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学者姓名：郑相涵
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With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data. © 2024 Elsevier B.V.
Keyword ：
Certificateless Certificateless Data security Data security Electronic healthcare system Electronic healthcare system Mobile communication Mobile communication Sanitizable signature Sanitizable signature
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| GB/T 7714 | Zhan, Y. , Yang, Y. , Yi, B. et al. A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system [J]. | Computer Communications , 2024 , 228 . |
| MLA | Zhan, Y. et al. "A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system" . | Computer Communications 228 (2024) . |
| APA | Zhan, Y. , Yang, Y. , Yi, B. , He, R. , Shi, R. , Zheng, X. . A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system . | Computer Communications , 2024 , 228 . |
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With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data.
Keyword ：
Certificateless Certificateless Data security Data security Electronic healthcare system Electronic healthcare system Mobile communication Mobile communication Sanitizable signature Sanitizable signature
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| GB/T 7714 | Zhan, Yonghua , Yang, Yang , Yi, Bixia et al. A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system [J]. | COMPUTER COMMUNICATIONS , 2024 , 228 . |
| MLA | Zhan, Yonghua et al. "A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system" . | COMPUTER COMMUNICATIONS 228 (2024) . |
| APA | Zhan, Yonghua , Yang, Yang , Yi, Bixia , He, Renjie , Shi, Rui , Zheng, Xianghan . A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system . | COMPUTER COMMUNICATIONS , 2024 , 228 . |
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Sorting is an important construction waste management tool to increase recycling rates and reduce pollution. Previous studies have used robots to improve the efficiency of construction waste recycling. However, in large construction sites, it is difficult for a single robot to accomplish the task quickly, and multiple robots working together are a better option. Most construction waste recycling robotic systems are developed based on a client-server framework, which means that all robots need to be continuously connected to their respective cloud servers. Such systems are low in robustness in complex environments and waste a lot of computational resources. Therefore, in this paper, we propose a pixel-level automatic construction waste recognition platform with high robustness and low computational resource requirements by combining multiple computer vision technologies with edge computing and cloud computing platforms. Experiments show that the computing platform proposed in this study can achieve a recognition speed of 23.3 fps and a recognition accuracy of 90.81% at the edge computing platform without the help of network and cloud servers. This is 23 times faster than the algorithm used in previous research. Meanwhile, the computing platform proposed in this study achieves 93.2% instance segmentation accuracy on the cloud server side. Notably, this system allows multiple robots to operate simultaneously at the same construction site using only a single server without compromising efficiency, which significantly reduces costs and promotes the adoption of automated construction waste recycling robots.
Keyword ：
cloud computing cloud computing computer vision computer vision construction waste management construction waste management edge computing edge computing multi-robot multi-robot waste recycling waste recycling
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| GB/T 7714 | Wang, Zeli , Yang, Xincong , Zheng, Xianghan et al. Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing [J]. | BUILDINGS , 2024 , 14 (12) . |
| MLA | Wang, Zeli et al. "Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing" . | BUILDINGS 14 . 12 (2024) . |
| APA | Wang, Zeli , Yang, Xincong , Zheng, Xianghan , Huang, Daoyin , Jiang, Binfei . Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing . | BUILDINGS , 2024 , 14 (12) . |
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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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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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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 et al. "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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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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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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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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