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A Latency-Aware and Fault-Tolerant Framework for Resource Scheduling and Data Management in Fog-Enabled Smart City Transportation Systems

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Computers, Materials & Continua 2025年第1期82卷 1377-1399页

作者： Ibrar Afzal Noor ul Amin Zulfiqar Ahmad Abdulmohsen Algarni department of Computer science and information technology Hazara UniversityMansehra21300Pakistan department of Computer science King Khalid UniversityAbha61421Saudi Arabia

Thedeployment of the Internet of Things(IoT)with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses,smart cities,and smart transportation *** computing tackles a range of challenges,including processing,storage,bandwidth,latency,and reliability,by locally distributing secure information through end *** of endpoints,fog nodes,and back-end cloud infrastructure,it provides advanced capabilities beyond traditional cloud *** smart environments,particularly within smart city transportation systems,the abundance of devices and nodes poses significant challenges related to power consumption and system *** address the challenges of latency,energy consumption,and fault tolerance in these environments,this paper proposes a latency-aware,faulttolerant framework for resource scheduling and data management,referred to as the FORD framework,for smart cities in fog *** framework is designed to meet the demands of time-sensitive applications,such as those in smart transportation *** FORD framework incorporates latency-aware resource scheduling to optimize task execution in smart city environments,leveraging resources from both fog and cloud *** simulation-based executions,tasks are allocated to the nearest available nodes with minimum *** the event of execution failure,a fault-tolerantmechanism is employed to ensure the successful completion of *** successful execution,data is efficiently stored in the cloud data center,ensuring data integrity and reliability within the smart city ecosystem.

关键词： Fog computing smart cities smart transportation data management fault tolerance resource scheduling

GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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science China(information sciences) 2025年第1期68卷 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue QIAO Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology department of Computer science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

关键词： graph representation learning graph neural networks graph contrastive learning graph augmentations graph pooling

A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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Computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni department of Computer science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

Quantum public-key crypto via EPR pairs

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science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 58-63页

作者： Xiang-Qun Fu Hong-Wei Li Jian-Hong Shi Tan Li Wan-Su Bao Henan Key Laboratory of Quantum information and Cryptography Information Engineering University Synergetic Innovation Center of Quantum information and Quantum Physics University of Science and Technology of China

With the development of quantum computing technology, quantum public-key cryptography is gradually becoming an alternative to the existing classical public-key cryptography. This paper designs a quantum trapdoor one-way function via EPR pairs and quantum measurements. Based on this, a new quantum public-key cryptosystem is presented, which offers forward security,and can resist the chosen-plaintext attack and chosen-ciphertext attack. Compared with the existing quantum public-key cryptos,eavesdropping can be automatically detected in this new quantum public-key cryptosystem under a necessary condition, which is also detailed in the paper.

关键词： quantum public-key crypto trapdoor one-way function EPR pairs

Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia department of Computer science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

Local magnetic moment oscillation around an Anderson impurity on graphene

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science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 192-201页

作者： Shuai Li Zhen Ma Jin-Hua Gao School of science Hebei University of Science and Technology School of Electronics and information Zhengzhou University of Light Industry School of Physics and Wuhan National High Magnetic Field Center Huazhong University of Science and Technology

We theoretically investigate the spin resolved Friedel oscillation （FO） and quasiparticle interference （QPI） in graphene induced by an Anderson *** the impurity becomes magnetic,the resulted FO becomes spin dependent,which gives rise to a local magnetic moment oscillation with an envelop decaying as r-2in real space in the doping ***,at half filling,the electron density and local magnetic moment will not oscillate but decay as *** spin resolved FO has both sublattice and spin ***,the local magnetic moment decay at half filling only occurs at one sublattice of graphene,which is quite like the phenomenon observed in the STM experiment (***ález-Herrero et al.,science 352,437 （2016）).We further give an analytic formula about such spin dependent FO based on the stationary phase ***,we study the interference of quasiparticles around the magnetic impurity by calculating the spin dependent Fourier-transformed local density of states （FT-LDOS）.Our work gives a comprehensive understanding about the local magnetic moment oscillation around an Anderson impurity on graphene.

关键词： Friedel oscillation local magnetic moment graphene Anderson impurity

Buried interface bridging for inverted cesium-formamidinium triiodide perovskite solar cells with long operational stability

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science China Chemistry 2025年第1期68卷 163-173页

作者： Chenhui Zhang Chunjun Liang Fulin Sun Ting Zhu Xinghai Huang Yuzhu Guo Xinyu Guo Kunyang Ge Dan Li Fangtian You Zhiqun He Key Laboratory of Luminescence and Optical information Ministry of Education Institute of Optoelectronic TechnologyBeijing Jiaotong University department of Physics School of Physical Science and Engineering Beijing Jiaotong University

In the field of perovskite solar cells(PSCs), the research on defects in the buried interface has been relatively limited due to its non-exposure; however, this interface significantly impacts the performance enhancement of inverted PSCs. This study employs phenylethylammonium chloride(PEACl) molecules as a buffer layer to modify the buried interface of p-i-n structured PSCs,aiming to enhance the uniformity of self-assembled monolayers(SAMs) and facilitate the uniform nucleation and growth of perovskite films on the substrate. Furthermore, the introduction of the PEACl buffer layer effectively passivates defects at the bottom of the perovskite layer and notably enhances the crystal quality of the perovskite film by mitigating residual stress,thereby reducing nonradiative recombination loss. Following these optimizations, the MA-free PSCs treated with PEACl achieve a power conversion efficiency(PCE) of 24.11%, with significant improvements in storage, thermal stability, and operational stability. Particularly noteworthy is the device's performance in an unencapsulated state, whereas after 1,500 hours of continuous light operation stability testing, it retains 97% of its original efficiency. This study not only enriches the systematic understanding of the characteristics of the buried interface in PSCs but also contributes significantly to advancing the commercial production of perovskite photovoltaic technology.

关键词： defect-induced recombination buried interfaces inverted perovskite solar cells crystallization lattice mismatch

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in Engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of Computer science and Engineering College of Engineering GuindyAnna UniversityChennai600025India department of information technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

An Improved Practical Byzantine Fault-Tolerant Algorithm Based on XGBoost Grouping for Consortium Chains

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Computers, Materials & Continua 2025年第1期82卷 1295-1311页

作者： Xiaowei Wang Haiyang Zhang Jiasheng Zhang Yingkai Ge Kexin Cui Zifu Peng Zhengyi Li Lihua Wang College of Electronic and information Engineering Shandong University of Science and TechnologyQingdao266590China

In response to the challenges presented by the unreliable identity of the master node,high communication overhead,and limited network support size within the Practical Byzantine Fault-Tolerant(PBFT)algorithm for consortium chains,we propose an improved PBFT algorithm based on XGBoost grouping called XG-PBFT in this ***-PBFT constructs a dataset by training important parameters that affect node performance,which are used as classification indexes for *** XGBoost algorithm then is employed to train the dataset,and nodes joining the system will be grouped according to the trained grouping *** them,the nodes with higher parameter indexes will be assigned to the consensus group to participate in the consensus,and the rest of the nodes will be assigned to the general group to receive the consensus *** order to reduce the resource waste of the system,XG-PBFT optimizes the consensus protocol for the problem of high complexity of PBFT ***,we evaluate the performance of *** experimental results show that XG-PBFT can significantly improve the performance of throughput,consensus delay and communication complexity compared to the original PBFT algorithm,and the performance enhancement is significant compared to other algorithms in the case of a larger number of *** results demonstrate that the XG-PBFT algorithm is more suitable for large-scale consortium chains.

关键词： Consortium chain PBFT grouping reputation XGBoost