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学者姓名：郑相涵
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Abstract ：
As a distributed machine learning framework, federated learning has received considerable attention in recent years and has been researched and applied in various scenarios. However, the system heterogeneity due to the physical characteristics of various terminal devices has led to the straggler effect, making the practical implementation of federated learning challenging. Therefore, we propose a semi-asynchronous federated optimization method based on buffer pre-aggregation. This method allows every participant to engage in training through pre-aggregation and establishes a training time framework based on the pre-aggregation time. It updates the model adaptively using a semi-asynchronous communication method combined with lag factors, improving communication efficiency while maintaining stable accuracy. Experimental results on datasets demonstrate that our proposed method can effectively accelerate the training process of federated learning compared to existing federated optimization methods.
Keyword ：
Distributed Machine Learning Distributed Machine Learning Federated Learning Federated Learning Semi-Asynchronous Communication Semi-Asynchronous Communication
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| GB/T 7714 | Chen, Yimi , Zheng, Xianghan , Zhan, Yichen . Semi-asynchronous federation optimization method based on buffer pre-aggregation [J]. | 2024 5TH INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKS AND INTERNET OF THINGS, CNIOT 2024 , 2024 : 13-18 . |
| MLA | Chen, Yimi 等. "Semi-asynchronous federation optimization method based on buffer pre-aggregation" . | 2024 5TH INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKS AND INTERNET OF THINGS, CNIOT 2024 (2024) : 13-18 . |
| APA | Chen, Yimi , Zheng, Xianghan , Zhan, Yichen . Semi-asynchronous federation optimization method based on buffer pre-aggregation . | 2024 5TH INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKS AND INTERNET OF THINGS, CNIOT 2024 , 2024 , 13-18 . |
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对于大规模运动模拟问题而言,近邻点的搜索效率将对整体的运算效率产生显著影响.本文基于关联性分析建立kd-tree的最大深度dmax与粒子总数N的自适应关系式,提出了kd-tree自动终止准则,即ATC-kd-tree,同时还考虑了叶子节点大小阈值no对近邻搜索效率的影响.试验表明,ATC-kd-tree具有更高的近邻搜索效率,相较于不使用自动终止准则的kd-tree搜索效率最高提升46％,且适用性更强,可求解不同N值的近邻搜索问题,解决了粒子总数N发生改变时需要再次率定最大深度dmax的问题.同时,本文还提出了网格搜索法组合坐标下降法的两步参数优化算法GSCD法.通过2维阿米巴虫形状的参数优化试验发现,GSCD法可更为快速地率定ATC-kd-tree的可变参数,其优化效率比网格搜索法最高提升了205％,相较于改进网格搜索法最高提升了90％.研究结果表明,ATC-kd-tree和GSCD法不仅提高了近邻搜索的效率,也为复杂运动中近邻粒子搜索问题提供了一种更为高效的解决方案,能够显著降低计算资源的消耗,进一步提升模拟的精度和效率.
Keyword ：
kd-tree kd-tree 坐标下降法 坐标下降法 粒子近邻搜索 粒子近邻搜索 网格搜索法 网格搜索法 自适应 自适应
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| GB/T 7714 | 张挺 , 王宗锴 , 林震寰 et al. 基于自动终止准则改进的kd-tree粒子近邻搜索研究 [J]. | 工程科学与技术 , 2024 , 56 (6) : 217-229 . |
| MLA | 张挺 et al. "基于自动终止准则改进的kd-tree粒子近邻搜索研究" . | 工程科学与技术 56 . 6 (2024) : 217-229 . |
| APA | 张挺 , 王宗锴 , 林震寰 , 郑相涵 . 基于自动终止准则改进的kd-tree粒子近邻搜索研究 . | 工程科学与技术 , 2024 , 56 (6) , 217-229 . |
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Semi-supervised learning (SSL) employs unlabeled data with limited labeled samples to enhance deep networks, but imbalance degrades performance due to biased pseudo-labels skewing decision boundaries. To address this challenge, we propose two optimization conditions inspired by our theoretical analysis. These conditions focus on aligning class distributions and representations. Additionally, we introduce a plug-and-play method called Basis Transformation based distribution alignment (BTDA) that efficiently aligns class distributions while considering inter-class relationships. BTDA mitigates the negative impact of biased pseudo-labels through basis transformation, which involves a learnable transition matrix. Extensive experiments demonstrate the effectiveness of integrating existing SSL methods with BTDA in image classification tasks with class imbalance. For example, BTDA achieves accuracy improvements ranging from 2.47 to 6.66% on CIFAR10-LT and SVHN-LT datasets, and a remarkable 10.95% improvement on the tail class, even under high imbalanced rates. Despite its simplicity, BTDA achieves state-of-the-art performance in SSL with class imbalance on representative datasets.
Keyword ：
Basis transformation Basis transformation Class-imbalanced datasets Class-imbalanced datasets Distribution alignment Distribution alignment Image classification Image classification Inter-class bias Inter-class bias Semi-supervised learning Semi-supervised learning
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| GB/T 7714 | Ye, Jinhuang , Gao, Xiaozhi , Li, Zuoyong et al. Btda: basis transformation based distribution alignment for imbalanced semi-supervised learning [J]. | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2024 , 15 (9) : 3829-3845 . |
| MLA | Ye, Jinhuang et al. "Btda: basis transformation based distribution alignment for imbalanced semi-supervised learning" . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 15 . 9 (2024) : 3829-3845 . |
| APA | Ye, Jinhuang , Gao, Xiaozhi , Li, Zuoyong , Wu, Jiawei , Xu, Xiaofeng , Zheng, Xianghan . Btda: basis transformation based distribution alignment for imbalanced semi-supervised learning . | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS , 2024 , 15 (9) , 3829-3845 . |
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Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class relationships.BTDA implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Classification (of information) Classification (of information) Image classification Image classification Linear transformations Linear transformations Machine learning Machine learning Transfer matrix method Transfer matrix method
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| GB/T 7714 | Ye, Jinhuang , Wu, Jiawei , Li, Zuoyong et al. Rethinking Distribution Alignment for Inter-class Fairness [C] . 2024 : 10-21 . |
| MLA | Ye, Jinhuang et al. "Rethinking Distribution Alignment for Inter-class Fairness" . (2024) : 10-21 . |
| APA | Ye, Jinhuang , Wu, Jiawei , Li, Zuoyong , Zheng, Xianghan . Rethinking Distribution Alignment for Inter-class Fairness . (2024) : 10-21 . |
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In mobile edge computing (MEC) systems, unmanned aerial vehicles (UAVs) facilitate edge service providers (ESPs) offering flexible resource provisioning with broader communication coverage and thus improving the Quality of Service (QoS). However, dynamic system states and various traffic patterns seriously hinder efficient cooperation among UAVs. Existing solutions commonly rely on prior system knowledge or complex neural network models, lacking adaptability and causing excessive overheads. To address these critical challenges, we propose the DisOff, a novel profit-aware cooperative offloading framework in UAV-enabled MEC with lightweight deep reinforcement learning (DRL). First, we design an improved DRL with twin critic-networks and delay mechanism, which solves the $Q$ -value overestimation and high variance and thus approximates the optimal UAV cooperative offloading and resource allocation. Next, we develop a new multiteacher distillation mechanism for the proposed DRL model, where the policies of multiple UAVs are integrated into one DRL agent, compressing the model size while maintaining superior performance. Using the real-world datasets of user traffic, extensive experiments are conducted to validate the effectiveness of the proposed DisOff. Compared to benchmark methods, the DisOff enhances ESP profits while reducing the DRL model size and training costs.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Computational modeling Computational modeling Computation offloading Computation offloading deep reinforcement learning (DRL) deep reinforcement learning (DRL) Internet of Things Internet of Things mobile edge computing (MEC) mobile edge computing (MEC) model compression model compression Optimization Optimization Quality of service Quality of service Resource management Resource management Training Training unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Chen, Zheyi , Zhang, Junjie , Zheng, Xianghan et al. Profit-Aware Cooperative Offloading in UAV-Enabled MEC Systems Using Lightweight Deep Reinforcement Learning [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) : 21325-21336 . |
| MLA | Chen, Zheyi et al. "Profit-Aware Cooperative Offloading in UAV-Enabled MEC Systems Using Lightweight Deep Reinforcement Learning" . | IEEE INTERNET OF THINGS JOURNAL 11 . 12 (2024) : 21325-21336 . |
| APA | Chen, Zheyi , Zhang, Junjie , Zheng, Xianghan , Min, Geyong , Li, Jie , Rong, Chunming . Profit-Aware Cooperative Offloading in UAV-Enabled MEC Systems Using Lightweight Deep Reinforcement Learning . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) , 21325-21336 . |
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Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.
Keyword ：
Access control Access control Blockchain Blockchain DIDs DIDs Multi-domain environments Multi-domain environments Smart contracts Smart contracts Verifiable credentials Verifiable credentials
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| GB/T 7714 | Du, Zhiqiang , Li, Yunliang , Fu, Yanfang et al. Blockchain-based access control architecture for multi-domain environments [J]. | PERVASIVE AND MOBILE COMPUTING , 2024 , 98 . |
| MLA | Du, Zhiqiang et al. "Blockchain-based access control architecture for multi-domain environments" . | PERVASIVE AND MOBILE COMPUTING 98 (2024) . |
| APA | Du, Zhiqiang , Li, Yunliang , Fu, Yanfang , Zheng, Xianghan . Blockchain-based access control architecture for multi-domain environments . | PERVASIVE AND MOBILE COMPUTING , 2024 , 98 . |
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Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles. To address these challenges, we propose a dynamic privacy-preserving anonymous authentication scheme for condition matching in fog-cloud-based VANETs. The scheme employs general Elliptic Curve Cryptosystem (ECC) technology and fog-cloud computing methods to decrease computational overhead for On-Board Units (OBUs) and supports multiple TAs for improved service quality and robustness. Furthermore, certificateless technology alleviates TAs of key management burdens. The security analysis indicates that our solution satisfies the communication security and privacy requirements. Experimental simulations verify that our method achieves optimal overall performance with lower computational costs and smaller communication overhead compared to state-of-the-art solutions.
Keyword ：
authenticated key agreement authenticated key agreement conditional privacy-preserving conditional privacy-preserving dynamic group dynamic group fog-cloud computing fog-cloud computing VANETs VANETs
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| GB/T 7714 | Zhan, Yonghua , Xie, Weipeng , Shi, Rui et al. Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs [J]. | SENSORS , 2024 , 24 (6) . |
| MLA | Zhan, Yonghua et al. "Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs" . | SENSORS 24 . 6 (2024) . |
| APA | Zhan, Yonghua , Xie, Weipeng , Shi, Rui , Huang, Yunhu , Zheng, Xianghan . Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs . | SENSORS , 2024 , 24 (6) . |
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Face recognition technology is widely used in various fields, such as law enforcement, payment systems, transportation, and access control. Traditional face authentication systems typically establish a facial feature template database for identity verification. However, this approach poses various security risks, such as the risk of plaintext feature data stored in cloud databases being leaked or stolen. To address these issues, in recent years, a face recognition technology based on homomorphic encryption has gained attention. Based on homomorphic encryption, face recognition can encrypt facial feature values and achieve feature matching without exposing the feature information. However, due to the encryption, face recognition in the ciphertext domain often requires considerable time. In this paper, we introduce the big data stream processing engine Flink to achieve parallel computation of face recognition in the ciphertext domain based on homomorphic encryption. We analyze the security, accuracy, and acceleration of this approach. Ultimately, we verify that this approach achieves recognition accuracy close to plaintext and significant efficiency improvement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Keyword ：
Access control Access control Face recognition Face recognition Privacy-preserving techniques Privacy-preserving techniques
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| GB/T 7714 | Wang, Gong , Zheng, Xianghan , Zeng, Lingjing et al. A Privacy-Preserving Face Recognition Scheme Combining Homomorphic Encryption and Parallel Computing [C] . 2024 : 38-52 . |
| MLA | Wang, Gong et al. "A Privacy-Preserving Face Recognition Scheme Combining Homomorphic Encryption and Parallel Computing" . (2024) : 38-52 . |
| APA | Wang, Gong , Zheng, Xianghan , Zeng, Lingjing , Xie, Weipeng . A Privacy-Preserving Face Recognition Scheme Combining Homomorphic Encryption and Parallel Computing . (2024) : 38-52 . |
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In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.
Keyword ：
computer vision computer vision construction waste management construction waste management long-distance target detection long-distance target detection unmanned aerial vehicle unmanned aerial vehicle
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| GB/T 7714 | Wang, Zeli , Yang, Xincong , Zheng, Xianghan et al. Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle [J]. | SENSORS , 2024 , 24 (9) . |
| MLA | Wang, Zeli et al. "Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle" . | SENSORS 24 . 9 (2024) . |
| APA | Wang, Zeli , Yang, Xincong , Zheng, Xianghan , Li, Heng . Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle . | SENSORS , 2024 , 24 (9) . |
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With the rapid development of Ethereum, vast amounts of data are recorded on the blockchain through transactions, encompassing diverse and extensive textual information. While Long Short-Term Memory (LSTM) models have shown remarkable effectiveness in sentiment analysis tasks in recent years, they often encounter situations where different features have equal importance when processing such textual data. Therefore, this study introduces a Bidirectional LSTM model with a Multi-Head Attention mechanism (MABLSTM) designed for sentiment analysis tasks in Ethereum transaction texts. BLSTM consists of two distinct and independent LSTMs that consider information flow from two directions, capturing contextual information from both the past and the future. The outputs from the BLSTM layer are enhanced using a multi-head attention mechanism to amplify the importance of sentiment words and blockchain-specific terms. This paper evaluates the effectiveness of MABLSTM on Ethereum transaction data through experiments conducted on an Ethereum transaction dataset, comparing MABLSTM with CNN, SVM, ABLSTM and ABCDM. The results demonstrate the effectiveness and superiority of MABLSTM in sentiment analysis tasks. This approach accurately analyzes sentiment polarity in Ethereum transaction texts, providing valuable information for Ethereum participants and researchers to support decision-making and emotional analysis.
Keyword ：
Attention Mechanism Attention Mechanism BLSTM BLSTM Deep Learning Deep Learning Ethereum Ethereum MABLST MABLST Sentiment Analysis Sentiment Analysis
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| GB/T 7714 | Zheng, Xianghan , Zhang, Wenyan , Zhang, Jianxian et al. Ethereum Public Opinion Analysis Based on Attention Mechanism [J]. | COGNITIVE COMPUTING - ICCC 2023 , 2024 , 14207 : 100-115 . |
| MLA | Zheng, Xianghan et al. "Ethereum Public Opinion Analysis Based on Attention Mechanism" . | COGNITIVE COMPUTING - ICCC 2023 14207 (2024) : 100-115 . |
| APA | Zheng, Xianghan , Zhang, Wenyan , Zhang, Jianxian , Xie, Weipeng . Ethereum Public Opinion Analysis Based on Attention Mechanism . | COGNITIVE COMPUTING - ICCC 2023 , 2024 , 14207 , 100-115 . |
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