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学者姓名：郑相涵
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Abstract ：
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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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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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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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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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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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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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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It has been widely known that long non-coding RNA (lncRNA) plays an important role in gene expression and regulation. However, due to a few characteristics of lncRNA (e.g., huge amounts of data, high dimension, lack of noted samples, etc.), identifying key lncRNA closely related to specific disease is nearly impossible. In this paper, the authors propose a computational method to predict key lncRNA closely related to its corresponding disease. The proposed solution implements a BPSO based intelligent algorithm to select possible optimal lncRNA subset, and then uses ML-ELM based deep learning model to evaluate each lncRNA subset. After that, wrapper feature extraction method is used to select lncRNAs, which are closely related to the pathophysiology of disease from massive data. Experimentation on three typical open datasets proves the feasibility and efficiency of our proposed solution. This proposed solution achieves above 93% accuracy, the best ever.
Keyword ：
Binary Particle Swarm Optimization Binary Particle Swarm Optimization Expression Profile Expression Profile Extreme Learning Machine Extreme Learning Machine Long Non-Coding RNA Long Non-Coding RNA
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| GB/T 7714 | Yang, Wenqing , Zheng, Xianghan , Huang, QiongXia et al. Combining BPSO and ELM Models for Inferring Novel lncRNA- Disease Associations [J]. | INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING , 2023 , 19 (2) . |
| MLA | Yang, Wenqing et al. "Combining BPSO and ELM Models for Inferring Novel lncRNA- Disease Associations" . | INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 19 . 2 (2023) . |
| APA | Yang, Wenqing , Zheng, Xianghan , Huang, QiongXia , Liu, Yu , Chen, Yimi , Song, ZhiGang . Combining BPSO and ELM Models for Inferring Novel lncRNA- Disease Associations . | INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING , 2023 , 19 (2) . |
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