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Automatic Estimation for Visual Quality Changes of Street Space via Street-View Images and Multimodal large Language Models

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ieee ACCESS 2024年 12卷 87713-87727页

作者： Liang, Hao Zhang, Jiaxin Li, Yunqin Wang, Bowen Huang, Jingyong Nanjing Forestry Univ Coll Landscape Architecture Nanjing 210037 Peoples R China Nanchang Univ Architecture & Design Coll Nanchang 330031 Peoples R China Osaka Univ Grad Sch Engn Div Sustainable Energy & Environm Engn Osaka 5650871 Japan Osaka Univ Grad Sch Informat Sci & Technol Osaka 5650871 Japan

Estimating Visual Quality of Street Space (VQoSS) is pivotal for urban design, environmental sustainability, civic engagement, etc. Recent advancements, notably in deep learning, have enabled large-scale analysis. However, traditional deep learning approaches are hampered by extensive data annotation requirements and limited adaptability across diverse VQoSS tasks. Multimodal large Language Models (MLLMs) have recently demonstrated proficiency in various computer vision tasks, positioning them as promising tools for automated VQoSS assessment. In this paper, we pioneer the application of MLLMs to VQoSS change estimation, with our empirical findings affirming their effectiveness. In addition, we introduce Street Quality Generative Pre-trained Transformer (SQ-GPT), a model that distills knowledge from the current most powerful but inaccessible (not free) GPT-4V, requiring no human efforts. SQ-GPT approaches GPT-4V's performance and is viable for large-scale VQoSS change estimation. In a case study of Nanjing, we showcase the practicality of SQ-GPT and knowledge distillation pipeline. Our work promises to be a valuable asset for future urban studies research.

关键词： visualization Task analysis Estimation Deep learning Image color analysis data models Context modeling Smart cities large language models Smart city visual quality deep learning multimodal large language models

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Exploring Users Pointing Performance on large Displays with Different Curvatures in Virtual Reality

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第11期29卷 4535-4545页

作者： Ullah, A. K. M. Amanat Delamare, William Hasan, Khalad Univ British Columbia Okanagan BC Canada Univ Bordeaux ESTIA Inst Technol EstiaR F-64210 Bidart France

large curved displays inside Virtual Reality environments are becoming popular for visualizing high-resolution content during analytical tasks, gaming or entertainment. Prior research showed that such displays provide a wide field of view and offer users a high level of immersion. However, little is known about users' performance (e.g., pointing speed and accuracy) on them. We explore users' pointing performance on large virtual curved displays. We investigate standard pointing factors (e.g., target width and amplitude) in combination with relevant curve-related factors, namely display curvature and both linear and angular measures. Our results show that the less curved the display, the higher the performance, i.e., faster movement time. This result holds for pointing tasks controlled via their visual properties (linear widths and amplitudes) or their motor properties (angular widths and amplitudes). Additionally, display curvatures significantly affect the error rate for both linear and angular conditions. Furthermore, we observe that curved displays perform better or similar to flat displays based on throughput analysis. Finally, we discuss our results and provide suggestions regarding pointing tasks on large curved displays in VR.

关键词： Task analysis Performance evaluation visualization data visualization Focusing Error analysis Throughput Virtual large Display Pointing Performance Display Curvatures Fitts Law

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Precise Embodied data Selection with Haptic Feedback while Retaining Room-Scale Visualisation Context

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 602-612页

作者： Dai, Shaozhang Li, Yi Ens, Barrett Besancon, Lonni Dwyer, Tim Monash Univ Clayton Vic 3800 Australia Univ British Columbia Vancouver BC Canada Linkoping Univ Linkoping Sweden

Room-scale immersive data visualisations provide viewers a wide-scale overview of a large dataset, but to interact precisely with individual data points they typically have to navigate to change their point of view. In traditional screen-based visualisations, focus-and-context techniques allow visualisation users to keep a full dataset in view while making detailed selections. Such techniques have been studied extensively on desktop to allow precise selection within large data sets, but they have not been explored in immersive 3D modalities. In this paper we develop a novel immersive focus-and-context technique based on a "magic portal" metaphor adapted specifically for data visualisation scenarios. An extendable-hand interaction technique is used to place a portal close to the region of interest. The other end of the portal then opens comfortably within the user's physical reach such that they can reach through to precisely select individual data points. Through a controlled study with 12 participants, we find strong evidence that portals reduce overshoots in selection and overall hand trajectory length, reducing arm and shoulder fatigue compared to ranged interaction without the portal. The portals also enable us to use a robot arm to provide haptic feedback for data within the limited volume of the portal region. In a second study with another 12 participants we found that haptics provided a positive experience (qualitative feedback) but did not significantly reduce fatigue. We demonstrate applications for portal-based selection through two use-case scenarios.

关键词： immersive analytics focus-and-context remote interaction portal haptic feedback

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Advancements in large-Scale Image and Text Representation Learning: A Comprehensive Review and Outlook

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ieee ACCESS 2025年 13卷 49922-49933页

作者： Qin, Yang Ding, Shuxue Xie, Huiming Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab AI Algorithm Engn Sch Artificial Intelligence Guilin 541004 Peoples R China Guilin Univ Aerosp Technol Engn Comprehens Training Ctr Guilin 541004 Peoples R China

large-scale image and text representation learning is critical in determining the performance of multimodal tasks involving images and text, such as visual question answering and image captioning. Most existing research on large-scale image and text representation learning relies on Transformer networks for pre-training, i.e., learning generic semantic representations from large-scale image-to-text pairs. These representations are then fine-tuned and transferred to downstream multimodal tasks. This paper first provides a brief analysis of the advantages of pre-training models. It then comprehensively summarizes the relevant research on large-scale image and text representation learning based on pre-training. The focus is on pre-training model architectures, pre-training tasks, and image-text datasets. Finally, we provide a summary and outlook of large-scale image and text representation learning.

关键词： Transformers Image coding Representation learning Feature extraction Encoding Vectors data models visualization Analytical models Semantics Image-and-text large-scale representation learning pre-training transformer self-supervised learning

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GraphFederator: Federated Visual analysis for Multi-party Graphs 17

GraphFederator: Federated Visual Analysis for Multi-party Gr...

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17th Pacific visualization Conference (PacificVis)

作者： Han, Dongming Zhu, Haiyang Chen, Wei Pan, Rusheng Liu, Yijing Zhou, Jiehui Feng, Haozhe Zhang, Tianye Wang, Xumeng Zhu, Minfeng Tao, Jianrong Fan, Changjie Zhang, Xiaolong (Luke) Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou Peoples R China Zhejiang Univ Minist Educ Lab Art & Archaeol Image Hangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Zhejiang Univ Hangzhou Peoples R China Netease Fuxi AI Lab Hangzhou Peoples R China Penn State Univ University Pk PA 16802 USA Wuchan Zhongda Digital Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798350393811;9798350393804

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

关键词： Federated learning Graph analysis Graph visual analysis Representation learning

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HSViz-II: Octet Layered Hierarchy Simplified visualizations for Distributed Firewall Policy analysis

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ieee ACCESS 2024年 12卷 936-948页

作者： Lee, Hyunjung Lee, Suryeon Kim, Kyounggon Kim, Huy Kang Korea Secur Comp Corp KOSCOM Seoul 07329 South Korea Seoul Womens Univ Dept CyberSecur Seoul 01797 South Korea Naif Arab Univ Secur Sci Dept Forens Sci Riyadh 14812 Saudi Arabia Korea Univ Sch Cybersecur Seoul 02841 South Korea

Enterprises typically install firewalls at communication points to their internal networks with the primary objective of protecting their core assets from external cyber attackers. This ensures unauthorized access is controlled and prevented. However, overly permissive policies and services with vulnerabilities can be exploited by attackers, providing them with pathways into internal systems. Therefore, firewall policies must be meticulously applied and managed. Given the significant ramifications of firewall policies, they must be continuously managed with high importance. As the number of policies increases and the amount of information to be processed grows, the process becomes complex, and there are limitations to managing policies from a human cognitive perspective. An increase in unmanaged misuse policies can inadvertently introduce security risks through unintended allowance policies. In the case of large-scale network networks operating multiple firewalls, a different form of misuse policy check and management is required compared to managing a single firewall policy. The proposed tool, HSViz-II, not only visualizes misuse of a single firewall policy but also visualizes four misuse cases in a distributed firewall environment, providing a detailed breakdown based on Octets. It displays the distribution of anomalous policies by dividing the Source IP into Octet Layers. For the four anomalous policy cases, it offers five views based on dividing the Source IP into Octet Layers and three overall views for upstream firewall, downstream firewall, and both, totaling 60 views. The processing speed for each function was measured using four sets of actual upstream and downstream firewall policies, comprising eight different firewall policies in total. Firewall operators can use this tool to grasp the distribution status of misuse policies in single and multiple firewalls and check the status of misuse policies by Octet. By offering a method for firewall operators to ac

关键词： Firewalls (computing) visualization data visualization Security Three-dimensional displays IP networks Distributed databases Guidelines Anomaly detection Firewall policy visualization policy analysis data visualization rule anomaly detection distributed firewall rule anomaly detection

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Scalable Comparative visualization of Ensembles of Call Graphs

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第3期29卷 1691-1704页

作者： Kesavan, Suraj P. P. Bhatia, Harsh Bhatele, Abhinav Brink, Stephanie Pearce, Olga Gamblin, Todd Bremer, Peer-Timo Ma, Kwan-Liu Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94551 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, such as hardware configurations, system software choices, and application parameters, and are interested in detecting and understanding bottlenecks in different executions. They often collect hierarchical performance profiles represented as call graphs, which combine performance metrics with their execution contexts. The crucial task of exploring multiple call graphs together is tedious and challenging because of the many structural differences in the execution contexts and significant variability in the collected performance metrics (e.g., execution runtime). In this paper, we present Ensemble CallFlow to support the exploration of ensembles of call graphs using new types of visualizations, analysis, graph operations, and features. We introduce ensemble-Sankey, a new visual design that combines the strengths of resource-flow (Sankey) and box-plot visualization techniques. Whereas the resource-flow visualization can easily and intuitively describe the graphical nature of the call graph, the box plots overlaid on the nodes of Sankey convey the performance variability within the ensemble. Our interactive visual interface provides linked views to help explore ensembles of call graphs, e.g., by facilitating the analysis of structural differences, and identifying similar or distinct call graphs. We demonstrate the effectiveness and usefulness of our design through case studies on large-scale parallel codes.

关键词： Codes Measurement Tools Runtime Libraries Task analysis data visualization Performance analysis software visualization visual analytics hierarchical data coordinated and multiple views

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Reimagining TaxiVis through an Immersive Space-Time Cube metaphor and reflecting on potential benefits of Immersive Analytics for urban data exploration 31

Reimagining TaxiVis through an Immersive Space-Time Cube met...

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ieee Conference on Virtual Reality and 3D User Interfaces (VR)

作者： Wagner, Jorge Silva, Claudio T. Stuerzlinger, Wolfgang Nedel, Luciana Univ Fed Rio Grande do Sul Porto Alegre RS Brazil NYU New York NY 10003 USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798350374025;9798350374032

Current visualization research has identified the potential of more immersive settings for data exploration, leveraging VR and AR technologies. To explore how a traditional visualization system could be adapted into an immersive framework, and how it could benefit from this, we decided to revisit a landmark paper presented ten years ago at ieee VIS. TaxiVis, by Ferreira et al., enabled interactive spatio-temporal querying of a large dataset of taxi trips in New York City. Here, we reimagine how TaxiVis' functionalities could be implemented and extended in a 3D immersive environment. Among the unique features we identify as being enabled by the Immersive TaxiVis prototype are alternative uses of the additional visual dimension, a fully visual 3D spatio-temporal query framework, and the opportunity to explore the data at different scales and frames of reference. By revisiting the case studies from the original paper, we demonstrate workflows that can benefit from this immersive perspective. Through reporting on our experience, and on the vision and reasoning behind our design decisions, we hope to contribute to the debate on how conventional and immersive visualization paradigms can complement each other and on how the exploration of urban datasets can be facilitated in the coming years.

关键词： Human-centered computing-visualization-visualization systems and tools Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual reality

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BeeVis: A Comprehensive Honey Bee data visualization, Exploration, and analysis System

BeeVis: A Comprehensive Honey Bee Data Visualization, Explor...

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ieee SoutheastCon Conference on Engineering the Future

作者： Feng, Tinghao Arkle, Sam Campell, Christopher Tashakkori, Rahman Appalachian State Univ Comp Sci Dept Boone NC 28608 USA

ISBN: (纸本)9798350317107;9798350317114

Honey bees play an important role in pollinating major crops and fruits across the world. Unfortunately, in recent years, many honey bee colonies have been lost due to various causes that, at some point, were referred to as the Colony Collapse Disorder (CCD). Some research, including the Appalachian Multi-Purpose Apiary Systems (AppMAIS) have acquired a large amount of data such as audio and video recordings as well as humidity, temperature, weight, and genetic data from honey beehives to determine the health of their bees. The amount of data produced by AppMAIS is a good example of Big data, which is simply impossible to analyze with traditional computing approaches. Such analysis requires sophisticated visualization and exploratory tools to study honey bee behavior successfully and the environmental factors affecting them. This paper will provide details on the design, implementation, and application of a comprehensive visualization system called BeeVis, which has been developed as part of the AppMAIS project for visualizing, exploring, and analyzing honey bee data for 29 hives in the Western Region of North Carolina. BeeVis has assisted us in gaining significant knowledge about the health of the AppMAIS hives. A significant contribution of BeeVis in our research is the ability to provide options for comparison of our hives at different development stages.

关键词： data visualization

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Dash Sylvereye: A Python Library for Dashboard-Driven visualization of large Street Networks

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ieee ACCESS 2023年 11卷 121142-121161页

作者： Garcia-Robledo, Alberto Zangiabady, Mahboobeh Conahcyt CentroGeo Queretaro 76709 Mexico Univ Twente Dept Design & Anal Commun Syst DACS NL-7522 NB Enschede Netherlands

State-of-the-art open graph visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, we present Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data. This is achieved by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by Dash. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries *** and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation.

关键词： data visualization data analysis software libraries component architectures complex networks graphical user interfaces graphics vehicle dynamics

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