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IEEE Transactions on Artificial Intelligence 2025年

作者： Dey, Tanushree Bera, Somnath Mukherjee, Anwesha De, Debashis Buyya, Rajkumar Maulana Abul Kalam Azad University of Technology Department of Computer Science and Engineering West Bengal India Mahishadal Raj College Department of Computer Science West Bengal Mahishadal721628 India The University of Melbourne Research Visitor of the CLOUDS Lab School of Computing and Information Systems Australia Lab School of Computing and Information Systems ParkvilleVIC3052 Australia

Crop yield prediction is a significant area of precision agriculture. In this paper, we propose a crop yield prediction framework named FLyer, based on federated learning and edge computing. In FLyer, the soil and environmental data are locally processed inside the edge servers, and the model parameters are transmitted between the edge servers and the cloud with encrypted gradients. LSTM is used as the local and global models for data analysis. As the LSTM model can capture the temporal dependencies and hold the sequential nature of the data, we use LSTM in FLyer. By encrypting the gradients, the gradient information leakage ratio is reduced, and data privacy is protected. For gradient encryption, we use AES-256, and for data encryption during local storage we use RSA and AES-256. The results demonstrate that FLyer diminishes the latency by 39% and energy consumption by 40% than the conventional edge-cloud framework respectively. The experimental results show that the global model in FLyer achieves above 99% accuracy, precision, recall, and F1-score in crop yield prediction. The results also present that the local models also achieve >94% accuracy in crop yield prediction. © 2020 IEEE.

关键词： Federated learning

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Statistical Testing for Long-Range Dependence in the Workload of a Single-Server Queue 23rd

Statistical Testing for Long-Range Dependence in the Work...

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23rd International Conference on information Technologies and Mathematical Modelling, ITMM 2024

作者： Igolkin, V. Rumyantsev, A. Stochastic Modeling of Information-computing and Telecom. Systems (SMITS) Lab Institute of Applied Mathematical Research Karelian Research Center Russian Academy of Sciences Petrozavodsk Russian Federation Petrozavodsk State University Karelia Petrozavodsk Russian Federation

ISBN: (纸本)9783031883064

In this paper we empirically evaluate the workload process of a single-server queue with service times having heavy-tailed distribution, in an effort to investigate the applicability of statistical detection methods for long memory (long range dependence). While it is known that theoretically (under specific moment conditions) such a workload process has long memory, it is not easy to evidence this property in simulation or within real data. Using numerical experiments, we compare two known statistical testing methods, and demonstrate moderate optimism regarding their applicability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： hypothesis testing Long memory long range dependence M/G/1 queue Pareto distribution

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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Uncertainty-aware Superpoint Graph Transformer for Weakly Supervised 3D Semantic Segmentation

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IEEE Transactions on Fuzzy systems 2025年

作者： Fan, Yan Wang, Yu Zhu, Pengfei Hui, Le Xie, Jin Hu, Qinghua Tianjin University College of Intelligence and Computing Tianjin300350 China Ministry of Education of the People's Republic of China Engineering Research Center of City Intelligence and Digital Governance China Haihe Laboratory of Itai Tianjin China Northwestern Polytechnical University School of Electronics and Information Shaanxi Key Laboratory of Information Acquisition and Processing China Nanjing University of Science and Technology Pca Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

Weakly supervised 3D semantic segmentation has successfully mitigated the labor-intensive and time-consuming task of annotating 3D point clouds. However, reliably utilizing the minimal point-wise annotations for unlabeled data in complex and large-scale scenes is still challenging, such as only 20 points labeled in 2 million points. To tackle this challenge, we propose a new Uncertainty-aware Superpoint Graph Transformer (UaSGT) framework that utilizes minimal annotations for unlabeled data learning through reliable long-range supervision propagation from labeled superpoints to unlabeled superpoints. First, we propose a superpoint graph transformer to achieve long-range supervision propagation along the attention-based fuzzy subsets defined on superpoints. The attention-based fuzzy subset measures the membership of unlabeled superpoints to clusters centered on labeled superpoints. Second, we employ an uncertainty-aware membership rectification technique on the fuzzy subset to ensure reliable propagation among superpoints within the same category. This technique integrates an uncertainty prediction module to mask the influence of unreliable membership and a spatial prior refinement module to reduce uncertainty in intra-class membership degrees. Finally, experimental results on two large-scale benchmarks S3DIS and ScanNet-V2 demonstrate the superiority of our approach compared to the state-of-the-art with at least 90% annotation reduction, and our method also achieves comparable performance to fully supervised methods with less than 0.1% labeled points. Our code is available at https://***/fanyan0411/UaSGT. © 2025 IEEE.

关键词： Semantic Segmentation

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Constraining multimodal distribution for domain adaptation in stereo matching

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Pattern Recognition 2025年 167卷

作者： Shen, Zhelun Li, Zhuo Wu, Chenming Rao, Zhibo Liu, Lina Dai, Yuchao Zhang, Liangjun Robotics and Autonomous Driving Lab Baidu Research Sunnyvale United States Institute of Computing Technology Chinese Academy of Science China School of Information Engineering Nanchang Hangkong University China Institute of Cyber-Systems and Control Zhejiang University China School of Electronics and Information Northwestern Polytechnical University China

Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play core contributions to its success. However, in unsupervised domain adaptation scenarios, we observe that these two operations often yield multimodal disparity probability distributions in target domains, resulting in degraded generalization. In this paper, we propose a novel approach, Constrain Multi-modal Distribution (CMD), to address this issue. Specifically, we introduce uncertainty-regularized minimization and anisotropic soft argmin to encourage the network to produce predominantly unimodal disparity distributions in the target domain, thereby improving prediction accuracy. Experimentally, we apply the proposed method to multiple representative stereo-matching networks and conduct domain adaptation from synthetic data to unlabeled real-world scenes. Results consistently demonstrate improved generalization in both top-performing and domain-adaptable stereo-matching models. The code for CMD will be available at: https://***/gallenszl/CMD. © 2025

关键词： Gaussian distribution

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Subgrid informed neural networks for high-resolution flood mapping

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Journal of Hydrology 2025年 660卷

作者： Herath, Herath Mudiyanselage Viraj Vidura Marshall, Lucy Saha, Abhishek Rasnayaka, Sanka Seneviratne, Sachith Faculty of Science and Engineering Macquarie University SydneyNSW Australia Faculty of Engineering The University of Sydney SydneyNSW Australia Delft Institute of Applied Mathematics Delft University of Technology Delft Netherlands Hydroinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore Transport Health and Urban Systems Research Lab Melbourne School of Design The University of Melbourne MelbourneVIC Australia Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC Australia

Physics-based hydrodynamic models are essential for accurate flood prediction but are computationally expensive, limiting their applicability for real-time forecasting and probabilistic analyses. Conversely, pure machine learning (ML) models offer both computational efficiency and accuracy but often lack interpretability. To address this gap, we propose SGUnet, a physics-informed ML model and a hybrid theory-guided data science approach, for rapid, high-resolution flood mapping. It utilizes a neural network with U-Net architecture and integrates subgrid-based coarse-grid hydrodynamic model predictions as initial estimates, upskilling them to achieve fine-grid model accuracy. Unlike traditional hydrodynamic models, the subgrid method embeds fine-scale topographic details within coarse-grid cells, enhancing both computational efficiency and predictive accuracy. SGUnet processes flood depth raster patches (512 × 512 pixels) and corresponding digital elevation models as inputs. It functions as a deep learning-based corrector, refining flood predictions from numerical simulators. Trained through supervised learning, SGUnet learns to correct deviations in coarse-grid predictions using fine-grid model outputs as target values. The model is evaluated across three large Australian watersheds—Wollombi, Chowilla, and Burnett River—using HEC-RAS flood simulations with subgrid formulation. SGUnet reduces root mean squared error by a factor of 4.5–5.3 compared to coarse-grid models, achieves a critical success index exceeding 0.9 for flood extent mapping, and delivers a 50x speed-up over fine-grid hydrodynamic models. Furthermore, SGUnet outperforms a state-of-the-art ML-based upskilling model in depth and extent predictions. By effectively correcting flood artifacts from coarse-grid models, SGUnet achieves near fine-grid accuracy with significantly reduced computational cost, demonstrating its potential for real-time flood risk assessment. © 2025 The Authors

关键词： Deep learning

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Nanoscaffold Ba_0.6Sr_0.4TiO₃:Nd₂O₃ ferroelectric memristors crossbar array for neuromorphic computing and secure encryption

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Journal of Materiomics 2025年第5期11卷

作者： Zhang, Weifeng Xu, Jikang Wang, Yongrui Zhang, Yinxing Wang, Yu Li, Pengfei Jia, Yongqing Zhao, Zhen Li, Changliang Yang, Biao Hou, Yue Guo, Zhenqiang Huang, Zeze Qi, Yincheng Yan, Xiaobing Institute of Life Science and Green Development Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province College of Electronic and Information Engineering Hebei Baoding 071002 China Hebei Key Lab of Optic-Electronic Information and Materials College of Physics Science & Technology Hebei Baoding 071002 China Department of Electronic and Communication Engineering Intelligent Vision Computing Research Institute North China Electric Power University Hebei Baoding 071003 China

Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endurance, and dispersed switching voltages. Here, we introduce a silicon-based integrated (Ba_0.6Sr_0.4TiO₃)_0.5(Nd₂O₃)_0.5 (BSTN) nanoscaffolded ferroelectric thin film memristor with a vertically self-assembled nanocomposite structure (VSNs) optimally oriented on La_0.67Sr_0.33MnO₃/SrTiO₃/P[sbnd]Si substrates. This device demonstrates a widely tunable ferroelectric domain range (0°–180°), high remnant polarization (21.04 μC/cm²), and a greater number of unitary states (16 states or 4 bits). It exhibits high durability, enduring over 10⁹ switching cycles. The switching mechanism combines ferroelectric polarization and oxygen vacancy migration, enabling the simulation of biological synaptic functions via bi-directional conductance tunability. Additionally, we implemented a low-power (0.57 pJ per event) multi-factor secure encryption system for smart locks using 16×16 BSTN memristor crossbar arrays and a pressure sensor. Under multiple factors (disordered inputs, specific users, and corresponding passwords) the system recognized passwords with 97.6% accuracy and a 3.8% loss rate after 500 iterations. Overall, this work establishes a robust foundation for advancing multilevel storage, neuromorphic computing, and AI chip applications based on ferroelectric memristors. © 2025 The Authors

关键词： Ferroelectric memristor Interface channel Multi-factor secure encryption system Nanoscaffolded

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Investigating the impact of corruption and culture on electronic government maturity 72

Investigating the impact of corruption and culture on electr...

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72nd Annual Meeting of the Academy of Management, AOM 2012

作者： Krishnan, Satish 01-02 Information Systems Research Lab 1 Department of Information Systems School of Computing National University of Singapore Computing 2 15 Computing Drive Singapore117418 Singapore

Utilizing the concept of "fit as moderation" from strategy literature, this study investigates (1) the impacts of systematic (bad) and idiosyncratic (good) corruption on electronic government (e-government) maturity;and (2) the moderating effects of national culture on the relationships of systematic and idiosyncratic corruption with e-government maturity. The findings and the implications of our study are discussed.

关键词： Government data processing

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Tight chosen ciphertext attack(CCA)-secure hybrid encryption scheme with full public verifiability

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Science China(information Sciences) 2014年第11期57卷 162-175页

作者： KANG Li TANG XiaoHu LIU JiaFen School of Economic Information Engineering Southwestern University of Finance and Economics Research Center for Payment Systems of China Southwestern University of Finance and Economics Information Security and National Computing Grid Lab Southwest Jiaotong University

In this paper, we propose a new "full public verifiability" concept for hybrid public-key encryption schemes. We also present a new hybrid public-key encryption scheme that has this feature, which is based on the decisional bilinear Diffie–Hellman assumption. We have proven that the new hybrid public-key encryption scheme is secure against adaptive chosen ciphertext attack in the standard model. The "full public verifiability"feature means that the new scheme has a shorter ciphertext and reduces the security requirements of the symmetric encryption scheme. Therefore, our new scheme does not need any message authentication code, even when the one-time symmetric encryption scheme is passive attacks secure. Compared with all existing publickey encryption schemes that are secure to the adaptive chosen ciphertext attack, our new scheme has a shorter ciphertext, efficient tight security reduction, and fewer requirements(if the symmetric encryption scheme can resist passive attacks).

关键词： public-key encryption(PKE) hybrid PKE public verifiability passive attack(PA) security chosen ciphertext attack(CCA) security decisional bilinear Diffie–Hellman

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