uist vis 2024

source/publications.json

@@ -1,4 +1,124 @@
-[ {
+[{
+  "id": "BloomWind",
+  "paper": "source/projects/BloomWind/BloomWind.pdf",
+  "teaser": "source/projects/BloomWind/BloomWind.png",
+  "title": "Blowing Seeds across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media Posts",
+  "video": "https://youtu.be/myZ4di4MWMg",
+  "embedVideo": "https://www.youtube.com/embed/myZ4di4MWMg",
+  "authors": [
+    "Jianing Yin", "Hanze Jia", "Buwei Zhou", "Tan Tang", "Lu Ying", "Shuainan Ye", "Tai-Quan Peng", "Yingcai Wu"
+  ],
+  "source": "IEEE VIS",
+  "transaction": "IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2024)",
+  "year": 2024,
+  "abstract": "Propagation analysis refers to studying how information spreads on social media, a pivotal endeavor for understanding social sentiment and public opinions. Numerous studies contribute to visualizing information spread, but few have considered the implicit and complex diffusion patterns among multiple platforms. To bridge the gap, we summarize cross-platform diffusion patterns with experts and identify significant factors that dissect the mechanisms of cross-platform information spread. Based on that, we propose an information diffusion model that estimates the likelihood of a topic/post spreading among different social media platforms. Moreover, we propose a novel visual metaphor that encapsulates cross-platform propagation in a manner analogous to the spread of seeds across gardens. Specifically, we visualize platforms, posts, implicit cross-platform routes, and salient instances as elements of a virtual ecosystem — gardens, flowers, winds, and seeds, respectively. We further develop a visual analytic system, namely BloomWind, that enables users to quickly identify the cross-platform diffusion patterns and investigate the relevant social media posts. Ultimately, we demonstrate the usage of BloomWind through two case studies and validate its effectiveness using expert interviews.",
+  "DOI": "10.1109/TVCG.2024.3456181"
+}, {
+  "id": "VisCourt",
+  "paper": "source/projects/VisCourt/VisCourt.pdf",
+  "teaser": "source/projects/VisCourt/VisCourt.png",
+  "title": "Smartboard: Visual Exploration of Team Tactics with LLM Agent",
+  "video": "https://youtu.be/--jEM9hSDi4",
+  "embedVideo": "https://www.youtube.com/embed/--jEM9hSDi4",
+  "authors": [
+    "Ziao Liu", "Xiao Xie", "Moqi He", "Wenshuo Zhao", "Yihong Wu", "Liqi Cheng", "Hui Zhang", "Yingcai Wu"
+  ],
+  "source": "IEEE VIS",
+  "transaction": "IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2024)",
+  "year": 2024,
+  "abstract": "Tactics play an important role in team sports by guiding how players interact on the field. Both sports fans and experts have a demand for analyzing sports tactics. Existing approaches allow users to visually perceive the multivariate tactical effects. However, these approaches require users to experience a complex reasoning process to connect the multiple interactions within each tactic to the final tactical effect. In this work, we collaborate with basketball experts and propose a progressive approach to help users gain a deeper understanding of how each tactic works and customize tactics on demand. Users can progressively sketch on a tactic board, and a coach agent will simulate the possible actions in each step and present the simulation to users with facet visualizations. We develop an extensible framework that integrates large language models (LLMs) and visualizations to help users communicate with the coach agent with multimodal inputs.Based on the framework, we design and develop Smartboard, an agent-based interactive visualization system for fine-grained tactical analysis, especially for play design. Smartboard provides users with a structured process of setup, simulation, and evolution, allowing for iterative exploration of tactics based on specific personalized scenarios. We conduct case studies based on real-world basketball datasets to demonstrate the effectiveness and usefulness of our system.",
+  "DOI": "10.1109/TVCG.2024.3456200"
+},{
+  "id": "VisCourt",
+  "paper": "source/projects/VisCourt/VisCourt.pdf",
+  "teaser": "source/projects/VisCourt/VisCourt.png",
+  "title": "VisCourt: In-Situ Guidance for Interactive Tactic Training in Mixed Reality",
+  "video": "https://youtu.be/NTKKY1E9_Jk",
+  "embedVideo": "https://www.youtube.com/embed/NTKKY1E9_Jk",
+  "authors": [
+    "Liqi Cheng", "Hanze Jia", "Lingyun Yu", "Yihong Wu", "Shuainan Ye", "Dazhen Deng", "Hui Zhang", "Xiao Xie", "Yingcai Wu"
+  ],
+  "source": "UIST",
+  "transaction": "ACM Symposium on User Interface Software and Technology (UIST 2024)",
+  "year": 2024,
+  "abstract": "In team sports like basketball, understanding and executing tactics—coordinated plans of movements among players—are crucial yet complex, requiring extensive practice. These tactics require players to develop a keen sense of spatial and situational awareness. Traditional coaching methods, which mainly rely on basketball tactic boards and video instruction, often fail to bridge the gap between theoretical learning and the real-world application of tactics, due to shifts in view perspectives and a lack of direct experience with tactical scenarios. To address this challenge, we introduce VisCourt, a Mixed Reality (MR) tactic training system, in collaboration with a professional basketball team. To set up the MR training environment, we employed semi-automatic methods to simulate realistic 3D tactical scenarios and iteratively designed visual in-situ guidance. This approach enables full-body engagement in interactive training sessions on an actual basketball court and provides immediate feedback, significantly enhancing the learning experience. A user study with athletes and enthusiasts shows the effectiveness and satisfaction with VisCourt in basketball training and offers insights for the design of future SportsXR training systems.",
+  "DOI": "10.1145/3654777.3676466"
+},{
+  "id": "AdversaFlow",
+  "titleKey": [
+      "Honorable Mention"
+    ],
+  "paper": "source/projects/AdversaFlow/AdversaFlow.pdf",
+  "teaser": "source/projects/AdversaFlow/AdversaFlow.png",
+  "title": "AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial Flow",
+  "DOI": "10.1109/TVCG.2024.3456150",
+  "authors": ["Dazhen Deng", "Chuhan Zhang", "Huawei Zheng", "Yuwen Pu", "Shouling Ji", "Yingcai Wu"],
+  "source": "IEEE VIS",
+  "transaction": "IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2024)",
+  "year": 2024,
+  "abstract": "In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.",
+  "video": "https://youtu.be/f16uy4e3U34",
+  "embedVideo": "https://www.youtube.com/embed/f16uy4e3U34",
+  "volume": 1,
+  "issue": 1,
+  "page": [1,11],
+  "demo": ""
+},{
+  "id": "Ferry",
+  "paper": "source/projects/ferry/ferry.pdf",
+  "teaser": "source/projects/ferry/ferry.png",
+  "video": "https://youtu.be/ckWQGHQwdb4",
+  "embedVideo": "https://www.youtube.com/embed/ckWQGHQwdb4",
+  "title": "Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts",
+  "DOI": "10.1109/TVCG.2024.3456328",
+  "authors": [
+    "Zhongsu Luo", "Kai Xiong", "Jiajun Zhu", "Ran Chen", "Xinhuan Shu", "Di Weng", "Yingcai Wu"
+  ],
+  "source": "IEEE VIS",
+  "transaction": "IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2024)",
+  "year": 2024,
+  "abstract": "Understanding the input and output of data wrangling scripts is crucial for various tasks like debugging code and onboarding new data. However, existing research on script understanding primarily focuses on revealing the process of data transformations, lacking the ability to analyze the potential scope, i.e., the space of script inputs and outputs. Meanwhile, constructing input/output space during script analysis is challenging, as the wrangling scripts could be semantically complex and diverse, and the association between different data objects is intricate. To facilitate data workers in understanding the input and output space of wrangling scripts, we summarize ten types of constraints to express table space and build a mapping between data transformations and these constraints to guide the construction of the input/output for individual transformations. Then, we propose a constraint generation model for integrating table constraints across multiple transformations. Based on the model, we develop Ferry, an interactive system that extracts and visualizes the data constraints describing the input and output space of data wrangling scripts, thereby enabling users to grasp the high-level semantics of complex scripts and locate the origins of faulty data transformations. Besides, Ferry provides example input and output data to assist users in interpreting the extracted constraints and checking and resolving the conflicts between these constraints and any uploaded dataset. Ferry’s effectiveness and usability are evaluated through two usage scenarios and two case studies, including understanding, debugging, and checking both single and multiple scripts, with and without executable data. Furthermore, an illustrative application is presented to demonstrate Ferry’s flexibility.",
+  "volume": 1,
+  "issue": 1,
+  "page": [
+    1,
+    11
+  ]
+},{
+  "id": "TeamScouter",
+  "paper": "source/projects/TeamScouter/TeamScouter.pdf",
+  "teaser": "source/projects/TeamScouter/TeamScouter.png",
+  "title": "Team-Scouter: Simulative Visual Analytics of Soccer Player Scouting",
+  "DOI": "10.1109/TVCG.2024.3456216",
+  "authors": ["Anqi Cao", "Xiao Xie", "Runjin Zhang", " Yuxin Tian", "Mu Fan", "Hui Zhang", "Yingcai Wu"],
+  "source": "IEEE VIS",
+  "transaction": "IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2024)",
+  "year": 2024,
+  "abstract": "In soccer, player scouting aims to find players suitable for a team to increase the winning chance in future matches. To scout suitable players, coaches and analysts need to consider whether the players will perform well in a new team, which is hard to learn directly from their historical performances. Match simulation methods have been introduced to scout players by estimating their expected contributions to a new team. However, they usually focus on the simulation of match results and hardly support interactive analysis to navigate potential target players and compare them in fine-grained simulated behaviors. In this work, we propose a visual analytics method to assist soccer player scouting based on match simulation. We construct a two-level match simulation framework for estimating both match results and player behaviors when a player comes to a new team. Based on the framework, we develop a visual analytics system, Team-Scouter, to facilitate the simulative-based soccer player scouting process through player navigation, comparison, and investigation. With our system, coaches and analysts can find potential players suitable for the team and compare them on historical and expected performances. For an in-depth investigation of the players' expected performances, the system provides a visual comparison between the simulated behaviors of the player and the actual ones. The usefulness and effectiveness of the system are demonstrated by two case studies on a real-world dataset and an expert interview.",
+  "video": "https://youtu.be/iw8TBUmTnVc",
+  "embedVideo": "https://www.youtube.com/embed/iw8TBUmTnVc",
+  "volume": 1,
+  "issue": 1,
+  "page": [1,11],
+  "demo": ""
+},{
+  "id": "SNIL",
+  "paper": "source/projects/SNIL/SNIL.pdf",
+  "teaser": "source/projects/SNIL/SNIL.png",
+  "title": "SNIL: Generating Sports News From Insights With Large Language Models",
+  "DOI": "10.1109/TVCG.2024.3392683",
+  "authors": ["Liqi Cheng", "Dazhen Deng", "Xiao Xie", "Rihong Qiu", "Mingliang Xu", "Yingcai Wu"],
+  "source": "IEEE TVCG",
+  "transaction": "IEEE Transactions on Visualization and Computer Graphics",
+  "year": 2024,
+  "abstract": "To enhance the appeal and informativeness of data news, there is an increasing reliance on data analysis techniques and visualizations, which poses a high demand for journalists' abilities. While numerous visual analytics systems have been developed for deriving insights, few tools specifically support and disseminate viewpoints for journalism. Thus, this work aims to facilitate the automatic creation of sports news from natural language insights. To achieve this, we conducted an extensive preliminary study on the published sports articles. Based on our findings, we propose a workflow - 1) exploring the data space behind insights, 2) generating narrative structures, 3) progressively generating each episode, and 4) mapping data spaces into communicative visualizations. We have implemented a human-AI interaction system called SNIL, which incorporates user input in conjunction with large language models (LLMs). It supports the modification of textual and graphical content within the episode-based structure by adjusting the description. We conduct user studies to demonstrate the usability of SNIL and the benefit of bridging the gap between analysis tasks and communicative tasks through expert and fan feedback.",
+  "video": "",
+  "embedVideo": "",
+  "volume": 0,
+  "issue": 0,
+  "page": [1,15],
+  "demo": ""
+},{
   "id": "textlin",
   "paper": "source/projects/textlin/textlin.pdf",
   "teaser": "source/projects/textlin/textlin.png",