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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2

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Computers, Materials & Continua 2024年第7期80卷 679-694页

作者： Zhilin Li Yuxin Li Chunyu Yan Peng Yan Xiutong Li Mei Yu Tingchi Wen Benliang Xie College of Big Data and Information Engineering Guizhou UniversityGuiyang550025China Power Semiconductor Device Reliability Engineering Center of the Ministry of Education Guiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China The Mushroom Research Centre Guizhou UniversityGuiyang550025China The Engineering Research Center of Southwest Bio-Pharmaceutical Resources Ministry of EducationGuizhou UniversityGuiyang550025China

Diseases in tea trees can result in significant losses in both the quality and quantity of tea *** monitoring can help to prevent the occurrence of large-scale diseases in tea ***,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring *** paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these *** proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions ***,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease ***,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational *** results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.

关键词： Disease identification coordinate attention mechanism multi-scale feature extraction model pruning

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RGCNU: Recurrent Graph Convolutional Network With Uncertainty Estimation for Remaining Useful Life Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1640-1642页

作者： Qiwu Zhu Qingyu Xiong Zhengyi Yang Yang Yu School of Big Data&Software Engineering Chongqing UniversityChongqing 400044 Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqing 400044China School of Biology and Engineering Guizhou Medical UniversityGuiyang 550025China

Dear Editor,This letter focuses on the problem of remaining useful life(RUL)prediction of equipment. Existing graph neural network(GCN)-based approaches merely provide the point estimation of RUL. However,the estimated RUL often varies widely due to the model parameters and the noise in data. It is important to know the uncertainty in predictions for reliable risk analysis and maintenance decision *** map the relationship between noisy condition monitoring data and RUL with uncertainty.

关键词： letter estimation prediction

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks 42

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Wang, Puwei Sun, Zhouxing Zhan, Ying Li, Haoran Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing100872 China Guizhou University of Finance and Economics Guiyang City550025 China

ISBN: (纸本)9798350334142

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks. © 2023 IEEE.

关键词： Resource allocation

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Reinforcement Learning Based data-Driven Control Method for Dynamic Systems and Its Application to FLUIDMechatronix Platform 16th

Reinforcement Learning Based Data-Driven Control Method for...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zou, Zonghan Gao, Jingjing Yang, Xu Huang, Jian Zhang, Tao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819617760

With the increasing complexity of industrial process, accurate models of automatic control systems are difficult to be established, which makes model-based control methods become less effective. On this premise, a reinforcement learning (RL) based data-driven control method for dynamic systems is proposed in this paper. For our purposes, a model-free RL controller with an Actor-Critic structure is developed by using neural networks (NN), combining the merits of both value-based and policy-based RL algorithms. Policy of the actor is assessed by the critic, updating parameters of RL controller in real-time for online optimization. The effectiveness of the RL controller is demonstrated by an experimental study on FLUIDMechatronix platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 734-754页

作者： Yong Zhang Ying Hu Xiaozhi Gao Dunwei Gong Yinan Guo Kaizhou Gao Wanqiu Zhang School of Information and Control Engineering China University of Mining and TechnologyXuzhouChina The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchunChina School of Computing University of Eastern FinlandKuopioFinland The Macao Institute of Systems Engineering Macao University of Science and TechnologyTaipaChina

In real life,a large amount of data describing the same learning task may be stored in different institutions(called participants),and these data cannot be shared among par-ticipants due to privacy *** case that different attributes/features of the same instance are stored in different institutions is called vertically distributed *** pur-pose of vertical‐federated feature selection(FS)is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature *** solve this problem,in the paper,an embedded vertical‐federated FS algorithm based on particle swarm optimisation(PSO‐EVFFS)is proposed by incorporating evolutionary FS into the SecureBoost framework for the first *** optimising both hyper‐parameters of the XGBoost model and feature subsets,PSO‐EVFFS can obtain a feature subset,which makes the XGBoost model more *** the same time,since different participants only share insensitive parameters such as model loss function,PSO‐EVFFS can effec-tively ensure the privacy of participants'***,an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each ***,the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation ***-mental results show that the proposed algorithm can significantly improve the classifi-cation performance of selected feature subsets while fully protecting the data privacy of all participants.

关键词： Evolutionary optimization feature selection privacy protection vertical‐federated learning

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Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

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Science China(Technological Sciences) 2024年第8期67卷 2579-2593页

作者： LIU JiaJun ZHANG Jie LENG JieWu Shanghai Engineering Research Center of Industrial Big Data and Intelligent System Institute of Artificial IntelligenceDonghua UniversityShanghai 201620China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of TechnologyGuangzhou 510006China

Supplier selection is an important business activity in order to realize the purchasing function in supply chain *** supplier selection process includes four stages,i.e.,bidding inviting,bidding,group decision-making,and results disclosure,involving the participation of manufacturing service demanders(MSDs),manufacturing service suppliers(MSSs),and ***,all the participants have raised concerns about the increased transparency in supplier ***,this study proposes a transparent supplier selection method by considering the engagement of *** this method,the Bayesian best-worst method(Bayesian BWM)is used to aggregate decision-makers'preferences into the overall optimal weights of the alternative MSSs,and the MSS with the largest weight is considered the suitable MSS for ***,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract,where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making ***,a case study of supplier selection in purchasing vibration acceleration sensors is *** result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers'preferences,and blockchain can provide credible information about the supplier selection process for MSSs,MSDs,and *** this way,the transparency of supplier selection is enhanced.

关键词： blockchain supplier selection supply chain transparency Bayesian BWM group decision-making

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Dynamic-prototype Contrastive Fine-tuning for Continual Few-shot Relation Extraction with Unseen Relation Detection 31

Dynamic-prototype Contrastive Fine-tuning for Continual Few-...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhao, Simiao Tan, Zhen Pang, Ning Xiao, Weidong Zhao, Xiang National Key Laboratory of Information Systems Engineering National University of Defense Technology China Laboratory for Big Data and Decision National University of Defense Technology China

ISBN: (纸本)9798891761964

Continual Few-shot Relation Extraction (CFRE) aims to continually learn new relations from limited labeled data while preserving knowledge about previously learned relations. Facing the inherent issue of catastrophic forgetting, previous approaches predominantly rely on memory replay strategies. However, they often overlook task interference in continual learning and the varying memory requirements for different relations. To address these shortcomings, we propose a novel framework, DPC-FT, which features: 1) a lightweight relation encoder for each task to mitigate negative knowledge transfer across tasks;2) a dynamic prototype module to allocate less memory for easier relations and more memory for harder relations. Additionally, we introduce the None-Of-The-Above (NOTA) detection in CFRE and propose a threshold criterion to identify relations that have never been learned. Extensive experiments demonstrate the effectiveness and efficiency of our method in CFRE, making our approach more practical and comprehensive for real-world scenarios. © 2025 Association for Computational Linguistics.

关键词： knowledge transfer

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RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network 17th

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Plac...

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17th European Conference on Computer Vision, ECCV 2022

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031250552

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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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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