Grey-box Adversarial Attack And Defence For Sentiment Classification

Glove: Global Vectors for Word Representation

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Convolutional Neural Networks for Sentence Classification

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Explaining and Harnessing Adversarial Examples

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XLNet: Generalized Autoregressive Pretraining for Language Understanding

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Adversarial examples in the physical world

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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks

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Categorical Reparameterization with Gumbel-Softmax

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Practical Black-Box Attacks against Machine Learning

Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security · doi:10.1145/3052973.3053009 · Cited by 3,495 · see citing works

Boosting Adversarial Attacks with Momentum

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Attention-based LSTM for Aspect-level Sentiment Classification

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ZOO

Chen, P., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C. (2017) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/3128572.3140448 · Cited by 1,782 · see citing works

Universal Sentence Encoder

Cer, D., Yang, Y., Kong, S.-Y., Hua, N., Limtiaco, N., John, R. S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al. (2018) · arXiv · doi:10.48550/arxiv.1803.11175 · Cited by 1,267 · see citing works

Convolutional Neural Networks for Sentence ClassificationPreprint

Kim, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5882 · Cited by 1,112 · see citing works

Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

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Generating Adversarial Examples with Adversarial Networks

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HotFlip: White-Box Adversarial Examples for Text Classification

Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2006 · Cited by 948 · see citing works

Generating Natural Language Adversarial Examples

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When does label smoothing help?

Müller, R., Kornblith, S., & Hinton, G. (2019) · arXiv · doi:10.48550/arxiv.1906.02629 · Cited by 859 · see citing works

Adversarial Attacks on Deep-learning Models in Natural Language Processing

Zhang, W. E., Sheng, Q. Z., Alhazmi, A., & Li, C. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3374217 · Cited by 618 · see citing works

Counter-fitting Word Vectors to Linguistic Constraints

Mrkšić, N., Séaghdha, D. Ó., Thomson, B., Gašić, M., Rojas-Barahona, L. M., Su, P.-H., Vandyke, D., Wen, T.-H., & Young, S. J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1018 · Cited by 356 · see citing works

ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

Wieting, J., & Gimpel, K. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1042 · Cited by 318 · see citing works

Adversarial Attacks on Deep Learning Models in Natural Language Processing: A SurveyPreprint

Zhang, W. E., Sheng, Q. Z., Alhazmi, A., & Li, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.06796 · Cited by 253 · see citing works

Generating Adversarial Examples with Adversarial NetworksPreprint

Xiao, C., Li, B., Zhu, J.-Y., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.02610 · Cited by 230 · see citing works

Certified Robustness to Adversarial Word Substitutions

Jia, R., Raghunathan, A., Göksel, K., & Liang, P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1423 · Cited by 225 · see citing works

Did you hear that? Adversarial Examples Against Automatic Speech RecognitionPreprint

Alzantot, M., Balaji, B., & Srivastava, M. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.00554 · Cited by 192 · see citing works

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

Huang, P.-S., Stanforth, R., Welbl, J., Dyer, C., Yogatama, D., Gowal, S., Dvijotham, K., & Kohli, P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1419 · Cited by 116 · see citing works

Reevaluating Adversarial Examples in Natural Language

Morris, J. X., Lifland, E., Lanchantin, J., Ji, Y., & Qi, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.341 · Cited by 96 · see citing works

HotFlip: White-Box Adversarial Examples for Text ClassificationPreprint

Ebrahimi, J., Rao, A., Lowd, D., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.06751 · Cited by 79 · see citing works

Adversarial Texts with Gradient MethodsPreprint

Gong, Z., Wang, W., Li, B., Song, D. X., & Ku, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07175 · Cited by 53 · see citing works

TextAttack: A Framework for Adversarial Attacks in Natural Language ProcessingPreprint

Morris, J. X., Lifland, E., Yoo, J. Y., & Qi, Y. (2020) · arXiv (Cornell University) · Cited by 42 · see citing works

How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context

Lau, J. H., Armendariz, C. S., Lappin, S., Purver, M., & Shu, C. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00315 · Cited by 40 · see citing works

Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood EnsemblePreprint

Zhou, Y. X., Zheng, X., Hsieh, C., Chang, K.-W., & Huang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11627 · Cited by 39 · see citing works

Adversarial Attack on Sentiment Classification

Tsai, Y.-T., Yang, M.-C., & Chen, H. (2019) · doi:10.18653/v1/w19-4824 · Cited by 26 · see citing works

Certified Robustness to Adversarial Word SubstitutionsPreprint

Jia, R., Raghunathan, A., Göksel, K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00986 · Cited by 16 · see citing works

Achieving Verified Robustness to Symbol Substitutions via Interval Bound PropagationPreprint

Huang, P.-S., Stanforth, R., Welbl, J., Dyer, C., Yogatama, D., Gowal, S., Dvijotham, K., & Kohli, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01492 · Cited by 15 · see citing works

Elephant in the Room: An Evaluation Framework for Assessing Adversarial Examples in NLPPreprint

Xu, Y., Zhong, X., Yepes, A. J., & Lau, J. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.07820 · Cited by 8 · see citing works

How Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in ContextPreprint

Lau, J. H., Armendariz, C. S., Lappin, S., Purver, M., & Shu, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00881 · Cited by 2 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

Wang, Y., Sun, T. S., Li, S., Yuan, X., Ni, W., Hossain, E., & Poor, H. V. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3319492 · Cited by 207 · see citing works

A Survey of Adversarial Defenses and Robustness in NLP

Goyal, S., Doddapaneni, S., Khapra, M. M., & Ravindran, B. (2023) · ACM Computing Surveys · doi:10.1145/3593042 · Cited by 147 · see citing works

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Omar, M., Choi, S., Nyang, D., & Mohaisen, A. (2022) · IEEE Access · doi:10.1109/access.2022.3197769 · Cited by 96 · see citing works

A Survey on Cyber-Attacks for Cyber-Physical Systems: Modeling, Defense, and Design

Lian, Z., Shi, P. J., & Chen, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3495046 · Cited by 85 · see citing works

Decision-Based Adversarial Attack With Frequency Mixup

Li, X.-C., Zhang, X.-Y., Yin, F., & Liu, C. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3156809 · Cited by 45 · see citing works

Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

Vitorino, J., Oliveira, N. R. B. D., & Praça, I. (2022) · Future Internet · doi:10.3390/fi14040108 · Cited by 44 · see citing works

Evading text based emotion detection mechanism via adversarial attacks

Bajaj, A., & Vishwakarma, D. K. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126787 · Cited by 25 · see citing works

Secure IoT Search Engine: Survey, Challenges Issues, Case Study, and Future Research Direction

Hatcher, W. G., Qian, C., Liang, F., Liao, W., Blasch, E., & Yu, W. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3153171 · Cited by 24 · see citing works

Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution

Liu, A., Yu, H., Hu, X., Li, S., Li, L., Ma, F., Yang, Y., & Wen, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.522 · Cited by 24 · see citing works

Are Neural Ranking Models Robust?

Wu, C. H., Zhang, R., Guo, J., Fan, Y., & Cheng, X. (2022) · ACM Transactions on Information Systems · doi:10.1145/3534928 · Cited by 23 · see citing works

Rethinking Textual Adversarial Defense for Pre-Trained Language Models

Wang, J., Bao, R., Zhang, Z., & Zhao, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3192097 · Cited by 18 · see citing works

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction

Xie, Y., Wang, D., Chen, P., Xiong, J., Liu, S., & Koyejo, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.43 · Cited by 16 · see citing works

Temporal characteristics-based adversarial attacks on time series forecasting

Shen, Z., & Li, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125950 · Cited by 16 · see citing works

Emotion categorization from facial expressions: A review of datasets, methods, and research directions

Shehu, H. A., Browne, W. N., & Eisenbarth, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129367 · Cited by 23 · see citing works

Fast Gradient Sign Method (FGSM) Variants in White Box Settings: A Comparative Study

Lad, A., Bhale, R., & Belgamwar, S. (2024) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict60155.2024.10544606 · Cited by 12 · see citing works

An anti-attack method for emotion categorization from images

Shehu, H. A., Browne, W. N., & Eisenbarth, H. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109456 · Cited by 10 · see citing works

LEAP: Efficient and Automated Test Method for NLP Software

Xiao, M., Xiao, Y., Dong, H., Ji, S., & Zhang, P. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00052 · Cited by 8 · see citing works

Mitigating adversarial attacks on transformer models in credit scoring

Schwab, B., & Kriebel, J. (2025) · European Journal of Operational Research · doi:10.1016/j.ejor.2025.05.029 · Cited by 7 · see citing works

Evaluating the Adversarial Robustness of Text Classifiers in Hyperdimensional Computing

Moraliyage, H., Kahawala, S., Silva, D. D., & Alahakoon, D. (2022) · Conference on Human System Interactions · doi:10.1109/hsi55341.2022.9869459 · Cited by 7 · see citing works

A Secure Adversarial Attack Detector Framework for Monitoring Network Intrusion in IoT Environment

Verma, P., Vidyarthi, A., Mishra, A. K., Ali, J., Bharot, N., & Breslin, J. G. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3614806 · Cited by 6 · see citing works

A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models

Vázquez-Hernández, M., Morales-Rosales, L. A., Algredo‐Badillo, I., Gregorio, S. I. F., Rodriguez-Rangel, H., & Córdoba-Tlaxcalteco, M. L. (2024) · Applied Sciences · doi:10.3390/app14114614 · Cited by 6 · see citing works

An AI-driven solution to prevent adversarial attacks on mobile Vehicle-to-Microgrid services

Omara, A., & Kantarcı, B. (2024) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.103016 · Cited by 5 · see citing works

Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation

Dingeto, H., & Kim, J. (2023) · Applied Sciences · doi:10.3390/app13158830 · Cited by 4 · see citing works

Textual adversarial attacks by exchanging text‐self words

Liu, H., Yu, J., Ma, J., Li, S., Ji, B., Yi, Z., Li, M., Peng, L., & Liu, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.23083 · Cited by 5 · see citing works

Exploring Public Data Vulnerabilities in Semi-Supervised Learning Models through Gray-box Adversarial Attack

Jo, J., Kim, J., & Suh, Y.-J. (2024) · Electronics · doi:10.3390/electronics13050940 · Cited by 4 · see citing works

Supervised contrastive learning for robust text adversarial training

Li, W., Zhao, B., An, Y., Shangguan, C., Ji, M., & Yuan, A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07871-5 · Cited by 4 · see citing works

Adversarial Attacks on Deep Learning-based Network Intrusion Detection Systems: A Taxonomy and ReviewPreprint

Hasan, M. M., Islam, R., Mamun, Q., Islam, M. Z., & Gao, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5096420 · Cited by 5 · see citing works

MAFD: Multiple Adversarial Features Detector for Enhanced Detection of Text-Based Adversarial Examples

Jin, K., Xiong, Y., Lou, S., & Yu, Z. X. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11710-0 · Cited by 3 · see citing works

On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks Against LLMs

Bahar, A. A. M., & Wazan, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3574108 · Cited by 4 · see citing works

Adversarial Attacks and Defenses on Large Language Models: A Systematic Review

Li, R., Liu, J., Zhen, J., Bao, Y., Qiang, Y., Xu, Y., Gu, Y., Jin, Y., Yu, D., He, Y., et al. (2025) · IEEE International Conference on Big Data and Artificial Intelligence (BDAI) · doi:10.1109/bdai66031.2025.11325628 · Cited by 3 · see citing works

IAE: Irony-Based Adversarial Examples for Sentiment Analysis Systems

Yi, X., & Huang, J. (2024) · IEEE Access · doi:10.1109/access.2024.3435573 · Cited by 3 · see citing works

ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model

Deng, C., Liu, M., Qin, Y., Zhang, J., Duan, H., & Sun, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.125 · Cited by 2 · see citing works

GAN-Based Defense Mechanism Against Word-Level Attacks for Arabic Transformer-Based Model

Alshalan, H., & Rekabdar, B. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698308 · Cited by 2 · see citing works

Emotion Detection and its Effect on Decision-making

Shehu, H. A. (2023) · doi:10.26686/wgtn.23530485 · Cited by 2 · see citing works

Robust deep neural network surrogate models with uncertainty quantification via adversarial training

Zhang, L., & Li, J. (2023) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11610 · Cited by 1 · see citing works

OMG-Attack: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks

Tal, O. B., Haviv, A., & Bermano, A. H. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00397 · Cited by 1 · see citing works

LST2A: Lexical-Syntactic Targeted Adversarial Attack for Texts

Zhou, G., Qiu, P., Fan, M., Chen, C., Li, Y., & Zhou, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679640 · Cited by 1 · see citing works

Alert-ME: An Explainability-Driven Defense Against Adversarial Examples in Transformer-Based Text Classification

Sabir, B., Gao, Y., Abuadbba, A., & Babar, M. A. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3624268 · Cited by 4 · see citing works

Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models

Birshert, A., & Artemova, E. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-15168-2_1 · Cited by 2 · see citing works

Adversarial Robustness of Medical Image Classifiers via Denoised Smoothing

Tran, K., Ly, L., & Luong, N. H. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-4288-5_4 · Cited by 1 · see citing works

A Sentiment Detection Tool for Multiple Domains

Shrivastava, P., & Sharma, D. K. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-8892-8_32

Adversarial Attacks

Kuribayashi, M. (2022) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-19-1524-6_3

An adversarial text generation method at multiple granularity

Wang, H., Lin, W., & Hu, D. (2023) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei60616.2023.10527839

Adversarial Attacks on Deep Learning-Based Network Intrusion Detection Systems: A Taxonomy and ReviewPreprint

Hasan, M. M., Islam, R., Mamun, Q., Islam, M. Z., & Gao, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4863302 · Cited by 1 · see citing works

Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models

Vázquez-Hernández, M., Algredo‐Badillo, I., Villaseñor-Pineda, L., Báez, M. L., López-Pimentel, J. C., & Morales-Rosales, L. A. (2025) · Applied Sciences · doi:10.3390/app15020855

NLP Security and Ethics, in the Wild

Lent, H., Galinkin, E., Chen, Y., Pedersen, J. M., Derczynski, L., & Bjerva, J. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00762

Minimal adversarial attack on the $$L_0$$ norm

Maillard, P.-F., & Roy, B. K. (2025) · Journal of Computer Virology and Hacking Techniques · doi:10.1007/s11416-025-00558-0

Text Adversarial Attacks With Dynamic Outputs

Wang, W., Liang, S., Zhang, Y., Chen, Z., Xiao, Y., & Cao, X. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3723158

Adversarial Attacks in Computer Vision

Tarsała, P., & Kasprzyk, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14163-7_17

Text-Muddler: an advanced adversarial paradigm for disrupting NLP-based neural architectures in sentiment analysis frameworks

Bajaj, A. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09903-1

Detection of Adversarial Examples Through Chaotic Features Extracted From Ordinal Patterns

Singh, H., Déniz, Ó., Pedraza, A., Singh, S., & Bueno, G. (2026) · IET Image Processing · doi:10.1049/ipr2.70323

Neural networks security under realistic scenario

Maho, T. (2023) · doi:10.70675/690e25b2z1fb9z4d7fza6cbzcb09e2b773cf

Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models

Chen, Y., Zhang, C., Luo, D., D'Haro, L. F., Tan, R. T., & Li, H. (2024) · arXiv · doi:10.48550/arxiv.2405.14646

Universal Adversarial Attacks with Natural Triggers for Text Classification

Song, L., Yu, X., Peng, H.-T., & Narasimhan, K. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.291 · Cited by 49 · see citing works

White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks

Gil, Y., Chai, Y., Gorodissky, O., & Berant, J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1139 · Cited by 47 · see citing works

LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification

Xu, J., Zhao, L., Yan, H., Zeng, Q., Liang, Y., & Sun, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1554 · Cited by 21 · see citing works

Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness

Sung, Y. Y., Gor, M., Fleisig, E., Mondal, I., & Boyd-Graber, J. L. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.27 · Cited by 1 · see citing works

A Hybrid Adversarial Attack for Different Application Scenarios

Du, X., Yu, J., Yi, Z., Li, S., Ma, J., Tan, Y., & Wu, Q. (2020) · Applied Sciences · doi:10.3390/app10103559 · Cited by 8 · see citing works