PKU Visualization

Original link: http://vis.pku.edu.cn/blog/oldvisonline/ Recently, researchers from the University of Oxford and Yuan Xiaoru’s research group at the School of Intelligence at Peking University have worked closely with researchers from the Hong Kong University of Science and Technology, Fudan University, Huawei and other institutions to systematically collect visualizations created in the early history of various countries […]

Original link: http://vis.pku.edu.cn/blog/newsrecxplain/ The Internet has made it easier than ever to distribute news articles to more people, and people’s access to information is overflowing with data content and digital objects. Recommender systems (RSs for short) have become indispensable. However, RSs always lack transparency and diversity, and users have little control over “what is recommended”.

Original link: http://vis.pku.edu.cn/blog/%E4%BC%97%E5%8C%85%E8%A7%82%E7%82%B9%EF%BC%9A%E7% BB%93%E5%90%88%E4%BC%97%E5%8C%85%E6%99%BA%E6%85%A7%E5%92%8C%E6%95%B0%E6%8D% AE%E5%88%86%E6%9E%90%E4%BB%A5%E5%A2%9E%E5%BC%BA%E5%9B%A0%E6%9E%9C%E5%88%86/ Causal analysis is the process by which people understand and explain the relationship between different events. People use causal analysis to explain what has happened, to predict future events, and to help make decisions. However, the facts show that single-person causal analysis may face various challenges, and various unintentional biases,

Original link: http://vis.pku.edu.cn/blog/visual_caption/ In everyday conversation, people bring up topics that are unfamiliar to others. In online meetings such as Zoom, instant subtitles can help people understand what others are saying. In these scenarios, this work proposes to use visual images to aid in conveying information. Specifically, this work designed an AI-assisted plug-in [1] based

Original link: http://vis.pku.edu.cn/blog/how-space-is-told/ Using augmented reality (AR) to guide tourists to visit cultural sites and narrate the stories behind them is an effective way of sightseeing tour (Figure 1), which can improve the participation of tourists while providing route navigation function, And strengthen tourists’ images of scenic spots. However, in the current research related to

Original link: http://vis.pku.edu.cn/blog/calvi/ While visualizations can be effective at conveying information, sometimes they can also convey misleading information to readers. This situation is called visualization misinformation. In the face of misinformation, an interesting question is: To what extent can the public recognize visual misinformation? To answer it, we first need to have a metric that

Original link: http://vis.pku.edu.cn/blog/networknarratives%EF%BC%9A%E8%A7%86%E8%A7%89%E7%BD%91%E7%BB%9C%E6 %8E%A2%E7%B4%A2%E4%B8%8E%E5%88%86%E6%9E%90%E7%9A%84%E6%95%B0%E6%8D%AE%E4%B9 %8B%E6%97%85networknarratives-data-tours-for-visual-networ/ Exploring the network takes a lot of effort, and based on strong expertise, such a process is time-consuming and challenging. In order to solve these problems, the author developed a semi-automatic data cruising system NetworkNarratives[1,2] to help network analysts explore complex networks. This paper [1] is the first to propose

Original link: http://vis.pku.edu.cn/blog/chartdetective%EF%BC%9A%E4%BB%8E%E5%A4%8D%E6%9D%82%E7%9A%84%E7 %9F%A2%E9%87%8F%E5%9B%BE%E4%B8%AD%E8%BD%BB%E6%9D%BE%E8%80%8C%E5%87%86%E7%A1 %AE%E5%9C%B0%E6%8F%90%E5%8F%96%E4%BA%92%E5%8A%A8%E6%95%B0/ Extracting underlying data from rasterized charts is tedious and inaccurate; values ​​may be partially occluded or indistinguishable, and the quality of the images limits the accuracy of data recovery. To address these issues, the authors developed a semi-automatic system to easily and accurately extract underlying data using vector graphics. The

Original link: http://vis.pku.edu.cn/blog/linset-zip/ Based on the existing Linear Diagram, this work uses a certain strategy to compress multiple disjoint sets into one line to make the layout more compact, thereby improving the space efficiency of the view and improving the efficiency of comparison between sets. background Linear Diagram [2] is a classic collection visualization method.

Original link: http://vis.pku.edu.cn/blog/dehumor/ While humor is an important communication skill, understanding it can often be challenging – using it successfully requires a combination of engaging content construction and appropriate oral delivery (such as pauses). Previous work emphasizes the textual and audio features of punchlines, but ignores the build-ups for punchlines in longer contexts. In addition,

Original link: http://vis.pku.edu.cn/blog/chart-reader/ Data visualization enables people to effectively explore data and communicate insights effectively. But visualization relies on visual ability, and people who are blind or have low vision have visual deficits. People who are blind or have low vision primarily use a screen reader as an aid to reading, which is not compatible

Original link: http://vis.pku.edu.cn/blog/data-hunches/ The data presented in the visualization is not necessarily perfect and there may be some errors. For data quality issues, only domain experts are often aware of them. After discovering data quality problems in the visualization, experts can provide feedback through written notification, conversation, etc., but such feedback methods are very inefficient.

Original link: http://vis.pku.edu.cn/blog/%E6%8E%A2%E7%B4%A2%E4%BB%80%E4%B9%88%E6%98%AF%E4% B8%8D%E5%AD%98%E5%9C%A8%E7%9A%84-%E5%9F%BA%E4%BA%8E%E5%9B%BE%E5%85%83%E7%9A %84%E7%BC%BA%E5%A4%B1%E5%80%BC%E5%88%86%E6%9E%90%E5%8F%AF%E8%A7%86%E5%8C%96 / Missing values ​​are a common problem in datasets, and the analysis of missing values ​​is often challenging. Aiming at the problem of missing multivariate data, this paper proposes a primitive-based visualization MissiG, which analyzes the three proposed missing modes—quantitative missing (AM), joint missing (JM) and conditional missing (CM). A