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What are Table Cartograms Good for Anyway? An Algebraic Analysis

COMPUTER GRAPHICS FORUM 2021年第3期40卷 61-73页

作者： McNutt, A. Univ Chicago Dept Comp Sci Chicago IL 60637 USA

Unfamiliar or esoteric visual forms arise in many areas of visualization. While such forms can be intriguing, it can be unclear how to make effective use of them without long periods of practice or costly user studies. In this work we analyze the table cartogram-a graphic which visualizes tabular data by bringing the areas of a grid of quadrilaterals into correspondence with the input data, like a heat map that has been "area-ed" rather than colored. Despite having existed for several years, little is known about its appropriate usage. We mend this gap by using Algebraic visualization Design to show that they are best suited to relatively small tables with ordinal axes for some comparison and outlier identification tasks. In doing so we demonstrate a discount theory-based analysis that can be used to cheaply determine best practices for unknown visualizations.

关键词： CCS Concepts • Human-centered computing → visualization design and evaluation methods visualization theory concepts and paradigms

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Artificial Intelligence Based Segmentation for Skin-Layer visualization from Optical Coherence Tomography Images

Artificial Intelligence Based Segmentation for Skin-Layer Vi...

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2025 ieee International conference on Consumer Electronics, ICCE 2025

作者： Ramadhan, Gilang Titah Pusparani, Yori Prisilla, Ardha Ardea Shen, Wei-Cheng Cheng, Hsu-Tang Liau, Ben-Yi Jan, Yih-Kuen Chang, Wen-Thong Lin, Chih-Yang Lung, Chi-Wen Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Department of Fashion Design LaSalle College Jakarta Jakarta Indonesia Department of Creative Product Design Asia University Taichung Taiwan Division of Plastic Surgery Asia University Hospital Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Department of Kinesiology and Community Health University of Illinois Urbana United States Department of Mechanical Engineering National Central University Taoyuan Taiwan

ISBN: (纸本)9798331521165

Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92% accuracy in predicting SC and 95% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications. © 2025 ieee.

关键词： Optical coherence tomography

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Forecasting and visualization using Dash for Improved Stock Analysis

Forecasting and Visualization using Dash for Improved Stock ...

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2023 International conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Rose, G. Rebekah Sathwika, G. Hema, K. Dhanalakshmi, D. Mandala, Sowmya Hariharan, Shanmugasundaram Vardhaman College of Engineering Department of Cse Hyderabad India

ISBN: (纸本)9798350313987

Since stock market is a dynamic, transforming, and sophisticated investment system, one of the important financial considerations is to make a long-term investment. In this perspective, financial market analysis plays an important role in determining a new strategy to outline and visualize the stock market forecasts to maximize the profit from investments. Stock investment gives best returns when compared to other investment approaches. Despite their volatility, share prices and other statistical elements can be visualized to carefully select the stock to invest. The proposed concept includes the Dash (Dynamic Adaptive Streaming Hypertext) module in python language to create dynamic charts of financial data analysis, which is strongly supported by the in-built finance Python library package. To forecast the future stock values, this research study employs a machine learning system. © 2023 ieee.

关键词： Dash Python finance Machine Learning Predictions Stock Market

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Unsupervised Representation Learning for Visual Robotics Grasping 7

Unsupervised Representation Learning for Visual Robotics Gra...

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7th ieee International conference on Advanced Robotics and Mechatronics

作者： Wang, Shaochen Zhou, Zhangli Wang, Hao Li, Zhijun Kan, Zhen Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

Despite tremendous success achieved by deep learning in the field of robotic vision, it still requires massive amounts of manual annotations and expensive computational resources to train a high-performance grasping detection model. The difficulties (e.g., complicated object geometry, sensor noise) all pose challenges for grasping unknown objects. In this paper, self-supervised representation learning pre-training is investigated to tackle the issues like expensive data annotation and poor generalization to improve visual robotics grasping. The proposed framework has two primary characteristics: 1) Siamese networks integrated with metric learning capture commonalities between similar objects from unlabeled data in an unsupervised fashion. 2) A well-designed encoder-decoder architecture with skip-connections, fusing low-level contour information and high-level semantic information, enables a spatially precise and semantically rich representation. A key aspect of using self-supervised pre-training model is that it alleviates the burden on data annotation and accelerates model training. By fine-tuning on a small number of labeled data, our method improves the baseline which does not use deep representation learning by 9.5 points on the Cornell dataset. Our final grasping system is capable to grasp unseen objects in a variety of scenarios on a 7DoF Franka Emika Panda robot. A video is available at https://***/Xd0hhYD-IOE.

关键词： Representation learning Training visualization Uncertainty Annotations Shape Semantics

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A Closed Space Parameter Monitoring and visualization System using Power Business Intelligence (Power BI) 2

A Closed Space Parameter Monitoring and Visualization System...

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2nd International conference for Innovation in Technology, INOCON 2023

作者： Jain, Samyak C. Gaydhane, Minal A. Koli, Omkar M. Saraf, Rutuj P. Mhaske, Gayatri C. Pol, Praveen V. Vishwakarma Institute of Technology Department of Instrumentation and Control Pune India

ISBN: (纸本)9798350320923

To comprehend a process or any aspect of the environment, it is necessary to measure a variety of parameters. Temperature, pressure, and other parameters can all be included in these parameters. It is crucial to monitor various parameters in small or enclosed areas so that a basic understanding of a system can be acquired. In this paper, a system including the Power Business Intelligence tool is developed to measure variables such as temperature, humidity, pressure, soil moisture, and air quality. A user can comprehend and monitor the data in real time while the system is measuring. A dashboard is also constructed. This data can be uploaded to Google Sheets automatically and utilised for other things. An experimental prototype is designed and tested, and the test results are verified practically. © 2023 ieee.

关键词： Soil moisture

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Research on Supermarket Marketing Data Analysis Based on Business Intelligence 6

Research on Supermarket Marketing Data Analysis Based on Bus...

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6th International conference on information and Computer Technologies, ICICT 2023

作者： Mei, Zhao Mingjie, Li National University College of Computing and Information Technologies Manila Philippines

ISBN: (纸本)9798350300956

In recent years, with the rapid development of the new retail industry, consumers have more comparison and choice when purchasing goods, which leads to increasingly fierce competition in the supermarket industry and continuous compression of profit space. If you want to improve the competitiveness of supermarkets, you can conduct business intelligence analysis and sales forecast on a large number of data generated by supermarket operation and management, thus providing an important basis for supermarket operation and management strategy adjustment. This paper uses the marketing data of a global supermarket for four years as the data base, analyzes the current business situation from different angles, uses python to conduct data preprocessing, analysis and visualization, and explores the sales strategy to improve sales through sales analysis, commodity analysis and user analysis. It uses the data to find new growth points, and obtains methods to further improve the supermarket sales. Finally, the integrated learning algorithms XGBoost, lightGBM and RandomForest in machine learning are used to build a prediction model and extract four different types of feature set data. The average score values predicted by the three models for 'Sales' are different. Among the four types of feature set data, the Average Score value obtained from RandomForest is higher than XGBoost and lightGBM models, and the Average Score value obtained from the 'subcateall' feature set data is higher than the value obtained from the other three feature set data, which is 81.25%, indicating that RandomForest has the best prediction effect among the three models. © 2023 ieee.

关键词： Sales

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Filling the Void: Data-Driven Machine Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

Filling the Void: Data-Driven Machine Learning-based Reconst...

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2024 Workshops of the International conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Biswas, Ayan Mishra, Aditi Majumder, Meghanto Hazarika, Subhashis Most, Alexander Castorena, Juan Bryan, Christopher Mccormick, Patrick Ahrens, James Lawrence, Earl Hagberg, Aric Los Alamos National Lab Los Alamos United States Arizona State University Tempe United States Fujitsu Research of America Inc Santa Clara United States

ISBN: (纸本)9798350355543

As high-performance computing systems continue to advance, the gap between computing performance and I/O capabilities is widening. This bottleneck limits the storage capabilities of increasingly large-scale simulations, which generate data at never-before-seen granularities while only being able to store a small subset of the raw data. Recently, strategies for data-driven sampling have been proposed to intelligently sample the data in a way that achieves high data reduction rates while preserving important regions or features with high fidelity. However, a thorough analysis of how such intelligent samples can be used for data reconstruction is lacking. We propose a data-driven machine learning approach based on training neural networks to reconstruct full-scale datasets based on a simulation's sampled output. Compared to current state-of-the-art reconstruction approaches such as Delaunay triangulation-based linear interpolation, we demonstrate that our machine learning-based reconstruction has several advantages, including reconstruction quality, time-to-reconstruct, and knowledge transfer to unseen timesteps and grid resolutions. We propose and evaluate strategies that balance the sampling rates with model training (pretraining and fine-tuning) and data reconstruction time to demonstrate how such machine learning approaches can be tailored for both speed and quality for the reconstruction of grid-based datasets. © 2024 ieee.

关键词： computing methodologies data reduction machine learning machine learning approaches modeling and simulation neural networks reconstruction scientific visualization simulation types and techniques

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Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMU-GPS Sensor Fusion and Data Filtering Algorithm 15

Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software ...

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15th International conference on information Technology and Electrical Engineering, ICITEE 2023

作者： Arfakhsyad, Azfar Azdi Rahman, Aufa Nasywa Kinanti, Larasati Awwalur Rizqi, Ahmad Ataka Muhammad, Hannan Nur Universitas Gadjah Mada Department of Electrical Engineering and Information Technology Yogyakarta Indonesia Universitas Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350304466

Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity. © 2023 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Hybrid Approach to Enhance Security and Friendliness of Visual Secret Sharing Scheme 10

Hybrid Approach to Enhance Security and Friendliness of Visu...

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10th International conference on Emerging Trends in Engineering and Technology - Signal and information Processing, ICETET-SIP-22 2022

作者： Panchbhai, Vishal V. Varade, Suchita W. Priyadarshini College of Engineering Department of Electronics & Telecommunication Engineering Nagpur India

ISBN: (纸本)9781665467414

In Block based Progressive Visual Secret Sharing (BPVSS) scheme, secret image will be recovered block by block from noise-like and meaningful shares. It is found that only meaningful shares are user-friendly. But the meaningful shares generated through BPVSS techniques show poor visual quality so it is suspicious to intruder while transmission over internet. Thus, this paper proposes a meaningful share generating strategy based on BPVSS and LSB-based steganography to solve the above-mentioned problem with less processing power and time. The proposed system delivers superior standard performance parameter readings and has various advantages, including (1) it is suited for grayscale as well as color secret images, (2) the contrast of the recovered image is the same as the original secret image, and (3) computational power and time required to recover secret image is very less. © 2022 ieee.

关键词： visualization Steganography Image color analysis information processing Gray-scale Market research Hybrid power systems

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Research on Forest Landscape based on Big data and CiteSpace visualization Analysis 2

Research on Forest Landscape based on Big data and CiteSpace...

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2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Lin, Xinya Xu, Han Dong, Jianwen Fujian Agriculture and Forestry University Fuzhou China

ISBN: (纸本)9798350323818

In order to fully grasp the development and research status of forest landscape ecology, this paper analyzed the literatures related to forest landscape from 1982 to 2020 on CNKI and Wanfang Data Service platform. Based on Python language environment, this paper uses Scrapy framework to extract literature data, and uses CiteSpace visual analysis software to draw knowledge map. It analyzes and visualizes the amount of articles published, research groups, research hotspots and trends. The results showed that: (1) the development process of forest landscape ecology research was divided into three stages: initial exploration period from 1982 to 2002, rapid development period from 2003 to 2008, and stable development period from 2009 to present. (2) The number of scholars in this field is relatively large and scattered, and the influential core author group and institutional group has not yet been formed;(3) The hot research directions are forest landscape pattern, sustainable development of forest, and forest landscape planning and design. (4) The focus of forest landscape ecology research has gradually shifted from geographic information system to eco-Tourism, urban forest and planning and design, and the research has become specialized and meticulous. © 2023 ieee.

关键词： Big data

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