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学者姓名：叶少珍
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Abstract ：
Global pandemics such as COVID-19 have resulted in significant global social and economic disruption.Although polymerase chain reaction(PCR)is recommended as the standard test for identifying the SARS-CoV-2,conven-tional assays are time-consuming.In parallel,although artificial intelligence(AI)has been employed to contain the disease,the implementation of AI in PCR analytics,which may enhance the cognition of diagnostics,is quite rare.The information that the amplification curve reveals can reflect the dynamics of reactions.Here,we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices(μPADs)to detect synthetic RNA templates of the SARS-CoV-2 ORFlab gene.The μPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments.During analysis,real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks,including RNN,LSTM,and GRU.Of these,the GRU is found to be most effective and accurate.Based on the experimentally obtained datasets,qualitative forecasting can be made as early as 13 cycles,which significantly enhances the efficiency of the PCR tests by 67.5％(～40 min).Also,an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles.To further improve PCR testing efficiency,we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers,which enables real-time quantitative analysis of PCR tests.The presented approach is the first to integrate Al for on-chip PCR data analysis.It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation.It is also capable of fully exploring the dynamics and intrinsic features of each reaction.This work leverages method-ologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field.It is universally applicable and can be extended to multiple areas of fundamental research.
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| GB/T 7714 | Hao Sun , Linghu Xiong , Yi Huang et al. AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease [J]. | 自然科学基础研究（英文） , 2022 , 2 (3) : 476-486 . |
| MLA | Hao Sun et al. "AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease" . | 自然科学基础研究（英文） 2 . 3 (2022) : 476-486 . |
| APA | Hao Sun , Linghu Xiong , Yi Huang , Xinkai Chen , Yongjian Yu , Shaozhen Ye et al. AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease . | 自然科学基础研究（英文） , 2022 , 2 (3) , 476-486 . |
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With the deepening research and cross-fusion in the modern remote sensing image area, the classification of high spatial resolution remote sensing images has captured the attention of the researchers in the field of remote sensing. However, due to the serious phenomenon of same object, different spectrum and same spectrum, different object of high-resolution remote sensing image, the traditional classification strategy is hard to handle this challenge. In this paper, a remote sensing image scene classification model based on SENet and Inception-V3 is proposed by utilizing the deep learning method and transfer learning strategy. The model first adds a dropout layer before the full connection layer of the original Inception-V3 model to avoid over-fitting. Then we embed the SENet module into the Inception-V3 model for optimizing the network performance. In this paper, global average pooling is used as squeeze operation, and then two fully connected layers are used to construct a bottleneck structure. The model proposed in this paper is more non-linear, can better fit the complex correlation between channels, and greatly reduces the amount of parameters and computation. In the training process, this paper adopts the transfer learning strategy, makes full use of existing models and knowledge, improves training efficiency, and finally obtains scene classification results. The experimental results based on AID high-score remote sensing scene images show that SE-Inception has faster convergence speed and more stable training effect than the original Inception-V3 training. Compared with other traditional methods and deep learning networks, the improved model proposed in this paper has greater accuracy improvement. © 2020 Z. L. Cai et al.
Keyword ：
Deep learning Deep learning Image classification Image classification Learning systems Learning systems Remote sensing Remote sensing Transfer learning Transfer learning
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| GB/T 7714 | Cai, Z.L. , Weng, Q. , Ye, S.Z. . RESEARCH on SE-INCEPTION in HIGH-RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION [C] . 2020 : 539-545 . |
| MLA | Cai, Z.L. et al. "RESEARCH on SE-INCEPTION in HIGH-RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION" . (2020) : 539-545 . |
| APA | Cai, Z.L. , Weng, Q. , Ye, S.Z. . RESEARCH on SE-INCEPTION in HIGH-RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION . (2020) : 539-545 . |
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Image dehazing is a crucial image processing step for outdoor vision systems. However, images recovered through conventional image dehazing methods that use either haze-relevant priors or heuristic cues to estimate transmission maps may not lead to sufficiently accurate haze removal from single images. The most commonly observed effects are darkened and brightened artifacts on some areas of the recovered images, which cause considerable loss of fidelity, brightness, and sharpness. This paper develops a variational image dehazing method on the basis of a color-transfer image dehazing model that is superior to conventional image dehazing methods. By creating a color-transfer image dehazing model to remove haze obscuration and acquire information regarding the coefficients of the model by using the devised convolutional neural network-based deep framework as a supervised learning strategy, an image fidelity, brightness, and sharpness can be effectively restored. The experimental results verify through quantitative and qualitative evaluations of either synthesized or real haze images, and the proposed method outperforms existing single image dehazing methods.
Keyword ：
color transfer color transfer deep learning deep learning Image dehazing Image dehazing
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| GB/T 7714 | Yin, Jia-Li , Huang, Yi-Chi , Chen, Bo-Hao et al. Color Transferred Convolutional Neural Networks for Image Dehazing [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2020 , 30 (11) : 3957-3967 . |
| MLA | Yin, Jia-Li et al. "Color Transferred Convolutional Neural Networks for Image Dehazing" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 30 . 11 (2020) : 3957-3967 . |
| APA | Yin, Jia-Li , Huang, Yi-Chi , Chen, Bo-Hao , Ye, Shao-Zhen . Color Transferred Convolutional Neural Networks for Image Dehazing . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2020 , 30 (11) , 3957-3967 . |
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传统高空间分辨率遥感影像(简称"高分遥感影像")分类方法的"同物异谱"、"异物同谱"现象较为严重,深度学习方法为高分遥感影像分类提出了一种新的解决方案.然而,遥感影像训练样本少容易导致网络过拟合现象的发生.利用深度学习方法,结合迁移学习策略,提出了一种改进的Inception-V3的遥感图像场景分类模型.首先在原始Inception-V3模型的全连接层之前添加Dropout层,以进一步避免过拟合现象的发生;训练过程中采用迁移学习策略,充分利用已有模型及知识,提高训练效率.基于AID和NWPU-RESISC45两个大型高分遥感场景影像的实验结果表明,改进的Inception-V3较原始的Inception-V3训练收敛速度更快,训练效果更平稳;与其他传统方法和深度学习网络相比,本文提出的模型的分类精度也有较大的提升,验证了该模型的有效性.
Keyword ：
Inception-V3 Inception-V3 卷积神经网络 卷积神经网络 场景分类 场景分类 深度学习 深度学习 迁移学习 迁移学习 遥感图像分类 遥感图像分类
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| GB/T 7714 | 蔡之灵 , 翁谦 , 叶少珍 et al. 基于Inception-V3模型的高分遥感影像场景分类 [J]. | 国土资源遥感 , 2020 , 32 (3) : 80-89 . |
| MLA | 蔡之灵 et al. "基于Inception-V3模型的高分遥感影像场景分类" . | 国土资源遥感 32 . 3 (2020) : 80-89 . |
| APA | 蔡之灵 , 翁谦 , 叶少珍 , 简彩仁 . 基于Inception-V3模型的高分遥感影像场景分类 . | 国土资源遥感 , 2020 , 32 (3) , 80-89 . |
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针对低照度图像增强问题,提出一种基于生成式对抗网络(generative adversarial networks, GAN)的循环式图像增强网络.引入无监督学习方式,通过降低循环一致性损失和对抗性损失,估计低照度图像的原始光照图;利用建立的图像增强模型公式,对光照不足环境下采集到的图像进行亮度等方面的增强.在人工合成低照度图像数据集和真实自然低照度图像数据集上,均进行了质化和量化评价.实验表明,与现有的一些图像增强方法相比,该方法具有更好的图像增强效果,能够由低照度图像复原出生动、清晰、直观、自然的高质量图像.
Keyword ：
卷积神经网络 卷积神经网络 图像增强 图像增强 图像复原 图像复原 生成式对抗网络 生成式对抗网络
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| GB/T 7714 | 黄路遥 , 叶少珍 . 基于GAN的低照度图像增强算法研究 [J]. | 福州大学学报(自然科学版) , 2020 , 48 (05) : 551-557 . |
| MLA | 黄路遥 et al. "基于GAN的低照度图像增强算法研究" . | 福州大学学报(自然科学版) 48 . 05 (2020) : 551-557 . |
| APA | 黄路遥 , 叶少珍 . 基于GAN的低照度图像增强算法研究 . | 福州大学学报(自然科学版) , 2020 , 48 (05) , 551-557 . |
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Deep learning computation is often used in single-image dehazing techniques for outdoor vision systems. Its development is restricted by the difficulties in providing a training set of degraded and ground-truth image pairs. In this paper, we develop a novel model that utilizes cycle generative adversarial network through unsupervised learning to effectively remove the requirement of a haze/depth data set. Qualitative and quantitative experiments demonstrated that the proposed model outperforms existing state-of-the-art dehazing models when tested on both synthetic and real haze images.
Keyword ：
Image dehazing Image dehazing transmission estimation transmission estimation unsupervised learning unsupervised learning
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| GB/T 7714 | Huang, Lu-Yao , Yin, Jia-Li , Chen, Bo-Hao et al. TOWARDS UNSUPERVISED SINGLE IMAGE DEHAZING WITH DEEP LEARNING [C] . 2019 : 2741-2745 . |
| MLA | Huang, Lu-Yao et al. "TOWARDS UNSUPERVISED SINGLE IMAGE DEHAZING WITH DEEP LEARNING" . (2019) : 2741-2745 . |
| APA | Huang, Lu-Yao , Yin, Jia-Li , Chen, Bo-Hao , Ye, Shao-Zhen . TOWARDS UNSUPERVISED SINGLE IMAGE DEHAZING WITH DEEP LEARNING . (2019) : 2741-2745 . |
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Collaborative filtering algorithms have obvious advantages in recommendation accuracy, and Bandit's algorithm is a strategy to address diversity needs. The COFIBA algorithm combines the collaborative filtering algorithm with the Bandit algorithm to provide a solution for recommending the balance of diversity and accuracy. However, COFIBA does not consider the influence of time characteristics, and COFIBA is a cumulative regret. It is relatively slow to solve the problem of diversity. Therefore, this paper proposes a learning-based model. On the one hand, it introduces the openness characteristics of users to achieve diversity recommendation, and relies on the 'exploration-feedback-update' strategy to adjust the user's openness. At the same time, the time factor is incorporated into the COFIBA algorithm as a feature, and the change of user interest with time is analyzed to ensure the accuracy of recommendation. The experimental results show that the combination algorithm with time and open features has a significant improvement in the diversity and accuracy of the results compared with the COFIBA algorithm. © 2019 IEEE.
Keyword ：
Big data Big data Cloud computing Cloud computing Collaborative filtering Collaborative filtering Signal filtering and prediction Signal filtering and prediction Social networking (online) Social networking (online)
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| GB/T 7714 | Lin, Yuxiang , Ye, Shaozhen . The influence of bandit-based user openness feature on recommendation diversity and accuracy [C] . 2019 : 1624-1628 . |
| MLA | Lin, Yuxiang et al. "The influence of bandit-based user openness feature on recommendation diversity and accuracy" . (2019) : 1624-1628 . |
| APA | Lin, Yuxiang , Ye, Shaozhen . The influence of bandit-based user openness feature on recommendation diversity and accuracy . (2019) : 1624-1628 . |
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Deep learning computation is often used in single-image de-hazing techniques for outdoor vision systems. Its development is restricted by the difficulties in providing a training set of degraded and ground-truth image pairs. In this paper, we develop a novel model that utilizes cycle generative adversarial network through unsupervised learning to effectively remove the requirement of a haze/depth data set. Qualitative and quantitative experiments demonstrated that the proposed model outperforms existing state-of-the-art dehazing models when tested on both synthetic and real haze images. © 2019 IEEE.
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| GB/T 7714 | Huang, Lu-Yao , Yin, Jia-Li , Chen, Bo-Hao et al. Towards Unsupervised Single Image Dehazing with Deep Learning [C] . 2019 : 2741-2745 . |
| MLA | Huang, Lu-Yao et al. "Towards Unsupervised Single Image Dehazing with Deep Learning" . (2019) : 2741-2745 . |
| APA | Huang, Lu-Yao , Yin, Jia-Li , Chen, Bo-Hao , Ye, Shao-Zhen . Towards Unsupervised Single Image Dehazing with Deep Learning . (2019) : 2741-2745 . |
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Secure data ownership management is significant for realizing personal and private data sharing, which can be widely used with consumer electronics. The notion of the decentralized privacy (DP) is introduced by Zyskind et al., and accordingly the first DP system is implemented through blockchain and off-blockchain distributed hashtable. To address the efficiency and overhead issues, we present a conceptually simple solution directly from smart contracts with cryptographic primitives. Our system is called the smart contract-based decentralized privacy (SCDP) system. We propose the basic SCDP system as a warm-up to introduce the design principle based on symmetric encryption. Moreover, the strong SCDP system is provided by using ciphertext-policy attribute-based encryption for supporting more flexible scenarios of access control and also eliminating some limitations of the basic system. © 2019 IEEE.
Keyword ：
Access control Access control Blockchain Blockchain Cryptography Cryptography Data privacy Data privacy Data Sharing Data Sharing Information management Information management
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| GB/T 7714 | He, Yunmin , Chen, Yu-Chi , Guo, Zhong-Yi et al. SCDP: Smart contract-based decentralized privacy system for securing data ownership management [C] . 2019 : 881-882 . |
| MLA | He, Yunmin et al. "SCDP: Smart contract-based decentralized privacy system for securing data ownership management" . (2019) : 881-882 . |
| APA | He, Yunmin , Chen, Yu-Chi , Guo, Zhong-Yi , Tso, Raylin , Ye, Shaozhen . SCDP: Smart contract-based decentralized privacy system for securing data ownership management . (2019) : 881-882 . |
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Background Feelings of depression can be caused by negative life events (NLE) such as the death of a family member, a quarrel with one's spouse, job loss, or strong criticism from an authority figure. The automatic and accurate identification of negative life event language patterns (NLE-LP) can help identify individuals potentially in need of psychiatric services. An NLE-LP combines a person (subject) and a reasonable negative life event (action), e.g. or < boyfriend:break_up>. Methods This paper proposes an analogical reasoning framework which combines a word representation approach and a pattern inference method to mine/extract NLE-LPs from psychiatric consultation documents. Word representation approaches such as skip-gram (SG) and continuous bag-of-words (CBOW) are used to generate word embeddings. Pattern inference methods such as cosine similarity (COSINE) and cosine multiplication similarity (COSMUL) are used to infer patterns. Results Experimental results show our proposed analogical reasoning framework outperforms the traditional methods such as positive pairwise mutual information (PPMI) and hyperspace analog to language (HAL), and can effectively mine highly precise NLE-LPs based on word embeddings. CBOW with COSINE of analogical reasoning is the best word representation and inference engine. In addition, both word embeddings and the inference engine provided by the analogical reasoning framework can further be used to improve the HAL model. Conclusions Our proposed framework is a very simple matching function based on these word representation approaches and is applied to significantly improve HAL model mining performance.
Keyword ：
Analogical reasoning Analogical reasoning Language pattern mining Language pattern mining Negative life event Negative life event
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| GB/T 7714 | Wu, Jheng-Long , Xiao, Xiang , Yu, Liang-Chih et al. Using an analogical reasoning framework to infer language patterns for negative life events [J]. | BMC MEDICAL INFORMATICS AND DECISION MAKING , 2019 , 19 (1) . |
| MLA | Wu, Jheng-Long et al. "Using an analogical reasoning framework to infer language patterns for negative life events" . | BMC MEDICAL INFORMATICS AND DECISION MAKING 19 . 1 (2019) . |
| APA | Wu, Jheng-Long , Xiao, Xiang , Yu, Liang-Chih , Ye, Shao-Zhen , Lai, K. Robert . Using an analogical reasoning framework to infer language patterns for negative life events . | BMC MEDICAL INFORMATICS AND DECISION MAKING , 2019 , 19 (1) . |
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