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Zhongkai Shangguan

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Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data

Nov 06, 2021
Zhongkai Shangguan, Zihe Zheng, Lei Lin

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Nowadays social media platforms such as Twitter provide a great opportunity to understand public opinion of climate change compared to traditional survey methods. In this paper, we constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic modeling and natural language processing, we show the relationship between the number of tweets about climate change and major climate events; the common topics people discuss climate change; and the trend of sentiment. Our dataset was published on Kaggle (\url{https://www.kaggle.com/leonshangguan/climate-change-tweets-ids-until-aug-2021}) and can be used in further research.

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NTIRE 2021 Multi-modal Aerial View Object Classification Challenge

Jul 02, 2021
Jerrick Liu, Nathan Inkawhich, Oliver Nina, Radu Timofte, Sahil Jain, Bob Lee, Yuru Duan, Wei Wei, Lei Zhang, Songzheng Xu, Yuxuan Sun, Jiaqi Tang, Xueli Geng, Mengru Ma, Gongzhe Li, Xueli Geng, Huanqia Cai, Chengxue Cai, Sol Cummings, Casian Miron, Alexandru Pasarica, Cheng-Yen Yang, Hung-Min Hsu, Jiarui Cai, Jie Mei, Chia-Ying Yeh, Jenq-Neng Hwang, Michael Xin, Zhongkai Shangguan, Zihe Zheng, Xu Yifei, Lehan Yang, Kele Xu, Min Feng

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In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR. This challenge is composed of two different tracks using EO andSAR imagery. Both EO and SAR sensors possess different advantages and drawbacks. The purpose of this competition is to analyze how to use both sets of sensory information in complementary ways. We discuss the top methods submitted for this competition and evaluate their results on our blind test set. Our challenge results show significant improvement of more than 15% accuracy from our current baselines for each track of the competition

* Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, 588-595  
* 10 pages, 1 figure. Conference on Computer Vision and Pattern Recognition 
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Neural Process for Black-Box Model Optimization Under Bayesian Framework

Apr 03, 2021
Zhongkai Shangguan, Lei Lin, Wencheng Wu, Beilei Xu

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There are a large number of optimization problems in physical models where the relationships between model parameters and outputs are unknown or hard to track. These models are named as black-box models in general because they can only be viewed in terms of inputs and outputs, without knowledge of the internal workings. Optimizing the black-box model parameters has become increasingly expensive and time consuming as they have become more complex. Hence, developing effective and efficient black-box model optimization algorithms has become an important task. One powerful algorithm to solve such problem is Bayesian optimization, which can effectively estimates the model parameters that lead to the best performance, and Gaussian Process (GP) has been one of the most widely used surrogate model in Bayesian optimization. However, the time complexity of GP scales cubically with respect to the number of observed model outputs, and GP does not scale well with large parameter dimension either. Consequently, it has been challenging for GP to optimize black-box models that need to query many observations and/or have many parameters. To overcome the drawbacks of GP, in this study, we propose a general Bayesian optimization algorithm that employs a Neural Process (NP) as the surrogate model to perform black-box model optimization, namely, Neural Process for Bayesian Optimization (NPBO). In order to validate the benefits of NPBO, we compare NPBO with four benchmark approaches on a power system parameter optimization problem and a series of seven benchmark Bayesian optimization problems. The results show that the proposed NPBO performs better than the other four benchmark approaches on the power system parameter optimization problem and competitively on the seven benchmark problems.

* This paper has been accepted to AAAI-MLPS 2021 
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Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report

Aug 18, 2020
Jing Shi, Zhiheng Li, Haitian Zheng, Yihang Xu, Tianyou Xiao, Weitao Tan, Xiaoning Guo, Sizhe Li, Bin Yang, Zhexin Xu, Ruitao Lin, Zhongkai Shangguan, Yue Zhao, Jingwen Wang, Rohan Sharma, Surya Iyer, Ajinkya Deshmukh, Raunak Mahalik, Srishti Singh, Jayant G Rohra, Yipeng Zhang, Tongyu Yang, Xuan Wen, Ethan Fahnestock, Bryce Ikeda, Ian Lawson, Alan Finkelstein, Kehao Guo, Richard Magnotti, Andrew Sexton, Jeet Ketan Thaker, Yiyang Su, Chenliang Xu

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This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester

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