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https://ceur-ws.org/Vol-2881/xpreface.pdf
Preface to the proceedings of the CIKM AnalytiCup
2020
Dimitar Dimitrova , Xiaofei Zhub
a
GESIS - Leibniz Institute for the Social Sciences, Unter Sachsenhausen 6-8 50667 Cologne, Germany
b
College of Computer Science and Engineering, Chongqing University of Technology, No. 69 Hongguang Avenue, Banan
District, 400054 Chongqing, China
The AnalytiCup is an annual open competition at the International Conference on Information
and Knowledge Management (CIKM). The AnalytiCup offers compelling data challenges aimed
at members of the industry and academia. The 2020 edition of the CIKM AnalytiCup hosted two
data challenges. The final solutions of each challenge were presented at the CIKM AnalytiCup
2020 event, which was held in conjunction with the 29th ACM International Conference on
Information and Knowledge Management (CIKM’2020).
In the following, we provide a short description of each challenge including the number of
teams:
• COVID-19 Retweet Prediction Challenge (32 teams with more than 200 partici-
pants)
The goal of this challenge is to predict the popularity of COVID-19-related tweets in
terms of the number of their retweets. Retweeting—re-posting original content without
any change—is a popular function in Twitter and can be seen as amplifying the spread of
original messages. This makes retweet prediction a crucial task when studying online
information diffusion processes during a time of crisis such as the current COVID-19
pandemic.
The winners of the COVID-19 Retweet Prediction Challenge received non-cash prizes
worth 2.500€ provided by L3S Research Center, University of Hannover, Germany. The
challenge has been also supported by GESIS – Leibniz Institute for the Social Sciences,
Germany, Chongqing University of Technology, China, Heinrich-Heine-University Düs-
seldorf, Germany.
• Alibaba-Tsinghua Adversarial Challenge on Object Detection (1701 teams with
more than 2000 participants)
In recent years, with vigorous technological development, AI has gradually become the
driver of many practical applications. Meanwhile, AI security also has become more
critical. As the AI defender in the future, Alibaba Security unions Tsinghua University to
convene ”Challengers” for the safety of AI models based on different data modalities, such
as text, image, video, and audio. This challenge contains target and non-target attacks
Proceedings of the CIKM AnalytiCup 2020, 22 October, 2020, Gawlay (Virtual Event), Ireland, 2020
Envelope-Open dimitar.dimitrov@gesis.org (D. Dimitrov); zxf@cqut.edu.cn (X. Zhu)
GLOBE http://dimitardimitrov.info/ (D. Dimitrov); https://atai.cqut.edu.cn/zhuxiaofei (X. Zhu)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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under different scenarios, and the goal is to attack the model by generating adversarial
examples to lead the model misjudging.
The winners of the Alibaba-Tsinghua Adversarial Challenge on Object Detection received
68000¥ provided by Alibaba Security.
Each challenge brings together the competition winners presenting their solutions, academic
and industry speakers contributing to the main debates. We thank all authors and participants
for their efforts in making this edition of the CIKM AnalytiCup an active and intellectually
challenging forum of research discussions. We also thank L3S Research Center and Alibaba
Security for financially supporting CIKM AnalytiCup 2020. Finally we thank GESIS – Leibniz
Institute for the Social Sciences, Germany, Chongqing University of Technology, China, Heinrich-
Heine-University Düsseldorf, Germany, and Tsinghua University, China for the material support
of the CIKM AnalytiCup 2020.
In the following, we list the papers describing the winning solutions in each data challenge
and the program committee.
COVID-19 Retweet Prediction Challenge
• T Vinayaka Raj CIKM AnalytiCup 2020: COVID-19 Retweet Prediction with Personalized
Attention
• Tam T. Nguyen, Karamjit Singh, Sangam Verma, Hardik Wadhwa, Siddharth Vimal, Lalasa
Dheekollu, Sheng Jie Lui, Divyansh Gupta, Dong Yang Yin, Zha Wei Word and Graph
Embeddings for COVID-19 Retweet Prediction
• Daichi Takehara Feature Extraction for Deep Neural Networks: A Case Study on the COVID-
19 Retweet Prediction Challenge
• Guangyuan Piao, Weipeng Huang Regression-enhanced Random Forests with Personalized
Patching for COVID-19 Retweet Prediction
Alibaba-Tsinghua Adversarial Challenge on Object Detection
• Honglin Li, Yunqing Zhao Fool Object Detectors with 𝐿0 -Norm Patch Attack
• Ye Liu, Xiaofei Zhu, Xianying Huang Efficient Warm Restart Adversarial Attack for Object
Detection
• Shudeng Wu, Tao Dai, Shu-Tao Xia DPAttack: Diffused Patch Attacks against Universal
ObjectDetection
• Yusheng Zhao, Huanqian Yan, Xingxing Wei Object Hider: Adversarial Patch Attack
Against Object Detectors
• Jiayu Bao Sparse Adversarial Attack to Object Detection
• Yanghao Zhang, Fu Wang, Wenjie Ruan Fooling Object Detectors:Adversarial Attacks by
Half-Neighbor Masks
Program Committee
• Huayu Chen, Tsinghua University, China
• Dimitar Dimitrov, GESIS - Leibniz Institute for the Social Sciences, Germany
• Yinpeng Dong, Tsinghua University, China
• Yao Feng, Tsinghua University, China
• Zhongkai Hao, University of Science and Technology of China, China
• Chang Liu, Shanghai Jiao Tong University, China
• Bowen Sun, Shanghai Jiao Tong University, China
• Zhengyi Wang, Tsinghua University, China
• Chengyang Ying, Tsinghua University, China
• Xiaofei Zhu, Chongqing University of Technology, China
• Xinning Zhou, Tsinghua University, China
CIKM AnalytiCup 2020 chairs
Dimitar Dimitrov, GESIS - Leibniz Institute for the Social Sciences
Xiaofei Zhu, Chongqing University of Technology, China