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学者姓名：董晨
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Abstract ：
Digital microfluidic biochips (DMFBs) have a significant stride in the applications of medicine and the biochemistry in recent years. DMFBs based on micro -electrode -dot -array (MEDA) architecture, as the nextgeneration DMFBs, aim to overcome drawbacks of conventional DMFBs, such as droplet size restriction, low accuracy, and poor sensing ability. Since the potential market value of MEDA biochips is vast, it is of paramount importance to explore approaches to protect the intellectual property (IP) of MEDA biochips during the development process. In this paper, an IP authentication strategy based on the multi-PUF applied to MEDA biochips is presented, called bioMPUF, consisting of Delay PUF, Split PUF and Countermeasure. The bioMPUF strategy is designed to enhance the non -linearity between challenges and responses of PUFs, making the challenge-response pairs (CRPs) on the MEDA biochips are difficult to be anticipated, thus thwarting IP piracy attacks. Moreover, based on the easy degradation of MEDA biochip electrodes, a countermeasure is proposed to destroy the availability of piracy chips. Experimental results demonstrate the feasibility of the proposed bioMPUF strategy against the brute force attack and modeling attack.
Keyword ：
Hardware security Hardware security IP protection IP protection MEDA biochips MEDA biochips Modeling attack Modeling attack Multi-PUF Multi-PUF
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| GB/T 7714 | Dong, Chen , Guo, Xiaodong , Lian, Sihuang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) . |
| MLA | Dong, Chen et al. "Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 36 . 3 (2024) . |
| APA | Dong, Chen , Guo, Xiaodong , Lian, Sihuang , Yao, Yinan , Chen, Zhenyi , Yang, Yang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) . |
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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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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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In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Biochips Biochips Digital microfluidics Digital microfluidics Genetic algorithms Genetic algorithms Health care Health care Routing algorithms Routing algorithms
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| GB/T 7714 | Huang, Huichang , Yang, Zhongliao , Zhong, Jiayuan et al. Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare [C] . 2024 : 209-223 . |
| MLA | Huang, Huichang et al. "Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare" . (2024) : 209-223 . |
| APA | Huang, Huichang , Yang, Zhongliao , Zhong, Jiayuan , Xu, Li , Dong, Chen , Bao, Ruishen . Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare . (2024) : 209-223 . |
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With the development of technology, robots are gradually being used more and more widely in various fields. Industrial robots need to perform path planning in the course of their tasks, but there is still a lack of a simple and effective method to implement path planning in complex industrial scenarios. In this paper, an improved whale optimization algorithm is proposed to solve the robot path planning problem. The algorithm initially uses a logistic chaotic mapping approach for population initialization to enhance the initial population diversity, and proposes a jumping mechanism to help the population jump out of the local optimum and enhance the global search capability of the population. The proposed algorithm is tested on 12 complex test functions and the experimental results show that the improved algorithm achieves the best results in several test functions. The algorithm is then applied to a path planning problem and the results show that the algorithm can help the robot to perform correct and efficient path planning. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Industrial robots Industrial robots Mapping Mapping Motion planning Motion planning Optimization Optimization Robot programming Robot programming
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| GB/T 7714 | Huang, Peixin , Dong, Chen , Chen, Zhenyi et al. An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm [C] . 2024 : 209-222 . |
| MLA | Huang, Peixin et al. "An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm" . (2024) : 209-222 . |
| APA | Huang, Peixin , Dong, Chen , Chen, Zhenyi , Zhen, Zihang , Jiang, Lei . An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm . (2024) : 209-222 . |
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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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Addressing privacy concerns and the evolving nature of user preferences, it is crucial to explore collaborative training methods for federated recommendation models that match the performance of centralized models while preserving user privacy. Existing federated recommendation models primarily rely on static relational data, overlooking the temporal patterns that dynamically evolve over time. In domains like travel recommendations, factors such as the availability of attractions, introduction of new activities, and media coverage constantly change, influencing user preferences. To tackle these challenges, we propose a novel approach called FedNTF. It leverages an LSTM encoder to capture multidimensional temporal interactions within relational data. By incorporating tensor factorization and multilayer perceptrons, we project users and items into a latent space with time encoding, enabling the learning of nonlinear relationships among diverse latent factors. This approach not only addresses the privacy concerns by preserving the confidentiality of user data but also enables the modeling of temporal dynamics to enhance the accuracy and relevance of recommendations over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Factorization Factorization Learning systems Learning systems Long short-term memory Long short-term memory Recommender systems Recommender systems Signal encoding Signal encoding Tensors Tensors User profile User profile
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| GB/T 7714 | Ye, Jingzhou , Lin, Hui , Wang, Xiaoding et al. Efficient and Reliable Federated Recommendation System in Temporal Scenarios [C] . 2024 : 97-107 . |
| MLA | Ye, Jingzhou et al. "Efficient and Reliable Federated Recommendation System in Temporal Scenarios" . (2024) : 97-107 . |
| APA | Ye, Jingzhou , Lin, Hui , Wang, Xiaoding , Dong, Chen , Liu, Jianmin . Efficient and Reliable Federated Recommendation System in Temporal Scenarios . (2024) : 97-107 . |
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Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Biochips Biochips Chemical reactions Chemical reactions Digital microfluidics Digital microfluidics Health care Health care Scheduling algorithms Scheduling algorithms Simulated annealing Simulated annealing
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| GB/T 7714 | Yang, Zhongliao , Huang, Huichang , Liu, Zeyi et al. Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare [C] . 2024 : 266-281 . |
| MLA | Yang, Zhongliao et al. "Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare" . (2024) : 266-281 . |
| APA | Yang, Zhongliao , Huang, Huichang , Liu, Zeyi , Dong, Chen , Xu, Li . Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare . (2024) : 266-281 . |
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The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Cloud computing Cloud computing Covariance matrix Covariance matrix Evolutionary algorithms Evolutionary algorithms Resource allocation Resource allocation
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| GB/T 7714 | Luo, Jihai , Dong, Chen , Chen, Zhenyi et al. A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm [C] . 2024 : 47-62 . |
| MLA | Luo, Jihai et al. "A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm" . (2024) : 47-62 . |
| APA | Luo, Jihai , Dong, Chen , Chen, Zhenyi , Xu, Li , Chen, Tianci . A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm . (2024) : 47-62 . |
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Spiking neural networks are widely deployed in neuromorphic devices to simulate brain function. In this situation, SNN security becomes significant while lacking in-depth research. Nowadays, most existing work generate adversarial sample by adding perturbations to the pixel domain, which may be appropriate for fooling spiking neural networks on the static datasets than neuromorphic ones. Therefore, this work proposes a spike perturbation superimposed attack algorithm, named SPS, which generates adversarial samples using the accumulation of multiple perturbation spike trains to generate more general adversarial sample. Our work aims to the adversarial attack again SNNs and design the adversarial sample from a different perspective than the pixel domain, but rather the spike trains domain. Firstly, the SPS algorithm uses the surrogate gradient to calculate the spike trains’ gradient and perturbs the spike trains according to the gradient direction. Then, the method of spike trains superposition accumulates multiple perturbation spike trains and generates adversarial spike trains. Finally, the adversarial spike trains are mapped to the adversarial sample. The experimental results show that the attack success rate of SPS is better than state-of-the-art works, and the mean values are 95.42%. Experiments are also conducted on neuromorphic and static datasets to investigate the SPS performance under different conditions further. The results show that the SPS attack works perfect on both image and neuromorphic datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keyword ：
Neural networks Neural networks Pixels Pixels
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| GB/T 7714 | Lin, Xuanwei , Dong, Chen , Liu, Ximeng et al. Spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain [C] . 2023 : 457-471 . |
| MLA | Lin, Xuanwei et al. "Spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain" . (2023) : 457-471 . |
| APA | Lin, Xuanwei , Dong, Chen , Liu, Ximeng , Cheng, Dong . Spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain . (2023) : 457-471 . |
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