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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Deep Convolutional Neural Network Approach for COVID-19 Detection

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Computer Systems science & engineering 2022年第7期42卷 201-211页

作者： Yu Xue Bernard-Marie Onzo Romany F.Mansour Shoubao Su School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharja72511Egypt Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the *** by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected *** accurate detection of Covid-19 cases poses some questions to scientists and *** two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected ***-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is *** image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the *** imaging approach can detect certain characteristic changes in the lung associated with *** this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray *** entire model proposed is categorized into three stages:dataset,data pre-processing andﬁnal stage being training and classiﬁcation.

关键词： COVID-19 deep learning convolutional neural network X-ray

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Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts

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Journal of Computer science & Technology 2022年第1期37卷 67-82页

作者： Que-Ping Kong Zi-Yan Wang Yuan Huang Xiang-Ping Chen Xiao-Cong Zhou Zi-Bin Zheng Gang Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China School of Software Engineering Sun Yat-sen UniversityZhuhai 519082China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China Peking University Shenzhen Graduate School Shenzhen 518000China

Ethereum blockchain is a new internetware with tens of millions of smart contracts running on *** from general programs,smart contracts are decentralized,tamper-resistant and permanently ***,to avoid resource abuse,Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by *** is necessary to optimize smart contracts to save ***,since developers are not familiar with the operating environment of smart contracts(i.e.,Ethereum virtual machine)or do not pay attention to resource consumption during development,there are many optimization opportunities for smart *** fill this gap,this paper defines six gas-inefficient patterns from more than 25,000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is *** evaluate the prevalence and economic benefits of gas-inefficient patterns,this paper conducts an empirical study on more than 160,000 real smart *** promising experimental results demonstrate that 52.75%of contracts contain at least one gas-inefficient pattern proposed in this *** these patterns are removed from the contract,at least 0.30 can be saved per contract.

关键词： smart contract anti-pattern detection optimization empirical study

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FLEKE: Federated Locate-then-Edit Knowledge Editing

arXiv

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arXiv 2025年

作者： Zhao, Zongkai Xu, Guozeng Li, Xiuhua Wei, Kaiwen Zhong, Jiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns. To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse. In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations. Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FLEKE task with our FedEdit framework. Our code is available at https://***/zongkaiz/FLEKE. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Study on the influence of environmentally friendly plasticizers on the properties of polyvinyl chloride 3

Study on the influence of environmentally friendly plasticiz...

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2023 3rd International Conference on Advanced Materials and Chemical engineering, AMCE 2023

作者： Wu, Hailong Zhou, Biyun Cen, Chuanfu Cao, Yu School of Data Science and Software Engineering Wu Zhou University Guangxi Wuzhou543002 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wu Zhou University Guangxi Wuzhou543002 China School of Mechanical and Resource Engineering Wu Zhou University Guangxi Wuzhou543002 China

In this work, the torque rheology, mechanical properties, migration resistance, thermal deformation, and Vicat softening point temperature of PVC plasticized by four environmentally friendly plasticizers are investigated. The experimental results reveal that the plasticizer's heavy metal content meets production requirements while falling below the limiting standard. Plasticizer melting points differ, as do the enthalpy and torque of the entire melting process, resulting in different energy consumption when melting plasticizers. Also, the mechanical properties of PVC are not different, but the mechanical properties of PVC plasticized by epoxy soybean oil are the most prominent. Besides, the Vicat softening point temperature of PVC plasticized by four environmentally friendly plasticizers is within the normal standard range, and the migration amount in different solvents is changed in a small range, which will not affect human health. © Published under licence by IOP Publishing Ltd.

关键词： Plasticizers

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CB-GAN: Generate Sensitive data with a Convolutional Bidirectional Generative Adversarial Networks 28th

CB-GAN: Generate Sensitive Data with a Convolutional Bidir...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Hu, Richa Li, Dan Ng, See-Kiong Zheng, Zibin School of Software Engineering Sun Yat-Sen University Zhuhai528478 China Institute of Data Science National University of Singapore Singapore117602 Singapore School of Computing National University of Singapore Singapore117417 Singapore

ISBN: (纸本)9783031306778

In the era of big data, numerous data measurements collected from all walks of life are playing important roles in various data mining applications. Not all data owners (or keepers) could develop feasible learning models for knowledge discovery’s sake. Oftentimes, the original data need to be passed to or shared with researchers or data scientists for better mining insights, especially in the medical, financial, and industrial fields. However, concerns about sensitivity and privacy limit the availability and completeness of shared (or passed) data and the quality of mining results. In this paper, we propose a novel Convolutional Bidirectional Generative Adversarial Networks (CB-GAN) framework to generate sensitive synthetic data. The Convolutional Neural Networks are utilized to capture the feature correlations of the original data, and the Generative Adversarial Networks with Autoencoders are combined to synthesize realistically distributed data. To demonstrate the feasibility of the model, we evaluated it from three aspects: how similar are the distributions of the synthetic data to the original data, how well can the synthetic data accomplish future data mining tasks, and how much sensitive information has been hidden. Various experimental results showed the superiority of the proposed method compared with the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Deep Learning in Automatic Recognition of Mongolian Compound Nouns 5

Deep Learning in Automatic Recognition of Mongolian Compound...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Si, Ha La, Du Ding, Yang College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China School of Software and Big Data Inner Mongolia Electronic Information Vocational Technical College Hohhot China

ISBN: (纸本)9798350355925

Mongolian compound nouns often become the vehicle for the creation of new concepts and terms due to their diverse combinations and flexible expressive power. In the field of natural language shallow syntactic analysis, the recognition of Mongolian compound nouns plays an important role, and its performance will directly affect the processing effect of related research. In the current academic research, the research on automatic recognition of Mongolian compound nouns is still insufficient, especially in the automatic recognition of Mongolian compound nouns using deep learning technology, which is almost in a blank state. In this study, we firstly construct a Mongolian compound noun annotated corpus, and on the basis of the constructed annotated corpus, we propose a Mongolian compound noun automatic recognition model based on deep learning for the fact that there is no deep learning method for recognising Mongolian compound nouns at present. The model takes Conditional Random Field (CRF) as the basic framework to make full use of contextual information by considering the joint probability distribution of the whole sequence. During the model construction process, the TextCNN model is incorporated and by designing a multi-scale convolutional kernel, the model is able to effectively capture the local features of Mongolian compound nouns. The experimental results show that the automatic Mongolian compound noun recognition model based on deep learning proposed in this study performs well on the Mongolian compound noun recognition task, with an F1 value of 93.40%. © 2024 IEEE.

关键词： Contrastive Learning

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

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3H-ObjectDet: A Target Detection Method Based on Haar Wavelet Transform Downsampling and Hybrid Attention Mechanism for Medical Fracture Image 4

3H-ObjectDet: A Target Detection Method Based on Haar Wavele...

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4th International Conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Xue, Junmin Rao, Yunbo Yao, Qinwei Wu, Jiangang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Beijing Youan Hospital Affiliated to Capital Medical University Beijing China Operation Data Center of Postal Savings Bank of China Beijing China

ISBN: (纸本)9798331534622

In recent years, in order to reduce the incidence of misdiagnosis and missed diagnosis in fracture diagnosis and maximize the protection of patients' lives and health, deep learning has achieved rapid development in the field of fracture diagnosis. Target detection models can obtain the location and category of fractures in images simultaneously, and the detection results output are more intuitive, making it convenient for clinical doctors to use directly. However, the following difficulties have troubled clinical use: (1) inefficient extraction of image features, (2) difficulty in accurate detection classification, and (3) large calculation volume causing delayed detection output. This paper proposed a medical fracture image target detection method based on wavelet transform downsampling and mixed attention mechanism, and made relevant innovations: (1) introduced a mixed attention mechanism instead of traditional convolution operation in the feature extraction of the backbone network, (2) innovated the detection classification and localization module to effectively identify features, and (3) simultaneously enhanced feature extraction ability through wavelet transform downsampling and reduced computation. The innovative detection model achieved a 21.2% increase in accuracy and a 10.1% increase in mAP@0.5 in the rib fracture dataset, while reducing computation by 14.8%. In the upper limb fracture and the hand fracture dataset, the high performance indicators have also been significantly increased, showing the high efficiency and generalization of the innovative detection model. © 2024 IEEE.

关键词： Wavelet transforms

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Earthquake big data processing and visualization based on PyEcharts 3

Earthquake big data processing and visualization based on Py...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wu, Jiandong Wang, Qiang Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang China Software School of East China University of Technology Nanchang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

This paper mainly uses the Python language and the pyecharts library to mine the earthquake data that occurred around the world in 2020, and finally displays it with pie charts, bar charts and other graphics. It lays a foundation for better use and processing of earthquake big data in the future, and also demonstrates the application of big data in other industries. © 2023 SPIE.

关键词： Visualization

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