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Biosensors 2025年第4期15卷 255-255页

作者： Chellamani, Narmatha Albelwi, Saleh Ali Shanmuganathan, Manimurugan Amirthalingam, Palanisamy Paul, Anand Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Pharmacy Practice Faculty of Pharmacy University of Tabuk Tabuk71491 Saudi Arabia Biostatistics and Data Science Department The School of Public Health LSU Health Sciences Center New Orleans New OrleansLA70112 United States

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to PPG signals can predict BGLs with high accuracy. However, training DNN models requires large and diverse datasets, which are typically distributed across multiple healthcare institutions. Privacy concerns and regulatory restrictions further limit data sharing, making conventional centralized machine learning (ML) approaches less effective. To address these challenges, this study proposes a federated learning (FL)-based solution that enables multiple healthcare organizations to collaboratively train a global model without sharing raw patient data, thereby enhancing model performance while ensuring data privacy and security. In the data preprocessing stage, continuous wavelet transform (CWT) is applied to smooth PPG signals and remove baseline drift. Adaptive cycle-based segmentation (ACBS) is then used for signal segmentation, followed by particle swarm optimization (PSO) for feature selection, optimizing classification accuracy. The proposed system was evaluated on diverse datasets, including VitalDB and MUST, under various conditions with data collected during surgery and anesthesia. The model achieved a root mean square error (RMSE) of 19.1 mg/dL, demonstrating superior predictive accuracy. Clarke error grid analysis (CEGA

关键词： Wavelet transforms

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Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System

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Journal of Field Robotics 2025年第0期

作者： Li, Zhibin Deng, Xun Chen, Tinghui Yang, Yuhang Chen, Linlin Yang, Xiwen Hu, Zhenzhen Hu, Lun Hu, Pengwei Li, Shuai Luo, Xin Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China School of Software Engineering Chengdu University of Information Technology Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China School of Software Engineering Xinjiang University Urumqi China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland VTT-Technical Research Centre of Finland Oulu Finland College of Computer and Information Science Southwest University Chongqing China

Robots are frequently utilized in manufacturing, aviation, and other industries, which enhance industrial production efficiency and quality. Specifically, robots perform high-precision tasks like welding, assembly and material handling, which reduce the intensity of manual labor in factories. In the logistics field, robots automatically sort and deliver goods, thereby speeding up supply chain operations. However, the prolonged operation of robots suffers from a decline in positioning accuracy, which makes them unable to satisfy task requirements. To address this challenging issue, this study designs an efficient calibration system integrating the Levenberg–Marquardt algorithm with fuzzy proportion integration differentiation controller and radial basis function neural network. The innovations of this method include: (1) integrating the fuzzy proportion integration differentiation controller into the updating rules of Levenberg–Marquardt algorithm, which further enhances the identification performance of kinematic errors;(2) adopting the radial basis function neural network to handle the robot dynamic errors, which addresses the complexity of dynamic error sources. Extensively experimental robot positioning points are gathered on an HSR JR680 robot, and then experimental validations are conducted by using the designed calibration system. The experiments indicate that the developed algorithm outperforms these existing advanced algorithms. © 2025 Wiley Periodicals LLC.

关键词： Radial basis function networks

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A Hybrid Deep Learning-based Person Re-identification Across Multi-Cameras

A Hybrid Deep Learning-based Person Re-identification Across...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Manaschai Aonon Phasit Charoenkwan Phudinun Singkhamfu Porntida Kaewkamol Data Science Consortium Faculty of Engineering Chiang Mai University Chiang Mai Thailand Modern Management and Information Technology College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand Software Engineering College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Surveillance systems play an essential role in enhancing security and ensuring safety within society. Public spaces and critical areas are commonly monitored through multi-camera networks for tracking individuals in real-time to enable quick responses to potential security threats. However, variations in lighting, camera angles, and occlusions pose significant challenges to the process of recognizing individuals across multiple camera views. This research proposes a Person Re-Identification (Re-ID) with deep learning and feature extraction method to improve the accuracy of individual matching and identification despite these challenges. This research encompasses multiple neural network topologies, such as ResNet50, ResNet101, and OSNet architectures with traditional computer vision methodologies such as Scale-Invariant Feature Transform (SIFT) to examine the body part weighting coefficients and feature selection mechanisms. The results demonstrated that integrating dimensionality reduction via principal component analysis (PCA) coupled with semantic segmentation techniques yields statistically significant improvements in re-identification accuracy. The ResNet101 architecture emerges as the superior methodology, achieving performance with a Rank-1 accuracy of 0.88 and mean Average Precision (mAP) of 0.94 on the test dataset.

关键词： Deep learning Accuracy Surveillance Computer architecture Transforms Feature extraction Cameras Security Identification of persons Principal component analysis

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Hyperbolic Variational Graph Auto-Encoder for Next POI Recommendation 25

Hyperbolic Variational Graph Auto-Encoder for Next POI Recom...

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Proceedings of the ACM on Web Conference 2025

作者： Yuwen Liu Lianyong Qi Xingyuan Mao Weiming Liu Fan Wang Xiaolong Xu Xuyun Zhang Wanchun Dou Xiaokang Zhou Amin Beheshti Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software College of Computer Science and Technology China University of Petroleum(East China) Qingdao China College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Macquarie University Sydney Australia Department of Computer Science and Technology Nanjing University Nanjing China Faculty of Business and Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400712746

Next Point-of-Interest (POI) recommendation has become a crucial task in Location-Based Social Networks (LBSNs), which provide personalized recommendations by predicting the user's next check-in locations. Commonly used models including Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) have been widely explored. However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. Specifically, we utilize a Hyperbolic Graph Convolutional Network (Hyperbolic GCN) to model hierarchical structures and tree-like relationships by converting node embeddings from euclidean space to hyperbolic space. Then we use Variational Graph Auto-Encoder (VGAE) to convert node embeddings to probabilistic distributions, enhancing the capture of deeper latent features and providing a more robust model structure. Furthermore, we combine the Mamba4Rec recommender and Rotary Position Embedding (RoPE) and propose Rotary Position Mamba (RPMamba) to effectively utilize POI embeddings rich in sequential information, which improves the accuracy of the next POI recommendation. Extensive experiments on three public datasets demonstrate the superior performance of the HVGAE model.

关键词： graph convolutional network

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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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Fusion+: A Multi-Sensor Image Fusion for Very High-Resolution Satellite Imagery

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IEEE Access 2025年 13卷 62856-62869页

作者： Hestrio, Yohanes Fridolin Surya Candra, Danang Prima Brahmantara, Randy Prabowo, Yudhi Dimara Sakti, Anjar Luthfi Ramadhan, Mgs M. Loong Yap, Yong Azizah, Kurniawati Hafizhuddin Hilman, Muhammad Phinn, Stuart Liang Lim, Sin Jatmiko, Wisnu University of Indonesia Faculty of Computer Science Depok16424 Indonesia Research Center for Geoinformatics Bogor16912 Indonesia Center for Data and Information Bogor16912 Indonesia Research Center for Hydrodynamics Technology Bogor16912 Indonesia Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and Technology Bandung40132 Indonesia Multimedia University Faculty of Engineering Cyberjaya75450 Malaysia The University of Queensland Remote Sensing Research Center School of Earth and Environmental Sciences BrisbaneQLD4072 Australia

A very detailed image, such as very high-resolution satellite imagery, is necessary for many applications. By enhancing the image’s detail and reducing its color distortion, we can optimize its use. Image fusion is a method to achieve this goal. This paper proposes a novel method of multi-sensor image fusion called Fusion+. This method enhances spatial resolution, sharpens the image using the kernel of image enhancement, and preserves radiometric consistency using the histogram specification. In addition, the Fusion+ has a shift adjustment between the input images to address the geometric issue, especially for images from multiple sensors. The proposed method can be used for both single-sensor and multi-sensor image fusion. The fused image obtained from the proposed method also increased the image’s detail and was sharper than the other methods. The universal image quality index, peak signal-to-noise ratio, and structural similarity index measure are also sufficient to maintain the radiometric consistency of the original image. The results also showed that the fused image had maximum detail increase, minimum color distortion, and a natural color appearance. Fusion+ obtained an average SSIM score of 0.779, an average PSNR of 29.342, and an average UQI of 0.973 on single-sensor fused images, as well as SSIM averages of 0.556, an average PSNR of 27.896, and an average UQI of 0.947 on multi-sensor fused images. The Fusion+ method demonstrates the potential for improved image fusion quality, which can contribute to improved accuracy and reliability in the analysis of very high-resolution remote sensing data, especially in multi-sensor applications. © 2013 IEEE.

关键词： Satellite imagery

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Implementation of the OQDS Method for Principal Component Analysis 30th

Implementation of the OQDS Method for Principal Component...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in Computer science, Computer Engineering and Applied Computing, CSCE 2024

作者： Chiyonobu, Miho Takata, Masami Kimura, Kinji Nakamura, Yoshimasa Graduate School of Humanities and Sciences Nara Women’s University Nara Japan Research Group of Information and Communication Technology for Life Nara Women’s University Nara Japan Faculty of Engineering University of Fukui Fukui Japan Faculty of Data Science Osaka Seikei University Osaka Japan

ISBN: (纸本)9783031856372

In this paper, we propose a method for computing partial singular values and the corresponding singular vectors. PCA (Principal Component Analysis) requires only larger singular values and the corresponding singular vectors. Generally, it is obtained by combining the bisection method and the inverse iteration method. However, there are some input matrices, such as the glued Kimura matrix, for which the inverse iteration method fails. Therefore, the OQDS (Orthogonal QD with Shift) method is adopted in this paper. The OQDS method can compute smaller singular values and the corresponding right singular vectors from a bi–diagonal matrix with high accuracy, in the case that the matrix is not split during the decomposition. However, usually, split occurs. Under split, it is not clear which side of the split the smaller singular values fall on. Therefore, to adopt the OQDS method to the PCA, it is necessary to consider how to deal with split. Thus, in this paper, we propose a new implementation of the OQDS method that is not effected by split. Experiments have confirmed that the method is fast while maintaining reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Orthogonal QD with Shift method Principal Component Analysis Singular Value Decomposition

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Fast Computation Method for Stopping Condition of Range Restricted GMRES Method 30th

Fast Computation Method for Stopping Condition of Range Re...

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ISBN: (纸本)9783031856372

In this paper, we propose a method for fast computation of the stopping condition of the Range Restricted General Minimum Residual (RRGMRES) method. The RRGMRES method is iterative. As the number of iterations increases, the matrix size increases. The stopping condition for the iterations requires a condition number. The condition number is expressed as the ratio of the largest singular value to the smallest singular value. The proposed method employs the Cholesky LR method and inverse iteration. By comparing the experimental results with the conventional method, the proposed method has been 10 times faster than the conventional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Choquet integral

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Analyzing Students’ Emerging Roles Based on Quantity and Heterogeneity of Individual Contributions in Small Group Online Collaborative Learning Using Bipartite Network Analysis

arXiv

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arXiv 2025年

作者： Feng, Shihui Gibson, David Gašević, Dragan Faculty of Education University of Hong Kong Pok Fu Lam Road Hong Kong Institute of Data Science University of Hong Kong Pok Fu Lam Road Hong Kong Curtin University Perth Australia UNESCO France Faculty of Information Technology Monash University ClaytonVIC Australia

Understanding students’ emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning analytic methods based on student–subtask bipartite networks to gauge two conceptual dimensions—quantity and heterogeneity of individual contribution to subtasks—for understanding students’ emerging roles in online collaborative learning in small groups. We analyzed these two dimensions and explored the changes of individual emerging roles within seven groups of high school students (N = 21) in two consecutive collaborative learning projects. We found a significant association in the changes between assigned leadership roles and changes in the identified emerging roles between the two projects, echoing the importance of externally facilitated regulation scaffolding in CSCL. We also collected qualitative data through a semi-structured interview to further validate the quantitative analysis results, which revealed that student perceptions of their emerging roles were consistent with the quantitative analysis results. This study contributes new learning analytic methods for collaboration analytics as well as a two-dimensional theoretical framework for understanding students’ emerging roles in small group CSCL. © 2025, CC BY.

关键词： Collaborative learning

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