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Partial Label Learning with Noisy Labels

Annals of data science 2025年第1期12卷 199-212页

作者： Zhao, Pan Tang, Long Pan, Zhigeng School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing210044 China Research Institute of Talent Big Data Nanjing University of Information Science & Technology Nanjing210044 China

Partial label learning (PLL) is a particular problem setting within weakly supervised learning. In PLL, each sample corresponds to a candidate label set in which only one label is true. However, in some practical application scenarios, the emergence of label noise can make some candidate sets lose their true labels, leading to a decline in model performance. In this work, a robust training strategy for PLL, derived from the joint training with co-regularization (JoCoR), is proposed to address this issue in PLL. Specifically, the proposed approach constructs two separate PLL models and a joint loss. The joint loss consists of not only two PLL losses but also a co-regularization term measuring the disagreement of the two models. By automatically selecting samples with small joint loss and using them to update the two models, our proposed approach is able to filter more and more suspected samples with noise candidate label sets. Gradually, the robustness of the PLL models to label noise strengthens due to the reduced disagreement of the two models. Experiments are conducted on two state-of-the-art PLL models using benchmark datasets under various noise levels. The results show that the proposed method can effectively stabilize the training process and reduce the model's overfitting to noisy candidate label sets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Phase locked loops

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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense Network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of Computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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A Hybrid Music Recommendation Model Based on Personalized Measurement and Game Theory

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Chinese Journal of Electronics 2023年第6期32卷 1319-1328页

作者： WU Yun LIN Jian MA Yanlong The State Key Laboratory of Public Big Data Guizhou University College of Computer Science and Technology Guizhou University

Music recommendation algorithms, from the perspective of real-time, can be classified into two categories: offline recommendation algorithms and online recommendation algorithms. To improve music recommendation accuracy, especially for the new music(users have no historic listening records on it), and real-time recommendation ability, and solve the interest drift problem simultaneously, we propose a hybrid music recommendation model based on personalized measurement and game theory. This model can be separated into two parts: an offline recommendation part(OFFLRP) and an online recommendation part(ONLRP). In the offline part, we emphasize users personalization. We introduce two metrics named user pursue-novelty degree(UPND) and music popularity(MP) to improve the traditional items-based collaborative filtering algorithm. In the online part, we try to solve the interest drift problem, which is a thorny problem in the offline part. We propose a novel online recommendation algorithm based on game theory. Experiments verify that the hybrid music recommendation model has higher new music recommendation accuracy, decent dynamical personalized recommendation ability, and real-time recommendation capability, and can substantially mitigate the problem of interest drift.

关键词： Measurement Collaborative filtering Heuristic algorithms Market research Real-time systems data models Classification algorithms

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Fine-Tuned BERT Model for Hate Speech Detection in Political Discourse 22

Fine-Tuned BERT Model for Hate Speech Detection in Political...

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22nd International Conference on ICT and Knowledge Engineering, ICT and KE 2024

作者： Nwaiwu, Steve Jongsawat, Nipat Tungkasthan, Anucha Thaloey, Jarinya RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand RMUTT Big Data Analytics and Management Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350391411

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models. © 2024 IEEE.

关键词： Natural language processing systems

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Neighbor Library-Aware Graph Neural Network for Third Party Library Recommendation

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Tsinghua science and technology 2023年第4期28卷 769-785页

作者： Ying Jin Yi Zhang Yiwen Zhang School of Artificial Intelligence and Big Data Hefei UniversityHefei 230601China School of Computer Science and Technology Anhui UniversityHefei 230601China

Modern software development has moved toward agile growth and rapid delivery,where developers must meet the changing needs of users *** such a situation,plug-and-play Third-Party Libraries(TPLs)introduce a considerable amount of convenience to ***,selecting the exact candidate that meets the project requirements from the countless TPLs is challenging for *** works have considered setting up a personalized recommender system to suggest TPLs for ***,these approaches rarely consider the complex relationships between applications and TPLs,and are unsatisfactory in accuracy,training speed,and convergence *** this paper,we propose a new end-to-end recommendation model called Neighbor Library-Aware Graph Neural Network(NLA-GNN).Unlike previous works,we only initialize one type of node embedding,and construct and update all types of node representations using Graph Neural Networks(GNN).We use a simplified graph convolution operation to alternate the information propagation process to increase the training efficiency and eliminate the heterogeneity of the app-library bipartite graph,thus efficiently modeling the complex high-order relationships between the app and the *** experiments on large-scale real-world datasets demonstrate that NLA-GNN achieves consistent and remarkable improvements over state-of-the-art baselines for TPL recommendation tasks.

关键词： Third-Party Library(TPL) TPL recommendation Graph Neural Network(GNN) bipartite graph

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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

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Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization Fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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Route selection for opportunity-sensing and prediction of waterlogging

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Frontiers of Computer science 2024年第4期18卷 147-159页

作者： Jingbin WANG Weijie ZHANG Zhiyong YU Fangwan HUANG Weiping ZHU Longbiao CHEN College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Department of Computer Science and Technology Xiamen UniversityXiamen 361005China

Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.

关键词： waterlogging prediction sparse crowdsensing active learning route selection graph convolutional network

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Survey on age-related macular degeneration detection in OCT image

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International Journal of Computers and Applications 2024年第2期46卷 101-112页

作者： Deepti, S. Renu Karthik, P.C. Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology Kattankulathur India

Age-related Macular Degeneration (AMD) is the most common eye disease that causes visual impairment in elder people. Prevalently, AMD is detected by Spectral Domain Optical Coherence Tomography (SD-OCT) for diagnosis and monitoring purposes. In this work, 50 papers are reviewed to examine various approaches and their performance metrics. This paper reviewed various existing works related to AMD detection using different imaging modalities for quality images with high resolution. The investigation of case studies is analyzed on the basis of data collected from various patients undergoing several treatments and scans obtained from various imaging modalities. Furthermore, the analysis extends to the works that used different AI techniques, such as Deep Learning (DL), Machine Learning (ML), and Unsupervised Framework, for the detection of AMD-related diseases. Also, the analysis of different performance measures is done that shows the variations in terms of performance metrics such as accuracy, sensitivity, specificity, and so on. The outcome of this deep analysis shows that the system with the DL model reaches its utmost performance in AMD detection. Finally, gaps and challenges that are still lagging with this research area are summarized, which motivates the researchers to take away the work in an effective way. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diagnosis

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