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A self-supervised graph convolutional model for recommendation with exponential moving average

Neural Computing and Applications 2025年第9期37卷 6777-6793页

作者： Chen, Rui Pang, Kangning Wang, Zonglin Liu, Qingfang Tang, Cundong Chang, Yanshuo Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China Information Center Shijiazhuang Posts and Telecommunications Technical College Shijiazhuang050021 China School of Information Science and Technology Northwest University Xi’an710127 United States Insititute for Silk Road Research Xi’an University of Finance and Economics Xi’an710100 China

Recommendation algorithms based on graph convolutional networks can integrate user and item node information along with their interaction topology, better capturing the intricate relationships between users and items, thereby enhancing the accuracy of recommender systems. However, existing methods often overlook the impact of noise in user behavior data on recommendation performance. Additionally, when there are too many convolutional layers in the graph, the node representations tend to smoothing, resulting in an inability to accurately distinguish user preferences. To address these issues, we propose a self-supervised graph convolutional model for recommendation with exponential moving average (SGCERec). Specifically, we first employ exponential moving average (EMA) techniques from the field of time-series analysis to denoise the raw user interaction data. Then, by applying layer filtering technique to update the propagation of information and the representation of nodes within the graph convolutional network, we effectively deepen the model hierarchy, enabling the model to gain a deeper understanding of the features and structures of the graph data, thereby improving the performance and effectiveness of the recommender systems. Finally, experimental results on three real datasets show that SGCERec outperforms state-of-the-art recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Recommender systems

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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Reparameterizable Dual-Resolution Network for Real-Time Semantic Segmentation

SSRN

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SSRN 2025年

作者： Yang, Guoyu Shi, Daming Wang, Yuan College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China Zhejiang University of Technology China

Semantic segmentation plays a key role in applications such as autonomous driving and medical image. Although existing real-time semantic segmentation models achieve a commendable balance between accuracy and speed, their multi-path blocks still affect overall speed. To address this issue, this study proposes a Reparameterizable Dual-Resolution Network (RDRNet) dedicated to real-time semantic segmentation. Specifically, RDRNet employs a two-branch architecture, utilizing multi-path blocks during training and reparameterizing them into single-path blocks during inference, thereby enhancing both accuracy and inference speed simultaneously. Furthermore, we propose the Reparameterizable Pyramid Pooling Module (RPPM) to enhance the feature representation of the pyramid pooling module without increasing its inference time. Experimental results on the Cityscapes, CamVid, and Pascal VOC 2012 datasets demonstrate that RDRNet outperforms existing state-of-the-art models in terms of both performance and speed. Specifically, without using ImageNet pre-trained weight, RDRNet achieves 76.8\% mIoU and 134.6 FPS on the Cityscapes validation set with an RTX 3090. The code is available at \url{https://***/gyyang23/RDRNet}. © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A review on machine learning techniques for network security

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Journal of Cyber Security Technology 2025年

作者： Nazir, Rashid Laghari, Asif Ali Dahri, Fida Hussain Shoulin, Yin Alhakeem, Zaineb M. Hakim, Heba Mughal, Zeeshan Ahmed Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Software college Shenyang Normal University Shenyang China School of Computer Science and Engineering Southeast University Nanjing China Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq

Network security encompasses the strategies and techniques to protect networks from unauthorized access and potential threats. Network security is essential to protect layers of networks and data transferring on them. Data integrity and the underlying network infrastructure are necessary in today’s digital landscape. This paper explores the role of machine learning (ML) in enhancing network security by developing intelligent tools and methodologies. We critically analyze various ML techniques utilized in this field, their practical applications, and the advancements achieved in fortifying networks against cyber threats. We address ML’s challenges and limitations in network security, concluding with a discussion of open research issues for future investigation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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Prediction of Solar Energy Production Using Low-Cost Data Logger and ANN Algorithm 9th

Prediction of Solar Energy Production Using Low-Cost Data L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Raif, Mourad Ledmaoui, Younes El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This article introduces a novel method for predicting solar energy production using an affordable data logger and the Artificial Neural Network (ANN) algorithm. The main goal of this research is to create a precise predictive model for solar energy output, addressing the critical need for accurate forecasting in the renewable energy industry. The study capitalizes on the accessibility and cost-efficiency of a low-cost data logger to collect detailed environmental and solar irradiance data. The ANN algorithm is utilized to analyze the collected dataset, providing a robust and effective approach for forecasting solar energy production. Extensive experiments demonstrate that the developed model achieves an impressive R2 score of 0.95, indicating a high degree of accuracy and reliability in predicting solar energy output. This significant R2 score highlights the proposed methodology’s effectiveness, demonstrating its potential for practical applications in solar energy planning and management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data logger Machine learning Photovoltaic prediction Renewable energy

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Leveraging Predictive Maintenance for Photovoltaic Systems 9th

Leveraging Predictive Maintenance for Photovoltaic Systems

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9th International Conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

This paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. Through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. The seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Battery management systems

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EF-IMR: Embedded Flash with Interlaced Magnetic Recording Technology

EF-IMR: Embedded Flash with Interlaced Magnetic Recording Te...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Chenlin Ma Xiaochuan Zheng Kaoyi Sun Tianyu Wang Yi Wang College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Radio Frequency Heterogeneous Integration

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Interlaced Magnetic Recording (IMR), a technology that improves storage density through track overlap, introduces significant latency due to Read-Modify-Write (RMW) operations. Writing to overlapped tracks affects underlying tracks, requiring additional I/O operations to read, back up, and rewrite them, resulting in significant head movement latency. We propose EF-IMR, a new architecture that ensures crash consistency in IMR while minimizing RMW latency and head movement. EF-IMR reduces head movement during RMW operations and decreases redundant RMW operations. Evaluations under real-world, intensive I/O workloads show that EF-IMR reduces RMW latency by 20.11 % and head movement latency by 89.37% compared to existing methods.

关键词： Tracking Europe Writing Magnetic heads computer crashes Delays Magnetic recording

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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A Study on the Construction and Practical Application of the Smart Education Data Platform at Guizhou Traffic Technician and Transportation college 14

A Study on the Construction and Practical Application of the...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Li, Hong Zhang, Hong Deng, Shaoxun Li, Bo Guizhou Bit Software Co. Ltd. Research and Development Department Guiyang China Guizhou Traffic Technician and Transportation College Department of Management Engineering Guiyang China Guizhou University School of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350356670

This paper investigates the digital transformation at Guizhou Traffic Technician and Transportation college, aligned with the 'China Education Modernization 2035' agenda. It introduces a microservices-based data platform aimed at upgrading educational IT systems, enhancing instructional quality, and informing decisions through smarter data use. The approach integrates microservices, cloud, big data, and AI to achieve efficient data handling and analytics, leading to improved management and decision-making processes. The study documents the platform's architecture, security, challenges, and solutions, along with successes like data visualization and automated reporting. Findings emphasize the project's relevance for vocational education's digital transition, pointing out future challenges, including data security and technology updates. The research concludes with key takeaways for institutions undergoing digitalization, underscoring the paramountcy of data security and tech optimization. © 2024 IEEE.

关键词： Big data

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Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images

arXiv

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arXiv 2025年

作者： Fang, Sheng Li, Kaiyu Li, Zhe Zhao, Jianli Zhang, Xingli College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China

Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical remote sensing

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