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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Enhancing Supply Chain Resilience: Integrating Blockchain for Transparency and Risk Mitigation in Logistics

Enhancing Supply Chain Resilience: Integrating Blockchain fo...

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Ramkumar, M. Siva Arun, M. Gopan, Gokul Giri, Jayant Al-Qawasmi, Khaled Sns College of Technology Dept of Ece India Saveetha University Department of Mechanical Engineering Chennai602105 India YeshwantraoChavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Saveetha University Department of Vlsi Microelectronics TN Chennai602105 India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331521394

Supply chain resilience is vital for ensuring smooth operations, especially in logistics, where transparency and risk mitigation are critical for managing disruptions. Integrating blockchain technology offers a robust solution to enhance supply chain visibility and minimize risks across logistics networks. Traditional supply chain systems often face challenges such as lack of transparency, inefficient tracking, and susceptibility to fraud, leading to risk management vulnerabilities. These issues are exacerbated by the reliance on centralized systems, which can lead to information silos and delayed decision-making in times of disruption. We propose a Supply Chain Resilience using Blockchain Technology (SCR-BCT) framework to address these challenges. SCR-BCT leverages blockchain's decentralized and immutable nature to enhance transparency, facilitate real-time tracking, and ensure the authenticity of data across the supply chain. The framework enables automated compliance checks and secure transaction records by integrating smart contracts and distributed ledgers. The proposed method can monitor shipments, track products from origin to destination, and trigger risk mitigation protocols during unforeseen events like delays or disruptions. This system empowers all stakeholders with realtime visibility and ensures accountability at each stage of the logistics process. The research findings suggest that SCR-BCT significantly enhances supply chain resilience by improving transparency, reducing fraud, and enabling faster, data-driven responses to risks, leading to more reliable and efficient logistics operations. © 2025 ieee.

关键词： Risk perception

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Semantic Prompting with Image Token for Continual Learning

Semantic Prompting with Image Token for Continual Learning

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Jisu Han Jaemin Na Wonjun Hwang Ajou University Korea Tech. Innovation Group KT Korea

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks. Recently, prompt-based learning has emerged to leverage pre-trained models to be prompted to learn subsequent tasks without the reliance on the rehearsal buffer. Although this approach has demonstrated outstanding results, existing methods depend on preceding task-selection process to choose appropriate prompts. However, imperfectness in task-selection may lead to negative impacts on the performance particularly in the scenarios where the number of tasks is large or task distributions are imbalanced. To address this issue, we introduce a novel task-agnostic approach that focuses on the visual semantic information of image tokens eliminating the preceding task prediction. By leveraging the ability of the pre-trained model to discriminate between similar tokens, our method not only subdivides the prompt but also eliminates the need for additional forward pass. Consequently, we achieve competitive performance on four benchmarks while significantly reducing training time compared to state-of-the-art methods. The code is available at https://***/pilsHan/I-Prompt

关键词： Continuing education Training visualization Computer vision Image resolution Image coding Codes Semantics Benchmark testing

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Detecting Origin Attribution for Text-to-Image Diffusion Models

Detecting Origin Attribution for Text-to-Image Diffusion Mod...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Katherine Xu Lingzhi Zhang Jianbo Shi University of Pennsylvania Adobe Inc.

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Modern text-to-image (T2I) diffusion models can generate images with remarkable realism and creativity. These advancements have sparked research in fake image detection and attribution, yet prior studies have not fully explored the practical and scientific dimensions of this task. In addition to attributing images to 12 state-of-the-art T2I generators, we provide extensive analyses on what inference stage hyperparameters and image modifications are discernible. Our experiments reveal that initialization seeds are highly detectable, along with other subtle variations in the image generation process to some extent. We further investigate what visual traces are leveraged in image attribution by perturbing high-frequency details and employing midlevel representations of image style and structure. Notably, altering high-frequency information causes only slight reductions in accuracy, and training an attributor on style representations outperforms training on RGB images. Our analyses underscore that fake images are detectable and attributable at various levels of visual granularity.

关键词： Training visualization Image forensics Accuracy Image recognition Image synthesis Text to image Diffusion models Generators Testing

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ELMGS: Enhancing Memory and Computation Scalability Through coMpression for 3D Gaussian Splatting

ELMGS: Enhancing Memory and Computation Scalability Through ...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Muhammad Salman Ali Sung-Ho Bae Enzo Tartaglione LTCI Télécom Paris Institut Poly technique de Paris France Kyung Hee University Republic of Korea

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

3D models have recently been popularized by the potentiality of end-to-end training offered first by Neural Radiance Fields and most recently by 3D Gaussian Splatting models. The latter has the big advantage of naturally providing fast training convergence and high editability. However, as the research around these is still in its infancy, there is still a gap in the literature regarding the model's scalability. In this work, we propose an approach enabling both memory and computation scalability of such models. More specifically, we propose an iterative pruning strategy that removes redundant information encoded in the model. We also enhance compressibility for the model by including a differentiable quantization and entropy coding estimator in the optimization strategy. Our results on popular benchmarks showcase the effectiveness of the proposed approach and open the road to the broad deployability of such a solution even on resource-constrained devices.

关键词： Training Solid modeling visualization Three-dimensional displays Quantization (signal) Computational modeling Scalability Roads Benchmark testing Standards

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Analyzing Logs of Large-Scale Software Systems Using Time Curves visualization

Analyzing Logs of Large-Scale Software Systems Using Time Cu...

引用

ieee International conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Dmytro Borysenkov Adriano Vogel Sören Henning Esteban Perez-Wohlfeil Dynatrace Research Linz Austria JKU/Dynatrace Co-Innovation Lab Johannes Kepler University Linz Austria

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Logs are crucial for analyzing large-scale software systems, offering insights into system health, performance, security threats, potential bugs, etc. However, their chaotic na-ture-characterized by sheer volume, lack of standards, and variability-makes manual analysis complex. The use of clustering algorithms can assist by grouping logs into a smaller set of templates, but lose the temporal and relational context in doing so. On the contrary, Large Language Models (LLMs) can provide meaningful explanations but struggle with processing large collections efficiently. Moreover, representation techniques for both approaches are typically limited to either plain text or traditional charting, especially when dealing with large-scale systems. In this paper, we combine clustering and LLM summarization with event detection and Multidimensional Scaling through the use of Time Curves to produce a holistic pipeline that enables efficient and automatic summarization of vast collections of software system logs. The core of our approach is the proposal of a semimetric distance that effectively measures similarity between events, thus enabling a meaningful representation. We show that our method, based on logs collected from different applications, can explain the behavior of a system over time without prior knowledge. We also show how the approach can be used to detect general trends as well as outliers in parallel and distributed systems by overlapping multiple projections. As a result, we expect a significant reduction in the time required to analyze system-wide issues, identify performance bottlenecks and security risks, debug applications, etc.

关键词： visualization Software algorithms Pipelines Text summarization Manuals Software systems Market research Security Proposals Standards

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Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge

Enhancing Scene Graph Generation with Hierarchical Relations...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Bowen Jiang Zhijun Zhuang Shreyas S. Shivakumar Camillo J. Taylor GRASP Laboratory University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

This work introduces an enhanced approach to generating scene graphs by incorporating both a relationship hierarchy and commonsense knowledge. Specifically, we begin by proposing a hierarchical relation head that exploits an informative hierarchical structure. It jointly predicts the relation super-category between object pairs in an image, along with detailed relations under each super-category. Following this, we implement a robust commonsense validation pipeline that harnesses foundation models to critique the results from the scene graph prediction system, removing nonsensical predicates even with a small language-only model. Extensive experiments on Visual Genome and Open-Image V6 datasets demonstrate that the proposed modules can be seamlessly integrated as plug-and-play enhancements to existing scene graph generation algorithms. The results show significant improvements with an extensive set of reasonable predictions beyond dataset annotations. Codes are available at https://***/bowen-upenn/scene_graph_commonsense 1 1 This work has been accepted at the 2025 ieee/CVF Winter conference on Applications of Computer Vision (WACV).

关键词： Computer vision visualization Head Foundation models Computational modeling Pipelines Genomics Predictive models Prediction algorithms Commonsense reasoning

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Towards Personalized Navigation in XR: Design Recommendations to Accommodate Individual Differences

Towards Personalized Navigation in XR: Design Recommendation...

引用

2025 ieee conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Lee, Jong-In Stuerzlinger, Wolfgang College of Performance Visualization and Fine Arts Texas A&m University United States School of Interactive Arts and Technology Simon Fraser University Canada

ISBN: (纸本)9798331514846

Navigation interfaces in Extended Reality (XR) have traditionally targeted universal solutions that perform well for all users. However, research has shown that users exhibit distinct preferences and performance patterns when using different navigation techniques. This position paper argues for the necessity of and strategies for designing personalized navigation interfaces that accommodate individual differences in spatial abilities, navigation strategies, and individual needs. Drawing from empirical findings from previous work investigating locomotion and wayfinding techniques and research in spatial cognition and navigation, we demonstrate how different user groups respond uniquely to navigation interface components. Based on these insights, we propose design recommendations for developing adaptive navigation interfaces that cater to individual user characteristics while maintaining usability. Furthermore, we discuss opportunities for standardization in user assessment, interface adaptation, and inclusive design. This approach could lead to more inclusive and effective navigation solutions for XR environments. © 2025 ieee.

关键词： Augmented Reality Extended Reality Locomotion Virtual Reality Wayfinding

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An Encoder-Agnostic Weakly Supervised Method For Describing Textures

An Encoder-Agnostic Weakly Supervised Method For Describing ...

引用

ieee Workshop on Applications of Computer Vision (WACV)

作者： Shangbo Mao Deepu Rajan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recent advances in Large Language Models (LLMs) have enabled the semantic description of textures in natural language, aiming to capture them in richer detail. However, most methods are confined to either depending on supervised training with pairs of images and manually annotated visual attributes that most texture datasets lack or using Vision-Language Models (VLMs) such as CLIP. In this paper, we develop an encoder-agnostic Weakly supervised Texture Description Generator (WTDG) that employs a novel Scaled Ranked Kullback-Leibler divergence (SR-KL) loss between image and text modalities. Within the SR-KL loss formulation, we leverage category information, which is always available as ground-truths for all benchmark texture recognition datasets. We further extend our proposed WTDG to assist in texture recognition by using its generated texture descriptions. Thus, we develop a multimodal framework, called $T e x^2$ , which is adept at simultaneous generation of texture description and recognition. Our approach exhibits promising performance in describing and recognizing textures on benchmark datasets.

关键词： Training visualization Computer vision Accuracy Large language models Semantics Natural languages Benchmark testing Generators

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DREAM MR: Distraction Regulation for Effective Attention Management using Mixed Reality

DREAM MR: Distraction Regulation for Effective Attention Man...

引用

2025 ieee conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Bezanson, Katherine Rojas-Muñoz, Edgar Texas A&m University College of Performance Visualization & Fine Arts United States

ISBN: (纸本)9798331514846

Public places can be overstimulating, making focusing on a task difficult. This can be particularly problematic to people in the neurodivergent spectrum. This paper presents the initial plan to develop DREAM MR, an assistive framework to regulate the amount of distractions a person perceives from the environment. The objective of the framework is to temporarily and seamlessly attenuate elements in the vicinity of a workspace that have the potential to distract users from their current task. For each object, the amount of attenuation to apply over time will be determined by calculating three criteria: potential for distraction and overstimulation, relevance to the task, and ability to promote connectedness to the environment. DREAM MR will achieve this by leveraging recent advancements in Mixed Reality such as real-time environment tracking and understanding and video pass-through. This framework will promote personal accessibility and functionality in public spaces for all individuals. © 2025 ieee.

关键词： Human-centered computing [Accessibility]: Accessibility technologies Computing methodologies [Artificial intelligence]: Computer vision - Computer vision tasks Human-centered computing [Human computer interaction (HCI)]: Interaction paradigms - Mixed / augmented reality

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Proximity Zones Based on Perceived Danger in Human-Robot Interaction 25

Proximity Zones Based on Perceived Danger in Human-Robot Int...

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ACM/ieee International conference on Human-Robot Interaction (HRI)

作者： Suhwan Jung Hyochang Kim Hyunmin Kang Meen Jong Kim Heena Noh Yong Gu Ji Department of Industrial Engineering Yonsei University South Korea Stanford Center at the Incheon Global Campus Stanford University South Korea Automatic Driving Technology Research Division Korea Automotive Technology Institute South Korea

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

This study addresses the challenge of ensuring pedestrian safety in shared spaces during human-robot interactions. As mobile robots become increasingly integrated into public environments, there is a need to better understand how pedestrians perceive robots in proximity. This research explores the effects of robot lateral distance, robot velocity, and pedestrian state on pedestrian perception, with a particular focus on proximity zones during human-robot interactions. Conducted in a virtual reality environment with a within-subject design, the study measured perceived danger through button presses and visualized proximity zones using contour density plots. Findings reveal that increasing lateral distance significantly decreases button press likelihood, particularly beyond 160 cm. Robot velocities of 1 mls and 1.5 mls showed no significant difference, suggesting pedestrians perceive them similarly due to their alignment with average walking speed. Proximity zones were consistently wider and denser on the left than the right, highlighting spatial asymmetry in perception. These results provide quantitative and visual insights for designing mobile robots in shared spaces. Future research should examine environmental and group dynamics and robot design to enhance safety and public acceptance in human-robot interactions.

关键词： Legged locomotion Presses visualization Pedestrians Density measurement Human-robot interaction Data visualization Virtual reality Safety

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