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A Real-Time Lightweight Perceptron for Cloud–Edge Collaborative Predictive Maintenance of Online Service Systems

IEEE Internet of Things Journal 2025年第9期12卷 12640-12657页

作者： Zhang, Linzi Shi, Yong Wang, Donghan Communication University of China School of Economics and Management Beijing100024 China Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science Key Laboratory of Big Data Mining and Knowledge Management Beijing100190 China

In the era of expanding cloud–edge online service systems, predictive maintenance (PdM) based on key performance indicators (KPIs), such as CPU utilization, response rate, and network bandwidth, is essential for system operational reliability and security. Traditional data-driven PdM approaches for cloud–edge services often monitor individual indicators in isolation, neglecting their interrelationships. Although convolutional modules have been utilized for collaborative representation learning of localized high-dimensional KPIs, the high-computational complexity of CNNs and the limited kernel size hinder their efficacy in modeling high-dimensional, time-sensitive KPIs. To address these challenges, we propose a lightweight multichannel multilayer perceptron (MCMLP) framework for collaborative PdM. This framework enables efficient pointwise manipulation to capture inner-indicator hidden patterns within KPIs sequences without relying on grid-based convolution. Our MCMLP enhances locality through a two-stage dual-channel feature extraction process that captures spatial and temporal dependencies, integrating inner-indicator and cross-timeline features. The proposed MCMLP can also be easily plugged into existing CNN-based detectors as a substitute, offering a computationally economical alternative. Empirical results in real-world KPI data from various cloud–edge service scenarios demonstrate that MCMLP significantly outperforms traditional CNN-based methods, with a roughly 22% reduction in training time at a 95% confidence level (α = 0.05 ). This study also provides actionable insights for deploying MCMLP in cloud–edge service systems. © 2014 IEEE.

关键词： Predictive maintenance

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UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework 31

UnifiedGEC: Integrating Grammatical Error Correction Approac...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhao, Yike Wang, Xiaoman Lan, Yunshi Qian, Weining School of Data Science and Engineering East China Normal University Shanghai Engineering Research Center of Big Data Management China

ISBN: (纸本)9798891761988

Grammatical Error Correction is an important research direction in NLP field. Although many models of different architectures and datasets across different languages have been developed to support the research, there is a lack of a comprehensive evaluation on these models, and different architectures make it hard for developers to implement these models on their own. To address this limitation, we present UnifiedGEC, the first open-source GEC-oriented toolkit, which consists of several core components and reusable modules. In UnifiedGEC, we integrate 5 widely-used GEC models and compare their performance on 7 datasets in different languages. Additionally, GEC-related modules such as data augmentation, prompt engineering are also deployed in it. Developers are allowed to implement new models, run and evaluate on existing benchmarks through our framework in a simple way. Code, documents and detailed results of UnifiedGEC are available at https://***/AnKate/UnifiedGEC. ©2025 Association for Computational Linguistics.

关键词： Error correction

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Intelligent Pavement Condition Rating System for Cycle Routes and Greenways 11

Intelligent Pavement Condition Rating System for Cycle Route...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Haider Shah, Syed M. Qureshi, Waqar Shahid O’ Dea, Gerard Ullah, Ihsan Insigth SFI Research Center for Data Analytics University of Galway Galway Ireland School of Computer Science University of Galway Galway Ireland Transport Infrastructure Ireland Ireland

ISBN: (纸本)9789897587450

This study introduces an intelligent framework for assessing cycling infrastructure, addressing the limitations of traditional pavement evaluation methods. At the core of the system is the CRSI, a 1-to-5 rating scale specifically designed to evaluate cycle routes based on critical factors like surface quality, vegetation encroachment, and drainage. A dataset of over 40,000 frames, extracted from videos captured using handlebar-mounted GoPro cameras and annotated by experts, forms the foundation of the system. Four deep learning (DL) models LeNet, AlexNet, EfficientNet-B2, and Swin Transformer-Tiny were trained and evaluated for Cycle Route Surface Index (CRSI) classification. Among all models, Swin Transformer-Tiny performed the best, achieving an impressive accuracy of 99.90%. To further test its robustness, we evaluated the system on four new videos, from which four separate frame sets were generated. Among these, Swin Transformer-Tiny again delivered the highest accuracy, reaching 86.67%, confirming its reliability across different datasets. This CRSI-based framework provides a scalable, automated solution for evaluating cycling infrastructure, empowering transportation agencies to improve maintenance and ensure safer, more accessible cycling networks. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Convolutional neural networks

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Digitizing Highways:Unleashing Potential Through data Element Circulation for Safety,Cost Efficiency,and Environmental Impact Optimization

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Journal of Systems science and Information 2025年第1期13卷 23-60页

作者： Yanyan JIA Wen LONG Yingjie TIAN School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

As the era of large-scale highway maintenance arrives,the maintenance strategies have transitioned to a holistic approach that prioritizes safety,economic feasibility,and environmental *** research introduces a multi-objective optimization model for highway maintenance that incorporates the interplay of decision-maker preferences across three key objectives:Highway safety performance,maintenance engineering cost,and carbon *** study employs a large-sample data analysis on a subset of the Lianhuo Highway network,which includes 2,842 pavement *** approach mitigates the impact of outliers,ensuring a substantial data buffer that fortifies the model’s capacity for generalization and bolsters its *** findings reveal a Pareto-optimal relationship among the three scrutinized variables.A particularly noteworthy observation is the M-shaped trajectory of carbon emissions,which initially rise,then decline,and ultimately rebound,contingent upon the selected maintenance ***,an examination of the relationship between maintenance costs and safety performance discloses a trend of diminishing marginal returns,illustrating that the incremental gains in safety performance attenuate as maintenance investment escalates.

关键词： data element circulation highway maintenance multi-objective optimization

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Hydrogen Energy Demand management in China:A Department Scenario Analysis Method

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Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research Institute of Decision-Making Science and Big Data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Advancing Air Mobility: Few-Shot Learning in Airspace research and Development

Advancing Air Mobility: Few-Shot Learning in Airspace Resear...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Samani, Saeideh Beecham, Nena Bonner, Joshua Phojanamongkolkij, Nipa NASA Langley Research Center Research Aerospace Engineer Crew Systems and Aviation Operations Branch HamptonVA23681 United States NASA Langley Research Center Department of Data Science George Washington University HamptonVA23681 United States NASA Langley Research Center Information Data Analytics Services Branch HamptonVA23681 United States NASA Langley Research Center Systems Engineering and Engineering Methods Branch HamptonVA23681 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The advancement of Air Mobility, particularly in the context of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), represents a transformative shift in aviation's role in modern society. A comprehensive understanding of requirement consistency is paramount for fostering interoperability, standardization, and cost-effectiveness within airspace systems. This paper introduces a novel approach utilizing a pretrained Sentence Transformers model and few-shot learning to address this crucial aspect task of flagging potentially inconsistent requirements. Few-shot learning supports the development of this future through ensuring the accuracy and consistency of identified requirements with little human oversight. This approach offers a promising solution to the challenges of requirement consistency identification in airspace systems. By harnessing the power of advanced NLP techniques with fine-tuned models, stakeholders can enhance efficiency, accuracy, and scalability;ultimately fostering improved interoperability, standardization, and cost-effectiveness in airspace management. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Air mobility

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Glycemic Control and Diabetes Duration in Relation to Subsequent Myocardial Infarction among Patients with Coronary Heart Disease and Type 2 Diabetes

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Biomedical and Environmental sciences 2025年第1期38卷 27-36页

作者： Furong Li Yan Dou Chunbao Mo Shuang Wang Jing Zheng Dongfeng Gu Fengchao Liang School of Public Health and Emergency Management Southern University of Science and TechnologySchool of MedicineShenzhen 518055GuangdongChina Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine Southern University of Science and TechnologyShenzhen 518055GuangdongChina Department of Epidemiology National Center for Cardiovascular DiseasesFuwai HospitalChinese Academy of Medical Sciences&Peking Union Medical College/National Center for Cardiovascular DiseasesBeijing 100037China Shenzhen Health Development Research and Data Management Center Shenzhen 518106GuangdongChina

Objective This study aimed to investigate the impact of glycemic control and diabetes duration on subsequent myocardial infarction(MI)in patients with both coronary heart disease(CHD)and type 2 diabetes(T2D).Methods We conducted a retrospective cohort study of 33,238 patients with both CHD and T2D in Shenzhen,*** were categorized into 6 groups based on baseline fasting plasma glucose(FPG)levels and diabetes duration(from the date of diabetes diagnosis to the baseline date)to examine their combined effects on subsequent *** proportional hazards regression models were used,with further stratification by age,sex,and comorbidities to assess potential *** Over a median follow-up of 2.4 years,2,110 patients experienced *** to those with optimal glycemic control(FPG<6.1 mmol/L)and shorter diabetes duration(<10 years),the fullyadjusted hazard ratio(HR)(95%Confidence Interval[95%CI])for those with a diabetes duration of≥10 years and FPG>8.0 mmol/L was 1.93(95%CI:1.59,2.36).The combined effects of FPG and diabetes duration on MI were largely similar across different age,sex,and comorbidity groups,although the excess risk of MI associated with long-term diabetes appeared to be more pronounced among those with atrial *** Our study indicates that glycemic control and diabetes duration significant influence the subsequent occurrence of MI in patients with both CHD and *** management strategies emphasizing strict glycemic control may be particularly beneficial for patients with longer diabetes duration and atrial fibrillation.

关键词： Coronary heart disease Type 2 diabetes Myocardial infarction Diabetes duration Fasting plasma glucose

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A Novel Natural Language Processing Tool Improves Colonoscopy Auditing of Adenoma and Serrated Polyp Detection Rates

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Journal of Gastroenterology and Hepatology (Australia) 2025年第5期40卷 1230-1237页

作者： Chew, Melissa Yu, Catherine Stojevski, Leanne Conilione, Paul Gust, Anthony Suleiman, Mani Swansson, Will Anderson, Bennett Garg, Mayur Lewis, Diana Department of Gastroenterology Northern Health Melbourne VIC Australia Department of Client Data Management Northern Health Melbourne VIC Australia Department of Data Science and Analytics Northern Health Melbourne VIC Australia Department of Research Northern Centre of Health Education and Research Melbourne VIC Australia Department of Research and Industry Engagement La Trobe University Melbourne VIC Australia Department of Medicine Northern Health Clinical School University of Melbourne Melbourne VIC Australia

Background and Study Aims: Determining adenoma detection rate (ADR) and serrated polyp detection rate (SDR) can be challenging as they usually involve manual matching of colonoscopy and histology reports. This study aimed to validate a Natural Language Processing (NLP) code that enables rapid and efficient data extraction to calculate ADR and SDR. Design: A NLP code was developed to automatically extract colonoscopy quality indicators from colonoscopy and histology reports at a tertiary health service. These reports were manually reviewed to verify the concordance of ADR and SDR between the two methods. This process was applied in the initial training phase, repeated following modification of the code, and again with a validation cohort. Results: Included in the training and test phases were 5911 colonoscopies, with 2022 in the validation phase. The NLP code extracted patient names with 99.9% concordance and had a 98.9% accuracy in ADR and SDR in the training phase. Search terms were subsequently modified to take into consideration spelling variations and overlapping terminologies. Using data from the same cohort, accuracy of the NLP improved to 100%, excluding four colonoscopies that had missing histology reports in the test phase. Using a validated cohort, NLP had a 99.9% accuracy in ADR and SDR. The total time taken for auditing using NLP in the validation phase was less than 1 h. Conclusions: An automatic NLP code had an accuracy of almost 100% in determining ADR and SDR in a tertiary colonoscopy service. Wider adoption of NLP enables significant improvements in colonoscopy audits that is accurate and time efficient. © 2025 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

关键词： adenoma detection rate colonoscopy audit colorectal cancer screening natural language processing serrated polyp detection rate

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A deep federated learning approach for cross-regional railway image analysis 2

A deep federated learning approach for cross-regional railwa...

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2nd International Conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Li, Chenghao Li, Guohua Du, Wenran Li, Rui Yang, Taocun Zhang, Hongsen Big Data Research and Innovation Center China Academy of Railway Science Beijing China Research Management Department China Academy of Railway Science Beijing China Office Department China Railway Guangzhou Group Co. Ltd. Beijing China

ISBN: (纸本)9781510689053

Cross-regional railway data analysis is vital for improving safety and operational efficiency on a global scale. However, the diverse climates, traffic patterns, and operational practices across multiple regions, coupled with strict data privacy regulations, often hinder large-scale centralized training efforts. To overcome these obstacles, we propose a federated deep learning framework that transmits only model parameters after they have been updated within each local node. This approach maintains compliance with privacy requirements while taking advantage of distributed data diversity. Our experiments draw on a dataset of 500, 000 images collected from ten geographically distinct railway regions, reflecting both harsh and moderate climates as well as differing infrastructure statuses. The results indicate a 10.2% improvement in foreign object detection accuracy and a 40% reduction in communication overhead compared to single-region models. Such findings highlight the potential of federated learning to unify decentralized data for more effective anomaly detection applications in the railway sector, without compromising confidentiality or incurring excessive training costs. By maintaining shorter communication intervals and employing asynchronous updates, the proposed framework aligns well with real-world railway network structures. Overall, this work underscores the feasibility of privacy-preserving, large-scale collaboration in modern railway analytics. Specifically, it can accommodate ongoing expansions in infrastructure. © 2025 SPIE.

关键词： Differential privacy

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