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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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Ontology-Based Decision-Making Support in Real-Time Strategy Game 4

Ontology-Based Decision-Making Support in Real-Time Strategy...

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Wang, Yaochu Yang, Jian Yang, Guoli School of Automation Science and Engineering South China University of Technology Guangzhou China Advanced Institute of Big Data Department of Big Data Intelligence Beijing China

ISBN: (纸本)9798350384062

Real-Time Strategy (RTS) games have attracted millions of players due to their characteristics of diverse scenes and flexible decision-making mechanisms. However, the mechanisms, contents, and operations of RTS games could be quite complex for beginners. The frustration and poor beginner experience caused by the high level of difficulty in getting started may lead novice players to give up on a specific RTS game due to a lack of sense of achievement. To address this issue and enhance the gaming experiences of beginners, this article proposes to assist beginners in familiarizing themselves with the mechanisms of the games and evaluating the opponents' abilities based on ontology. Firstly, an ontology will be built according to the properties of game units, which can provide beginners with a clear and comprehensive conceptual framework to understand game mechanisms and operations. Secondly, a knowledge-based reasoning method will be proposed to help beginners evaluate their opponents' abilities. Using a specific RTS game as a demonstration, the ontology is built and a reasoning example is proposed. By analyzing and reasoning about the abilities of their opponents' game units, this reasoning method can help beginners develop more effective strategies and tactics to enhance their experience in the game. © 2024 IEEE.

关键词： Ontology

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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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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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Robust Deep One-Class Classification Time Series Anomaly Detection

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Computers, Materials and Continua 2025年第3期83卷 5181-5197页

作者： Yang, Zhengdao Wang, Xuewei Chen, Yuling Dou, Hui Sang, Haiwei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China College of Computer Science and Technology Weifang University of Science and Technology Weifang261000 China School of Mathematics and Big Data Guizhou Education University Guiyang550018 China

Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these challenges, we propose a novel anomaly detection method called Robust One-Class Classification Detection (ROC). This approach utilizes an autoencoder (AE) to learn features while constraining the context vectors from the AE within a sufficiently small hypersphere, akin to One-Class Classification (OC) methods. By simultaneously optimizing two hypothetical objective functions, ROC captures various aspects of normality. We categorize the input raw time series into clean and outlier sequences, reducing the impact of outliers on compressed feature representation. Experimental results on public datasets indicate that our approach outperforms existing baseline methods and substantially improves model robustness. Copyright © 2025 The Authors.

关键词： Support vector machines

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm 7

Research on Mining Association Rules for Classroom Concentra...

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7th IEEE Information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Bai, Jingqiu Kong, Huafeng Hu, Wenhan Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China The Main Research Direction Is the Computer Application China

ISBN: (纸本)9798350334197

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future. © 2023 IEEE.

关键词： Association rules

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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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A Progressive Skip Reasoning Fusion Method for Multi-Modal Classification 24

A Progressive Skip Reasoning Fusion Method for Multi-Modal C...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qian Liang, Xinyan Qian, Yuhua Cui, Zhihua Wen, Jie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798400706868

In multi-modal classification tasks, a good fusion algorithm can effectively integrate and process multi-modal data, thereby significantly improving its performance. Researchers often focus on the design of complex fusion operators and have proposed numerous fusion operators, while paying less attention to the design of feature fusion usage, specifically how features should be fused to better facilitate multi-modal classification tasks. In this article, we propose a progressive skip reasoning fusion network (PSRFN) to make some attempts to address this issue. Firstly, unlike most existing multi-modal fusion methods that only use one fusion operator in a single stage to fuse all view features, PSRFN utilizes the progressive skip reasoning (PSR) block to fuse all views with a fusion operator at each layer. Specifically, each PSR block utilizes all view features and the fused features from the previous layer to jointly obtain the fused features for the current layer. Secondly, each PSR block utilizes a dual-weighted fusion strategy with learnable parameters to adaptively allocate weights during the fusion process. The first level of weighting assigns weights to each view feature, while the second level assigns weights to the fused features from the previous layer and the fused features obtained from the first level of weighting in the current layer. This strategy ensures that the PSR block can dynamically adjust the weights based on the actual contribution of features. Finally, to enable the model to fully utilize feature information from different levels for feature fusion, the skip connections are adopted between PSR blocks. Extensive experiment results on six real multi-modal datasets show that a better usage for fusion operator is indeed able to improve performance. © 2024 ACM.

关键词： Classification (of information)

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Hierarchical Privacy Protection Model in Advanced Metering Infrastructure Based on Cloud and Fog Assistance

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Computers, Materials & Continua 2024年第8期80卷 3193-3219页

作者： Linghong Kuang Wenlong Shi Jing Zhang School of Computer Science and Mathematics Fujian University of TechnologyFuzhou350118China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118China

The Advanced Metering Infrastructure(AMI),as a crucial subsystem in the smart grid,is responsible for measuring user electricity consumption and plays a vital role in communication between providers and ***,with the advancement of information and communication technology,new security and privacy challenges have emerged for *** address these challenges and enhance the security and privacy of user data in the smart grid,a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance(HPPM-AMICFA)is proposed in this *** proposed model integrates cloud and fog computing with hierarchical threshold encryption,offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart *** methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance,thereby assigning appropriate protection ***,a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs,ensuring secure aggregation and encryption of user *** results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs,thereby safeguarding user data in the smart grid.

关键词： AMI cloud and fog assistance fuzzy comprehensive analysis hierarchical threshold encryption

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