensional space. For a time-consuming multiple kernel clustering task, it's meaningful to find a secure and efficient outsourcing scheme in the edge-end collaborative architecture, which utilizes edge computing resources while resisting untrusted edge servers. However, existing secure outsourcing schemes cannot align well with the distributed and real-Time characteristics of edge computing due to their complex encryption processes. In this paper, we propose a secure multiple kernel fuzzy clustering outsourcing scheme based on a novel matrix blinding method. The proposed novel matrix blinding method conducts two related encryption operations with disturbance terms, which avoids specific disturbance elimination computations, to reduce the computational burdens in the decryption phase. Additionally, we introduce a sampling verification method to detect the server's deceptive behaviors. The theoretical analysis demonstrates that our scheme guarantees data privacy and has the capability to verify incorrect results. The experimental results indicate that our scheme is 6.73% superior to other schemes on average when conducting matrix outsourcing computation and enhances the efficiency of conducting the MKFC algorithm by 10.44% to 55.70% on different datasets. © 2014 IEEE.

关键词： Differential privacy

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Crysformer:An attention-based graph neural network for properties prediction of crystals

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Chinese Physics B 2023年第9期32卷 15-20页

作者：王田陈家辉滕婧史金钢曾新华 Hichem Snoussi Institute of Artificial Intelligence SKLSDEBeihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Academy for Engineering and Technology Fudan UniversityShanghai 200433China Charles Delaunay Institute University of Technology of TroyesTroyes Cedex 10004France

We present a novel approach for the prediction of crystal material properties that is distinct from the computationally complex and expensive density functional theory(DFT)-based ***,we utilize an attention-based graph neural network that yields high-accuracy *** approach employs two attention mechanisms that allow for message passing on the crystal graphs,which in turn enable the model to selectively attend to pertinent atoms and their local environments,thereby improving *** conduct comprehensive experiments to validate our approach,which demonstrates that our method surpasses existing methods in terms of predictive *** results suggest that deep learning,particularly attention-based networks,holds significant promise for predicting crystal material properties,with implications for material discovery and the refined intelligent systems.

关键词： deep learning property prediction crystal attention networks

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Person Re-Identification Algorithm Based on Improved ResNet

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IAENG International Journal of Applied Mathematics 2024年第5期54卷 894-901页

作者： Shen, Wenrui Wang, Zhifeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance in real-world environments such as criminal investigation and statistical analysis of commercial foot traffic and has received extensive attention from the academic community. However, traditional methods such as manual extraction cannot adapt to large-scale data volumes, and deep learning-based methods at this stage suffer from interference in complex environments such as similar costumes, perspective changes, and occlusion. Therefore, in this paper, we investigate the above problems. Firstly, we expand the dataset by introducing random erasure-based preprocessing of pedestrian images to enhancing the robustness and generalization capability of neural networks. Secondly, a composite attention mechanism is introduced after the network residual layer to enhance the spatial information capability and feature expression. Finally, the union loss composed of Circle Loss, Ternary Loss, and Cross Entropy Loss was chosen for network training in the loss optimization phase. Findings from the experiments reveal that the improved method proposed in this experiment achieves 96.0%Rank-1 and 88.3%mAP in Market1501, which reflects the validity of the approach proposed in this manuscript, and provides valuable reference suggestions for Person Re-Identification related research. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Image enhancement

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Small Object Detection in Aerial Drone Imagery based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第9期51卷 1346-1354页

作者： Pan, Junyu Zhang, Yujun School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In recent years, the utilization of unmanned aerial vehicles (UAVs) for aerial target detection has gained significant attention due to their high-altitude perspective and maneuverability, which offer novel opportunities and tremendous potential in this field. However, detecting targets in UAV aerial images remains highly challenging due to the presence of numerous small targets with limited feature information, as well as issues like target occlusion and complex backgrounds that severely impact detection accuracy. To address these challenges, we propose a detection model called BDC-YOLOv8 that aims to enhance accuracy for small targets while minimizing computational complexity. Specifically, we augment the YOLOv8 architecture by incorporating a dedicated detection head tailored for small targets to improve performance when encountering such objects. Additionally, we restructure the neck network of the model to better extract and fuse feature information from targets with significant scale variations. Furthermore, we introduce the concept of DynamicHead to enhance the detection head by incorporating various attention mechanisms suitable for our task ahead of the original detection head, thereby enhancing the model’s capability to detect objects of different scales and complex backgrounds. Moreover, we introduce Convolutional Block Attention Module (CBAM) to identify regions of interest in densely populated areas. Extensive experiments conducted on the VisDrone2019 dataset yield promising results where our model achieves a mean Average Precision (mAP) score of 38% and an AP50 score of 59.6%. Compared to the original YOLOV8 model, improvements are observed with increases in mAP by 2.5% and AP50 by 3.7%, respectively. Notably, our model demonstrates a significant enhancement in detecting small targets with an increase in APs evaluation metric by 4.1%. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Aerial photography

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mRS-DBFT: An improved consensus mechanism suitable for end-edge-cloud hybrid trust models 5

mRS-DBFT: An improved consensus mechanism suitable for end-e...

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5th International Conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Zhai, Xue Pang, Shanchen Qiao, Sibo Yu, Shihang Qingdao Institute of Software College Computer Science and Technology Qingdao China Tiangong University School of Mechanical Engineering Tianjin China

ISBN: (纸本)9798350309034

As a three-terminal collaborative architecture of terminal, edge and cloud, end-edge-cloud is jointly participated by multiple institutions or organizations, and has the characteristics of a hybrid trust architecture of 'partially trustworthy, globally untrustworthy'. Traditional consensus algorithm designs are mostly used in relatively unified trust model environments and do not provide a mechanism for cross-organizational cooperation. Therefore, they cannot effectively solve the trust and consensus issues between organizations. We propose an improved DPoS-PBFT consensus mechanism based on reputation scoring (mRS-DBFT). The core idea of the mRS-DBFT is to add a reputation scoring mechanism based on the DPOS-PBFT consensus algorithm. The reputation score is calculated based on factors such as the node's historical behavior, contribution, and credibility. Nodes with higher reputation score have greater influence and decision-making power in the consensus process. This mechanism can solve the problem of mutual distrust among nodes of different organizations in the end-edge-cloud architecture, and each node participates in the consensus process based on its reputation score, thus forming a credible and consistent system, achieving cross-organizational consensus, and ensuring node trust and data consistency in an open end-edge-cloud environment. The experimental results demonstrate that the mRS-DBFT consensus algorithm helps select nodes with higher stability to participate in the consensus by evaluating the reputation of computing devices, it can reduce fluctuations in delay and improve the stability of the consensus process. © 2023 IEEE.

关键词： consensus mechanism delegated proof of stake end-edge-cloud computing practical byzantine fault tolerance reputation scoring

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Improved Infrared Road Object Detection Algorithm Based on Attention Mechanism in YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第6期51卷 673-680页

作者： Luo, Zilong Tian, Ying School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In response to these challenges, this paper proposes an infrared road object detection algorithm based on an attention mechanism. By incorporating the CPCA module, which utilizes attention mechanisms, into the YOLOv8s model, the algorithm enhances the model’s focus on unobstructed areas and highly illuminated sections, extracting crucial feature information to improve both accuracy and robustness. Additionally, the original model’s downsampling layer is replaced with the Context Grided Network Block Downsampling (CGBD) module, which not only preserves feature edge information but also effectively handles local and contextual features, thereby enhancing the overall feature capturing capabilities of the model. To address the issue of equal aspect ratios in the model’s original loss function, the proposed algorithm adopts the superior Weighted Intersection over Union (WIoU). This not only addresses the shortcomings of the original loss function (CIoU) but also demonstrates increased sensitivity in classification tasks. Experimental results show that the improved algorithm, compared to YOLOv8s, achieves a 1.4% increase in mean average precision (mAP), along with notable improvements in precision and recall. Furthermore, when compared to mainstream model algorithms, the enhanced model significantly outperforms in infrared road object detection tasks, providing validation of its effectiveness. © (2024), (International Association of Engineers). All rights reserved.

关键词： Object detection

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A Multi-Resolution Feature Fusion Method for Pedestrian Re-identification

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第9期51卷 1393-1403页

作者： Qin, Haitian Xu, Yang Chen, Xupeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and locating missing persons. However, variations in lighting, background, resolution, and other imaging conditions captured by different cameras create significant challenges in pedestrian feature extraction, often leading to poor recognition accuracy. To overcome these challenges, this paper presents a Multi-resolution Feature Fusion (MRFF) method for pedestrian re-identification, based on the Pedestrian Re-identification Relational Network (RNFPR). This approach incorporates the Coordinate Attention (CA) module into the DenseNet161 network to enhance feature extraction capabilities. Improving the discriminative and recognition accuracy of features requires learning and fusing pedestrian features from multiple low-resolution images. This process enhances the expressive power of feature maps, ultimately improving pedestrian recognition performance. Additionally, this method introduces a multi-resolution feature fusion module that segments and integrates multi-resolution features from image data. This enables the model to effectively combine feature information from various resolution levels, resulting in a more comprehensive feature representation. Experimental results show that the MRFF method achieves a 1.3% increase in mean Average Precision (mAP) and a 1.0% improvement in Rank-1 accuracy on the Market1501 dataset. For the DukeMTMC-reID dataset, it provides a 0.2% increase in mAP and a 0.7% enhancement in Rank-1 accuracy. Consequently, the MRFF approach results in an overall mAP increase of 0.7% on the DukeMTMC-ReID dataset, significantly improving pedestrian gender re-identification accuracy. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Security systems

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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Research on Social Recommendation Algorithm Based on PSO_KFCM Clustering and CBAM Attention Mechanism of Graph Neural Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第8期51卷 936-948页

作者： Teng, Yue Yang, Kai School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Fuzzy clustering

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Disentangled Hypergraph Collaborative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Xiao Meng, Shunmei Li, Qianmu Xu, Xiaolong Qi, Lianyong Dou, Wanchun Zhang, Jing Zhang, Xuyun Nanjing University of Science and Technology China Nanjing University State Key Lab. for Novel Software Technology China Nanjing University of Information Science and Technology School of Computer and Software China College of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Macquarie University School of Computing Australia

ISBN: (纸本)9798350304855

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores. © 2023 IEEE.

关键词： Collaborative filtering

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