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学者姓名：董晨
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Abstract ：
Electronic tickets (e-tickets) are gradually being adopted as a substitute for paper-based tickets to bring convenience to customers, corporations, and governments. However, their adoption faces a number of practical challenges, such as flexibility, privacy, secure storage, and inability to deploy on IoT devices such as smartphones. These concerns motivate the current research on e-ticket systems, which seeks to ensure the unforgeability and authenticity of e-tickets while simultaneously protecting user privacy. Many existing schemes cannot fully satisfy all these requirements. To improve on the current state-of-the-art solutions, this paper constructs a blockchain-enhanced privacy-preserving e-ticket system for IoT devices, dubbed PriTKT, which is based on blockchain, structure-preserving signatures (SPS), unlinkable redactable signatures (URS), and zero-knowledge proofs (ZKP). It supports flexible policy-based ticket purchasing and ensures user unlinkability. According to the data minimization and revealing principle of GDPR, PriTKT empowers users to selectively disclose subsets of (necessary) attributes to sellers as long as the disclosed attributes satisfy ticket purchasing policies. In addition, benefiting from the decentralization and immutability of blockchain, effective detection and efficient tracing of double spending of e-tickets are supported in PriTKT. Considering the impracticality of existing e-tickets schemes with burdensome ZKPs, we replace them with URS/SPS or efficient ZKP to significantly improve the efficiency of ticket issuing and make it suitable for use on smartphones.
Keyword ：
blockchain blockchain double-spending detection double-spending detection electronic tickets electronic tickets IoT IoT privacy-preserving privacy-preserving
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| GB/T 7714 | Zhan, Yonghua , Yuan, Feng , Shi, Rui et al. PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices [J]. | SENSORS , 2024 , 24 (2) . |
| MLA | Zhan, Yonghua et al. "PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices" . | SENSORS 24 . 2 (2024) . |
| APA | Zhan, Yonghua , Yuan, Feng , Shi, Rui , Shi, Guozhen , Dong, Chen . PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices . | SENSORS , 2024 , 24 (2) . |
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Active learning (AL) tries to maximize the model's performance when the labeled data set is limited, and the annotation cost is high. Although it can be efficiently implemented in deep neural networks (DNNs), it is questionable whether the model can maintain the ability to generalize well when there are significant distributional deviations between the labeled and unlabeled data sets. In this article, we consider introducing adversarial training and adversarial samples into AL to mitigate the problem of degraded generalization performance due to different data distributions. In particular, our proposed adversarial training AL (ATAL) has two advantages, one is that adversarial training by different networks enables the network to have better prediction performance and robustness with limited labeled samples. The other is that the adversarial samples generated by the adversarial training can effectively expand the labeled data set so that the designed query function can efficiently select the most informative unlabeled samples based on the expanded labeled data set. Extensive experiments have been performed to verify the feasibility and efficiency of our proposed method, i.e., CIFAR-10 demonstrates the effectiveness of our method-new state-of-the-art robustness and accuracy are achieved.
Keyword ：
Active learning (AL) Active learning (AL) adversarial learning adversarial learning adversarial samples adversarial samples Bayes methods Bayes methods data distribution data distribution Data models Data models Generative adversarial networks Generative adversarial networks Labeling Labeling robustness robustness Robustness Robustness Training Training Uncertainty Uncertainty
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| GB/T 7714 | Lin, Xuanwei , Liu, Ximeng , Chen, Bijia et al. ATAL: Active Learning Using Adversarial Training for Data Augmentation [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) : 4787-4800 . |
| MLA | Lin, Xuanwei et al. "ATAL: Active Learning Using Adversarial Training for Data Augmentation" . | IEEE INTERNET OF THINGS JOURNAL 11 . 3 (2024) : 4787-4800 . |
| APA | Lin, Xuanwei , Liu, Ximeng , Chen, Bijia , Wang, Yuyang , Dong, Chen , Hu, Pengzhen . ATAL: Active Learning Using Adversarial Training for Data Augmentation . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) , 4787-4800 . |
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Biological assays around "lab-on-a-chip (LoC)" are required in multiple concentration (or dilution) factors, satisfying specific sample concentrations. Unfortunately, most of them suffer from non-locality and are non-protectable, requiring a large footprint and high purchase cost. A digital geometric technique can generate arbitrary gradient profiles for digital microfluidic biochips (DMFBs). A next- generation DMFB has been proposed based on the microelectrode-dot-array (MEDA) architectures are shown to produce and disperse droplets by channel dispensing and lamination mixing. Prior work in this area must address the problem of reactant and waste minimization and concurrent sample preparation for multiple target concentrations. This paper proposes the first splitting-droplet sharing algorithm for reactant and waste minimization of multiple target concentrations on MEDAs. The proposed algorithm not only minimizes the consumption of reagents but also reduces the number of waste droplets by preparing the target concentrations concurrently. Experimental results on a sequence of exponential gradients are presented in support of the proposed method and demonstrate its effectiveness and efficiency. Compared to prior work, the proposed algorithm can achieve up to a 24.8% reduction in sample usage and reach an average of 50% reduction in waste droplets.
Keyword ：
Biochip Biochip Dilution Dilution Microelectrode-dot-array (MEDA) Microelectrode-dot-array (MEDA) Mixing tree Mixing tree Reactant minimization Reactant minimization Sample preparation Sample preparation
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| GB/T 7714 | Dong, Chen , Chen, Xiao , Chen, Zhenyi . Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees [J]. | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2024 , 40 (1) : 87-99 . |
| MLA | Dong, Chen et al. "Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees" . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 40 . 1 (2024) : 87-99 . |
| APA | Dong, Chen , Chen, Xiao , Chen, Zhenyi . Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2024 , 40 (1) , 87-99 . |
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The primary feature of Industry 4.0 is MPP (mass personalization production), which requires that consumers' individual requests are met in large-scale production. Under MPP, there is a multitude of subtasks decomposed from production tasks that are derived from individualized requests, and allocating these subtasks properly brings high economic benefits. However, existing approaches to achieve MPP, such as cloud manufacturing and social manufacturing, generally can not provide customers with deep participation in the entire production cycle, or respond to consumers' modification needs by a triggered mechanism. Besides, some methods are of centralized architecture, which is vulnerable to single point error and with large cloud load that is not conducive to quickly responding to consumers' dynamic demand changes. Therefore, this paper proposes a dynamic edge-cloud manufacturing mode for MPP, which can make subtask allocation with high economic benefit through distributed computing and implementing modifications of alternating direction method of multiplier (ADMM) algorithm. Also, it proposes an original improved ADMM algorithm, named Relaxation-Based ADMM algorithm, to increase the optimization speed in large-scale cases. The experimental results show that the proposed method generally obtains a superior solution under a certain iteration count.(c) 2023 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword ：
Cloud computing Cloud computing Convex optimization Convex optimization Edge computing Edge computing Industry 4.0 Industry 4.0 Intelligent manufacturing Intelligent manufacturing Mass personalization production Mass personalization production
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| GB/T 7714 | Dong, Chen , Luo, Jihai , Hong, Qiyu et al. A dynamic distributed edge-cloud manufacturing with improved ADMM algorithms for mass personalization production [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (8) . |
| MLA | Dong, Chen et al. "A dynamic distributed edge-cloud manufacturing with improved ADMM algorithms for mass personalization production" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 35 . 8 (2023) . |
| APA | Dong, Chen , Luo, Jihai , Hong, Qiyu , Chen, Zhenyi , Chen, Yuzhong . A dynamic distributed edge-cloud manufacturing with improved ADMM algorithms for mass personalization production . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (8) . |
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Due to technical barriers and economic costs, malicious circuits, known as hardware Trojans, are easily implanted in the complicated integrated circuit design and manufacturing process, which can lead to many disastrous consequences, such as denial of service, information leakage, performance degradation, etc. Research on how to detecting hardware Trojans has grown into a significantly open issue over the past decade. While, for very large scale integrated circuits, numerous new challenges deserve our full attention, including golden -free chip reference, automatic feature engineering, hardware Trojan localization, and scalable framework. In response to the above challenges, a fine-grained gate-level hardware Trojan detection approach is proposed in this paper, named GateDet, from improving earlier circuit graph modeling to developing a detection framework based on Bidirectional Graph Convolution Networks with a timely information fusion strategy. GateDet achieves automatic feature circuit extraction and further overcomes the original neighborhood limitation of Bidirectional Graph Convolution Network. Moreover, for large-scale training, it comprehensively considers the problems of sample imbalance and boundary network, and develops a circuit directed graph sampling method based on GraphSAINT, which improves the training performance of the directed graph framework. From experiments, GateDet shows high scalability on 24 benchmarks of TrustHub. It could be used to learn about adaptive structural feature extraction for different Trojans simultaneously. Compared to the existing gate-level detections, the fine-grained results of GateDet are more accurate and can be used to track suspicious structures, reducing manual review.
Keyword ：
Gate-level Gate-level Golden-free Golden-free Graph Neural Network Graph Neural Network Hardware Trojan Hardware Trojan Static detection Static detection
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| GB/T 7714 | Cheng, Dong , Dong, Chen , He, Wenwu et al. A fine-grained detection method for gate-level hardware Trojan base on bidirectional Graph Neural Networks [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (10) . |
| MLA | Cheng, Dong et al. "A fine-grained detection method for gate-level hardware Trojan base on bidirectional Graph Neural Networks" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 35 . 10 (2023) . |
| APA | Cheng, Dong , Dong, Chen , He, Wenwu , Chen, Zhenyi , Liu, Ximeng , Zhang, Hao . A fine-grained detection method for gate-level hardware Trojan base on bidirectional Graph Neural Networks . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2023 , 35 (10) . |
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Digital microfluidic, as an emerging and potential technology, diversifies the biochemical applications platform, such as protein dilution sewage detection. At present, a vast majority of universal cyberphysical digital microfluidic biochips (DMFBs) transmit data through wires via personal computers and microcontrollers (like Arduino), consequently, susceptible to various security threats and with the popularity of wireless devices, losing competitiveness gradually. On the premise that security be ensured first and foremost, calls for wireless portable, safe, and economical DMFBs are imperative to expand their application fields, engage more users, and cater to the trend of future wireless communication. To this end, a new cyber-physical DMFB called PortableLab is proposed in this paper, which guarantees data security through wireless sensors at low cost. After considering the security, computing consumption, and cost, a mobile module is added. In addition, the improved Advanced Encryption Standard (AES) and Cyclic Redundancy Check (CRC) algorithms are utilized to ensure the integrity and confidentiality of data transmission. Ultimately, all the security analysis, cost analysis, and experimental results on multiple protocols demonstrate the feasibility of the proposed PortableLab DMFB in time and space.
Keyword ：
DMFB DMFB healthcare healthcare low-cost low-cost mobile mobile security security wireless wireless
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| GB/T 7714 | Yao, Yinan , Qiu, Decheng , Liu, Huangda et al. A Reliable and Secure Mobile Cyber-Physical Digital Microfluidic Biochip for Intelligent Healthcare [J]. | IEEE ACCESS , 2023 , 11 : 137990-137998 . |
| MLA | Yao, Yinan et al. "A Reliable and Secure Mobile Cyber-Physical Digital Microfluidic Biochip for Intelligent Healthcare" . | IEEE ACCESS 11 (2023) : 137990-137998 . |
| APA | Yao, Yinan , Qiu, Decheng , Liu, Huangda , Yang, Zhongliao , Liu, Ximeng , Yang, Yang et al. A Reliable and Secure Mobile Cyber-Physical Digital Microfluidic Biochip for Intelligent Healthcare . | IEEE ACCESS , 2023 , 11 , 137990-137998 . |
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As we all know, population aging is a significant challenge faced by Chinese society, and ensuring the health and safety of elderly individuals has become an urgent topic of concern. Within the context of family safety, elderly individuals often experience falls or fainting due to age-related physical decline or underlying medical conditions. In response to this phenomenon, this paper presents a method for enhancing the wellbeing of family members by utilizing the YOLOv5 model to detect falls. Moreover, due to the built-in capability of YOLOv5 to read video from webcam, this technology can also be integrated into loT devices, turning these devices into a part of smart homes. Considering the specific nature of the home environment, CAU CAFall is considered to be the most suitable dataset. Various variations of the YOLOv5 model are experi-mented on a CAUCAFall dataset and achieve promising results. The YOLOv5x model achieved a precision of 82.2%, while the YOLOv5s model, with improved running speed, achieved an precision of 79.6%. Finally, we explored and selected the most suitable YOLOv5 model for home fall detection considering comprehensive evaluation metrics, and it is YOLOv5s. © 2023 IEEE.
Keyword ：
Automation Automation Deep learning Deep learning Fall detection Fall detection Intelligent buildings Intelligent buildings
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| GB/T 7714 | Ke, Yaojie , Yao, Yinan , Xie, Zhengye et al. Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5 [C] . 2023 : 942-949 . |
| MLA | Ke, Yaojie et al. "Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5" . (2023) : 942-949 . |
| APA | Ke, Yaojie , Yao, Yinan , Xie, Zhengye , Xie, Hepeng , Lin, Hui , Dong, Chen . Empowering Intelligent Home Safety: Indoor Family Fall Detection with YOLOv5 . (2023) : 942-949 . |
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In the era of big data, the number of internet users is increasing yearly, and each user receives a massive amount of information every day. The low-value density of massive text data makes it difficult to utilize its information. In the face of this problem, text summarization generation technology that can extract the paper's leading content and condense the paper's main idea is a powerful solution. In text summarization generation, abstractive summarization techniques with high readability, clear expression, and closer proximity to human language habits are often favored. However, mainstream technologies still have issues like summarizations' detachment from focus. In this paper, keyword information is employed to join the text summarization generation task, and a keyword information module is added to the Transformer to better perform the summarization generation task. Finally, the ROUGE evaluation standard is used to compare the performance of the above models on the large-scale Chinese short text summarization (LCSTS) dataset. The experimental results show that the model generates higher-quality summarizations using keyword information, and the related methods have considerable potential. Future work can explore better model structures, more suitable methods for utilizing keyword information, and more accurate keyword extraction techniques to improve the quality of abstractive summarizations. © 2023 IEEE.
Keyword ：
Deep learning Deep learning Large dataset Large dataset Model structures Model structures Natural language processing systems Natural language processing systems Text processing Text processing
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| GB/T 7714 | Lin, Jingsong , Guo, Xiaodong , Dong, Chen et al. Text Summarization Generation Based on Improved Transformer Model [C] . 2023 : 831-836 . |
| MLA | Lin, Jingsong et al. "Text Summarization Generation Based on Improved Transformer Model" . (2023) : 831-836 . |
| APA | Lin, Jingsong , Guo, Xiaodong , Dong, Chen , Lyu, Chenxi , Xu, Li , Chen, Zhenyi . Text Summarization Generation Based on Improved Transformer Model . (2023) : 831-836 . |
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In the realm of big data and cloud computing, the autonomous vehicle is an extremely promising and complex research topic, which is driven primarily by computer systems that employ AI technology, radar, GPS, visual computing, cloud computing, and other technologies to operate. autonomous vehicles use LIDAR to sense road information, and highly accurate maps are integrated with V2X (vehicle networking technology) to recognize relevant information such as traffic lights and speed limit signs. However, in some remote areas where map information needs to be completed, recognition of traffic lights and traffic signs cannot rely on positioning to achieve this. This paper uses the Efficientdet-d1 target detection algorithm built on Pytorch to simulate autonomous vehicles sensing pedestrian, vehicle, traffic light, and traffic sign information. This target detection algorithm uses EfficientNet-B1 as the backbone network and enhances the feature extraction process using four stacked BiFPN modules. The method involves using the open source big dataset LISA traffic light dataset and GTSRB German traffic sign dataset to train the model. Considering the uneven distribution of samples in the dataset, these classes are distributed into three Efficientdet-d1 target detection frameworks for pedestrians and various types of vehicles, traffic lights, and traffic signs, respectively. A multi-threaded approach allows the three detection processes to be executed simultaneously. The detection results are stored in a queue before aggregation for mapping to improve the speed of single-image execution. The experiments show the three prediction networks used achieved better results overall. The method proposed in this paper is a practical guide for autonomous vehicles to make road condition judgments during driving in remote areas.
Keyword ：
automatic driving automatic driving big data big data cloud computing cloud computing computer vision computer vision efficientdet efficientdet
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| GB/T 7714 | Lyu, Chenxi , Fan, Xinwen , Qiu, Zhenyu et al. Efficientdet Based Visial Perception for Autonomous Driving [J]. | 2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA , 2023 : 443-447 . |
| MLA | Lyu, Chenxi et al. "Efficientdet Based Visial Perception for Autonomous Driving" . | 2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA (2023) : 443-447 . |
| APA | Lyu, Chenxi , Fan, Xinwen , Qiu, Zhenyu , Chen, Jun , Lin, Jingsong , Dong, Chen . Efficientdet Based Visial Perception for Autonomous Driving . | 2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA , 2023 , 443-447 . |
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In the present era, the menace of malicious software is growing continuously, posing a significant challenge in the realm of cybersecurity. Currently, traditional machine learning algorithms have been widely applied to the analysis of malicious software and are considered effective methods. However, these approaches often require extensive feature engineering, feature learning, and feature representation, which can be time-consuming and resource-intensive. In this paper, a method for the identification of malicious software, named 'AntiMal,'is proposed. This method utilizes the Swin Transformer as the backbone network and combines it with image features for static analysis. It leverages the dataset provided by Microsoft for the Kaggle Microsoft Malware Classification Challenge. Initially, it converts the binary data of malicious code into grayscale images, treating every 8 bits as a pixel. Subsequently, it employs PyTorch to build and train the Swin Transformer, classifying the images in the validation set. The results demonstrate that this approach excels in the problem of malicious software classification, achieving an impressive accuracy of up to 95%, showcasing remarkable precision and generalization capabilities. Furthermore, it exhibits significant advantages when dealing with large datasets, requiring fewer computational resources, thus offering exceptional computational efficiency. © 2023 ACM.
Keyword ：
Classification (of information) Classification (of information) Computational efficiency Computational efficiency Deep learning Deep learning Large datasets Large datasets Learning algorithms Learning algorithms Learning systems Learning systems Malware Malware Static analysis Static analysis
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| GB/T 7714 | Lyu, Chenxi , Yao, Yinan , Guo, Xiaodong et al. AntiMal: an Approach of Malware Detection Employing Swin Transformer [C] . 2023 : 1-5 . |
| MLA | Lyu, Chenxi et al. "AntiMal: an Approach of Malware Detection Employing Swin Transformer" . (2023) : 1-5 . |
| APA | Lyu, Chenxi , Yao, Yinan , Guo, Xiaodong , Huang, Zihong , Dong, Chen , Zhang, Yuanyuan et al. AntiMal: an Approach of Malware Detection Employing Swin Transformer . (2023) : 1-5 . |
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