IEEE Computer Graphics and Applications最新文献_第2页

Circuit Mining in Transcriptomics Data. 转录组学数据中的电路挖掘。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-09-01 DOI: 10.1109/MCG.2025.3594562

Tobias Peherstorfer, Sophia Ulonska, Bianca Burger, Simone Lucato, Bader Al-Hamdan, Marvin Kleinlehner, Till F M Andlauer, Katja Buhler

引用次数: 0

Bridging Theory and Practice: A Multiphase Study of GenAI-Assisted Visualization Learning. 衔接理论与实践：基因人工智能辅助可视化学习的多阶段研究。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-09-01 DOI: 10.1109/MCG.2025.3553396

Mak Ahmad, Kwan-Liu Ma, Beatriz Sousa Santos, Alejandra J Magana, Rafael Bidarra

{"title":"Bridging Theory and Practice: A Multiphase Study of GenAI-Assisted Visualization Learning.","authors":"Mak Ahmad, Kwan-Liu Ma, Beatriz Sousa Santos, Alejandra J Magana, Rafael Bidarra","doi":"10.1109/MCG.2025.3553396","DOIUrl":"10.1109/MCG.2025.3553396","url":null,"abstract":"Understanding how students learn visualization skills is becoming increasingly crucial as generative AI transforms technical education. We present a systematic study examining how structured exposure to large language models via Observable's AI Assist platform impacts data visualization education through a multiphase investigation across two universities. Our mixed-methods approach with 65 graduate students (32 data science and 33 computer science) revealed that structured generative AI exposure following constructivist learning principles enabled sustained engagement and tool adoption while maintaining pedagogical rigor. Through a structured multiphase study incorporating preassessments, intervention observations, detailed assignment reflections, and postintervention evaluation within the academic term constraints, we identified specific patterns in how students integrate generative AI into their visualization workflows. The results from our mixed-methods analysis suggest potential strategies for adapting visualization education to an AI-augmented future while preserving essential learning outcomes. We contribute practical frameworks for integrating generative AI tools into visualization curricula and evidence-based insights on scaffolding student learning with AI assistance, with initial evidence of sustained impact over a three-week period following instruction.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"45 5","pages":"147-156"},"PeriodicalIF":1.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The State of Single-Cell Atlas Data Visualization in the Biological Literature. 生物学文献中单细胞图谱数据可视化的现状。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-09-01 DOI: 10.1109/MCG.2025.3583979

Mark S Keller, Eric Morth, Thomas C Smits, Simon Warchol, Grace Guo, Qianwen Wang, Robert Krueger, Hanspeter Pfister, Nils Gehlenborg

引用次数: 0

Embarrassingly Agile-Data Visualization Methodology in Emergency Responses. 令人尴尬的敏捷——应急响应中的数据可视化方法。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-09-01 DOI: 10.1109/MCG.2025.3595342

Barbora Kozlikova, Daniel Archambault, Johannes Dreesman, Andreas Kerren, Biagio Lucini, Cagatay Turkay, Melanie Tory, Daniel Keefe, Cindy Xiong Bearfield

引用次数: 0

Level Generation With Quantum Reservoir Computing. 量子储层计算的水平生成。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-09-01 DOI: 10.1109/MCG.2025.3591956

Joao S Ferreira, Pierre Fromholz, Hari Shaji, James R Wootton, Mike Potel

引用次数: 0

Challenges and Opportunities for the Visualization of Protein Energy Landscapes. 蛋白质能量景观可视化的挑战与机遇。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-09-01 DOI: 10.1109/MCG.2025.3592983

Jaume Ros, Alessio Arleo, Rafael Giordano Viegas, Vitor B P Leite, Fernando V Paulovich

引用次数: 0

FashionCook: A Visual Analytics System for Human-AI Collaboration in Fashion E-Commerce Design. FashionCook：时尚电子商务设计中人机协作的可视化分析系统。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-08-27 DOI: 10.1109/MCG.2025.3597849

Yuheng Shao, Shiyi Liu, Gongyan Chen, Ruofei Ma, Xingbo Wang, Quan Li

引用次数: 0

AnchorTextVis: A Visual Analytics Approach for Fast Comparison of Text Embeddings. 一个快速比较文本嵌入的可视化分析方法。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-08-13 DOI: 10.1109/MCG.2025.3598262

Jingzhen Zhang, Hongjiang Lv, Zhibin Niu

{"title":"AnchorTextVis: A Visual Analytics Approach for Fast Comparison of Text Embeddings.","authors":"Jingzhen Zhang, Hongjiang Lv, Zhibin Niu","doi":"10.1109/MCG.2025.3598262","DOIUrl":"https://doi.org/10.1109/MCG.2025.3598262","url":null,"abstract":"Visual comparison of text embeddings is crucial for analyzing semantic differences and comparing embedding models. Existing methods fail to maintain visual consistency in comparative regions and lack AI-assisted analysis, leading to high cognitive loads and time-consuming exploration processes. In this paper, we propose AnchorTextVis, a visual analytics approach based on AnchorMap-our dynamic projection algorithm balancing spatial quality and temporal coherence and LLMs to preserve users' mental map and accelerate the exploration process. We introduce the use of comparable dimensionality reduction algorithms that maintain visual consistency, such as AnchorMap from our previous work and Joint t-SNE. Building on this foundation, we leverage LLMs to compare and summarize, offering users insights. For quantitative comparisons, we define two complementary metrics, Shared KNN and Coordinate distance. Besides, we have also designed intuitive representation and rich interactive tools to compare clusters of texts and individual texts. We demonstrate the effectiveness and usefulness of our approach through three case studies and expert feedback.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MuCHEx: A Multimodal Conversational Debugging Tool for Interactive Visual Exploration of Hierarchical Object Classification. 多级对象分类的交互式可视化探索的多模态会话调试工具。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-08-12 DOI: 10.1109/MCG.2025.3598204

Reza Shahriari, Yichi Yang, Danish Nisar Ahmed Tamboli, Michael Perez, Yuheng Zha, Jinyu Hou, Mingkai Deng, Eric D Ragan, Jaime Ruiz, Daisy Zhe Wang, Zhitting Hu, Eric Xing

{"title":"MuCHEx: A Multimodal Conversational Debugging Tool for Interactive Visual Exploration of Hierarchical Object Classification.","authors":"Reza Shahriari, Yichi Yang, Danish Nisar Ahmed Tamboli, Michael Perez, Yuheng Zha, Jinyu Hou, Mingkai Deng, Eric D Ragan, Jaime Ruiz, Daisy Zhe Wang, Zhitting Hu, Eric Xing","doi":"10.1109/MCG.2025.3598204","DOIUrl":"https://doi.org/10.1109/MCG.2025.3598204","url":null,"abstract":"Object recognition is a fundamental challenge in computer vision, particularly for fine-grained object classification, where classes differ in minor features. Improved fine-grained object classification requires a teaching system with numerous classes and instances of data. As the number of hierarchical levels and instances grows, debugging these models becomes increasingly complex. Moreover, different types of debugging tasks require varying approaches, explanations, and levels of detail. We present MuCHEx, a multimodal conversational system that blends natural language and visual interaction for interactive debugging of hierarchical object classification. Natural language allows users to flexibly express high-level questions or debugging goals without needing to navigate complex interfaces, while adaptive explanations surface only the most relevant visual or textual details based on the user's current task. This multimodal approach combines the expressiveness of language with the precision of direct manipulation, enabling context-aware exploration during model debugging.","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interactive Texture Segmentation of 3D Scanned Models Leveraging Multiview Automatic Segmentation. 利用多视图自动分割的3D扫描模型的交互式纹理分割。

IF 1.4 4区计算机科学

IEEE Computer Graphics and Applications Pub Date : 2025-08-04 DOI: 10.1109/MCG.2025.3595378

Koki Madono, Takeo Igarashi, Hiroharu Kato, Taisuke Hashimoto, Fabrice Matulic, Tsukasa Takagi, Keita Higuchi

引用次数: 0