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IEEE Transactions on Artificial intelligence 2024年第2期5卷 647-660页

作者： Tian, Jiao Tsai, Pei-Wei Zhang, Kai Cai, Xinyi Xiao, Hongwang Yu, Ke Zhao, Wenyu Chen, Jinjun Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia Center for Health Computing Research Beijing Academy of Artificial Intelligence Beijing100084 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can detect this problem and make timely adjustments. In this article, we provide a new local-global class imbalance inconsistency quantification and utilize softmax as the activation and focal loss, a dynamically scaled cross-entropy loss, in federated XGBoost to mitigate local class imbalance. Moreover, we propose a simple but effective hyperparameter determination strategy based on local data distribution to adjust the sample weights among noncommunicating participants, synergetic focal loss, to solve the inconsistency of local and global class imbalance, a unique characteristic of federated learning. This strategy is perfectly integrated into the original classification algorithm. It requires no additional detectors or information transmission. Furthermore, a dynamical for loop is designed to capture an optimum hyperparameter combination. Finally, we conduct comprehensive tabular- and image-based experiments to show that synergetic focal loss used in federated XGBoost achieves faster convergency and significant accuracy improvement. Simulation results prove the effectiveness of the proposed principle of configuring sample weights. © 2023 IEEE.

关键词： Heuristic algorithms

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of Computer Science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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XGCN:a library for large-scale graph neural network recommendations

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Frontiers of Computer Science 2024年第3期18卷 247-249页

作者： Xiran SONG Hong HUANG Jianxun LIAN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Microsoft Research Asia Beijing 100080China

1 Introduction Graph Neural Networks(GNNs)have gained widespread adoption in recommendation systems,and nowadays there is a pressing need to effectively manage large-scale graph data[1].When it comes to large graphs,GNNs may encounter the scalability issue stemming from their multi-layer messagepassing ***,scaling GNNs has emerged as a crucial research area in recent years,with numerous scaling strategies being proposed.

关键词： scaling gained operations

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Graph Neural Network-Based User Preference Model for Social Network Access Control

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 21-36页

作者： Zhang, Yuan Xuchang Vocational Technical College Xuchang461000 China Henan Province Data Intelligence and Security Application Engineering Technology Research Center Xuchang461000 China

The popularity and deepening of social networks have increased the risk of personal information leakage for users. To enhance the security of social networks, this study constructed an access control model based on the preferences of social network users. This model utilizes graph neural networks to generate access control strategies based on user preferences, and introduces a multi-layer attention mechanism to optimize the graph neural network. To better capture user preference information, the study sets the learning rate to 0.0001. The experimental results demonstrated that in the Twitter dataset, the accuracy of the proposed model reached 95.7% and the F1 score reached 96.2%, which were significantly higher than those of other models. These results indicated that the model could more accurately classify access control in social networks and reduce false positives. The area under the receiver operation characteristic curve of the proposed model was 0.982, which was higher than other models. The decision time was 13.77 seconds, significantly lower than other models. This indicated that the model could more effectively distinguish different types of user access requests and provide more reliable guarantees for secure access to social networks. The user's preferred social network access control model based on graph neural networks has superior performance, effectively ensuring the information security of social network users and laying the foundation for further development of access control technology. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Access control models

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology 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 Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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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 adaptive defense automated defense programmable defense security services software-defined networking security management

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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial Intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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Deep learning-based semantic segmentation of human features in bath scrubbing robots

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Biomimetic intelligence & Robotics 2024年第1期4卷 70-79页

作者： Chao Zhuang Tianyi Ma Bokai Xuan Cheng Chang Baichuan An Minghuan Yin Hao Sun School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing *** study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog *** investigation focuses on semantic segmentation of human features using deep learning ***,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human ***,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human ***,the network is refined by integrating the spatial feature extraction module and the channel attention module based on *** results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over *** conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

关键词： 3D point cloud Human model LiDAR Semantic segmentation

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InDNI: An Infection Time Independent Method for Diffusion Network Inference 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Chen, Guoxin Wang, Yongqing Shao, Jiangli Shi, Boshen Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Diffusion network inference aims to reveal the message propagation process among users and has attracted many research interests due to the fundamental role it plays in some real applications, such as rumor-spread forecasting and epidemic controlling. Most existing methods tackle the task with exact node infection time. However, collecting infection time information is time-consuming and labor-intensive, especially when information flows are huge and complex. To combat the problem, we propose a new diffusion network inference algorithm that only relies on infection states. The proposed method first encodes several observation states into a node infection matrix and then obtains the node embedding via the variational autoencoder (VAE). Nodes with the least Wasserstein distance of embeddings are predicted for existing propagation edges. Meanwhile, to reduce the complexity, a novel clustering-based filtering strategy is designed for selecting latent propagation edges. Extensive experiments show that the proposed model outperforms the state-of-the-art infection time independent models while demonstrating comparable performance over infection time based models. © 2023, Springer Nature Switzerland AG.

关键词： Embeddings

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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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