文化智能

项目简介

文化智能小组聚焦于数字人文,致力于推动计算机科学、统计学与人文社会科学的深度交叉融合。面对”人文学科如何更好地拥抱人工智能时代”这一重要课题,我们坚持以人文学科专业知识为核心驱动,融合计算、统计与人工智能技术,通过数据可视化与人机交互技术搭建跨学科交流与协作的桥梁,为人文研究开拓新的分析视角与研究路径。

依托丰富的跨学科资源,我们与历史学、新闻学、语言学等多个领域的专家保持或曾保持紧密合作关系,形成了多元而富有潜力的研究布局与探索方向。本组的研究实践涵盖多个方面,包括人工智能技术在人文领域的应用、数据处理与分析、算法设计与统计建模、可视化与人机协同系统设计等。

相关发表
  • IEEE VAST Challenge 2021 Winner: Visual Analytics for Spatial-temporal Situation Awareness.
    Siqi Shen, Xingui Lai, Siming Chen*, Qinghong Wang , Junting Gao.
    IEEE Computer Graphics and Applications (CG&A), Accepted, 2022.
  • DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos.
    Shuai Chen, Sihang Li, Yanda Li, Junlin Zhu, Juanjuan Long, Siming Chen, Jiawan Zhang and Xiaoru Yuan.
    Computer Graphics Forum (EuroVis'22), 2022, Accepted.
    | Paper | pdf (6.0MB)
  • Semantics-Space-Time Cube: A Conceptual Framework for Systematic Analysis of Texts in Space and Time.
    Jie Li, Siming Chen, Wei Chen, Gennady Andrienko, Natalia Andrienko.
    IEEE Transactions on Visualization and Computer Graphics (TVCG), 26(4):1789 - 1806, 2020.
    | Paper | pdf (4.8M) | Video | mp4 (9.9M)
  • Contextualized Analysis of Movement Events.
    Siming Chen, Gennady Andrienko, Natalia Andrienko, Christos Doulkeridis, Athanasios Koumparos.
    in Proceedings of EuroVA'19 (Best Paper Award), Pages 49-53, Porto, Portugal, Jun.3-7, 2019.
    | Paper | pdf (4.9M) | Slides | pptx (12M)
  • COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.
    Jie Li, Siming Chen, Kang Zhang, Gennady Andrienko, Natalia Andrienko.
    IEEE Transactions on Visualization and Computer Graphics (TVCG), 25(8): 2554-2567, 2019.
    | Paper | pdf (3.8M) | Video | mp4 (16.8M)
  • Uncertainty-aware Visual Analytics for Exploring Human Behaviors from Heterogeneous Spatial Temporal Data.
    Siming Chen, Zuchao Wang, Jie Liang, Xiaoru Yuan.
    Journal of Visual Languages and Computing (ChinaVis'16, Honorable mention for Best Paper Award), 48: 187-198, 2018.
    | Paper | pdf (4.1M) | Slides | pdf (4.1M)