Zhongguoqiang

Ø Adaptive Perception in Open Environment, Subtask of the Major Project for New Generation of AI, PI;

Ø Underwater Signal Waveform Recognition Based on Deep Learning, the Joint Fund of the Equipments Pre-Research and Ministry of Education of China, PI;

Ø Network Abnormal Traffic Monitoring Based on IPv6 and Deep Learning, CERNET Innovation Project, PI;

Ø Development of Intelligent Wearable Equipment for Persons with Visual Impairment, Science and Technology Program of Qingdao, PI;

Ø Research on Deep Wavelet Networks and TheirApplications, National Laboratory of Pattern Recognition Funds for Open Research Topics, PI;

Ø Supervised Deep Learning Algorithms and Their Applications in Marine Environmental Data Analysis, National Natural Science Foundation of China (NSFC), PI;

Ø Research on Deep Learning and Large Scale Data Mining Algorithms and Their Applications, Fundamental Research Funds for the Central Universities, PI;

Ø Development of Oceanic Big Data Analysis and Forecasting Technology, Subtask of the National Key R&D Program of China, attendee;

Ø Indian ocean world, Social Sciences and Humanities Research Council of Canada (SSHRC), attendee;

Ø Research on Theories and Methods of Pattern Recognition and Their Applications,National Natural ScienceFoundation of China OutstandingYouth Fund, attendee.

Awards:

Ø 2014-2015, Outstanding Reviewer Award for the Pattern Recognition journal, Elsevier.

Ø 2015.8, the Third Prize of the Fifth “Huawei Cup”Intelligent Design Competition of Chinese University Students, two projects, director.

Ø 2016, Outstanding Teacher of Ocean University of China.

Ø 2017.8, the Second Prize of the Fifth “Huawei Cup”Intelligent Design Competition of Chinese University Students, two projects, director.

Ø 2017, Outstanding Reviewer Award for the Pattern Recognition journal, Elsevier.

Ø 2017, Outstanding Reviewer Award for the Neurocomputing journal, Elsevier.

Ø 2018.8, the First Prize of the Fifth “Huawei Cup”Intelligent Design Competition of Chinese University Students, one projects, director.

Ø 2018.8, the Second Prize of the Fifth “Huawei Cup”Intelligent Design Competition of Chinese University Students, two projects, director.

Ø 2018.8, the Third Prize of the Fifth “Huawei Cup”Intelligent Design Competition of Chinese University Students, one projects, director.

Ø 2018, Outstanding Reviewer Award for the Cognitive Systems Research journal, Elsevier.

Ø 2019, the Best Paper Award of BICS2019.

Ø 2019, Outstanding Finished Project of Science and Technology Program of Qingdao.

Ø 2019, Chinese Youth Award of Ocean University of China.

Publications^#:

Ø [B3] Yong Wang, Guoqiang Zhong, Xin Sun: Introduction to Machine Learning (Translation from English to Chinese, the 2^nd Edition). China Machine Press. 2018.

Ø [B2] Guoqiang Zhong, Kaizhu Huang: Semi-Supervised Learning: Background, Applications and Future Directions. Nova Science Publishers, Inc., NY USA. 2018.

Ø [B1] Yong Wang, Guoqiang Zhong, Xin Sun: Introduction to Machine Learning (Translation from English to Chinese). China Machine Press. 2016.

Ø [J33]Guoqiang Zhong, Wei Gao, Yongbin Liu, Youzhao Yang, Da-Han Wang, Kaizhu Huang: Generative Adversarial Networks with Decoder-Encoder Output Noises. Neural Networks 127: 19-28 (2020).

Ø [J32] Jianyuan Sun, Hui Yu, Guoqiang Zhong*, Junyu Dong, Shu Zhang, Hongchuan Yu: Random Shapley Forests: Cooperative Game Based Random Forests with Consistency. IEEE Transactions on Cybernetics (Accepted).

Ø [J31] Tao Li, Wencong Jiao, Li-Na Wang, Guoqiang Zhong*: Automatic DenseNet Sparsification. IEEE Access 8: 62561-62571 (2020).

Ø [J30]Guoqiang Zhong, Jianzhang Qu, Haizhen Wang, Benxiu Liu, Wencong Jiao, Zhenlin Fan, Hongli Miao, and Rachid Hedjam: Trace-Norm Regularized Multi-Task Learning for Sea State Bias Estimation. Journal of Ocean University of China (Accepted).

Ø [J29]Guoqiang Zhong, Tao Li, Wencong Jiao, Li-Na Wang, Junyu Dong, Cheng-Lin Liu: DNA Computing Inspired Deep Networks Design. Neurocomputing 382: 140-147 (2020).

Ø [J28]Guoqiang Zhong, Wenxue Liu, Hui Yao, Tao Li, Jinxuan Sun, Xiang Liu: Merging Similar Neurons for Deep Networks Compression. Cognitive Computation 12(3): 577-588 (2020).

Ø [J27] Jinxuan Sun, Guoqiang Zhong*, Yang Chen, Yongbin Liu, Tao Li, Kaizhu Huang: Generative Adversarial Networks with Mixture of t-Distributions Noise for Diverse Image Generation. Neural Networks, 122: 374-381 (2020).

Ø [J26] Guoqiang Zhong, Wencong Jiao, Wei Gao, Kaizhu Huang: Automatic Design of Deep Networks with Neural Blocks. Cognitive Computation 12(1): 1-12 (2020).

Ø [J25] Xiang Liu, Li-Na Wang, Wenxue Liu, Guoqiang Zhong*: Incremental Layers Resection: A Novel Method to Compress Neural Networks. IEEE Access 7: 172167-172177 (2019).

Ø [J24] Guoqiang Zhong, Kang Zhang, Hongxu Wei, Junyu Dong: Marginal Deep Architecture: Stacking Feature Learning Modules to Build Deep Learning Models. IEEE Access,7: 30220-30233, 2019.

Ø [J23] Guoqiang Zhong, Xiao Ling, Li-Na Wang: From Shallow Feature Learning to Deep Learning: Benefits from the Width and Depth of Deep Architectures. WIREs Data Mining and Knowledge Discovery, 9(1): 1255:1-1255:14 (2019).

Ø [J22] Xiao-Bo Jin, Guo-Sen Xie, Qiu-Feng Wang, Guoqiang Zhong, Guang-Gang Geng: Nonconvex Matrix Completion with Nesterov’s Acceleration. Big Data Analytics 3, 11 (2018).

Ø [J21] Jianyuan Sun, Guoqiang Zhong*, Kaizhu Huang, Junyu Dong: Banzhaf Random Forests: Cooperative Game Theory Based Random Forests with Consistency. Neural Networks, 106: 20-29 (2018).

Ø [J20] Hongli Miao, Yingting Guo, Guoqiang Zhong, Benxiu Liu, Guizhong Wang: A Novel model of Estimating Sea State Bias Based on Multi-layer Neural Network and Multi-source Altimeter Data. European Journal of Remote Sensing, 51(1): 616-626 (2018).

Ø [J19] Kaiquan Chen, Yao He, Guoqiang Zhong: The Transformation of Information Literacy Connotation in Artificial Intelligence Perspective and Target Positioning of Artificial Intelligence Education: Also on the Implementation Path of Artificial Intelligence Teaching in Basic Education. Distance Education Journal, 1: 61-71 (2018).

Ø [J18] Guoqiang Zhong, Benxiu Liu, Yingting Guo, Hongli Miao: Sea State Bias Estimation with Least Absolute Shrinkage and Selection Operator (LASSO). Journal of Ocean University of China, 17(5): 1019-1025 (2018).

Ø [J17] Qin Zhang, Guoqiang Zhong*, Junyu Dong: An Anchor-based Spectral Clustering Method. Frontiers of Information Technology & Electronic Engineering, 19(11): 1385-1396 (2018).

Ø [J16] Guoqiang Zhong, Shoujun Yan, Kaizhu Huang, Yajuan Cai, Junyu Dong: Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification. Cognitive Computation, 10(1): 179-186 (2018).

Ø [J15] Xiaoyi Pan, Jing Wang, Xudong Zhang, Yuan Mei, Lu Shi, Guoqiang Zhong: A Deep Learning Model for the Amplitude Inversion of Internal Waves Based on Optical Remote Sensing Images. International Journal of Remote Sensing, 39(3): 607-618 (2018).

Ø [J14] Xiaopeng Liu, Guoqiang Zhong, Junyu Dong: Natural Image Illuminant Estimation via Deep Non-negative Matrix Factorisation. IET Image Processing, 12(1): 121-125 (2018).

Ø [J13] Qin Zhang, Hui Wang, Junyu Dong, Guoqiang Zhong, Xin Sun: Prediction of Sea Surface Temperature using Long Short Term Memory. IEEE Geoscience and Remote Sensing Letters, 14(10): 1745-1749 (2017).

Ø [J12] Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: SLMOML: Online Metric Learning with Global Convergence. IEEE Transactions on Circuits and Systems for Video Technology, 28(10): 2460-2472 (2018).

Ø [J11] Feng Gao, Xiaopeng Liu, Junyu Dong, Guoqiang Zhong, Muwei Jian: Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks. Remote Sensing, 9 (5): 435 (2017).

Ø [J10] Partha Pratim Roy, Guoqiang Zhong, Mohamed Cheriet: Tandem HMMs Using Deep Belief Networks for Offline Handwriting Recognition. Frontiers of Information Technology and Electronic Engineering, 18(7): 978-988 (2017).

Ø [J9] Jianyuan Sun, Guoqiang Zhong, Junyu Dong, Hina Saeeda, Qin Zhang: Cooperative Profit Random Forests with Application in Ocean Front Recognition. IEEE Access, 5: 1398-1408 (2017).

Ø [J8] Qin Zhang, Jianyuan Sun, Guoqiang Zhong, Junyu Dong: Random Multi-Graphs: A Semi-Supervised Learning Framework for Classification of High Dimensional Data. Image and Vision Computing, 60: 30-37 (2017).

Ø [J7] Xiaopeng Liu, Guoqiang Zhong, Cong Liu, Junyu Dong: Underwater Image Colour Constancy Based on Deep Sparse Nonnegative Matrix Factorization. IET Image Processing, 11(1): 38-43 (2017).

Ø [J6] Guoqiang Zhong, Li-Na Wang, Xiao Ling, Junyu Dong: An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning. The Journal of Finance and Data Science, 2(4): 265-278 (2016).

Ø [J5] Heng Zhang, Guoqiang Zhong: Improving Short Text Classification by Learning Vector Representations of both Words and Hidden Topics. Knowl.-Based Syst., 102: 76-86 (2016).

Ø [J4] Guoqiang Zhong, Mohamed Cheriet: Tensor Representation Learning Based Image Patch Analysis for Text Identification and Recognition. Pattern Recognition, 48(4): 1207-1220 (2015).

Ø [J3] Guoqiang Zhong, Mohamed Cheriet: Large Margin Low Rank Tensor Analysis. Neural Computation, 26(4): 761-780 (2014).

Ø [J2] Guoqiang Zhong, Cheng-Lin Liu: Error-Correcting Output Codes Based Ensemble Feature Extraction. Pattern Recognition, 46(4): 1091-1100 (2013).

Ø [J1] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Joint Learning of Error-Correcting Output Codes and Dichotomizers from Data. Neural Computing and Applications, 21(4): 715-724 (2012).

Ø [C39] Zhenlin Fan, Guoqiang Zhong*, Hongxu Wei and Haitao Li: EDNet: A Mesoscale Eddy Detection Network with Multi-Modal Data. IJCNN, 2020.

Ø [C38] Ying Ma, Guoqiang Zhong*, Yanan Wang and Wen Liu: MetaCGAN: A Novel GAN Model for Generating High Quality and Diversity Images with Few Training Data. IJCNN, 2020.

Ø [C37] Guoqiang Zhong, Xin Lin, Kang Chen, Qingyang Li, Kaizhu Huang: Long Short-Term Attention. BICS, 2019.

Ø [C36] Wencong Jiao, Tao Li, Guoqiang Zhong*, Li-Na Wang: AutoML for DenseNet Compression. ICONIP, 2019.

Ø [C35] Yang Chen, Jinxuan Sun, Wencong Jiao, Guoqiang Zhong*: Recovering Super-Resolution Generative Adversarial Network for Underwater Images. ICONIP, 2019.

Ø [C34] Guoqiang Zhong, Wei Gao, Wencong Jiao, Biao shen, Dongdong Xia: Learnable Gabor Convolutional Networks. ICONIP, 2019.

Ø [C33] Li-Na Wang, Guoqiang Zhong*, yan shoujun, Junyu Dong, Kaizhu Huang: Enhanced LSTM with Batch Normalization. ICONIP, 2019.

Ø [C32] Guoqiang Zhong, Guohua Yue: Attention Recurrent Neural Networks for Image-Based Sequence Text Recognition. ACPR, 2019.

Ø [C31] Kang Zhang, Guoqiang Zhong*, Junyu Dong, Shengke Wang, Yong Wang: Stock Market Prediction Based on Generative Adversarial Network. IIKI, 2018.

Ø [C30] Li-Na Wang, Benxiu Liu, Haizhen Wang, Guoqiang Zhong*, Junyu Dong: Deep Gabor Scattering Network for Image Classification. PRCV, 2018.

Ø [C29] Guoqiang Zhong, Haizhen Wang, Wencong Jiao: MusicCNNs: A New Benchmark on Content-Based Music Recommendation. ICONIP, 2018.

Ø [C28] Guoqiang Zhong, Hui Yao, Huiyu Zhou: Merging Neurons for Structure Compression of Deep Networks. ICPR, 2018.

Ø [C27] Guoqiang Zhong, Yan Zheng, Xu-Yao Zhang, Hongxu Wei, Xiao Ling: Convolutional Discriminant Analysis. ICPR, 2018.

Ø [C26] Guoqiang Zhong, Hongxu Wei, Yuchen Zheng, Junyu Dong, Mohamed Cheriet: Deep Error Correcting Output Codes. ICPRAI, 2018.

Ø [C25] Qin Zhang, Junyu Dong, Guoqiang Zhong: Visual Texture Perception via Graph-Based Semi-supervised Learning. ICGIP, 2017.

Ø [C24] Guoqiang Zhong, Hongxu Wei, Yuchen Zheng, Junyu Dong: Marginal Deep Architectures. ACPR, 2017.

Ø [C23] Yanhai Gan, Huifang Chi, Ying Gao, Jun Liu, Guoqiang Zhong, Junyu Dong: Perception Driven Texture Generation. ICME, 2017.

Ø [C22] Xiaowei Zhou, Guoqiang Zhong, Lin Qi, Junyu Dong, Tuan D. Pham, Jianzhou Mao: Surface Height Map Estimation from a Single Image Using Convolutional Neural Networks. ICGIP, 2016.

Ø [C21] Xuecheng Han, Hui Yao, Guoqiang Zhong*: Handwritten Text Line Segmentation by Spectral Clustering. ICGIP, 2016.

Ø [C20] Guoqiang Zhong, Hui Yao, Yutong Liu, Chen Hong, Tuan Pham: Classification of Photographed Document Images Based on Deep-Learning Features. ICGIP, 2016.

Ø [C19] Guoqiang Zhong, Xiao Ling: The Necessary and Sufficient Conditions for the Existence of the Optimal Solution of Trace Ratio Problems. CCPR, 2016.

Ø [C18] Guoqiang Zhong, Yaxin Shi, Mohamed Cheriet: Relational Fisher Analysis: A General Framework for Dimensionality Reduction. IJCNN, 2016.

Ø [C17] Guoqiang Zhong, Yan Zheng, Sheng Li, Yun Fu: Scalable Large Margin Online Metric Learning. IJCNN, 2016.

Ø [C16] Guoqiang Zhong, Hui Xu, Pan Yang, Sijiang Wang, Junyu Dong: Deep Hashing Learning Networks. IJCNN, 2016.

Ø [C15] Jianwen Lou, Lin Qi, Junyu Dong, Hui Yu, Guoqiang Zhong: Learning Perceptual Texture Similarity and Relative Attributes from Computational Features. IJCNN, 2016.

Ø [C14] Pan Yang, Haoran Zhao, Lin Qi, Guoqiang Zhong: Self-Taught Recovery of Depth Data. APSIPA 2015: 1270-1275.

Ø [C13] Yajuan Cai, Guoqiang Zhong*, Yuchen Zheng, Kaizhu Huang, Junyu Dong: Is DeCAF Good Enough for Accurate Image Classification? ICONIP (2) 2015: 354-363.

Ø [C12] Yuchen Zheng, Yajuan Cai, Guoqiang Zhong*, Youssouf Chherawala, Yaxin Shi, Junyu Dong: Stretching Deep Architectures for Text Recognition. ICDAR 2015: 236-240.

Ø [C11] Guoqiang Zhong, Xin Mao, Yaxin Shi, Junyu Dong:3D Texture Recognition for RGB-D Images. CAIP (2) 2015: 518-528.

Ø [C10] Chengcheng Jia, Guoqiang Zhong, Yun Raymond Fu:Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery. AAAI 2014: 1228-1234.

Ø [C9] Yuchen Zheng, Guoqiang Zhong*, Jun Liu, Xiaoxu Cai, Junyu Dong: Visual Texture Perception with Feature Learning Models and Deep Architectures. CCPR 2014.

Ø [C8] Guoqiang Zhong, Mohamed Cheriet: An Empirical Evaluation of Supervised Dimensionality Reduction for Recognition. ICDAR2013.

Ø [C7] Guoqiang Zhong, Mohamed Cheriet: Adaptive Error-Correcting Output Codes. IJCAI 2013.

Ø [C6] Guoqiang Zhong, Mohamed Cheriet: Image Patches Analysis for Text Block Identification.ISSPA 2012: 1241-1246.

Ø [C5] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Low Rank Metric Learning with Manifold Regularization. ICDM 2011: 1266-1271.

Ø [C4] Guoqiang Zhong, Wu-Jun Li, Dit-Yan Yeung, Xinwen Hou, Cheng-Lin Liu: Gaussian Process Latent Random Field. AAAI 2010.

Ø [C3] Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Learning ECOC and Dichotomizers Jointly from Data. ICONIP (1) 2010: 494-502.

Ø [C2] Guoqiang Zhong, Xinwen Hou, Cheng-Lin Liu: Relative Distance-Based Laplacian Eigenmaps.CJKPR, 2009.

Ø [C1] Guoqiang Zhong, Lina Wang, Dazhi Meng: TheDifferential Geometrical Method of Modifying SVM and Its Application in Intron-Extron Classification. CCPR, 2007.

Ø [BC4] Guoqiang Zhong, Li-Na Wang, Qin Zhang, Estanislau Lima, Xin Sun, Junyu Dong, Hui Wang, Biao Shen: Oceanic Data Analysis with Deep Learning Models. Deep Learning: Fundamentals, Theory and Applications. Springer, 2018.

Ø [BC3] Guoqiang Zhong, Mohamed Cheriet: Low Rank Tensor Manifold Learning. Low-Rank and Sparse Modeling for Visual Analysis, 2014.

Ø [BC2] Mohamed Cheriet, Reza Farrahi Moghaddam, Ehsan Arabnejad Guoqiang Zhong: Manifold Learning for the Shape-Based Recognition of Historical Arabic Documents. Handbook of Statistics: Applications of Machine Learning, 2012.

Ø [BC1] Guoqiang Zhong, Kaizhu Huang, Xinwen Hou, Shiming Xiang: Local Tangent Space Laplacian Eigenmaps. Computational Intelligence, 2012.

#Bx: Book x; Jx: Journal x; Cx: Conference x; BCx: Book chapter x; *: Corresponding author.

Program co-chair: The eleventh International Conference on Information Sciences, Signal Processing and their Applications (ISSPA 2012), poster session chair;IDAS2014 chair; IDAS2015 chair; IJCNN2016 special session chair on Deep Learning for Brain-Like Computing and Pattern Recognition; CAAI-PR Expert Forum program chair (2016); CCF-CV Series Lectures program chair (2016); SFWICT2017 publication chair; ICGIP2017 publication chair; ICPR2018 poster session chair; ICGIP2018-2020 program chair; IJCNN2020 special session chair on Deep Learning for Brain-Like Computing and Pattern Recognition; The 11th International Conference on Awareness Science and Technology (iCAST 2020) special session chair.

Program committee member: the seventeenth International Conference on Neural Information Processing (ICONIP 2010), ISSPA 2012, ICDAR2015, IJCAI-ML track, ICFHR2016, CVIP2016, IJCNN2016, ICPR2018, ICDIS2019, ICONIP2019, ICONIP2020, IJCNN2020.

Reviewer for journals: ACM TIST, ACM TKDD, ACM JETC, ACM Computing Surveys Review, IEEE TNNLS, IEEE TYB, IEEE TKDE, IEEE TCSVT, IEEE Access, IEEE Signal Processing Letters, IEEE TII, PR, Knowledge-Based Systems, Neurocomputing, Cognitive Computation, Neural Computing and Applications,Scientific Reports, SN Computer Science, International Journal of Medical Informatics, Sustainable Computing: Informatics and Systems, Journal of Computational Methods in Sciences and Engineering,Autosoft, Big Data Analytics, Acta Automatica Sinica, ChineseJournalofComputers, Journalof Ocean University of China, China Communications.

Reviewer for conferences: ICONIP 2010, ICONIP 2011, ISSPA 2012, CCPR 2012, CCDM2014, CCPR2014, ICIMCS2014, IJCAI2015, BIC-TA 2015, ICRA2016, ICFHR2016, CVIP2016, IJCNN2016, ICPR2018, ICDIS2019, ICONIP2019, ICONIP2020, IJCNN2020.

PhD candidates: Machine learning (Spring, 2015-2018);

Master students: Elements of deep learning (Spring,2015-2020; Fall,2020), Formal methods (Fall,2015-2017);

Undergraduates: Experiments on introduction to computer science (Fall,2015-2017)，Graph Theory (Spring, 2015-2019), Machine learning (Fall, 2016-2019;Spring,2020), Natural Language Processing (Spring,2020).

Professor of Ocean University of China