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检索条件"任意字段=Conference on Visual Data Exploration and Analysis IV"

共 2452 条记录，以下是21-30 订阅

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Faces in the Fog: A Deep Dive into Face De-Identification Techniques and Their Comparative analysis

Faces in the Fog: A Deep Dive into Face De-Identification Te...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： Shaonti Goswami Sudipty Konica Paul Sanyam Jawlia Anurag Goel Department of CSE Delhi Technological University New Delhi India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Face de-identification have drawn a lot of attention due to growing privacy concerns and the growing requirement for anonymization in visual data. This review suggests a systematic exploration of current face de-identification methods, emphasizing three main deep learning-based methods: diffusion models, latent Variable based transformations, and GAN-based models. The in-depth analysis of this literature provides a comparison of how well each strategies protect privacy while retaining data utility by focusing on the advantages and disadvantages of them. Our analysis delves deeper into the benefits and limitations of these models, focusing on important issues such as adversarial robustness, systematic biases, and trade-offs between realistic image generation and privacy preservation. This comprehensive overview synthesizes data from several studies, identifies important gaps in existing approaches and provides guidance to researchers and industry practitioners directing to more reliable and ethical de-identification frameworks.

关键词： Deep learning data privacy Privacy visualization Systematics Reviews Focusing Diffusion models Protection Faces

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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2025 International conference on visual Analytics and data visualization, ICVADV 2025

作者： Nagalakshmi, Thirumalasetty Sambasivudu, M. Sai, Thota Siva Ratna Dept. of Cse Telangana India

ISBN: (纸本)9798331521394

Modern agriculture relies on plant leaf disease detection to reduce crop losses and guarantee food security. This study takes a look at the three main types of methods used to identify plant leaf diseases: conventional methods, techniques based on Machine Learning (ML), and methodology based on Deep Learning (DL). Although they provide essential insights, the scalability and precision of traditional methods-which depend on chemical analysis, image processing, and human inspection-are severely lacking. ML techniques, which make use of algorithms such as Random Forests (RF) and Support Vector Machines (SVM), automate feature-based categorization, which improves detection. Automated disease identification with high accuracy has recently been made possible by DL, which uses sophisticated methods like semantic segmentation, object recognition, and transfer learning in conjunction with architectures like Convolutional Neural Networks (CNNs). This study examines all of these approaches in detail, comparing and contrasting their advantages, disadvantages, and possible uses. Emerging developments, such as eXplainable Artificial Intelligence (XAI) and Internet of Things (IoT)-based systems, are also highlighted in the discussion, with an emphasis on how they will influence smart and sustainable agriculture in the future. © 2025 IEEE.

关键词： Semantic Segmentation

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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visual Analytics and data visualization (ICVADV), International conference on

作者： Thirumalasetty Nagalakshmi M Sambasivudu Thota Siva Ratna Sai Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

关键词： Support vector machines Deep learning Reviews Explainable AI Semantic segmentation Plants (biology) Transfer learning Food security Object recognition Diseases

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Knowledge-based Graph exploration analysis on Customer’s Preference of Insurance Services

Knowledge-based Graph Exploration Analysis on Customer’s Pr...

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International ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Yingfan Chen Chalermpon Kongjit College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics.

关键词： visualization Sentiment analysis Text analysis Social networking (online) Knowledge based systems Insurance Knowledge graphs History

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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visual Analytics and data visualization (ICVADV), International conference on

作者： P. Suresh T Sivakumar N. Revathy M. Pandi Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Master of Computer Applications Hindustan College of Arts and Science Coimbatore Tamil Nadu India Department of Information Technology SRM Madurai College for Engineering and Technology Sivagangai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance.

关键词： Technological innovation IEEE merchandise Heuristic algorithms visual analytics Layout Reinforcement learning Real-time systems Internet of Things Artificial intelligence Optimization

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Developing a Comprehensive Task Framework for Effective Workforce analysis 25

Developing a Comprehensive Task Framework for Effective Work...

引用

Companion Proceedings of the ACM on Web conference 2025

作者： Rebecca Dew Mingzhao Li Weidong Liu Sandya Baratha Raj Pearson Melbourne Australia Pearson Sydney Australia

ISBN: (纸本)9798400713316

Effective workforce analysis, planning, and management require a deep understanding of the tasks and skills associated with different roles. This paper introduces a novel methodology for developing a comprehensive task framework that leverages Large Language Models (LLMs) and large-scale job ads data. We first propose an innovative approach to task taxonomy design, which involves the decomposition and reconstruction of tasks into a hierarchical structure based on action-object pairings, systematically refined using LLMs. The methodology extends to integrating the taxonomy with occupation and skill linkages derived from job ads, ensuring alignment with real-world workforce dynamics. Finally, we demonstrate the practical value of this framework through a visual analytics system that enables interactive exploration and analysis of tasks, occupations, and associated skills, highlighting its potential to transform workforce analysis. Demo video: https://***/41txBZK

关键词： gpt

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AutoGaze: A Very Initial exploration in A SAM2-based Pipeline for Automated Eye-Object Interaction analysis in First-Person Videos

AutoGaze: A Very Initial Exploration in A SAM2-based Pipelin...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Qing Zhang Yifei Huang Jun Rekimoto The University of Tokyo Sony CSL Kyoto

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a novel automated workflow for analyzing eye-tracking data in first-person videos. Our system uses the Segment Anything Model 2 (SAM2) to segment and track objects, correlating them with gaze information to provide a detailed understanding of visual attention and hand-object interactions. The pipeline outputs a structured JSON file containing rich information about gaze behavior, object interactions, and their temporal relationships. We demonstrate the system’s effectiveness in a life science operational context and compare its performance with a multimodal Large Language Model (LLM) for automated annotation. Our findings highlight the potential of combining advanced computer vision techniques with LLMs for comprehensive and scalable analysis of gaze behavior in real-world scenarios.

关键词： visualization Solid modeling Three-dimensional displays Pipelines Gaze tracking Virtual reality User interfaces Life sciences Trajectory Videos

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visual Analyze the Impact of Structural Features and Node Attributes on Collaborative Relationships 19th

Visual Analyze the Impact of Structural Features and Node...

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19th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Lu, Qiang Ma, Cheng Wu, Hongfeng Zhang, Gaofeng Yu, Ye School of Computer Science and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9789819623754

Collaborative relationships are becoming more complex, diversified and multi-organization cross, which poses new challenges to collaborative relationship analysis. In order to analyze the effects of different structural features and node attributes on the overall cooperative relationships, and to support the exploration at the global, group and individual levels to promote the comprehensive analysis of cooperative relationships, we propose a visual analysis method based on graph contrast learning(GCL). The first step is to perturb the structural features and node attributes of the collaboration relationships through data augmentation. The second step is to use the graph convolutional network(GCN) to represent the structural features and node attributes as dense vectors in the high-dimensional vector space. The third step is to explore the influence of different structural features and node attributes through classification task outcome indicators. Based on this method, GCL4Vis, an interactive visual analysis system is designed, which is equipped with a variety of visual views to help users analyze the effects of different structural features and node attributes. Finally, this paper verified the usability and effectiveness of the GCL4Vis visual analysis system through specific case studies and structured interviews with users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Research on Early Warning of Drug Crime Behaviors Based on data-driven Method 24

Research on Early Warning of Drug Crime Behaviors Based on D...

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Proceedings of the 2024 4th International conference on Big data, Artificial Intelligence and Risk Management

作者： Shaobing Wu Changmei Wang Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Kunming Yunnan China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCV. It provides a useful exploration for combating Drug Crime.

关键词： data-driven

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Effect of Gaze visualization on Task Efficiency and User Behavior for Guidance Scenarios in Co-Located AR Collaboration

Effect of Gaze Visualization on Task Efficiency and User Beh...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Froso Sarri Katerina Mania School of Electrical & Computer Engineering Technical University of Crete Greece

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Augmented Reality (AR)-enabled collaboration enhances contextual guidance in shared environments, where unambiguous cues are essential, for swift and accurate task completion. Gaze-based cues offer clear, non-verbal indication of intent, reducing interaction ambiguity. While prior research has studied the effectiveness of gaze-based visualization in scenarios where collaborators share the same level of knowledge, less attention has been shown to guidance scenarios, where one user provides task-related information to another. Existing studies often focus on task outcomes (e.g., completion time, error rate) without analyzing gaze data to gain further insight into user behavior and attention. This study leverages gaze-based cues as an active means of communication in guidance scenarios, assessing their impact on both task performance and user behavior through gaze data analysis. The developed system incorporates gaze-based visualizations, including dynamic representations of gaze behavior, to deliver task-critical guidance. These are compared to verbal instructions using quantitative metrics for task effectiveness and gaze analysis, alongside subjective feedback, to determine whether they reduce ambiguity and enhance collaboration. Our study showed that gaze-based visualizations significantly reduced task related errors, with contextual triggered cues offering the best balance between speed and correctness. Gaze analysis revealed that gaze-based visualizations effectively directed participants’ attention to task-relevant areas, reducing unnecessary visual exploration. Participants reported lower perceived workload in gaze-based conditions, further supporting their effectiveness in enhancing collaboration and reducing ambiguity.

关键词： Human computer interaction Measurement Hands visualization Three-dimensional displays Error analysis Collaboration data visualization Information sharing Augmented reality

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