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Beyond Generating Code: Evaluating GPT on a data visualization Course 1

Beyond Generating Code: Evaluating GPT on a Data Visualizati...

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1st IEEE vIS Workshop on visualization Education, Literacy, and Activities (Eduvis) part of the IEEE vIS conference

作者： Chen, Zhutian Zhang, Chenyang Wang, Qianwen Troidl, Jakob Warchol, Simon Beyer, Johanna Gehlenborg, Nils Pfister, Hanspeter Harvard Univ Sch Engn & Appl Sci John A Paulson Sch Engn & Appl Sci Boston MA USA Harvard Boston MA USA Harvard Med Sch Biomed Informat Boston MA USA

ISBN: (纸本)9798350330304

This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard's CS171 data visualization course. While previous studies have focused on GPT's ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT's abilities in various visualization tasks, such as data interpretation, visualization design, visual data exploration, and insight communication. The evaluation utilized GPT-3.5 and GPT-4 through the APIs of OpenAI to complete assignments of CS171, and included a quantitative assessment based on the established course rubrics, a qualitative analysis informed by the feedback of three experienced graders, and an exploratory study of GPT's capabilities in completing border visualization tasks. Findings show that GPT-4 scored 80% on quizzes and homework, and Teaching Fellows could distinguish between GPT- and human-generated homework with 70% accuracy. The study also demonstrates GPT's potential in completing various visualization tasks, such as data cleanup, interaction with visualizations, and insight communication. The paper concludes by discussing the strengths and limitations of GPT in data visualization, potential avenues for incorporating GPT in broader visualization tasks, and the need to redesign visualization education.

关键词： visualization

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A SPATIO-TEMPORAL visual ANALYTICS TOOL FOR SIMULATED TRANSPORT ORIENTED-LAND USE URBAN PLANNING data 7

A SPATIO-TEMPORAL VISUAL ANALYTICS TOOL FOR SIMULATED TRANSP...

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7th International conference on Smart data and Smart Cities, SDSC 2022

作者： Merchan Zorro, L. vivas Meza, A.A. Bautista Acosta, O.D. Castro Barrera, H.E. Peña, J.R. Guzman, L.A. Hernandez, J.T. Imagine Research Group Sur Research Group University de Los Andes Colombia

This article presents a proposal framed in urban visual analytics, which aims to facilitate the exploration and analysis of zonal spatial indicators over time for a case of a large city-region. The context in which this exploration is carried out is the evaluation of the impact of interventions in land use and transportation, based on data produced by an integrated simulation system. The large amount of data to evaluate a scenario makes urgent the effort to design new interactive analysis environments. The resulting prototype (vIZ-BUS: visual Analytics for Bogota Urban Simulator) has, for its design, an analyst-centric approach to the analysis task. Our first web deployment has been very well received by urban planning specialists in order to evaluate scenarios for a sustainable and smart city-region. © 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： data visualization

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visual presentation and exploration of musical corpora: Case Study: Oskar Kolberg's Opera Omnia 23

Visual presentation and exploration of musical corpora: Case...

引用

10th International conference on Digital Libraries for Musicology, DLfM 2023

作者： Matuszewska, Anna Maria The Institute of Literary Research of the Polish Academy of Sciences Polish Academy of Sciences Poland

ISBN: (纸本)9798400708336

Extensive musical collections are growing with increasing momentum, and there are progressively more digital tools for analysing musical corpora. These tools visualize statistical information in diagrams, simplifying the analysis. This kind of data presentation is effective both for analysing single pieces of music and looking for trends for an entire collection. Increasingly, authors of digital collections, are also developing interfaces that allow viewing individual works in user-friendly analytical interfaces. However, interfaces that allow intuitive browsing of musical collections and comparison of its subsets are still rare. Accordingly, this paper proposes a methodology for working with musical data that is inspired by diagrammatic reasoning elaborated by Charles Sanders Peirce. The goal of the project is to develop a method that would enable researchers, especially those not associated with the creation of the selected corpus, to explore its characteristics quickly and without requiring theoretical preparation, classify its subsets, and detail the characteristics of individual compositions. The proposed interactive diagrammatic method for viewing and analysing musical data has been implemented on analytical dashboards in Tableau Public. Each dashboard contains a collection of diagrams presenting the results of computer-based music analysis of rhythm or melody of tunes from different perspectives. © 2023 ACM.

关键词： Music

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Implementing Hybrid (AI and data Analytics) Solutions for Optimal Performance and Cost Optimization for Image analysis with GPT-4 Turbo with vision for Predictive analysis 28

Implementing Hybrid (AI and Data Analytics) Solutions for Op...

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28th World Multi-conference on Systemics, Cybernetics and Informatics, WMSCI 2024

作者： Mateev, Mihail Computer Aided Engineering Department University of Architecture Civil Engineering and Geodesy 1 Hristo. Smirnenski Blvd Sofiya-grad Sofia1046 Bulgaria

ISBN: (纸本)9781950492794

Extracting crucial metadata from images and videos to assess real-world systems' state and propose sustainability measures is vital to contemporary predictive analysis systems. This practical application underscores the relevance and importance of this research. Many technologies are helping to solve this case, but one of the most exciting solutions is related to the latest LLM from OpenAI. GPT-4 Turbo with vision, developed by OpenAI, is a significant multi-modal model (LMM) capable of interpreting images and providing text-based answers to queries regarding those images. It combines capabilities in natural language processing and visual comprehension. The research is relevant for the latest GPT-4o. This research covers two essential topics: First, how to implement efficient image analysis and prediction models with GPT-4 Turbo with vision;second, how to optimize the cost of the solution using a hybrid approach where Artificial Intelligence meets data Analytics and offers cost-effective, high-efficient solutions for predictive analysis based on GPT-4v, vector Search, and different methods from data Analytics. The proposed approach considers using OpenAI LLM, data Analytics, and Digital Twins for four aspects of predictive analysis: image analysis, case decomposition, hybrid search, and creation of self-adaptive models to find possible trends and offer preventive actions. © 2024 by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Predictive analytics

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Managing the data Economy in Management 3rd

Managing the Data Economy in Management

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3rd International conference on Engineering Innovations and Sustainable Development, CEISD 2024

作者： Sheremetyeva, E.N. Mitropolskaya-Rodionova, N.v. Balanovskaya, A.v. Samara State University of Economics Samara Russia Moscow State Institute of International Relations University Ministry of Foreign Affairs of the Russian Federation Moscow Russia

ISBN: (纸本)9783031673719

The contribution presents research in the field of a new approach to management – management based on the data economy. Digital transformation is closely related to digital innovations, since the implementation of digital transformation requires technological innovations, new operating procedures, and the integration of methodological and technological approaches to the study of information systems that contribute to strategic changes, such as data-based management. The authors used system analysis, logical analysis, general scientific methods of cognition (scientific observation, analysis, synthesis, etc.), visual interpretation of research results. data management is based on the processes of collection, storage and use of information, organized according to the principles of efficiency, economy and security. Economic information is one of the key components in the functioning of any organization, enterprise or market. Its importance lies in the fact that it allows you to make informed decisions, control processes and predict results. The data economy management system is defined as a complex of interconnected components of organizational, methodological, software and information technology purposes, the joint action of which leads to the achievement of the set economic goals of effective management of activities, processes and results. The need to develop and implement a data management strategy comes to the fore in the face of a growing number of companies relying on an economic data management system through the development of intangible assets to create profits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： data Economics Management data Economics Management System data Integration Tools data Lakes data Management Strategy database Management

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MOSS: AI Platform for Discovery of Corrosion-Resistant Materials 23

MOSS: AI Platform for Discovery of Corrosion-Resistant Mater...

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32nd ACM International conference on Information and Knowledge Management (CIKM)

作者： Yin, Biao Josselyn, Nicholas Zhang, Ziming Rundensteiner, Elke A. Considine, Thomas A. Kelley, John v. Rinderspacher, Berend C. Jensen, Robert E. Snyder, James F. Worcester Polytech Inst Data Sci Program Worcester MA 01609 USA Worcester Polytech Inst Data Sci & ECE Dept Worcester MA 01609 USA Worcester Polytech Inst Data Sci & CS Dept Worcester MA 01609 USA DEVCOM Army Res Lab Army Res Directorate Aberdeen Proving Ground MD USA

ISBN: (纸本)9798400701245

Amid corrosion degradation of metallic structures causing expenses nearing $3 trillion or 4% of the GDP annually along with major safety risks, the adoption of AI technologies for accelerating the materials science life-cycle for developing materials with better corrosive properties is paramount. While initial machine learning models for corrosion assessment are being proposed in the literature, their incorporation into end-to-end tools for field experimentation by corrosion scientists remains largely unexplored. To fill this void, our university data science team in collaboration with the materials science unit at the Army Research Lab have jointly developed MOSS, an innovative AI-based digital platform to support material science corrosion research. MOSS features user-friendly iPadOS app for in-field corrosion progression data collection, deep-learning corrosion assessor, robust data repository system for long-term experimental data modeling, and visual analytics web portal for material science research. In this demonstration, we showcase the key innovations of the MOSS platform via use cases supporting the corrosion exploration processes, with the promise of accelerating the discovery of new materials. We open a MOSS video demo at: https://***/watch?v=CzcxMMRsxkE

关键词： Corrosion Science Automatic Assessment Deep Learning

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Diagnostics-LLavA: A visual Language Model for Domain-Specific Diagnostics of Equipment 16

Diagnostics-LLaVA: A Visual Language Model for Domain-Specif...

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16th Annual conference of the Prognostics and Health Management Society, PHM 2024

作者： Kumar, Aman Alam, Mahbubul Farahat, Ahmed Somineni, Maheshjabu Gupta, Chetan Industrial AI Lab Hitachi America Ltd. Santa ClaraCA95054 United States

ISBN: (纸本)9781936263059

The recent advancements in the area of Large language models (LLMs) have opened horizons for conversational assistant-based intelligent models capable of interpreting images, and providing textual response, also known as visual language models (vLMs). These models can assist equipment operators and maintenance technicians in complex Prognostics and Health Management (PHM) tasks such as diagnostics of faults, root cause analysis, and repair recommendations. Significant open-source contributions in the area of vLMs have been made. However, models trained in general data fail to perform well in complex tasks in specialized domains such as diagnostics and the repair of industrial equipment. Therefore, in this paper, we discuss our work on the development of Diagnostics-LLavA, a vLM suitable for interpreting images of specific industrial equipment, and provide better response than existing open source models in PHM tasks such as fault diagnostics and repair recommendation. We introduce Diagnostics-LLavA based on the architecture of LLavA and created one instance of Diagnostics-LLavA for the automotive repair domain, referred to as Automotive-LLavA. We demonstrate that our proposed Automotive-LLavA model performs better than the state-of-the-art open-source visual language models such as mPlugOWL and LLavA in both qualitative and quantitative experiments. © 2024 Prognostics and Health Management Society. All rights reserved.

关键词： visual languages

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Sequential Fusion Based Multi-Granularity Consistency for Space-Time Transformer Tracking 38

Sequential Fusion Based Multi-Granularity Consistency for Sp...

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38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Hu, Kun Yang, Wenjing Huang, Wanrong Zhou, Xianchen Cao, Mingyu Ren, Jing Tan, Huibin Natl Univ Def Technol Dept Intelligent Data Sci Coll Comp Sci & Technol Changsha Peoples R China Natl Univ Def Technol Coll Sci Changsha Peoples R China

ISBN: (纸本)1577358872

Regarded as a template-matching task for a long time, visual object tracking has witnessed significant progress in space-wise exploration. However, since tracking is performed on videos with substantial time-wise information, it is important to simultaneously mine the temporal contexts which have not yet been deeply explored. Previous supervised works mostly consider template reform as the breakthrough point, but they are often limited by additional computational burdens or the quality of chosen templates. To address this issue, we propose a Space-Time Consistent Transformer Tracker (STCFormer), which uses a sequential fusion framework with multi-granularity consistency constraints to learn spatiotemporal context information. We design a sequential fusion framework that recombines template and search images based on tracking results from chronological frames, fusing updated tracking states in training. To further overcome the overreliance on the fixed template without increasing computational complexity, we design three space-time consistent constraints: Label Consistency Loss (LCL) for label-level consistency, Attention Consistency Loss (ACL) for patch-level ROI consistency, and Semantic Consistency Loss (SCL) for feature-level semantic consistency. Specifically, in ACL and SCL, the label information is used to constrain the attention and feature consistency of the target and the background, respectively, to avoid mutual interference. Extensive experiments have shown that our STCFormer outperforms many of the best-performing trackers on several popular benchmarks.

关键词： Semantics

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A Comprehensive analysis on Refined BiLSTM-RNN Models for Accurate Forecasting of Bottom Hole Pressure in Managed Pressure Drilling Operations 14

A Comprehensive Analysis on Refined BiLSTM-RNN Models for Ac...

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14th IEEE International conference on Communication Systems and Network Technologies, CSNT 2025

作者： Nethi, v.R. Nagalakshmi Sharif, Md Ahammad Hymavathi, Bora Raj, Nitish Akhil, Banka Ashirvad, Muppidi Daniel Andhra Pradesh Rajahmundry533294 India

ISBN: (纸本)9798331531935

Managed Pressure Drilling (MPD) is a crucial technique in the oil and gas industry, ensuring optimal pressure conditions to reduce risks like bore instability and blowouts. Accurate Bottom Hole Pressure (BHP) forecasting is essential for real-time decision-making, but traditional methods often fail to capture the complex dynamics of drilling environments. This paper introduces a Bidirectional Long Short-Term Memory Recurrent Neural Network (BiLSTM-RNN) model for BHP forecasting in MPD. The process includes data exploration, cleaning, pattern identification, feature engineering, and dimensionality reduction to enhance computational efficiency. The BiLSTM-RNN model addresses the vanishing gradient problem and passes in time-series analysis. Simulations in Python achieved reliable error metrics, including MSE of 31942.15, MAE of 136.71, MAPE of 13.02, RMSE of 178.72, and an R2-score of 0.6, demonstrating its effectiveness in improving drilling performance and safety. © 2025 IEEE.

关键词： Long short-term memory

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A Comprehensive analysis of Satellite Image Denoising using Earth Observation data 7

A Comprehensive Analysis of Satellite Image Denoising using ...

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7th International conference on Computing Methodologies and Communication, ICCMC 2023

作者： Prema, P. Ramalingam, v.v. Department of Computing Technologies SRMIST SRM Nagar Potheri Kattankulathur India

ISBN: (纸本)9781665464086

Satellite images can be affected by noise during image acquisition, transmission, or poor environmental conditions. More precise images are increasingly necessary in both everyday activities and scientific research. Important information is also lost when noise in the image is reduced by attenuating the high-frequency image components. Noise, on the other hand, distorts images, lowering their quality of visual image. Due to this reason, investigations focus on removal of noise that can be done on images to improve image quality. To date, quite a lot of different approaches have been suggested to reduce noise, and each one has a unique set of benefits. To preserve the images' crucial information and enhance their visual appeal, various methods are offered as an efficient denoising technique. This analysis focuses on satellite noise removal using different methodology. As a performance metric, Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are used as metrics. © 2023 IEEE.

关键词： Mean square error

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