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A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 813-834页

作者： Chanhyeong Cho Byeori Kim Haehyun Cho Taek-Young Youn department of Cyber Security Dankook UniversityYongin16890Republic of Korea department of software Soongsil UniversitySeoul07027Republic of Korea

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly ***,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient *** paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative *** bundle includes updated information,allowing only the updated portions to be reencrypted when changes *** encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates *** proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext *** utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those *** approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data *** validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size *** show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size ***,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

关键词： Cloud collaboration mode of operation data update efficiency

Personalized federated few-shot node classification

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Science China(Information Sciences) 2025年第1期68卷 204-218页

作者： Yunfeng ZHAO Xintong HE Guoxian YU Jun WANG Yongqing ZHENG Carlotta DOMENICONI School of software Shandong University Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University department of Mathematics National University of Singapore department of Computer Science George Mason University

Personalized federated learning(PFL) aims to train customized models for individual clients in a decentralized setting, with the account of non-independent and identically distributed data across clients. However, most PFL methods adopt uniform classification layers for diverse clients and give rise to error-prone predictions, due to the task heterogeneity notably prominent in decentralized graph data scenarios. Although some PFL solutions setup client-specific classification layers for each client and optimize them only locally, they are corrupted with limited local training data. We propose an innovative solution called federated parameter decoupling and node augmentation(Fed PANo) to address these problems and to achieve personalized federated few-shot node classification, which is a prevalent and challenging but unexplored topic. Specifically, Fed PANo first separates the local model into the GNN and classifier to handle unique client-specific task variations. The GNN is trained through federated learning to capture shared knowledge of graph nodes across clients, while the classifier is custom-designed and trained individually for each client. Additionally, a generic classifier shared among clients is adopted to encourage the GNN's grasp of shared information. Then Fed PANo further proposes the node generator along with its local and collaborative training strategies to deal with the node scarcity of clients. Extensive experimental results on benchmark datasets confirm that Fed PANo outperforms eight competitive baselines across different settings.

关键词： personalized federated learning few-shot learning node classification task heterogeneity parameter decoupling node augmentation

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha department of software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India department of AI and software Engineering Kangwon National UniversitySamcheok25913Republic of Korea department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani Gyanendra Prasad Joshi Bhanu Shrestha department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia department of Artificial Intelligence&software Kangwon National UniversitySamcheok25913Republic of Korea department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

Propagating subduction initiation of the Caroline Plate at the Mussau Trench

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Science China Earth Sciences 2025年第1期68卷 253-269页

作者： Chun-Feng LI Yongkang DENG Tao WU Dongdong DONG Guoliang ZHANG Jiazheng ZHANG Yutao LIU Yaqing LI Fugui TANG Lulu ZHANG department of Marine Sciences Zhejiang University Laboratory of Marine Mineral Resources Laoshan Laboratory Key Lab of Marine Geology and Environment Institute of Oceanology Chinese Academy of Sciences Key Lab of Ocean and Marginal Sea Geology South China Sea Institute of Oceanology Chinese Academy of Sciences Science & Technology on Integrated Information System Lab Institute of Software Chinese Academy of Sciences

New seismic imaging from the Mussau Trench confirms that, under horizontal compression on an oceanic plate boundary, it is within the young and weak oceanic plate that a trench will develop, and it is the young plate, not so long after its birth, that starts to subduct toward the old plate. The Mesozoic Pacific Plate cannot easily bend, under the joint horizontal compression and vertical gravity pull, even with a pre-existing weak zone in place and with a large age offset across the plate boundary. The bending of the Caroline Plate near the northern end of the Mussau Trench started only after a long-time span of trench migration, and/or after a prolonged transition from initial compression to trench development. Highly contrasting trench deformation and sedimentary accretion between two parallel seismic sections support an evident migration of underthrusting or significant longitudinal heterogeneity along the trench. The swift transition from underthrusting to non-thrusting can be explained by a counter-clockwise rotation of the Caroline Plate. That miniature accretionary wedges develop even before the subduction initiates imply strong compression and uplifting before the underthrusting can kick-start. Rotation of the Caroline Plate have been halted as the Mussau Trench is seismically rather quiet and the Lyra Trough to the east shows relaxational extension.

关键词： Mussau Trench Subduction initiation Seismic reflection Caroline Plate

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Validation of Pervasive Cloud Task Migration with Colored Petri Net

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Tsinghua Science and Technology 2016年第1期21卷 89-101页

作者： Lianzhang Zhu Shouchao Tan Weishan Zhang Yong Wang Xiwei Xu department of software Engineering China University of Petroleum software Systems Research Group NICTA

Mobile devices are resource-limited, and task migration has become an important and attractive feature of mobile clouds. To validate task migration, we propose a novel approach to the simulation of task migration in a pervasive cloud environment. Our approach is based on Colored Petri Net（CPN）. In this research, we expanded the semantics of a CPN and created two task migration models with different task migration policies： one that took account of context information and one that did not. We evaluated the two models using CPN-based simulation and analyzed their task migration accessibility, integrity during the migration process, reliability, and the stability of the pervasive cloud system after task migration. The energy consumption and costs of the two models were also investigated. Our results suggest that CPN with context sensing task migration can minimize energy consumption while preserving good overall performance.

关键词： colored Petri net task migration pervasive cloud context information validation

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GPIO-Based Continuous Sliding Mode Control for Networked Control Systems Under Communication Delays With Experiments on Servo Motors

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 99-113页

作者： Kamal Rsetam Zhenwei Cao Zhihong Man Xian-Ming Zhang the School of software and Electrical Engineering Swinburne University of Technology the department of Automated Manufacturing Al Khwarizmi College of Engineering University of Baghdad IEEE

To handle input and output time delays that commonly exist in many networked control systems(NCSs), a new robust continuous sliding mode control(CSMC) scheme is proposed for the output tracking in uncertain single input-single-output(SISO) networked control systems. This scheme consists of three consecutive steps. First, although the network-induced delay in those systems can be effectively handled by using Pade approximation(PA), the unmatched disturbance cames out as another difficulty in the control design. Second, to actively estimate this unmatched disturbance, a generalized proportional integral observer(GPIO) technique is utilized based on only one measured state. Third, by constructing a new sliding manifold with the aid of the estimated unmatched disturbance and states, a GPIO-based CSMC is synthesized, which is employed to cope with not only matched and unmatched disturbances, but also networkinduced delays. The stability of the entire closed-loop system under the proposed GPIO-based CSMC is detailedly *** promising tracking efficiency and feasibility of the proposed control methodology are verified through simulations and experiments on Quanser's servo module for motion control under various test conditions.

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Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

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Journal of Computer Science & Technology 2014年第6期29卷 1048-1057页

作者： Muhammad Hussain department of software Engineering King Saud University

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.

关键词： mammogram enhancement lifting dyadic wavelet transform Tsallis entropy

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