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A visualization system for intelligent diagnosis and statistical analysis of oral diseases based on panoramic radiography

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Scientific reports 2025年第1期15卷 18222页

作者： Yue Hong Tianya Pan Shenji Zhu Miaoxin Hu Zhiguang Zhou Ting Xu Department of Stomatology First Affiliated Hospital Zhejiang University Hangzhou China. School of Medicine Zhejiang University Hangzhou China. Intelligent Big Data Visualization Lab Hangzhou Dianzi University Hangzhou China. Intelligent Big Data Visualization Lab Hangzhou Dianzi University Hangzhou China. zhgzhou@***. Department of Stomatology First Affiliated Hospital Zhejiang University Hangzhou China. xut@***. School of Medicine Zhejiang University Hangzhou China. xut@***.

Panoramic radiography is an essential auxiliary diagnostic tool for oral diseases. It is a difficult and time-consuming task to conduct extensive panoramic radiography interpretation. These challenges are exacerbated by the creation of electronic medical records and the investigation of oral diseases using collective data. So, we develop a visualization system based on panoramic radiographs. Its function focuses on the intelligent diagnosis and statistical analysis of oral diseases. Firstly, we provide a human-machine collaborative tool for the diagnosis and data extraction of oral diseases in panoramic radiographs. After that, the system generates electronic medical records, including visual charts of oral health status and radiology reports. We further develop statistical correlation analysis to visually evaluate and interactively explore the statistical data from oral health surveys. We conduct intelligent diagnosis, obtain the electronic medical records and do collective analysis based on 521 panoramic radiographs. The available analyses cover disease-prone teeth, disease distribution per tooth position and association of age, sex with oral diseases. The results are reported from a comprehensive case study showing that our system can improve the efficiency in disease detection and data mining. It can also fuel research studies in the field of public oral health and provide robust support for oral healthcare strategies.

关键词： Diagnosis Oral disease Panoramic radiography Statistical analysis visualization system

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Satori: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling

arXiv

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arXiv 2024年

作者： Li, Chenyi Wu, Guande Chan, Gromit Yeuk-Yin Turakhia, Dishita G. Quispe, Sonia Castelo Li, Dong Welch, Leslie Silva, Claudio Qian, Jing Tandon School of Engineering New York University New YorkNY United States Tandon School of Engineering New York University New York CityNY United States Adobe Research San JoseCA United States Visualization and Data Analytics Lab New York University New YorkNY United States Tandon School of Engineering New York University BrooklynNY United States Brown University ProvidenceRI United States New York University New York CityNY United States

Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a novel AR assistance system, Satori, that models both user states and environmental contexts to deliver proactive guidance. Our system combines the Belief-Desire-Intention (BDI) model with a state-of-the-art multi-modal large language model (LLM) to infer contextually appropriate guidance. The design is informed by two formative studies involving twelve experts. A sixteen within-subject study find that Satori achieves performance comparable to an designer-created Wizard-of-Oz (WoZ) system without relying on manual configurations or heuristics, thereby enhancing generalizability, reusability and opening up new possibilities for AR assistance. © 2024, CC BY.

关键词： Augmented reality

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SIRIUS: visualization OF MIXED FEATURES AS A MUTUAL INFORMATION NETWORK GRAPH

arXiv

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arXiv 2021年

作者： Adams, Jane Lydia DeLuca, Todd F. Danforth, Christopher M. Dodds, Peter Sheridan Zheng, Yuhang Anastasakis, Konstantinos Choi, Boyoon Min, Allison Bessey, Michael M. Data Visualization Lab Khoury College of Computer Sciences Northeastern University BostonMA United States University of Vermont Complex Systems Center BurlingtonVT United States Massachusetts Mutual Life Insurance Company SpringfieldMA United States

data scientists across disciplines are increasingly in need of exploratory analysis tools for data sets with a high volume of features of mixed data type (quantitative continuous and discrete categorical). We introduce Sirius, a novel visualization package for researchers to explore feature relationships among mixed data types using mutual information. The visualization of feature relationships aids data scientists in finding meaningful dependence among features prior to the development of predictive modeling pipelines, which can inform downstream analysis such as feature selection, feature extraction, and early detection of potential proxy variables. Using an information theoretic approach, Sirius supports network visualization of heterogeneous data sets (consisting of continuous and discrete data types), and provides a user interface for exploring feature pairs with locally significant mutual information scores. Mutual information algorithm and bivariate chart types are assigned on a data type pairing basis (continuous-continuous, discrete-discrete, and discrete-continuous). We show how this tool can be used for tasks such as hypothesis confirmation, identification of predictive features, suggestions for feature extraction, or early warning of data abnormalities. The accompanying website for this paper can be accessed at https://***/. All code and supplemental materials can be accessed at https://***/pdm9r/. © 2021, CC BY.

关键词： data visualization

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Stock Price Prediction and Portfolio Optimization Using Hidden Markov Model and Quadratic Programming

Stock Price Prediction and Portfolio Optimization Using Hidd...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： August C. Thio-Ac Daniel D. Cabrales Drexanne E. Calibara Maries V. Madrazo Agustine Christian C. Matawaran Genesis E. Micaller Edmon O. Fernandez Timothy M. Amado Romeo L. Jorda Lean Karlo S. Tolentino Lejan Alfred C. Enriquez Department of Electronics Engineering Technological University of the Philippines Manila Philippines Integrated Research and Training Center Technological University of the Philippines Manila Philippines Intelligent Systems Applications and Circuits Laboratory (ISAAC) Integrated Research and Training Center Technological University of the Philippines Manila Philippines Data Analytics Visualization Information Driven (DAVID) Research Lab Integrated Research and Training Center Technological University of the Philippines Manila Philippines Center for Artificial Intelligence and Nanoelectronics (CAIN) Integrated Research and Training Center Technological University of the Philippines Manila Philippines

This study introduces a decision support system for optimizing stock portfolios. It employs a Hidden Markov Model for 95% accurate stock price forecasts over a five-day period. The system utilizes Quadratic Programming to refine allocation, focusing on up to 15 blue-chip companies. Key indicators like MACD, RSI, and Bollinger Bands offer clear buy, sell, or hold recommendations. It actively monitors the Philippine Stock Exchange, notifying users of significant deviations. Developed in Python and C#, both models were trained on a consistent 5-year dataset for reliable performance. Results show high prediction accuracy, averaging 98.923% (high price) and 98.741% (low price), benefiting investment strategies. The study compared SPOT-allocated portfolios to random ones, revealing a notable difference in profit/loss allocation. This underscores its value for informed and profitable decision-making in stock trading.

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Survey on Visual Analysis of Event Sequence data

arXiv

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arXiv 2020年

作者： Guo, Yi Guo, Shunan Jin, Zhuochen Kaul, Smiti Gotz, David Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China Visual Analysis and Communication Lab University of North Carolina Chapel HillNC United States

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： visualization

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visualization of data from HACC Simulations by Paraview

Visualization of Data from HACC Simulations by Paraview

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IEEE Scientific visualization Conference (SciVis)

作者： Bao D. Nguyen Ngan V.T. Nguyen Vung Pham Tommy Dang Interactive Data Visualization Lab Texas Tech University

In this project, we visualize and analyze data of Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We investigate in point rendering as well as Smoothed-particle hydrodynamics (SPH) interpolation and apply them to visualize the data in a 3D environment. Paraview was used for the rendering task. Some parameters of points in the software were adjusted to find well-qualified visualizations. These approaches provide a clear vision to the impact of Active Galactic Nuclei (AGN) on surrounding matter, and thus, we can gain more information for understanding the nature of AGNs and the universe.

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Improving visualization interpretation using counterfactuals

arXiv

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arXiv 2021年

作者： Kaul, Smiti Borland, David Cao, Nan Gotz, David Dept. of Computer Science University of North Carolina at Chapel Hill United States RENCI University of North Carolina at Chapel Hill United States Intelligent Big Data Visualization Lab Tongji University China School of Information and Library Science University of North Carolina at Chapel Hill United States

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach;the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships. Copyright © 2021, The Authors. All rights reserved.

关键词： visualization

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Satori 悟り: Towards Proactive AR Assistant with Belief-Desire-Intention User Modeling 25

Satori 悟り: Towards Proactive AR Assistant with Belief-Desi...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Chenyi Li Guande Wu Gromit Yeuk-Yin Chan Dishita Gdi Turakhia Sonia Castelo Quispe Dong Li Leslie Welch Claudio Silva Jing Qian Tandon School of Engineering New York University New York New York USA Tandon School of Engineering New York University New York CIty New York USA Adobe Research San Jose California USA Tandon School of Engineering New York University New York City New York USA Visualization and Data Analytics Lab New York University New York New York USA Tandon School of Engineering Brooklyn New York USA Brown University Providence Rhode Island USA New York University New York City New York USA Tandon School of Engineering New York University Brooklyn New York USA

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Preface

Mathematics and Visualization

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Mathematics and visualization 2022年 vii-x页

作者： Childs, Hank Bennett, Janine C. Garth, Christoph Computer and Information Science University of Oregon EugeneOR United States Extreme-Scale Data Science and Analytics Sandia National Laboratories LivermoreCA United States Scientific Visualization Lab University of Kaiserslautern Kaiserslautern Germany

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The Circularity Assessment Protocol in Cities to Reduce Plastic Pollution

Community Science

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Community Science 2024年第1期3卷

作者： J. R. Jambeck T. Maddalene K. Youngblood A. Oposa H. Perello M. Werner I. Himelboim K. Romness J. Mathis C. Keisling A. L. Brooks College of Engineering Circularity Informatics Lab University of Georgia Athens GA USA Contribution: Conceptualization Methodology Formal analysis Writing - original draft Writing - review & editing Supervision Funding acquisition Save Philippine Seas Manila Philippines Contribution: Data curation Writing - original draft Writing - review & editing Visualization Project administration Contribution: Conceptualization Methodology Formal analysis Writing - review & editing Visualization Contribution: Methodology Investigation Data curation Writing - review & editing Project administration Contribution: Investigation Data curation Writing - review & editing Contribution: Formal analysis Writing - original draft College of Journalism and Mass Communication SEE Suite University of Georgia Athens GA USA Contribution: Investigation Writing - original draft Writing - review & editing University of St. Thomas St. Paul MN USA Contribution: Investigation Data curation Writing - original draft Contribution: Investigation Data curation Contribution: Conceptualization Methodology Formal analysis Writing - original draft

The circular economy has been promoted as a solution to plastic pollution, but cities and communities bear the brunt of plastic pollution. The Circularity Assessment Protocol (CAP) is a systems method of collaborative and open data collection for communities to use for decision- and policy-making. The CAP has been utilized in 51 cities in 14 countries and is illustrated here in Metro Manila. Results include identifying manufacturing and parent companies to bring to the table; documenting most (77%) products are in single-use multi-layer film packaging; a small, but growing formal refill and reuse system; 10% of to-go food containers composed of paper-based alternatives, and a snap-shot leakage concentration of plastics to the environment that is 1.8%–2.7% of current waste generation. Community narratives emerged from a collaborative workshop and are threaded throughout opportunities identified by the CAP process to inform circularity, future actions, and policy, as a scalable way to create systems change for plastic pollution from the ground up. The Circularity Assessment Protocol, or CAP, is a collaborative framework for communities to collect data on circular materials management, like plastic packaging or food waste. This framework is illustrated for plastic packaging with a local non-governmental partner in Manila, Philippines. From this framework, local community members help to define opportunities to reduce plastic pollution.

关键词： litter macroplastic circular economy

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