Zhao Liang

Associate Professor

Doctoral Supervisor

Master's Supervisor

Gender: Male

Alma Mater: Dalian University of Technology

Degree: Doctorate

Affiliated Institution: School of Software

Email: liangzhao@dlut.edu.cn

Office Location: Room 417, Integrated Building, School of Software

About

Profile Summary

I obtained my Bachelor, Master, and Ph.D. degrees from Dalian University of Technology, and I participated in a government-sponsored academic exchange at the University of British Columbia, Canada, during 2016-2017. My primary research areas include Big Data Computation, Multimodal Data Fusion, and Multimodal Medical Applications.

Academic Contributions

I have published over 60 papers in high-level national and international journals and conferences, with more than 40 of these as the first author or corresponding author. My research has led to the granting and acceptance of 15 national invention patents, as well as the approval of 2 software copyrights. I am also the author of the textbook Big Data Technologies and Applications.

Leadership in Research Projects

I have led over 10 research projects, including sub-projects of the National Key R&D Program, the National Natural Science Foundation, the ZF Domain Fund, the Natural Science Foundation of Liaoning Province, the Science and Technology Program of Dalian City, and the CCF Tencent Rhino-Bird Fund. Additionally, I have participated in more than 20 projects, including the National Key R&D Program, major national initiatives, the National Natural Science Foundation, and horizontal research projects.

Awards and Recognitions

My dedication to research and innovation has been recognized through various awards, including:

First Prize for Scientific and Technological Progress in Hainan Province;
Dalian Youth Science and Technology Star Award;
Liaoning Province Excellent Doctoral Dissertation Award;
ACM Dalian Excellent Doctoral Dissertation Award;
Dalian Youth Talent Award;
Supervising Teacher in the 2023 National Industrial Internet Innovation Competition;
Best Paper Awards at IEEE CCIS 2021 and IEEE PICom 2016 conferences.

Editorial and Conference Roles

I have served as editor of many journals and in key roles at conferences, including:

Associate Editor of the Journal of Artificial Intelligence Research
Workshop Chair for BIBM 2023-2024
Associate Editor of IJLAI Transactions on Science and Engineering
Youth Editorial Board Member of Big Data Mining and Analytics
Area Editor for CAAI Artificial Intelligence Research

Research

Multimodal Learning

Data fusion method is an important means of multi-modal data analysis and mining. However, the modal incompleteness, high-dimensionality and mismatch of multi-modal data pose severe challenges to the design of fusion and clustering methods. Our research team focuses on multi-modal alignment and incomplete multi-modal clustering to solve the challenges of sample misalignment and missing samples in real-world scenarios.

Cross-modal Text Generation

Our work focuses on the field of image generation and processing, and the functions implemented include: image generation based on text description, image editing and image repair, etc. The main knowledge involved in the work includes advanced technologies such as GAN networks, CNN networks, diffusion models, and autoregressive models to achieve innovative and efficient image generation and processing technologies.

Multimodal Medical Applications

Our team uses multi-modal medical images and medical relational data to perform tasks such as disease diagnosis, lesion segmentation, and condition classification. Specific research directions include: Brain disease diagnosis algorithm research, Soft tissue sarcoma diagnosis algorithm research, Chronic obstructive pulmonary disease (COPD) diagnosis algorithm research, Sepsis diagnosis algorithm research, Alzheimer's disease diagnosis algorithm research, Pulmonary disease diagnosis algorithm research,etc.

Multimodal Industrial Applications

The team's work focuses on time-series signal prediction, production process state classification, defect detection, and fault diagnosis, aiming for accurate prediction, intelligent classification, efficient detection, and rapid diagnosis in industrial settings. Key technologies include deep learning models such as Transformer and CNN, along with methods like time-series signal processing, multimodal feature fusion, graph convolution networks, and attention mechanisms, all aimed at overcoming technical challenges and expanding functionality to meet diverse industrial needs.

Publications

Monograph

Chen Z, Zhao L, Li Q, et al. Multimodal Data Fusion[M]//Advances in Computing, Informatics, Networking and Cybersecurity: A Book Honoring Professor Mohammad S. Obaidat’s Significant Scientific Contributions. Cham: Springer International Publishing, 2022: 53-91.

2024 to Present

Journals

1. Chen, Z., Lou, K., Liu, Z., Li, Y., Luo, Y., & Zhao, L. (2024). Joint long and short span self-attention network for multi-view classification. Expert Systems with Applications, 235, 121152.[pdf]
2. Zhao L, Xie Q, Li Z, et al. Dynamic Graph Guided Progressive Partial View-Aligned Clustering[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024. [pdf]
3. Zhao L, Wang X, Liu Z, et al. Learnable Graph Guided Deep Multi-view Representation Learning via Information Bottleneck[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024. [pdf]
4. Zhao L, Hu Q, Li X, et al. Multimodal Fusion Generative Adversarial Network for Image Synthesis[J]. IEEE Signal Processing Letters, 2024. [pdf]
5. Zhao L, Xie Q. Distribution-level multi-view clustering for unaligned data[J]. IEEE Signal Processing Letters, 2024. [pdf]
6. Yang Y, Chen T, Zhao L. From Segmentation to Classification: A Deep Learning Scheme for Sintered Surface Images Processing[J]. Processes, 2023, 12(1): 53.
7. Zhang Y, Lin R, Zhao L. A Time Compensation Based Algorithm For Predicting Surface Defects In Iron Ore Sintering[J]. Journal of Applied Science and Engineering, 2024, 27(12): 3761-3768.
8. Liu Z, Chen Z, Lou K, et al. CCIM-SLR: Incomplete multiview co-clustering by sparse low-rank representation[J]. Multimedia Tools and Applications, 2024: 1-31.
9. Chen Z, Lou K, Liu Z, et al. Joint long and short span self-attention network for multi-view classification[J]. Expert Systems with Applications, 2024, 235: 121152.
10. Yuan X, Chen Z, Bu X, et al. Knowledge graph fine-grained network with attribute transfer for recommendation[J]. Expert Systems with Applications, 2024, 257: 125074.
11. Yuan X, Wang W, Gao B, et al. Explicit and Implicit Feature Contrastive Learning Model for Knowledge Graph Link Prediction[J]. Sensors, 2024, 24(22): 7353.
12. Ma R, Wang X, Cao C, et al. GLSEC: Global and local semantic-enhanced contrastive framework for knowledge graph completion[J]. Expert Systems with Applications, 2024, 250: 123793.
13. Fu C, Wang M, Hu Q, et al. Text-Guided Co-Modulated Generative Adversarial Network for Image Inpainting[C]//2024 9th International Conference on Big Data Analytics (ICBDA). IEEE, 2024: 92-97.
14. Ma R, Gao B, Wang W, et al. MHEC: One-shot relational learning of knowledge graphs completion based on multi-hop information enhancement[J]. Neurocomputing, 2025, 614: 128760.
15. Ma R, Wang L, Wu H, et al. Historical Trends and Normalizing Flow for One-shot Temporal Knowledge Graph Reasoning[J]. Expert Systems with Applications, 2025, 260: 125366.

Conferences

16.Zhao L, Yuan Y, Xie Q, et al. Anchor Based Multi-view Clustering for Partially View-Aligned Data[C]// 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024: 1-5.[pdf]
17. Wu S, Ma R, Xie Q, et al. Pseudo-Label Guided Partially View-Aligned Clustering[C]// 2024 9th International Conference on Big Data Analytics (ICBDA). IEEE, 2024: 128-133.[pdf]
18. Chenyu Tian, Rui Lin, Liang Zhao. Rod Pumping System Fault Diagnosis Based on Multi-Model Ensemble Method[J], 2024 International Conference on Networking, Sensing and Control (ICNSC), 2024: 1-6.

2023

Journals

1. Ma, R., Ma, Y., Zhang, H., Mei, B., Lv, G., & Zhao, L. (2023). PANC: Prototype Augmented Neighbor Constraint instance completion in knowledge graphs. Expert Systems with Applications, 213, 119013.[pdf]
2. Ma, R., Bu, X., Chen, Z., Wu, H., Ma, Y., & Zhao, L. (2023). Knowledge graph preference migration network for recommendation. Expert Systems with Applications, 121256.[pdf]
4.Z. Chen, Y. Li, K. Lou and L. Zhao, "Incomplete Multi-View Clustering With Complete View Guidance," in IEEE Signal Processing Letters, vol. 30, pp. 1247-1251, 2023, doi: 10.1109/LSP.2023.3302234.[pdf]
5. X. Yuan, S. Gu, Z. Liu and L. Zhao, "Mining Multi-View Clustering Space with Interpretable Space Search Constraint," in IEEE Signal Processing Letters, doi: 10.1109/LSP.2023.3298284.[pdf]
6.L. Zhao, P. Huang, T. Chen, C. Fu, Q. Hu and Y. Zhang, "Multi-Sentence Complementarily Generation for Text-to-Image Synthesis," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2023.3297769.[pdf]
7. Zhao, L., Wang, X., Liu, Z., Yuan, H., Zhao, J., & Zhou, S. (2023). Deep probability multi-view feature learning for data clustering. Expert Systems with Applications, 217, 119458.[pdf]
8. Ma, R., Mei, B., Ma, Y. et al. One-shot relational learning for extrapolation reasoning on temporal knowledge graphs. Data Min Knowl Disc 37, 1591–1608 (2023). https://doi.org/10.1007/s10618-023-00935-7[pdf]
9. Zhao L, Jia C, Ma J, et al. Medical image segmentation based on self-supervised hybrid fusion network[J]. Frontiers in Oncology, 2023, 13: 1109786.[pdf]
10. Liu Z, Chen Z, Li Y, et al. IMC-NLT: Incomplete multi-view clustering by NMF and low-rank tensor[J]. Expert Systems with Applications, 2023, 221: 119742.[pdf]

Conferences

11.Y. Yang, T. Chen, L. Zhao, J. Gu, X. Tang and Y. Zhang, "Defects Clustering for Mineral Sintering Surface Based on Multi-source Data Fusion," 2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA), Qingdao, China, 2023, pp. 670-674, doi: 10.1109/CFASTA57821.2023.10243223.[pdf]
12. L. Zhao, Q. Xie, S. Wu and S. Ma, "An End-to-End Framework for Partial View-Aligned Clustering with Graph Structure," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10097067.[pdf]
13. L. Zhao, Z. Wang, Y. Yuan and F. Ding, "Unrestricted Anchor Graph Based GCN for Incomplete Multi-View Clustering," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096284.[pdf]
14. L. Zhao, Z. Wang, Z. Wang and Z. Chen, "Multi-View Graph Regularized Deep Autoencoder-Like NMF Framework," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096588.[pdf]
15. P. Huang, Y. Liu, C. Fu and L. Zhao, "Multi-Semantic Fusion Generative Adversarial Network for Text-to-Image Generation," 2023 IEEE 8th International Conference on Big Data Analytics (ICBDA), Harbin, China, 2023, pp. 159-164, doi: 10.1109/ICBDA57405.2023.10104850.[pdf]

2022

Journals

1. Liang Zhao, Chunyang Mo, Jiajun Ma, Zhikui Chen, Chenhui Yao c,Lstm-mfcn: a time series classifier based on multi-scale spatial–temporal features. Computer Communications,Volume 182, 15 January 2022, Pages 52-59[pdf]
2. Zhao, L. , Zhang, J. , Yang, T. , & Chen, Z. . (2022). Incomplete multi-view clustering based on weighted sparse and low rank representation. Applied Intelligence.[pdf]
3. Ma, R. , Guo, F. , Li, Z. , & Zhao, L. . (2022). Knowledge graph random neural networks for recommender systems. Expert Systems with Application(Sep.), 201.[pdf]
4.Ma R, Li Z, Ma Y, et al. Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs[J]. Applied Sciences, 2022, 12(9): 4284.[pdf]
5. Ma R, Guo F, Zhao L, et al. Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation[J]. Applied Sciences, 2022, 12(10): 4899.[pdf]
6. Zhao L, Ma J, Shao Y, et al. MM-UNet: A multimodality brain tumor segmentation network in MRI images[J]. Frontiers in oncology, 2022, 12: 950706.[pdf]

Conferences

7. Zhao L, Lin R, Liu Z, et al. Predicting The Likelihood of Patients Developing Sepsis Based on Compound Ensemble Learning[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 3235-3241.[pdf]
8. Zhao L, Shao Y, Jia C, et al. Time-series lung cancer CT dataset[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 3914-3915.[pdf]
9. Zhao, L., Li, X., Fu, C., Chen, Z. (2022). Image Attribute Modification Based on Text Guidance. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_16[pdf]
10. Ma R, Lv G, Zhao L, et al. Multi-attention User Information Based Graph Convolutional Networks for Explainable Recommendation[C]//International Conference on Knowledge Science, Engineering and Management. Cham: Springer International Publishing, 2022: 201-213[pdf]
11. Ma, R., Zhang, H., Mei, B., Lv, G., Zhao, L. (2023). SATCN: An Improved Temporal Convolutional Neural Network with Self Attention Mechanism for Knowledge Tracing. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1811. Springer, Singapore. https://doi.org/10.1007/978-981-99-2443-1_1[pdf]

2021

Journals

1. L. Zhao, T. Yang, J. Zhang, Z. Chen, Y. Yang and Z. J. Wang, "Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data," in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, pp. 1486-1496, April 2021, doi: 10.1109/TNNLS.2020.2984810.[pdf]
2. Zhao L, Yang T, Zhang J, et al. Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation[J]. IET Computer Vision, 2021, 15(8): 573-591.[pdf]
3. Ma R, Li Z, Guo F, et al. Hybrid attention mechanism for few‐shot relational learning of knowledge graphs[J]. IET Computer Vision, 2021, 15(8): 561-572.[pdf]
4. Zhang Y, Mo C, Ma J, et al. Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification[J]. Applied Sciences, 2021, 11(22): 10957.[pdf]
5. Zhao L, Zhang J, Wang Q, et al. Dual alignment self-supervised incomplete multi-view subspace clustering network[J]. IEEE Signal Processing Letters, 2021, 28: 2122-2126.[pdf]
6.Lin D, Lin J, Zhao L, et al. Multilabel aerial image classification with unsupervised domain adaptation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-13.[pdf]
7. Sun Q, Fang N, Liu Z, et al. HybridCTrm: Bridging CNN and transformer for multimodal brain image segmentation[J]. Journal of Healthcare Engineering, 2021, 2021.[pdf]
8. Lin D, Lin J, Zhao L, et al. Multilabel aerial image classification with a concept attention graph neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-12.[pdf]

Conferences

9. Y. Zhang, P. Huang, X. Li, S. Gao and L. Zhao, "Mask Image to Real Image Generation Based on Semantic Control Context Encoder," 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 2021, pp. 286-290, doi:10.1109/CCIS53392.2021.9754642.[pdf]
10. Youpeng W, Hongxiang L, Yiju G, et al. Amvae: Asymmetric multimodal variational autoencoder for multi-view representation[C]//Artificial Neural Networks and Machine Learning–ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I 30. Springer International Publishing, 2021: 391-402.[pdf]
11. Zhao, L., Li, X., Huang, P., Chen, Z., Dai, Y., Li, T. (2021). TRGAN: Text to Image Generation Through Optimizing Initial Image. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_76[pdf]

2020

Journals

1. Z. Chen, S. Zhang and L. Zhao, "Enhanced Attention-Based Back Projection Network for Image Super-Resolution in Sensor Network," in IEEE Sensors Journal, vol. 21, no. 22, pp. 25083-25089, 15 Nov.15, 2021, doi: 10.1109/JSEN.2020.3047889.[pdf]
2. Zhao L, Mo C, Sun T, et al. Aero Engine Gas-Path Fault Diagnose Based on Multimodal Deep Neural Networks[J]. Wireless Communications and Mobile Computing, 2020, 2020.[pdf]
3. Zhao L, Zhao T, Sun T, et al. Multi-view robust feature learning for data clustering[J]. IEEE Signal Processing Letters, 2020, 27: 1750-1754.[pdf]
4.Lin J, Zhao L, Wang Q, et al. DT-LET: Deep transfer learning by exploring where to transfer[J]. Neurocomputing, 2020, 390: 99-107.[pdf]

2019

Journals

1. Zhao L , Chen Z , Yang L T ,et al.Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data[J].ACM Transactions on Multimedia Computing Communications and Applications, 2019, 15(1s):1-21.DOI:10.1145/3241055.[pdf]
2. L. Zhao, Z. Chen, Y. Yang, L. Zou and Z. J. Wang, "ICFS Clustering With Multiple Representatives for Large Data," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 728-738, March 2019, doi: 10.1109/TNNLS.2018.2851979.[pdf]
3. Xiru Qiu , Zhikui Chen , Liang Zhao , Chengsheng Hu ，Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things[J].Future Generation Computer Systems, 2019, 100:523-530.DOI:10.1016/j.future.2019.05.055.[pdf]
4. Ma Y, Chen Z, Qiu X, et al. Robust and graph regularised non-negative matrix factorisation for heterogeneous co-transfer clustering[J]. International Journal of Computational Science and Engineering, 2019, 18(1): 29-38.[pdf]

2018

Journals

1. L. Zhao, Z. Chen, Y. Hu, G. Min and Z. Jiang, "Distributed Feature Selection for Efficient Economic Big Data Analysis," in IEEE Transactions on Big Data, vol. 4, no. 2, pp. 164-176, 1 June 2018, doi: 10.1109/TBDATA.2016.2601934.[pdf]
2. Zhao, Liang, Chen, Zhikui, Yang, & Yi, et al. (2018). Incomplete multi-view clustering via deep semantic mapping. Neurocomputing, 275(Jan.31), 1053-1062.[pdf]
3. L. Zhao, Z. Chen, Z. Yang, Y. Hu and M. S. Obaidat, "Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems," in IEEE Systems Journal, vol. 12, no. 2, pp. 1610-1620, June 2018, doi: 10.1109/JSYST.2016.2576026.[pdf]
4. Zhao, L., Chen, Z., & Wang, Z. J. (2018). Unsupervised Multiview Nonnegative Correlated Feature Learning for Data Clustering. IEEE Signal Processing Letters, 25(1), 60-64.[pdf]
5. Lin J, Zhao L, Li S, et al. Active-Learning-Incorporated Deep Transfer Learning for Hyperspectral Image Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(11): 4048-4062.[pdf]

Conferences

6. Chen Z, Li Q, Zhong F, et al. Adaptive Multimodal Hypergraph Learning for Image Classification[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE Computer Society, 2018: 252-257.[pdf]
7. Chen Z, Qiu X, Zhao L, et al. Dual Graph-Regularized Multi-view Feature Learning[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE Computer Society, 2018: 266-273.[pdf]
8. Liu G, Ying Z, Zhao L, et al. A New Deep Transfer Learning Model for Judicial Data Classification[C]//2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE Computer Society, 2018: 126-131.[pdf]

Chinese Paper

1.赵亮, 张洁, 陈志奎. 基于双图正则化的自适应多模态鲁棒特征学习[J]. 计算机科学, 2022, 49(4): 124-133. [pdf]
2.马瑞新, 郭芳清, 刘振娇, 陈志奎, 赵亮. 融合上下文信息与核密度估计的协同过滤推荐. 计算机技术与发展, 2021. [pdf]
3.陈志奎*, 刘振娇, 原旭, 罗方, 赵亮. 基于深度多模态与核密度估计的法律文书推荐模型. 西北师范大学学报(自然科学版). 2021, 57(01): 31-37. [pdf]
4.基于CSTAA模型的系统分析与设计课程教学创新设计. 马瑞新;原旭;赵亮.计算机教育,2024(02)
5.基于校企融合的系统分析与设计课程教学创新. 马瑞新;原旭;赵亮.软件导刊,2023(06)
6.基于JavaWeb的实验室管理系统设计与实现. 赵亮;刘建国;陈志奎.实验室研究与探索,2022(08)
7.知识图谱推理研究综述. 马瑞新;李泽阳;陈志奎;赵亮.计算机科学,2022(S1)
8.基于双图正则化的自适应多模态鲁棒特征学习. 赵亮 ;张洁 ;陈志奎 .计算机科学,2022(04)
9.融合上下文信息与核密度估计的协同过滤推荐. 马瑞新;郭芳清;刘振娇;陈志奎;赵亮.计算机技术与发展,2021(04)
10.基于深度多模态与核密度估计的法律文书推荐模型. 陈志奎;刘振娇;原旭;罗方;赵亮.西北师范大学学报(自然科学版),2021(01)
11.多模态特征融合的裁判文书推荐方法. 原旭;韩雪姣;陈志奎;钟芳明;赵亮.微电子学与计算机,2020(12)
12.大数据算法库教学实验平台设计与实现. 赵亮;陈志奎.实验技术与管理,2020(06)
13.一种基于BP神经网络的房价预测模型. 孙婷婷;沈毅;赵亮.电脑知识与技术,2019(28)
14.多模态数据融合算法研究. 赵亮.大连理工大学,2018

Students

Graduated Students

Xu Feng, Xinwei Li, Jiajun Ma, Tao Yang, Chunyang Mo

Jie Zhang, Zihao Wang, Zicheng Wang, Qinghao Hu, Xiao Wang

Chunjiang Fu, Songtao Wu, Zhanxin Gang

PhD Candidates

Yu Shao

Master's Candidate

Dexter, Chaoran Jia, Jian Zhang, Xiaoyuan Li, Sijia Hou, Qiongjie Xie,

Rui Lin, Ziyue Wang, Yukun Yuan, Yang Wang, Yihan Chen, Shubin Ma,

Yifan Guo, Tianqi Yue, PeiQi Wang, Zifan Liu, Zhiyuan Liu, Chuan Ding

Zhao Liang

Ph.D., Associate Professor, Doctoral Supervisor

About

Profile Summary

Academic Contributions

Leadership in Research Projects

Awards and Recognitions

My dedication to research and innovation has been recognized through various awards, including:

First Prize for Scientific and Technological Progress in Hainan Province;
Dalian Youth Science and Technology Star Award;
Liaoning Province Excellent Doctoral Dissertation Award;
ACM Dalian Excellent Doctoral Dissertation Award;
Dalian Youth Talent Award;
Supervising Teacher in the 2023 National Industrial Internet Innovation Competition;
Best Paper Awards at IEEE CCIS 2021 and IEEE PICom 2016 conferences.

Editorial and Conference Roles

I have served as editor of many journals and in key roles at conferences, including:

Associate Editor of the Journal of Artificial Intelligence Research
Workshop Chair for BIBM 2023-2024
Associate Editor of IJLAI Transactions on Science and Engineering
Youth Editorial Board Member of Big Data Mining and Analytics
Area Editor for CAAI Artificial Intelligence Research

Research

Multimodal learning

Data fusion method is an important means of multi-modal data analysis and mining. However, the modal incompleteness, high-dimensionality and mismatch of multi-modal data pose severe challenges to the design of fusion and clustering methods. This group is committed to studying multi-modal clustering problems under the above circumstances, and strives for more robust representation learning and clustering methods. The main research directions are multi-modal alignment and incomplete multi-modal clustering, which are used to solve the problems of sample misalignment and missing samples in reality.

Incremental Multimodal Feature Fusion
Complete Multimodal Feature Fusion
Incomplete Multimodal Feature Fusion
Unaligned Multimodal Feature Fusion
Partially Aligned Incomplete Multimodal Feature Fusion

Cross-modal generation

Our work focuses on the field of image generation and processing, and the functions implemented include: image generation based on text description, image editing and image repair, etc. The main knowledge involved in the work includes advanced technologies such as GAN networks, CNN networks, diffusion models, autoencoders, and autoregressive models to achieve innovative and efficient image generation and processing technologies.

Single-sentence Text-to-Image Generation
Multi-sentence Text-to-Image Generation
Text-guided Image Editing
Text-guided Image Inpainting

Medical group

Our team uses multi-modal medical images and medical relational data to perform tasks such as disease determination, lesion segmentation, and condition classification.Specific directions include: multi-modal medical segmentation network based on hybrid attention mechanism, incomplete modal brain tumor image segmentation based on statistical features and correlation fusion, brain tumor image segmentation based on self-supervised multi-modal fusion network, Vision Transformer-based lung cancer time series data classification network, sepsis condition prediction based on ensemble learning, etc. It aims to mine potential features from medical data that can assist doctors in disease diagnosis and treatment, highlight pathological characteristics of diseases, or provide auxiliary diagnosis results.

Multimodal Medical Datasets
Brain Disease Diagnosis Algorithm Research
Soft Tissue Sarcoma Diagnosis Algorithm Research
Chronic Obstructive Pulmonary Disease (COPD) Diagnosis Algorithm Research
Sepsis Diagnosis Algorithm Research
Alzheimer's Disease Diagnosis Algorithm Research
Pulmonary Disease Diagnosis Algorithm Research
Auxiliary Diagnosis Applications

Multimodal Industrial Applications

The team's work focuses on time-series signal prediction, production process state classification, defect detection, and fault diagnosis, aiming to achieve accurate prediction, intelligent classification, efficient detection, and rapid diagnosis in industrial settings. Key technologies include deep learning models such as Transformer and CNN, as well as methods like time-series signal processing, multimodal feature fusion, graph convolution networks, and attention mechanisms. The goal is to enhance prediction accuracy and efficiency, enable intelligent production state monitoring, and improve automation in defect detection and fault diagnosis, overcoming technical challenges in algorithm innovation and functional expansion to meet diverse industrial needs.

State Classification and Object Detection Based on Multimodal Sintering Dataset
Fault Diagnosis Based on Pumping Unit Work Sequence Data
Time Series Prediction Based on Industrial Open Dataset

Publications

Monograph

Chen Z, Zhao L, Li Q, et al. Multimodal Data Fusion[M]//Advances in Computing, Informatics, Networking and Cybersecurity: A Book Honoring Professor Mohammad S. Obaidat’s Significant Scientific Contributions. Cham: Springer International Publishing, 2022: 53-91.

Chinese Paper

1.赵亮, 张洁, 陈志奎. 基于双图正则化的自适应多模态鲁棒特征学习[J]. 计算机科学, 2022, 49(4): 124-133. [pdf]
2.马瑞新, 郭芳清, 刘振娇, 陈志奎, 赵亮. 融合上下文信息与核密度估计的协同过滤推荐. 计算机技术与发展, 2021. [pdf]
3.陈志奎*, 刘振娇, 原旭, 罗方, 赵亮. 基于深度多模态与核密度估计的法律文书推荐模型. 西北师范大学学报(自然科学版). 2021, 57(01): 31-37. [pdf]
4.基于CSTAA模型的系统分析与设计课程教学创新设计. 马瑞新;原旭;赵亮.计算机教育,2024(02)
5.基于校企融合的系统分析与设计课程教学创新. 马瑞新;原旭;赵亮.软件导刊,2023(06)
6.基于JavaWeb的实验室管理系统设计与实现. 赵亮;刘建国;陈志奎.实验室研究与探索,2022(08)
7.知识图谱推理研究综述. 马瑞新;李泽阳;陈志奎;赵亮.计算机科学,2022(S1)
8.基于双图正则化的自适应多模态鲁棒特征学习. 赵亮 ;张洁 ;陈志奎 .计算机科学,2022(04)
9.融合上下文信息与核密度估计的协同过滤推荐. 马瑞新;郭芳清;刘振娇;陈志奎;赵亮.计算机技术与发展,2021(04)
10.基于深度多模态与核密度估计的法律文书推荐模型. 陈志奎;刘振娇;原旭;罗方;赵亮.西北师范大学学报(自然科学版),2021(01)
11.多模态特征融合的裁判文书推荐方法. 原旭;韩雪姣;陈志奎;钟芳明;赵亮.微电子学与计算机,2020(12)
12.大数据算法库教学实验平台设计与实现. 赵亮;陈志奎.实验技术与管理,2020(06)
13.一种基于BP神经网络的房价预测模型. 孙婷婷;沈毅;赵亮.电脑知识与技术,2019(28)
14.多模态数据融合算法研究. 赵亮.大连理工大学,2018
15.不完整数据减法聚类填充算法研究与应用. 赵亮.大连理工大学,2014
16.基于AutoEncoder的增量式聚类算法. 原旭;杨镇楠;赵亮;陈志奎.微电子学与计算机,2016(03)
17.基于分布式减法聚类的不完整数据填充算法. 赵亮;陈志奎;张清辰.小型微型计算机系统,2015(07)
18.一种基于Hadoop的改进减法聚类算法. 原旭;陈志奎;赵亮;杨德礼.微电子学与计算机,2015(03)
19.基于离群点检测的K-means算法. 冷泳林;张清辰;赵亮;鲁富宇.渤海大学学报(自然科学版),2014(01)
20.基于物联网和超声导波的管道检测系统研究. 陈志奎;贾少攀;赵亮;张清辰.中国测试,2013(02)
21.基于物联网的桥梁压力信息采集系统. 卜范玉;王鑫;赵亮;张清辰.计算机系统应用,2013(03)
22.串口通信与网络通信的应用研究. 邢子羽;辛奇;赵亮;孙卓.电脑知识与技术,2010(22)

2024 to Present

Journals

1. Chen, Z., Lou, K., Liu, Z., Li, Y., Luo, Y., & Zhao, L. (2024). Joint long and short span self-attention network for multi-view classification. Expert Systems with Applications, 235, 121152.[pdf]
2. Zhao L, Xie Q, Li Z, et al. Dynamic Graph Guided Progressive Partial View-Aligned Clustering[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024. [pdf]
3. Zhao L, Wang X, Liu Z, et al. Learnable Graph Guided Deep Multi-view Representation Learning via Information Bottleneck[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024. [pdf]
4. Zhao L, Hu Q, Li X, et al. Multimodal Fusion Generative Adversarial Network for Image Synthesis[J]. IEEE Signal Processing Letters, 2024. [pdf]
5. Zhao L, Xie Q. Distribution-level multi-view clustering for unaligned data[J]. IEEE Signal Processing Letters, 2024. [pdf]

Conferences

6.Zhao L, Yuan Y, Xie Q, et al. Anchor Based Multi-view Clustering for Partially View-Aligned Data[C]// 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024: 1-5.[pdf]
7. Wu S, Ma R, Xie Q, et al. Pseudo-Label Guided Partially View-Aligned Clustering[C]// 2024 9th International Conference on Big Data Analytics (ICBDA). IEEE, 2024: 128-133.[pdf]

2023

Journals

1. Ma, R., Ma, Y., Zhang, H., Mei, B., Lv, G., & Zhao, L. (2023). PANC: Prototype Augmented Neighbor Constraint instance completion in knowledge graphs. Expert Systems with Applications, 213, 119013.[pdf]
2. Ma, R., Bu, X., Chen, Z., Wu, H., Ma, Y., & Zhao, L. (2023). Knowledge graph preference migration network for recommendation. Expert Systems with Applications, 121256.[pdf]
4.Z. Chen, Y. Li, K. Lou and L. Zhao, "Incomplete Multi-View Clustering With Complete View Guidance," in IEEE Signal Processing Letters, vol. 30, pp. 1247-1251, 2023, doi: 10.1109/LSP.2023.3302234.[pdf]
5. X. Yuan, S. Gu, Z. Liu and L. Zhao, "Mining Multi-View Clustering Space with Interpretable Space Search Constraint," in IEEE Signal Processing Letters, doi: 10.1109/LSP.2023.3298284.[pdf]
6.L. Zhao, P. Huang, T. Chen, C. Fu, Q. Hu and Y. Zhang, "Multi-Sentence Complementarily Generation for Text-to-Image Synthesis," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2023.3297769.[pdf]
7. Zhao, L., Wang, X., Liu, Z., Yuan, H., Zhao, J., & Zhou, S. (2023). Deep probability multi-view feature learning for data clustering. Expert Systems with Applications, 217, 119458.[pdf]
8. Ma, R., Mei, B., Ma, Y. et al. One-shot relational learning for extrapolation reasoning on temporal knowledge graphs. Data Min Knowl Disc 37, 1591–1608 (2023). https://doi.org/10.1007/s10618-023-00935-7[pdf]
9. Zhao L, Jia C, Ma J, et al. Medical image segmentation based on self-supervised hybrid fusion network[J]. Frontiers in Oncology, 2023, 13: 1109786.[pdf]
10. Liu Z, Chen Z, Li Y, et al. IMC-NLT: Incomplete multi-view clustering by NMF and low-rank tensor[J]. Expert Systems with Applications, 2023, 221: 119742.[pdf]

Conferences

11.Y. Yang, T. Chen, L. Zhao, J. Gu, X. Tang and Y. Zhang, "Defects Clustering for Mineral Sintering Surface Based on Multi-source Data Fusion," 2023 2nd Conference on Fully Actuated System Theory and Applications (CFASTA), Qingdao, China, 2023, pp. 670-674, doi: 10.1109/CFASTA57821.2023.10243223.[pdf]
12. L. Zhao, Q. Xie, S. Wu and S. Ma, "An End-to-End Framework for Partial View-Aligned Clustering with Graph Structure," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10097067.[pdf]
13. L. Zhao, Z. Wang, Y. Yuan and F. Ding, "Unrestricted Anchor Graph Based GCN for Incomplete Multi-View Clustering," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096284.[pdf]
14. L. Zhao, Z. Wang, Z. Wang and Z. Chen, "Multi-View Graph Regularized Deep Autoencoder-Like NMF Framework," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096588.[pdf]
15. P. Huang, Y. Liu, C. Fu and L. Zhao, "Multi-Semantic Fusion Generative Adversarial Network for Text-to-Image Generation," 2023 IEEE 8th International Conference on Big Data Analytics (ICBDA), Harbin, China, 2023, pp. 159-164, doi: 10.1109/ICBDA57405.2023.10104850.[pdf]

2022

Journals

1. Liang Zhao, Chunyang Mo, Jiajun Ma, Zhikui Chen, Chenhui Yao c,Lstm-mfcn: a time series classifier based on multi-scale spatial–temporal features. Computer Communications,Volume 182, 15 January 2022, Pages 52-59[pdf]
2. Zhao, L. , Zhang, J. , Yang, T. , & Chen, Z. . (2022). Incomplete multi-view clustering based on weighted sparse and low rank representation. Applied Intelligence.[pdf]
3. Ma, R. , Guo, F. , Li, Z. , & Zhao, L. . (2022). Knowledge graph random neural networks for recommender systems. Expert Systems with Application(Sep.), 201.[pdf]
4.Ma R, Li Z, Ma Y, et al. Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs[J]. Applied Sciences, 2022, 12(9): 4284.[pdf]
5. Ma R, Guo F, Zhao L, et al. Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation[J]. Applied Sciences, 2022, 12(10): 4899.[pdf]
6. Zhao L, Ma J, Shao Y, et al. MM-UNet: A multimodality brain tumor segmentation network in MRI images[J]. Frontiers in oncology, 2022, 12: 950706.[pdf]

Conferences

7. Zhao L, Lin R, Liu Z, et al. Predicting The Likelihood of Patients Developing Sepsis Based on Compound Ensemble Learning[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 3235-3241.[pdf]
8. Zhao L, Shao Y, Jia C, et al. Time-series lung cancer CT dataset[C]//2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022: 3914-3915.[pdf]
9. Zhao, L., Li, X., Fu, C., Chen, Z. (2022). Image Attribute Modification Based on Text Guidance. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_16[pdf]
10. Ma R, Lv G, Zhao L, et al. Multi-attention User Information Based Graph Convolutional Networks for Explainable Recommendation[C]//International Conference on Knowledge Science, Engineering and Management. Cham: Springer International Publishing, 2022: 201-213[pdf]
11. Ma, R., Zhang, H., Mei, B., Lv, G., Zhao, L. (2023). SATCN: An Improved Temporal Convolutional Neural Network with Self Attention Mechanism for Knowledge Tracing. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1811. Springer, Singapore. https://doi.org/10.1007/978-981-99-2443-1_1[pdf]

2021

Journals

1. L. Zhao, T. Yang, J. Zhang, Z. Chen, Y. Yang and Z. J. Wang, "Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data," in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 4, pp. 1486-1496, April 2021, doi: 10.1109/TNNLS.2020.2984810.[pdf]
2. Zhao L, Yang T, Zhang J, et al. Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation[J]. IET Computer Vision, 2021, 15(8): 573-591.[pdf]
3. Ma R, Li Z, Guo F, et al. Hybrid attention mechanism for few‐shot relational learning of knowledge graphs[J]. IET Computer Vision, 2021, 15(8): 561-572.[pdf]
4. Zhang Y, Mo C, Ma J, et al. Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification[J]. Applied Sciences, 2021, 11(22): 10957.[pdf]
5. Zhao L, Zhang J, Wang Q, et al. Dual alignment self-supervised incomplete multi-view subspace clustering network[J]. IEEE Signal Processing Letters, 2021, 28: 2122-2126.[pdf]
6.Lin D, Lin J, Zhao L, et al. Multilabel aerial image classification with unsupervised domain adaptation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-13.[pdf]
7. Sun Q, Fang N, Liu Z, et al. HybridCTrm: Bridging CNN and transformer for multimodal brain image segmentation[J]. Journal of Healthcare Engineering, 2021, 2021.[pdf]
8. Lin D, Lin J, Zhao L, et al. Multilabel aerial image classification with a concept attention graph neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-12.[pdf]

Conferences

9. Y. Zhang, P. Huang, X. Li, S. Gao and L. Zhao, "Mask Image to Real Image Generation Based on Semantic Control Context Encoder," 2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS), Xi'an, China, 2021, pp. 286-290, doi:10.1109/CCIS53392.2021.9754642.[pdf]
10. Youpeng W, Hongxiang L, Yiju G, et al. Amvae: Asymmetric multimodal variational autoencoder for multi-view representation[C]//Artificial Neural Networks and Machine Learning–ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I 30. Springer International Publishing, 2021: 391-402.[pdf]
11. Zhao, L., Li, X., Huang, P., Chen, Z., Dai, Y., Li, T. (2021). TRGAN: Text to Image Generation Through Optimizing Initial Image. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_76[pdf]

2020

Journals

1. Z. Chen, S. Zhang and L. Zhao, "Enhanced Attention-Based Back Projection Network for Image Super-Resolution in Sensor Network," in IEEE Sensors Journal, vol. 21, no. 22, pp. 25083-25089, 15 Nov.15, 2021, doi: 10.1109/JSEN.2020.3047889.[pdf]
2. Zhao L, Mo C, Sun T, et al. Aero Engine Gas-Path Fault Diagnose Based on Multimodal Deep Neural Networks[J]. Wireless Communications and Mobile Computing, 2020, 2020.[pdf]
3. Zhao L, Zhao T, Sun T, et al. Multi-view robust feature learning for data clustering[J]. IEEE Signal Processing Letters, 2020, 27: 1750-1754.[pdf]
4.Lin J, Zhao L, Wang Q, et al. DT-LET: Deep transfer learning by exploring where to transfer[J]. Neurocomputing, 2020, 390: 99-107.[pdf]

2019

Journals

1. Zhao L , Chen Z , Yang L T ,et al.Deep Semantic Mapping for Heterogeneous Multimedia Transfer Learning Using Co-Occurrence Data[J].ACM Transactions on Multimedia Computing Communications and Applications, 2019, 15(1s):1-21.DOI:10.1145/3241055.[pdf]
2. L. Zhao, Z. Chen, Y. Yang, L. Zou and Z. J. Wang, "ICFS Clustering With Multiple Representatives for Large Data," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 3, pp. 728-738, March 2019, doi: 10.1109/TNNLS.2018.2851979.[pdf]
3. Xiru Qiu , Zhikui Chen , Liang Zhao , Chengsheng Hu ，Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things[J].Future Generation Computer Systems, 2019, 100:523-530.DOI:10.1016/j.future.2019.05.055.[pdf]
4. Ma Y, Chen Z, Qiu X, et al. Robust and graph regularised non-negative matrix factorisation for heterogeneous co-transfer clustering[J]. International Journal of Computational Science and Engineering, 2019, 18(1): 29-38.[pdf]

2018

Journals

1. L. Zhao, Z. Chen, Y. Hu, G. Min and Z. Jiang, "Distributed Feature Selection for Efficient Economic Big Data Analysis," in IEEE Transactions on Big Data, vol. 4, no. 2, pp. 164-176, 1 June 2018, doi: 10.1109/TBDATA.2016.2601934.[pdf]
2. Zhao, Liang, Chen, Zhikui, Yang, & Yi, et al. (2018). Incomplete multi-view clustering via deep semantic mapping. Neurocomputing, 275(Jan.31), 1053-1062.[pdf]
3. L. Zhao, Z. Chen, Z. Yang, Y. Hu and M. S. Obaidat, "Local Similarity Imputation Based on Fast Clustering for Incomplete Data in Cyber-Physical Systems," in IEEE Systems Journal, vol. 12, no. 2, pp. 1610-1620, June 2018, doi: 10.1109/JSYST.2016.2576026.[pdf]
4. Zhao, L., Chen, Z., & Wang, Z. J. (2018). Unsupervised Multiview Nonnegative Correlated Feature Learning for Data Clustering. IEEE Signal Processing Letters, 25(1), 60-64.[pdf]
5. Lin J, Zhao L, Li S, et al. Active-Learning-Incorporated Deep Transfer Learning for Hyperspectral Image Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(11): 4048-4062.[pdf]

Conferences

6. Chen Z, Li Q, Zhong F, et al. Adaptive Multimodal Hypergraph Learning for Image Classification[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE Computer Society, 2018: 252-257.[pdf]
7. Chen Z, Qiu X, Zhao L, et al. Dual Graph-Regularized Multi-view Feature Learning[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE Computer Society, 2018: 266-273.[pdf]
8. Liu G, Ying Z, Zhao L, et al. A New Deep Transfer Learning Model for Judicial Data Classification[C]//2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE Computer Society, 2018: 126-131.[pdf]

Students

Graduating students

Xu Feng, Xinwei Li, Jiajun Ma,

Tao Yang, Chunyang Mo

Jie Zhang, Zihao Wang, Zicheng Wang,

Qinghao Hu, Xiao Wang

Chunjiang Fu, Songtao Wu, Zhanxin Gang

Doctor

Yu Shao

Master

Dexter, Chaoran Jia, Jian Zhang,

Xiaoyuan Li, Sijia Hou, Qiongjie Xie,

Rui Lin, Ziyue Wang, Yukun Yuan,

Yang Wang, Yihan Chen, Shubin Ma,

Yifan Guo, Tianqi Yue, PeiQi Wang,

Zifan Liu, Zhiyuan Liu, Chuan Ding