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ieee visualization in data science (vds)

作者： Rebecca Faust Carlos Scheidegger Chris North Department of Computer Science Virginia Tech. Department of Computer Science HDC Lab University of Arizona

Debugging programs is one of the most challenging and time consuming parts of programming. data science scripts present additional challenges as debugging often centers around more exploratory tasks, such as understanding the differences between results under different parameter settings. In fact, a common exploratory debugging practice is to run, modify, and re-run a script to observe the effects of the modification. Analysts perform this process frequently as they explore different settings and algorithms in their analysis. However, traditional debugging methods are not well suited to comparing across multiple executions of a script. They often require maintaining two instances of the debugging method and making manual, serial comparisons of program values. To address this gap, we present Aardvark, a comparative trace-based debugging method for identifying and visualizing the differences between two executions of data analysis scripts. Aardvark traces two consecutive instances of an analysis script, identifies the differences between them, and presents them through comparative visualizations. We present a prototype implementation in Python as well as an extension to support scripts in Jupyter notebooks. Finally, to demonstrate Aardvark, we provide two usage scenarios on real world analysis scripts.

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Multimodal Task Attention Residual Reinforcement Learning: Advancing Robotic Assembly in Unstructured Environment

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ieee ROBOTICS AND AUTOMATION LETTERS 2025年第4期10卷 3900-3907页

作者： Lin, Ze Wang, Chuang Wu, Sihan Xie, Longhan South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 510315 Peoples R China

Robotic assembly in dynamic and unstructured environments poses challenges for recent methods, due to background noise and wide-ranging errors. Directly learning from environments relies on complex models and extensive training iterations to adapt. Representation selection approaches, which depend on expert knowledge, can reduce training costs but suffer from poor robustness and high manual costs, limiting scalability. In response, this letter proposes a system that integrates task attention into residual reinforcement learning to address these challenges. By effectively segmenting task-relevant information from the background to leverage task attention, our approach mitigates the impact of environmental variability. Additionally, compared with existing baselines, our task attention mechanism based on instance segmentation and prompt-guided selection does not require additional offline training or local fine-tuning. Experimental evaluations conducted in both simulated and real environments demonstrate the superiority of our method over various baselines. Specifically, our system achieves high efficiency and effectiveness in learning and executing assembly tasks in dynamic and unstructured environments.

关键词： Training Force visualization Feature extraction Robotic assembly Robots Attention mechanisms data mining Costs Background noise Industrial robots computer vision for manufacturing compliant assembly

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Comprehensive Study on ChatGPT Sentiment Analysis and visualization

Comprehensive Study on ChatGPT Sentiment Analysis and Visual...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Katrojwar, Himanshu R. Atkar, Siddhesh D. Raut, Shreepad R. Bhoge, Harsh N. Agrawal, Rahul Dhule, Chetan G H Raisoni College of Engineering Department of Data Science Nagpur440016 India Nagpur440016 India

ISBN: (纸本)9798350344387

The rapid growth of artificial intelligence (AI) is poised to reshape industries and societal aspects, driven by its ability to simulate human intelligence and perform tasks requiring humanlike thinking and learning. ChatGpt has been leading this growth since its inception, enhancing user experience and garnering over 100 million users by February 2023. Social Networking sites have been a source for sharing the sentiments. This research explores tweets on ChatGpt in the field of sentiment analysis and its integration with data visualization techniques to enhance the comprehension of textual data. The research proposed a holistic approach by exploring the methodologies such as data collection, data preprocessing, tokenization, sentiment analysis using Natural Language Processing, accuracy assessment and visualization. For training and testing a dataset has been gathered from Kaggle which daily gets updated. The proposed study gives comprehensive results about sentiments on ChatGPT and explores different visualization techniques. © 2023 ieee.

关键词： Analysis Artificial Intelligence ChatGpt GPT Natural Language Processing Sentiment Twitter visualization

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FireExpert: Fire Event Identification and Assessment Leveraging Cross-Domain Knowledge and Large Language Model

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ieee TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 4794-4810页

作者： Luo, Guofeng Weng, Lijuan Li, Yunqian Sun, Yilu Hong, Yayao Wu, Yongyi Luo, Ruixiang Wang, Leye Wang, Cheng Chen, Longbiao Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities SCSC Xiamen 361005 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China

Fire events threaten the safety of residents and the health of ecosystems in affected areas, and post-disaster recovery efforts also require a large investment of resources and time. In recent years, the rising frequency of fire events has motivated local governments to strengthen their monitoring and emergency response efforts. However, current fire event identification methods can only identify the presence of a fire, without the ability to distinguish its specific category. In addition, when a fire occurs, the lack of information about the affected areas makes it challenging for emergency management authorities to take timely and effective rescue measures. To address these issues, we propose a two-stage framework for fire event identification and assessment. Specifically, in the first stage, based on multi-band fused remote sensing images and heterogeneous environmental images, the proposed framework not only identifies various fire events but also accurately identifies the boundaries of the fire events. In the second stage, integrating the results of fire event identification with social media data and domain knowledge, we present a real-time assessment agent for fire events based on the large language model. This agent enables timely and accurate analysis of the impact of fires on the affected areas. We evaluate our method on a real-world authority dataset, and results show that our framework identifies fire events with an F1-score of 61.0% and a mAP of 57.7%, which outperforms state-of-the-art baseline methods. In addition, the assessment results of fire events in real cases indicate that the proposed fire event assessment agent can assist emergency responders in obtaining timely and accurate information.

关键词： Remote sensing Emergency services Social networking (online) Satellites Satellite images Buildings visualization Monitoring Mobile computing Large language models Fire event identification impact assessment heterogeneous data remote sensing large language model

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The Digital Twin City in Enhancing Flood Evacuation Systems: Future Opportunities and Challenges

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ieee Access 2025年 13卷 32371-32383页

作者： Setijadi Prihatmanto, Ary Prasetyadi, Abdurrakhman Yoganingrum, Ambar Sutriadi, Ridwan Hadiana, Ana Bandung Institute of Technology School of Electrical and Informatics Engineering Bandung40132 Indonesia National Research and Innovation Agency Research Center for Data and Information Science Bandung40231 Indonesia Bandung Institute of Technology School of Architecture Planning and Policy Development Bandung40132 Indonesia

In recent decades, the increasing frequency and severity of natural disasters have necessitated the development of more effective disaster management systems. This paper explores the potential of digital twin technology to enhance flood evacuation systems. A digital twin-city model can simulate urban environments, predict flood inundation, and identify optimal evacuation routes in real-time. The study conducts a systematic literature review to assess current advancements, evaluating the integration of 3D city models, artificial intelligence, and Internet of things sensors in flood evacuation planning. Results indicate that digital twin technology significantly improves predictive capabilities and evacuation efficiency, although data integration, model accuracy, and computational demands remain. Future research should focus on enhancing data integration, refining simulation models, improving predictive analytics, developing advanced visualization tools, and validating models across diverse environments to ensure reliability and effectiveness. © 2025 The Authors.

关键词： Digital elevation model

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Rigel: Transforming Tabular data by Declarative Mapping

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 128-138页

作者： Chen, Ran Weng, Di Huang, Yanwei Shu, Xinhuan Zhou, Jiayi Sun, Guodao Wu, Yingcai State Key Lab of CAD&CG Zhejiang University Hangzhou China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Hangzhou China Microsoft Research Asia Beijing China Hong Kong University of Science and Technology Hong Kong Hong Kong Zhejiang University of Technology Hangzhou China

We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation. The recommended mappings are generated by enumerating and composing data variables based on the row, column, and cell channels, thereby revealing the possibility of alternative tabular forms and facilitating open-ended exploration in many data transformation scenarios, such as designing tables for presentation. In contrast to existing systems that transform data by composing operations (like transposing and pivoting), Rigel requires less prior knowledge on these operations, and constructing tables from the channels is more efficient and results in less ambiguity than generating operation sequences as done by the traditional by-example approaches. User study results demonstrated that Rigel is significantly less demanding in terms of time and interactions and suits more scenarios compared to the state-of-the-art by-example approach. A gallery of diverse transformation cases is also presented to show the potential of Rigel's expressiveness. © 2022 ieee.

关键词： data visualization

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Building Robust Information Systems For Remote Sensing data Management 64

Building Robust Information Systems For Remote Sensing Data ...

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2023 ieee 64th International Scientific Conference on Information Technology and Management science of Riga Technical University, ITMS 2023

作者： Osipovs, P.A. Fraimans, M. Bliznuks, D. Kovalovs, M. Riga Technical University Institute of Smart Computer Technologies Riga Latvia

ISBN: (纸本)9798350370294

The article describes the experience of building an information system for collecting and processing data from remote sensors. The specificity of the organization of a local system that provides storage, visualization [1] and processing of data from a certain set of external sensors is considered. Both the general architecture of the system and its modules are described, which provide a local graphical user interface, asynchronous data collection, visualization, notifications about reaching unacceptable values by the sensor, and processing of sensor data. The structure of interaction of the system with an external WEB-site through the implementation of the REST API [2] is shown. The issues of software emulation of sensors and risk management were also touched upon. © 2023 ieee.

关键词： Python

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Exploring Interactions with Printed data visualizations in Augmented Reality

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 418-428页

作者： Tong, Wai Chen, Zhutian Xia, Meng Lo, Leo Yu-Ho Yuan, Linping Bach, Benjamin Qu, Huamin Hong Kong University of Science and Technology Hong Kong Harvard University United States Carnegie Mellon University United States University of Edinburgh United Kingdom

This paper presents a design space of interaction techniques to engage with visualizations that are printed on paper and augmented through Augmented Reality. Paper sheets are widely used to deploy visualizations and provide a rich set of tangible affordances for interactions, such as touch, folding, tilting, or stacking. At the same time, augmented reality can dynamically update visualization content to provide commands such as pan, zoom, filter, or detail on demand. This paper is the first to provide a structured approach to mapping possible actions with the paper to interaction commands. This design space and the findings of a controlled user study have implications for future designs of augmented reality systems involving paper sheets and visualizations. Through workshops ($\mathrm{N}=20$) and ideation, we identified 81 interactions that we classify in three dimensions: 1) commands that can be supported by an interaction, 2) the specific parameters provided by an (inter)action with paper, and 3) the number of paper sheets involved in an interaction. We tested user preference and viability of 11 of these interactions with a prototype implementation in a controlled study ($\mathrm{N}=12$, HoloLens 2) and found that most of the interactions are intuitive and engaging to use. We summarized interactions (e.g., tilt to pan) that have strong affordance to complement 'point' for data exploration, physical limitations and properties of paper as a medium, cases requiring redundancy and shortcuts, and other implications for design. © 2022 ieee.

关键词： data visualization

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Scalable Volume visualization for Big Scientific data Modeled by Functional Approximation

Scalable Volume Visualization for Big Scientific Data Modele...

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2023 ieee International Conference on Big data, Bigdata 2023

作者： Sun, Jianxin Lenz, David Yu, Hongfeng Peterka, Tom Argonne National Laboratory LemontIL United States University of Nebraska-Lincoln School of Computing LincolnNE United States University of Nebraska-Lincoln Holland Computing Center LincolnNE United States

ISBN: (纸本)9798350324457

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches. © 2023 ieee.

关键词： big scientific dataset distributed computing functional approximation volume visualization

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 ieee Conference on Engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 ieee.

关键词： data visualization

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