Visual Informatics最新文献_第5页

Visual comparative analytics of multimodal transportation 多式联运的视觉比较分析

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2025-03-01 Epub Date: 2025-01-16 DOI: 10.1016/j.visinf.2025.01.001

Zikun Deng , Haoming Chen , Qing-Long Lu , Zicheng Su , Tobias Schreck , Jie Bao , Yi Cai

Contemporary urban transportation systems frequently depend on a variety of modes to provide residents with travel services. Understanding a multimodal transportation system is pivotal for devising well-informed planning; however, it is also inherently challenging for traffic analysts and planners. This challenge stems from the necessity of evaluating and contrasting the quality of transportation services across multiple modes. Existing methods are constrained in offering comprehensive insights into the system, primarily due to the inadequacy of multimodal traffic data necessary for fair comparisons and their inability to equip analysts and planners with the means for exploration and reasoned analysis within the urban spatial context. To this end, we first acquire sufficient multimodal trips leveraging well-established navigation platforms that can estimate the routes with the least travel time given an origin and a destination (an OD pair). We also propose TraDyssey, a visual analytics system that enables analysts and planners to evaluate and compare multiple modes by exploring acquired massive multimodal trips. TraDyssey follows a streamlined query-and-explore workflow supported by user-friendly and effective interactive visualizations. Specifically, a revisited difference-aware parallel coordinate plot (PCP) is designed for overall mode comparisons based on multimodal trips. Trip groups can be flexibly queried on the PCP based on differential features across modes. The queried trips are then organized and presented on a geographic map by OD pairs, forming a group-OD-trip hierarchy of visual exploration. Domain experts gained valuable insights into transportation planning through real-world case studies using TraDyssey.

当代城市交通系统通常依靠多种模式为居民提供出行服务。了解多式联运系统对于制定明智的规划至关重要，但对交通分析师和规划师来说，这本身也是一项挑战。这一挑战源于对多种交通方式的交通服务质量进行评估和对比的必要性。现有方法在提供对系统的全面见解方面受到限制，主要原因是缺乏进行公平比较所需的多模式交通数据，以及无法为分析师和规划师提供在城市空间背景下进行探索和合理分析的手段。为此，我们首先利用成熟的导航平台获取足够的多式联运出行数据，这些平台可以根据起点和终点（OD 对）估算出旅行时间最少的路线。我们还提出了 TraDyssey，这是一个可视化分析系统，使分析师和规划师能够通过探索获取的大量多式联运行程来评估和比较多种模式。TraDyssey 采用简化的查询和探索工作流程，并辅以用户友好和有效的交互式可视化。具体来说，基于多式联运的整体模式比较设计了一个重新设计的差异感知平行坐标图（PCP）。根据不同模式的差异特征，可以在平行坐标图上灵活地查询行程组。然后，查询到的行程按 OD 对在地理地图上进行组织和展示，形成一个可视化探索的组-OD-行程层次结构。领域专家通过使用 TraDyssey 进行实际案例研究，对交通规划获得了宝贵的见解。

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引用次数: 0

Leveraging personality as a proxy of perceived transparency in hierarchical visualizations 在层次可视化中，利用个性作为感知透明度的代理

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2025-03-01 Epub Date: 2025-02-22 DOI: 10.1016/j.visinf.2025.01.002

Tomás Alves , Carlota Dias , Daniel Gonçalves , Sandra Gama

Understanding which factors affect information visualization transparency continues to be one of the most relevant challenges in current research, especially since trust models how users build on the knowledge and use it. This work extends the current body of research by studying the user’s subjective evaluation of the visualization transparency of hierarchical charts through the clarity, coverage, and look and feel dimensions. Additionally, we extend the user profile to better understand whether personality facets manifest a biasing effect on the trust-building process. Our results show that the data encodings do not affect how users perceive visualization transparency while controlling for personality factors. Regarding personality, the propensity to trust affects how they judge the clarity of a hierarchical chart. Our findings provide new insights into the research challenges of measuring trust and understanding the transparency of information visualization. Specifically, we explore how personality factors manifest in this trust-building relationship and user interaction within visualization systems.

了解哪些因素会影响信息可视化透明度仍然是当前研究中最相关的挑战之一，特别是因为信任建模了用户如何建立和使用知识。这项工作通过研究用户通过清晰度、覆盖范围和外观和感觉维度对分层图表可视化透明度的主观评价，扩展了当前的研究主体。此外，我们扩展了用户档案，以更好地了解人格方面是否在信任建立过程中表现出偏见效应。我们的研究结果表明，在控制个性因素的情况下，数据编码不会影响用户对可视化透明度的感知。在人格方面，信任倾向会影响他们对等级图表清晰度的判断。我们的研究结果为衡量信任和理解信息可视化透明度的研究挑战提供了新的见解。具体来说，我们探讨了人格因素如何在可视化系统中表现出这种信任建立关系和用户交互。

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引用次数: 0

ArtEyer: Enriching GPT-based agents with contextual data visualizations for fine art authentication ArtEyer：为美术认证丰富基于gpt的代理与上下文数据可视化

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-11-16 DOI: 10.1016/j.visinf.2024.11.001

Tan Tang , Yanhong Wu , Junming Gao , Kejia Ruan , Yanjie Zhang , Shuainan Ye , Yingcai Wu , Xiaojiao Chen

Fine art authentication plays a significant role in protecting cultural heritage and ensuring the integrity of artworks. Traditional authentication methods require professionals to collect many reference materials and conduct detailed analyses. To ease the difficulty, we collaborate with domain experts to develop a GPT-based agent, namely ArtEyer, that offers accurate attributions, determines the origin and authorship, and executes visual analytics. Despite the convenience of the conversational user interface, novice users may still face challenges due to the hallucination issue and the steep learning curve associated with prompting. To face these obstacles, we propose a novel solution that places interactive data visualizations into the conversations. We create contextual visualizations from an external domain-dependent database to ensure data trustworthiness and allow users to provide precise instructions to the agent by interacting directly with these visualizations, thus overcoming the vagueness inherent in natural language-based prompting. We evaluate ArtEyer through an in-lab user study and demonstrate its usage with a real-world case.

美术鉴定在保护文化遗产、保证艺术品完整性方面具有重要作用。传统的鉴定方法需要专业人员收集大量的参考资料并进行详细的分析。为了减轻困难，我们与领域专家合作开发了一个基于gpt的代理，即ArtEyer，它提供准确的归属，确定来源和作者，并执行可视化分析。尽管会话式用户界面很方便，但由于幻觉问题和与提示相关的陡峭学习曲线，新手用户可能仍然面临挑战。为了面对这些障碍，我们提出了一种新颖的解决方案，将交互式数据可视化放入对话中。我们从外部领域相关数据库创建上下文可视化，以确保数据的可信度，并允许用户通过直接与这些可视化交互向代理提供精确的指令，从而克服基于自然语言的提示固有的模糊性。我们通过实验室用户研究来评估ArtEyer，并通过实际案例展示其使用情况。

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引用次数: 0

Computer Vision in Augmented, Virtual, Mixed and Extended Reality environments—A bibliometric review 增强、虚拟、混合和扩展现实环境中的计算机视觉——文献计量学综述

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-11-21 DOI: 10.1016/j.visinf.2024.11.002

Júlio Castro Lopes, Rui Pedro Lopes

This work describes a bibliometric analysis of the literature on the use of computer vision algorithms in Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), and Extended Reality (XR) environments. The analysis aims to highlight the evolution, trends, and effects of research in this field. This review provides an overview of immersive technologies and their applications, as well as the role of computer vision algorithms in enabling these technologies and the potential benefits of using such algorithms. This study identifies important authors, institutions, and research themes by using bibliometric indicators such as citation counts, co-citation analysis, and network analysis. The analysis also identifies gaps and opportunities for additional research in this area, as well as a critical assessment of the quality and relevance of the publications.

这项工作描述了在增强现实（AR）、虚拟现实（VR）、混合现实（MR）和扩展现实（XR）环境中使用计算机视觉算法的文献计量学分析。该分析旨在突出该领域研究的演变、趋势和影响。本文概述了沉浸式技术及其应用，以及计算机视觉算法在实现这些技术中的作用以及使用这些算法的潜在好处。本研究通过使用文献计量指标，如引文计数、共被引分析和网络分析，确定了重要的作者、机构和研究主题。该分析还确定了在这一领域进行进一步研究的差距和机会，并对出版物的质量和相关性进行了批判性评估。

引用次数: 0

Generative model-assisted sample selection for interest-driven progressive visual analytics 兴趣驱动的渐进式视觉分析生成模型辅助样本选择

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-10-24 DOI: 10.1016/j.visinf.2024.10.004

Jie Liu, Jie Li, Jielong Kuang

We propose interest-driven progressive visual analytics. The core idea is to filter samples with features of interest to analysts from the given dataset for analysis. The approach relies on a generative model (GM) trained using the given dataset as the training set. The GM characteristics make it convenient to find ideal generated samples from its latent space. Then, we filter the original samples similar to the ideal generated ones to explore patterns. Our research involves two methods for achieving and applying the idea. First, we give a method to explore ideal samples from a GM’s latent space. Second, we integrate the method into a system to form an embedding-based analytical workflow. Patterns found on open datasets in case studies, results of quantitative experiments, and positive feedback from experts illustrate the general usability and effectiveness of the approach.

我们提出兴趣驱动的渐进式视觉分析。其核心思想是从给定的数据集中过滤分析人员感兴趣的特征样本进行分析。该方法依赖于使用给定数据集作为训练集训练的生成模型（GM）。GM的特性使其能够方便地从潜在空间中寻找理想的生成样本。然后，我们对与理想生成的样本相似的原始样本进行过滤，以探索模式。我们的研究涉及实现和应用这个想法的两种方法。首先，我们给出了一种从GM的潜在空间中挖掘理想样本的方法。其次，我们将该方法集成到一个系统中，形成一个基于嵌入的分析工作流。案例研究中在开放数据集中发现的模式、定量实验的结果以及专家的积极反馈说明了该方法的一般可用性和有效性。

引用次数: 0

ChemNav: An interactive visual tool to navigate in the latent space for chemical molecules discovery ChemNav：一个交互式可视化工具，用于在化学分子发现的潜在空间中导航

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-10-10 DOI: 10.1016/j.visinf.2024.10.002

Yang Zhang, Jie Li, Xu Chao

In recent years, AI-driven drug development has emerged as a prominent research topic in computer chemistry. A key focus is the application of generative models for molecule synthesis, which create extensive virtual libraries of chemical molecules based on latent spaces. However, locating molecules with desirable properties within the vast latent spaces remains a significant challenge. Large regions of invalid samples in the latent space, called “dead zones”, can impede the exploration efficiency. The process is always time-consuming and repetitive. Therefore, we aim to propose a visualization system to help experts identify potential molecules with desirable properties as they wander in the latent space. Specifically, we conducted a literature survey about the application of generative networks in drug synthesis to summarize the tasks and followed this with expert interviews to determine their requirements. Based on the above requirements, we introduce ChemNav, an interactive visual tool for navigating latent space for desirable molecules search. ChemNav incorporates a heuristic latent space interpolation path search algorithm to enhance the efficiency of valid molecule generation, and a similar sample search algorithm to accelerate the discovery of similar molecules. Evaluations of ChemNav through two case studies, a user study, and experiments demonstrated its effectiveness in inspiring researchers to explore the latent space for chemical molecule discovery.

近年来，人工智能驱动的药物开发已成为计算机化学领域的一个突出研究课题。一个关键的焦点是分子合成生成模型的应用，它基于潜在空间创建了广泛的化学分子虚拟库。然而，在巨大的潜在空间中定位具有理想特性的分子仍然是一个重大挑战。潜在空间中存在大面积无效样本，称为“死区”，会影响探测效率。这个过程总是费时且重复的。因此，我们的目标是提出一个可视化系统，以帮助专家识别潜在分子在潜在空间中漫游时具有理想特性。具体来说，我们对生成网络在药物合成中的应用进行了文献调查，总结了任务，然后与专家进行了访谈，以确定他们的要求。基于上述需求，我们介绍了ChemNav，一个交互式可视化工具，用于导航潜在空间，以进行所需分子的搜索。ChemNav结合了一种启发式潜在空间插值路径搜索算法来提高有效分子生成的效率，以及一种相似样本搜索算法来加速相似分子的发现。通过两个案例研究、一个用户研究和实验对ChemNav进行了评估，证明了它在激励研究人员探索化学分子发现的潜在空间方面的有效性。

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引用次数: 0

Glyph design for communication initiation in real-time human-automation collaboration 实时人机协作中通信启动的字形设计

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-11-08 DOI: 10.1016/j.visinf.2024.09.006

Magnus Nylin , Jonas Lundberg , Magnus Bång , Kostiantyn Kucher

Initiating communication and conveying critical information to the human operator is a key problem in human-automation collaboration. This problem is particularly pronounced in time-constrained safety critical domains such as in Air Traffic Management. A visual representation should aid operators understanding why the system initiates the communication, when the operator must act, and the consequences of not responding to the cue. Data glyphs can be used to present multidimensional data, including temporal data in a compact format to facilitate this type of communication. In this paper, we propose a glyph design for communication initialization for highly automated systems in Air Traffic Management, Vessel Traffic Service, and Train Traffic Management. The design was assessed by experts in these domains in three workshop sessions. The results showed that the number of glyphs to be presented simultaneously and the type of situation were domain-specific glyph design aspects that needed to be adjusted for each work domain. The results also showed that the core of the glyph design could be reused between domains, and that the operators could successfully interpret the temporal data representations. We discuss similarities and differences in the applicability of the glyph design between the different domains, and finally, we provide some suggestions for future work based on the results from this study.

发起通信并向操作人员传递关键信息是人机协作的关键问题。这个问题在时间限制的安全关键领域尤其明显，例如空中交通管理。可视化表示应该帮助操作员理解系统启动通信的原因，操作员必须采取行动的时间，以及不响应提示的后果。数据符号可用于表示多维数据，包括紧凑格式的时态数据，以促进这种类型的通信。在本文中，我们提出了一种用于空中交通管理、船舶交通服务和火车交通管理中高度自动化系统的通信初始化的字形设计。该设计由这些领域的专家在三个研讨会上进行评估。结果表明，同时呈现的符号数量和情况类型是需要针对每个工作领域进行调整的特定领域的符号设计方面。结果还表明，符号设计的核心可以在域之间重用，操作符可以成功地解释时间数据表示。在此基础上，讨论了不同领域中字形设计适用性的异同，并对今后的工作提出了建议。

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引用次数: 0

ATVis: Understanding and diagnosing adversarial training processes through visual analytics ATVis：通过视觉分析来理解和诊断对抗性训练过程

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-10-24 DOI: 10.1016/j.visinf.2024.10.003

Fang Zhu , Xufei Zhu , Xumeng Wang , Yuxin Ma , Jieqiong Zhao

Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standard accuracy on normal data, a phenomenon that remains a contentious issue. In addition, the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve. This paper introduces ATVis, a visual analytics framework for examining and diagnosing adversarial training processes. Through multi-level visualization design, ATVis enables the examination of model robustness from various granularity, facilitating a detailed understanding of the dynamics in the training epochs. The framework reveals the complex relationship between adversarial robustness and standard accuracy, which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training. The effectiveness of the framework is demonstrated through case studies.

对抗性训练已经成为深度神经网络对抗对抗性扰动的主要策略，它减轻了利用模型漏洞生成错误预测的问题。尽管增强了鲁棒性，但对抗性训练通常会导致与正常数据的标准准确性之间的权衡，这一现象仍然是一个有争议的问题。此外，深度神经网络模型的不透明性使得检查和诊断对抗性训练过程的演变变得更加困难。本文介绍了ATVis，一种用于检查和诊断对抗性训练过程的可视化分析框架。通过多级可视化设计，ATVis能够从不同粒度检查模型鲁棒性，促进对训练时期动力学的详细理解。该框架揭示了对抗鲁棒性和标准准确性之间的复杂关系，这进一步提供了对对抗训练中观察到的驱动权衡的机制的见解。通过案例研究证明了该框架的有效性。

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引用次数: 0

Incidental visualizations: How complexity factors influence task performance 附带的可视化：复杂性因素如何影响任务性能

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-11-05 DOI: 10.1016/j.visinf.2024.10.005

João Moreira , Daniel Mendes , Daniel Gonçalves

Incidental visualizations convey information to a person during an ongoing primary task, without the person consciously searching for or requesting that information. They differ from glanceable visualizations by not being people’s main focus, and from ambient visualizations by not being embedded in the environment. Instead, they are presented as secondary information that can be observed without a person losing focus on their current task. However, despite extensive research on glanceable and ambient visualizations, the topic of incidental visualizations is yet a novel topic in current research. To bridge this gap, we conducted an empirical user study presenting participants with an incidental visualization while performing a primary task. We aimed to understand how complexity contributory factors — task complexity, output complexity, and pressure — affected primary task performance and incidental visualization accuracy. Our findings showed that incidental visualizations effectively conveyed information without disrupting the primary task, but working memory limitations should be considered. Additionally, output and pressure significantly influenced the primary task’s results. In conclusion, our study provides insights into the perception accuracy and performance impact of incidental visualizations in relation to complexity factors.

附带的可视化在一个人正在进行的主要任务中传递信息，而没有人有意识地搜索或请求这些信息。它们不同于可浏览的可视化，因为它们不是人们的主要焦点，也不同于环境可视化，因为它们没有嵌入到环境中。相反，它们被呈现为次要信息，可以被观察到，而不会让一个人失去对当前任务的注意力。然而，尽管对可浏览可视化和环境可视化进行了广泛的研究，但附带可视化的主题在当前的研究中仍然是一个新的主题。为了弥补这一差距，我们进行了一项经验用户研究，向参与者展示了在执行主要任务时附带的可视化。我们的目的是了解复杂性的促成因素-任务复杂性，输出复杂性和压力-如何影响主要任务的性能和附带的可视化精度。我们的研究结果表明，偶然的视觉化可以有效地传达信息，而不会干扰主要任务，但应考虑工作记忆的限制。此外，输出和压力显著影响了主要任务的结果。总之，我们的研究提供了与复杂性因素有关的偶然可视化的感知准确性和性能影响的见解。

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引用次数: 0

Intelligent CAD 2.0 智能 CAD 2.0

IF 3.8 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Visual Informatics

Pub Date : 2024-12-01 Epub Date: 2024-10-09 DOI: 10.1016/j.visinf.2024.10.001

Qiang Zou, Yingcai Wu, Zhenyu Liu, Weiwei Xu, Shuming Gao

Integrating modern artificial intelligence (AI) techniques, particularly generative AI, holds the promise of revolutionizing computer-aided design (CAD) tools and the engineering design process. However, the direction of “AI+CAD” remains unclear: how will the current generation of intelligent CAD (ICAD) differ from its predecessor in the 1980s and 1990s, what strategic pathways should researchers and engineers pursue for its implementation, and what potential technical challenges might arise?

As an attempt to address these questions, this paper investigates the transformative role of modern AI techniques in advancing CAD towards ICAD. It first analyzes the design process and reconsiders the roles AI techniques can assume in this process, highlighting how they can restructure the path humans, computers, and designs interact with each other. The primary conclusion is that ICAD systems should assume an intensional rather than extensional role in the design process. This offers insights into the evaluation of the previous generation of ICAD (ICAD 1.0) and outlines a prospective framework and trajectory for the next generation of ICAD (ICAD 2.0).

整合现代人工智能（AI）技术，尤其是生成式人工智能，有望彻底改变计算机辅助设计（CAD）工具和工程设计流程。然而，"AI+CAD "的发展方向仍不明确：当前一代的智能 CAD（ICAD）与上世纪八九十年代的前一代产品有何不同，研究人员和工程师应采取何种战略途径来实现这一目标，以及可能出现哪些潜在的技术挑战？本文首先分析了设计过程，并重新考虑了人工智能技术在这一过程中可以发挥的作用，重点介绍了人工智能技术如何重组人类、计算机和设计之间的交互路径。主要结论是，ICAD 系统应在设计过程中扮演内向型而非外向型的角色。这为评估上一代 ICAD（ICAD 1.0）提供了见解，并为下一代 ICAD（ICAD 2.0）勾勒出了一个前瞻性的框架和轨迹。

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引用次数: 0