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

作者： Yang, Xiaolong Wang, Lili College of Mathematics and Big Data Anhui University of Science＆Technology Anhui Huainan232001 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Anhui Huainan232001 China

ISBN: (纸本)9781510689053

Traditional approaches typically flatten the process when checking the conformance of complex processes. However, this flattening approach can result in the loss of dependencies between objects, reducing the accuracy of the conformance check. This paper proposes an object-centered alignment optimization method based on the heuristic A* algorithm to address this issue. This method effectively improves the efficiency of object-centered alignment in complex scenarios by considering both heuristic search and actual cost. The experimental results demonstrate that, in comparison to existing approaches, the proposed method presented in this paper observably enhances the efficiency of alignment in complex graph structures, exhibits superior adaptability, and gains a better understanding of the relationship between object-centric process behavior and data. These results provide new ideas and methods for improving conformance checking, which are of great theoretical and practical significance, especially in dynamic and complex environments where the potential for application has been further validated. © 2025 SPIE.

关键词： Detection and tracking algorithms Mining Alignment modeling Process modeling Algorithm development Beam propagation method Mathematical optimization Connectors Medicine Transportation

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Optimizing the Copy-on-Write Mechanism of Docker by Dynamic Prefetching

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Tsinghua science and technology 2021年第3期26卷 266-274页

作者： Yan Jiang Wei Liu Xuanhua Shi Weizhong Qiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHuazhong University of Science and TechnologyWuhan 430074China

Docker,as a mainstream container solution,adopts the Copy-on-Write(CoW)mechanism in its storage *** mechanism satisfies the need of different containers to share the same ***,when a single container performs operations such as modification of an image file,a duplicate is created in the upper readwrite layer,which contributes to the runtime *** the accessed image file is fairly large,this additional overhead becomes *** we present the concept of Dynamic Prefetching Strategy Optimization(DPSO),which optimizes the Co W mechanism for a Docker container on the basis of the dynamic prefetching *** the beginning of the container life cycle,DPSO pre-copies up the image files that are most likely to be copied up later to eliminate the overhead caused by performing this operation during application *** experimental results show that DPSO has an average prefetch accuracy of greater than 78%in complex scenarios and could effectively eliminate the overhead caused by the CoW mechanism.

关键词： Docker container Copy-on-Write(CoW) storage driver prefetch strategy

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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The periodic product recommendation system based on deep reinforcement learning and the multi-objective framework 12

The periodic product recommendation system based on deep rei...

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12th International Conference on Awareness science and technology, iCAST 2023

作者： Dat, Dang Tien Thang, Tran Ngoc Minh, Nguyen Anh Chen, Rung Ching Ngoc Anh, Nguyen Thi School of Applied Mathematics and Informatics Hanoi University of Science and Technology Viet Nam Big Data Lab CMC Institute of Science and Technology Viet Nam Department of Information Management Chaoyang University of Technology Taiwan

ISBN: (纸本)9798350324693

The primary objective of the recommendation system is to suggest suitable products to users. The need for a personalized recommendation system has become essential with the continuous growth in the number of users and the increasing diversity of products. In this paper, we proposed a novel approach that combines a reinforcement learning algorithm with user clustering to address the recommendation challenge for periodic products. Additionally, we extend our framework to accommodate multi-objective scenarios. Our contributions encompass the development of meaningful representation vectors for actions and states, along with the definition of reward vector aligning with users’ long-term preferences and optimizing profits. We apply our proposed method to the problem of recommending mobile packages in Vietnam. Notably, our approach yields favorable recommendation results and exhibits superior performance compared to alternative methods. © 2023 IEEE.

关键词： Reinforcement learning

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Resisting Membership Inference Attacks by Dynamically Adjusting Loss Targets

Resisting Membership Inference Attacks by Dynamically Adjust...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Ma, Xihua Tian, Youliang Ding, Zehua Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Institute of Cryptography and Data Security Guizhou University Guiyang550025 China

ISBN: (纸本)9798350327380

Machine learning (ML) models are susceptible to membership inference attacks (MIAs), which aim to infer whether a particular sample was involved in model training. Previous research suggests that the difference in loss distribution between member and non-member sample is an essential factor of MIAs. In the latest mitigation strategies to reduce the loss distribution discrepancy, the model owner must manually set a loss target for the training task. However, this can be challenging due to differences in datasets and model structures. We propose a new mitigation strategy based on existing studies, which can dynamically adjust the loss target during the training process according to the model structure and dataset characteristics, to achieve a reduced loss gap. We extensively evaluated our strategy in white-box and black-box environments, respectively. Our experimental results show that our approach avoids the problem of setting loss targets and even improves the model's resistance to attacks in most cases. Specifically, the accuracy of the attacks is reduced by an average of 4.92% and 11.3% in the black-box and white-box environments, respectively. © 2023 IEEE.

关键词： Machine learning

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Fair Personalized Learner Modeling Without Sensitive Attributes 25

Fair Personalized Learner Modeling Without Sensitive Attribu...

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34th ACM Web Conference, WWW 2025

作者： Xu, Hefei Hou, Min Wu, Le Liu, Fei Yang, Yonghui Bai, Haoyue Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China Institute of Dataspace Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400712746

Personalized learner modeling uses learners’ historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as learning resource recommendation and adaptive testing. However, prior studies have shown that CD models may unfairly correlate learners’ abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes. While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical. Furthermore, the presence of multiple sensitive attributes complicates fairness improvements. In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. We introduce a novel fairness objective tailored for personalized learner modeling and design a max-min strategy to facilitate both sensitive information inference and fair CD modeling. In the max step, we infer pseudo-labels by maximizing the fairness objective, while in the min step, we retrain the CD model by minimizing it. Additionally, we provide a theoretical guarantee that our framework reduces the upper bound of fairness generalization error. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods. © 2025 Copyright held by the owner/author(s).

关键词： Supervised learning

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Extremal Functions for an Improved Trudinger-Moser Inequality Involving L^(P)-Norm in R^(n)

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Journal of Partial Differential Equations 2023年第4期36卷 414-434页

作者： YANG Liu LI Xiaomeng College of Education Huaibei Institute of TechnologyHuaibei 235000China School of Mathematics and Big Data Chaohu UniversityHefei 230000China School of Mathematical Science Huaibei Normal UniversityHuaibei 235000China

Let W^(1,n)(R^(n))be the standard Sobolev *** any T>0 and p>n>2,we denote■Define a norm in W^(1,n)(R^(n))by■where 0≤α<λ_(n,p).Using a rearrangement argument and blow-up analysis,we will prove■can be attained by some function u_(0)∈W^(1,n)(R^(n))∩C^(1)(R^(n))with ||u_(0)||_(n,p)=1,here a_(n)=n■_(n-1)^(1/n-1) and ■_(n-1) is the measure of the unit sphere in R^(n).

关键词： Trudinger-Moser inequality extremal function blow-up analysis

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EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Multi-View Clustering with Gaussian Error Rank Minimization 41

EDISON: Enhanced Dictionary-Induced Tensorized Incomplete Mu...

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41st International Conference on Machine Learning, ICML 2024

作者： Gu, Zhibin Li, Zhendong Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China

This paper presents an efficient and scalable incomplete multi-view clustering method, referred to as Enhanced Dictionary-Induced tenSorized incomplete multi-view clustering with Gaussian errOr raNk minimization (EDISON). Specifically, EDISON employs an enhanced dictionary representation strategy as the foundation for inferring missing data and constructing anchor graphs, ensuring robustness to less-than-ideal data and maintaining high computational efficiency. Additionally, we introduce Gaussian error rank as a concise approximation of the true tensor rank, facilitating a comprehensive exploration of the diverse information encapsulated by various singular values in tensor data. Furthermore, we integrate a hyper-anchor graph Laplacian manifold regularization into the tensor representation, allowing for the simultaneous utilization of inter-view high-order correlations and intra-view local correlations. Extensive experiments demonstrate the superiority of the EDISON model in both effectiveness and efficiency compared to SOTA methods. Copyright 2024 by the author(s)

关键词： Tensors

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Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

Bilateral Network with Residual U-blocks and Dual-Guided Att...

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2023 China Automation Congress, CAC 2023

作者： Liao, Liang Wan, Liang Liu, Mingsheng Li, Shusheng College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Computer Science and Engineering Northeastern Univiversity Shenyang China

ISBN: (纸本)9798350303759

When some application scenarios need to use semantic segmentation technology, like automatic driving, the primary concern comes to real-time performance rather than extremely high segmentation accuracy. To achieve a good trade-off between speed and accuracy, two-branch architecture has been proposed in recent years. It treats spatial information and semantics information separately which allows the module to be composed of two networks both not heavy. However, the process of fusing features with two different scales becomes a performance bottleneck for many nowaday two-branch models. In this research, we design a new fusion mechanism for two-branch architecture which is guided by attention computation. To be precise, we use the Dual-Guided Attention (DGA) module we proposed to replace some multi-scale transformations with the calculation of attention which means we only use several attention layers of near linear complexity to achieve performance comparable to frequently-used multi-layer fusion. To ensure that our module can be effective, we use Residual U-blocks (RSU) to build one of the two branches in our networks which aims to obtain better multi-scale features. Extensive experiments on Cityscapes and CamVid dataset show the effectiveness of our method. On Cityscapes, our light version network without pretrain weight can achieve 71.1% mIoU at 163 FPS on a single Nvidia RTX 3070 using full resolution images(1024×2048pix). And the large version can achieve 77.9% mIoU with a speed of 43 FPS which still reaches the real-time criterion. Our code and module has been open sourced at https://***/LikeLidoA/Mymodule. © 2023 IEEE.

关键词： Computer vision

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WTUNet: A U-shaped semantic segmentation network combined with wavelet convolution 3

WTUNet: A U-shaped semantic segmentation network combined wi...

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3rd International Conference on Environmental Remote Sensing and Geographic Information technology, ERSGIT 2024

作者： Shi, Guangyue Wang, Lei Chen, Fang Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

Landslide extraction plays a critical role in facilitating effective rescue operations and emergency response. However, existing methods face difficulties in accurately extracting landslide areas due to complex terrain conditions, where variable textures, shapes, and spectral features can obscure critical details. These challenges are particularly evident in texture features, which spatial-domain methods alone struggle to capture reliably under varying lighting and background conditions. To address these issues, this study introduces WTUNet, a novel U-shaped semantic segmentation model that integrates frequency-domain feature extraction to enhance texture representation. WTUNet’s key innovation is the integration of a wavelet transform layer following each convolutional layer, enabling the model to capture frequency-domain features through wavelet decomposition while retaining spatial features from the convolutional layers. This dual-domain feature extraction approach is further optimized by dynamic feature fusion technology, which merges these diverse feature sets to enrich the semantic representation within feature maps, thereby substantially enhancing extraction performance. To evaluate WTUNet’s effectiveness, we conducted training and testing on the Bijie and Jiuzhaigou datasets, benchmarking it against three leading semantic segmentation models. Results demonstrate that wavelet transform integration meaningfully strengthens semantic feature representation, yielding at least a 2% Mean Intersection over Union(mIoU) improvement on the Bijie dataset and a 3% improvement on the Jiuzhaigou dataset, underscoring WTUNet's superior landslide extraction capabilities. © 2025 SPIE.

关键词： Semantic Segmentation

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