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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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GCINet: global convolution interaction network with a pre-trained reversible normalization method for long-term time series forecasting

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Neural Computing and applications 2025年第5期37卷 3227-3244页

作者： Fan, Jin Yang, Baoshun Sun, Danfeng Liu, Jie Chen, Qikai Wang, Zhipeng Wu, Jia Hangzhou Dianzi University Hangzhou China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Hangzhou China School of Computing Macquarie University Sydney Australia

Long-term time series forecasting is crucial in various domains, such as weather forecasting and disease forecasting. With the rise of Transformer-based models, we have witnessed a significant improvement in long-term prediction accuracy, as these models use self-attention to capture long-term temporal features in the data during feature extraction. However, this approach has several drawbacks: Firstly, self-attention has quadratic complexity, and reducing its complexity often results in reduced prediction accuracy. Secondly, the distribution of time series data frequently changes over time, a phenomenon known as distribution shift, which is not taken into consideration in most Transformer-based models. This leads to decreased prediction accuracy as the distribution deviates from the one used for model training. Therefore, we have developed a new time series forecasting model, GCINet, which introduces a new mode of interaction based on a convolutional network. This captures both long-term and short-term temporal features while reducing complexity during feature extraction. Additionally, GCINet includes an Inverse Trained Normalization method to address distribution shift, bringing a fresh perspective to the design of time series forecasting models. Our experiments on long-term forecasting tasks demonstrate that GCINet outperforms other commonly used models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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An Early Warning Method Based on Blending of Deep Generative Model and Oversampling Model for Online Learning

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IEEE Access 2025年 13卷 72248-72268页

作者： Zhang, Mingyan Wang, Yiqing Hung, Jui-Long Wang, Jie Duan, Chao Zhejiang Normal University College of Education Zhejiang Jinhua321004 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Jinhua321004 China Boise State University Department of Educational Technology BoiseID83725 United States

Early warning for learning performance requires to identify the maximum number of at-risk students as early as possible within a semester. However, educational data often suffer from the issue of data imbalance, making it challenging to simultaneously achieve both high precision (accurate identification) and high recall (comprehensive coverage) in at-risk student detection. Deep generative models and oversampling models are effective methods to solve data imbalance issues, which can improve classification performance. This paper proposes a method that combines the advantages of deep generative models and oversampling models to build a blending model for dealing with imbalanced educational data, which can effectively improve the precision, recall, F1-score and AUC for online learning early warning. First, we compare baseline models to select the best classifier, then choose the highest-precision deep generative model and the highest-recall oversampling model to construct blending models, which are shown to improve early warning prediction metrics. Finally, interpretable models are used to analyze differences in at-risk student prediction between the blending model, deep generative model, and oversampling model. The proposed models are validated on both extremely imbalanced datasets and new semester datasets. Results show that: (1) Compared to the baseline model, both the base learners built by the deep generative model and the oversampling model can improve the evaluation metrics of the model, the deep generative base learners achieve higher precision than the oversampling model, while the oversampling base learners achieve higher recall than the deep generative base learners. (2) The blending model composed of deep generative base learner and oversampling base learner can further improve the F1-score and AUC based on their individual strengths, the proposed blending model can also conduct effective early warning three units earlier than baseline models. (3) Compared

关键词： Contrastive Learning

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Sampled-Data Consensus for Multiagent Systems With Open Topology and Packet Loss

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3130-3142页

作者： Zhu, Haitao Lu, Jianquan Liu, Yang Zhang, Lingzhong Southeast University School of Mathematics Nanjing210096 China Zhejiang Normal University Zhejiang Key Laboratory of Intelligent Education Technology and Application the School of Mathematical Sciences Jinhua321004 China Zhejiang Normal University Hangzhou School of Automation Hangzhou311231 China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu215500 China

This article studies a more realistic issue in multiagent systems (MASs) named open topology, where the network scale tends to be variable as agents may join, leave, or be replaced. In order to reveal the influence that the variation of network scale brings on the dynamic behavior of MASs, a novel transition process containing impulse is provided. Then by applying the Halanay-like inequality, the distributed sampled-data control is designed to ensure the consensus property of the MASs under open topology. It is shown that the derived results can degenerate into the consensus criteria for general MASs with fixed-scale topology. Also, the potential packet loss phenomenon existing in controller is considered, which is integrated into the consensus performance via the average packet loss interval (APLI) condition, thereby guaranteeing the robustness of consensus performance against packet loss. Moreover, within the framework of open topology, it is proved that the admissible packet loss for MASs can be estimated in an average sense. At the end, the availability of this article is demonstrated by discussing a numerical example. © 2013 IEEE.

关键词： Network topology

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Research on the microstructure and corrosion-wear resistance mechanism of Ti–6Al–4V alloy under cryogenic burnishing

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Journal of Materials Research and technology 2025年 35卷 5854-5871页

作者： Hou, Wen-tao Lou, Ning Wang, Hai-dou Cai, Zhi-hai Xing, Zhi-guo Piao, Zhong-yu College of Mechanical Engineering Zhejiang University of Technology Zhejiang Hangzhou310012 China China International Science and Technology Cooperation Base on Intelligent Equipment Manufacturing in Special Service Environment Anhui University of Technology Ma'anshan243032 China Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education and Zhejiang Province Zhejiang University of Technology Hangzhou310012 China National Key Laboratory for Remanufacturing Army Academy of Armored Forces Beijing100072 China National Engineering Research Center for Remanufacturing Army Academy of Armored Forces Beijing100072 China

The synergistic effect of deep cryogenic treatment (DCT) coupled surface burnishing process (SBP) on the surface structure and properties of Ti–6Al–4V alloy was studied, and the surface modification layer was characterized using surface morphology, microhardness, corrosion wear and other testing methods. DCT is an extension of conventional heat treatment, and SBP is a common metal surface strengthening process. By preparing the surface nano-gradient structure, the surface strengthening of metal is enhanced. The surface hardness of the DCT-SBP sample is slightly higher than that of the SBP sample. In addition, the surface roughness of the DCT-SBP sample is lower. In the electrochemical corrosion test, the corrosion resistance of the DCT-SBP sample in Hank's solution was significantly better than that of the original sample and SBP sample. Especially compared with the original sample, the deep-cold rolling pattern increases by 0.256 V, the self-corrosion current density is the lowest, 1.317 × 10−7 A/cm2, which is an order of magnitude lower than the original sample, and the polarization resistance is the largest, 2.322 × 105 Ω cm2;the Rct value of charge transfer resistance is the highest, which is 2.989 × 105 Ω cm2. Then, in the corrosion-wear resistance test, the maximum wear scar width of the DCT-SBP sample was reduced by about 10%. It is smaller than the original sample under different load wear. And the synergistic mechanism of surface quality, microhardness, microstructure and corrosion-wear resistance of DCT-SBP specimens was analyzed. © 2025 The Authors

关键词： Titanium alloys

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Bilateral Pricing for Dynamic Association in Federated Edge Learning

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4684-4697页

作者： Pan, Bangqi Lu, Jianfeng Cao, Shuqin Liu, Jing Tian, Wenlong Li, Minglu Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Dalian University of Technology Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence China University of South China School of Computer Science and Technology Hengyang421001 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Correlation Information Enhanced Graph Anomaly Detection via Hypergraph Transformation

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IEEE Transactions on Cybernetics 2025年第6期55卷 2865-2878页

作者： Huang, Changqin Gao, Chengling Li, Ming Li, Yongzhi Wang, Xizhe Jiang, Yunliang Huang, Xiaodi Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China College of Education Zhejiang University Hangzhou310058 China Zhejiang Institute of Optoelectronics Jinhua321004 China China National Academy of Educational Sciences Beijing100088 China School of Information Engineering Huzhou University Huzhou313000 China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia

Graph anomaly detection (GAD) has attracted increasing interest due to its critical role in diverse real-world applications. Graph neural networks (GNNs) offer a promising avenue for GAD, leveraging their exceptional capacity to model complex graph structures and relationships. However, existing GNN-based models encounter challenges in addressing the GAD's fundamental issue - anomaly camouflage, where anomalies mimic normal instances, leading to indistinguishable features. In this article, we propose a novel approach, termed correlation information enhanced GAD (CIE-GAD). Specifically, drawing on the observation that the distribution of homophilic and heterophilic edges differs between abnormal and normal samples, we construct a hypergraph to learn the co-occurrence relationships among adjacent edges. By enhancing the extraction of sample correlation information, we effectively tackle feature similarity caused by anomaly camouflage, thereby enhancing the performance of GAD. Furthermore, we develop a spectral convolution mechanism based on node-level attention fusion, enabling the capture of multifrequency signals. This module performs adaptive fusion tailored to the unique frequency information requirements of each node, mitigating the local heterophily problem. Extensive experiments on various real-world GAD datasets demonstrate that the proposed CIE-GAD outperforms state-of-the-art methods. Notably, our approach achieves AUC-PR improvements of up to 3.47%, with an average gain of 1.5%, demonstrating its effectiveness in detecting anomalies in graph data. © 2025 IEEE.

关键词： Graph neural networks

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Between racialized and racializing: Chinese students’ dual experiences of racism in Tanzania

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Higher education 2025年 1-18页

作者： Jiang, Yuxiao Dai, Kun Li, Chao Department of Educational Administration and Policy Faculty of Education The Chinese University of Hong Kong Hong Kong China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Zhejiang China

Research on racism towards international students has predominantly focused on the dichotomy between whites and “Others” (e.g., Black or Asian) in Western contexts. However, relatively few studies have examined these racial dynamics in non-Western settings, despite the growing complexity and diversity of international student mobility, particularly within the Global South. To address this gap, this qualitative study investigates the experiences of racism among 14 Chinese students in Tanzania, drawing on theoretical notions of neo-racism and racial triangulation. The findings reveal that these students are racialized by local Black communities, encountering various interwoven forms of racism. Simultaneously, as they resist racism through detachment and desensitization, they also engage in racializing local Black individuals, paradoxically reinforcing the very racial hierarchies that marginalize them. This dual and interconnected process—where students both experience and perpetuate racism—underscores the fluidity and complexity of racial dynamics within Sino-Tanzanian racialization. Furthermore, this study highlights that this Sino-Tanzanian racialization operates in a triangular rather than binary structure, with the whiteness serving as a constant point of reference. The racial dynamic underlying these interactions is profoundly shaped by white supremacy and become complicit with Western narratives of anti-Black and anti-Chinese sentiments. By moving beyond existing Western-centric discourses, this study contributes to a more nuanced understanding of the intricate nature of racism in global higher education.

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MULTI-LABEL FEATURE SELECTION BASED ON BINARY HASHING LEARNING AND DYNAMIC GRAPH CONSTRAINTS

arXiv

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arXiv 2025年

作者： Guo, Cong Huang, Changqin Zhou, Wenhua Huang, Xiaodi Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China School of Computing Mathematics and Engineering Charles Sturt University Albury Australia

Multi-label learning poses significant challenges in extracting reliable supervisory signals from the label space. Existing approaches often employ continuous pseudo-labels to replace binary labels, improving supervisory information representation. However, these methods can introduce noise from irrelevant labels and lead to unreliable graph structures. To overcome these limitations, this study introduces a novel multi-label feature selection method called Binary Hashing and Dynamic Graph Constraint (BHDG), the first method to integrate binary hashing into multi-label learning. BHDG utilizes low-dimensional binary hashing codes as pseudo-labels to reduce noise and improve representation robustness. A dynamically constrained sample projection space is constructed based on the graph structure of these binary pseudo-labels, enhancing the reliability of the dynamic graph. To further enhance pseudo-label quality, BHDG incorporates label graph constraints and inner product minimization within the sample space. Additionally, an l2,1-norm regularization term is added to the objective function to facilitate the feature selection process. The augmented Lagrangian multiplier (ALM) method is employed to optimize binary variables effectively. Comprehensive experiments on 10 benchmark datasets demonstrate that BHDG outperforms ten state-of-the-art methods across six evaluation metrics. BHDG achieves the highest overall performance ranking, surpassing the next-best method by an average of at least 2.7 ranks per metric, underscoring its effectiveness and robustness in multi-label feature selection. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

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Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

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Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Li, Qi Liu, Gaozhan Zhang, Qianqian Han, Lidong Chen, Wei Li, Rui Xiong, Jinbo School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal University Hangzhou311121 China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou225127 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China School of Computer Science and Technology Xidian University Xi'an710071 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining810008 China Faculty of Software Fujian Normal University Fuzhou350108 China

Ciphertext-Policy Attribute-Based Encryption (CP-ABE) enables fine-grained access control on ciphertexts, making it a promising approach for managing data stored in the cloud-enabled Internet of Things. But existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute content. Additionally, resource-constrained IoT devices, especially those adopting wireless communication, frequently encounter affordability issues regarding decryption costs. In this paper, we propose an efficient and fine-grained access control scheme with fully hidden policies (named FHAC). FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them. A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access policy. Dictionary attacks are thwarted by providing unauthorized users with invalid values. The heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud servers. Additionally, users can verify the correctness of multiple outsourced decryption results simultaneously. Security analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption. © 2024 Chongqing University of Posts and Telecommunications

关键词： Ciphertext

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