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ieee symposium on large data analysis and visualization (LDAV)

作者： Jianxin Sun David Lenz Hongfeng Yu Tom Peterka University of Nebraska-Lincoln Argonne National Laboratory

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

Functional approximation as a high-order continuous representation provides a more accurate value and gradient query compared to the traditional discrete volume representation. Volume visualization directly rendered from functional approximation generates high-quality rendering results without high-order artifacts caused by trilinear interpolations. However, querying an encoded functional approximation is computationally expensive, especially when the input dataset is large, making functional approximation impractical for interactive visualization. In this paper, we proposed a novel functional approximation multi-resolution representation, Adaptive-FAM, which is lightweight and fast to query. We also design a GPU-accelerated out-of-core multi-resolution volume visualization framework that directly utilizes the Adaptive-FAM representation to generate high-quality rendering with interactive responsiveness. Our method can not only dramatically decrease the caching time, one of the main contributors to input latency, but also effectively improve the cache hit rate through prefetching. Our approach significantly outperforms the traditional function approximation method in terms of input latency while maintaining comparable rendering quality.

关键词： visualization Adaptation models Interpolation Three-dimensional displays Prefetching Rendering (computer graphics) Sampling methods Encoding Function approximation Testing

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Activity Monitoring and Management in Avatar-based Interactions

Activity Monitoring and Management in Avatar-based Interacti...

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ieee/SICE International symposium on System Integration (SII)

作者： Utsumi, Akira Nishimura, Shogo Ota, Akira Horikawa, Yukiko Miyashita, Takahiro Adv Telecommun Res Inst Int 2-2-2 Hikaridai Seika Kyoto 6190288 Japan

ISBN: (纸本)9798350398687

This paper describes the role of activity monitoring and management in a software platform (cybernetic avatar (CA) platform) to support avatar-based interactions. Avatar-based interaction differs in various aspects from conventional face-to-face interaction. For instance, it offers anonymity and parallel operability (i.e., a single operator can control multiple interactions simultaneously). On the other hand, it imposes a large burden on the operator due to the required avatar operations. Reducing the operator's burden is a crucial issue to consider in developing such a system. From the viewpoint of data analysis, it is easy to capture and record the activities of participants in avatar-based interaction because those interactions are carried out by explicit avatar operations via a network. In addition, the activities of human participants in such interaction are observed by sensor networks. Therefore, the data from avatar monitoring should contain many kinds of information on the human operators and interaction partners interacting with each other via the avatars. The activity monitoring and management functions enable various services to support human activities using avatars, including reduction of the operational burden. In this paper, we also address an operation recommendation system, as an example application of activity monitoring, which offers situation-dependent recommendations for the operator's speech and operations to reduce the operator's burden. Experimental results support the effectiveness of the proposed system.

关键词： data analysis Avatars System integration Software Behavioral sciences Monitoring Recommender systems

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Research on large Model Text-to-SQL Optimization Method for Intelligent Interaction in the Field of Construction Safety 5

Research on Large Model Text-to-SQL Optimization Method for ...

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5th International symposium on Computer Engineering and Intelligent Communications, ISCEIC 2024

作者： Zhao, Lu Jiang, Xin Tao, Feng Liu, Wen Wang, Xue Wu, Yu Hao Cccc Wuhan Zhixing International Engineering Consulting Ltd Technology Center Wuhan China

ISBN: (纸本)9798331518677

With the increasing amount of information and data in construction safety management, traditional business query and analysis methods are no longer sufficient to meet the demands for flexibility and complexity in on-site management. This study addresses the information query needs in the construction safety domain by proposing a graph-structured retrieval-enhanced large model Text-to-SQL optimization method. By standardizing database table information, addressing few-shot problems with example-based and enterprise safety domain knowledge prompt templates, and utilizing graph data-based knowledge retrieval enhancement techniques, the method enhances the large model's business understanding capability in the construction safety field and reduces computational resource consumption. Furthermore, by employing reasoning strategies such as chain-of-thought prompting, easy-to-hard progression, self-consistency, and self-correction, the accuracy of the large model in responding to complex construction safety business questions is significantly improved. Experimental results on the open-source model llama-3-sqlcoder-8b and the closed-source model GLM-4-Plus demonstrate that this method achieves substantial improvements in execution accuracy and syntactic accuracy, proving its universality and effectiveness. © 2024 ieee.

关键词： Structured Query Language

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ASSIMILATION OF SWOT ALTIMETRY AND SENTINEL-1 FLOOD EXTENT OBSERVATIONS FOR FLOOD REanalysis - A PROOF-OF-CONCEPT

ASSIMILATION OF SWOT ALTIMETRY AND SENTINEL-1 FLOOD EXTENT O...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Nguyen, T. H. Ricci, S. Piacentini, A. Emery, C. Suquet, R. Rodriquez Luque, S. Pena CECI UMR 5318 CERFACS CNRS Climate Modeling & Global Change F-31057 Toulouse 1 France Luxembourg Inst Sci & Technol L-4362 Esch Sur Alzette Luxembourg CS Grp F-31401 Toulouse 9 France Ctr Natl Etud Spatiales CNES Earth Observat Lab F-31401 Toulouse 9 France

ISBN: (纸本)9798350360332;9798350360325

In spite of astonishing advances and developments in remote sensing technologies, meeting the spatio-temporal requirements for flood hydrodynamic modeling remains a great challenge for Earth Observation. The assimilation of multi-source remote sensing data in 2D hydrodynamic models participates to overcome such a challenge. The recently launched Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite provides a global coverage of water surface elevation at a high resolution. SWOT provides complementary observation to radar and optical images, increasing the opportunity to observe and monitor flood events. This research work focuses on the assimilation of 2D flood extent maps derived from Sentinel-1 C-SAR imagery data, and water surface elevation from SWOT as well as in-situ water level measurements. An Ensemble Kalman Filter (EnKF) with a joint state-parameter analysis is implemented on top of a 2D hydrodynamic TELEMAC-2D model to account for errors in roughness, input forcing and water depth in flood-plain subdomains. The proposed strategy is carried out in an Observing System Simulation Experiment based on the 2021 flood event over the Garonne Marmandaise catchment. This work makes the most of the large volume of heterogeneous data from space for flood prediction in hindcast mode paves the way for nowcasting.

关键词： Fluvial floods data assimilation EnKF TELEMAC-2D Garonne Sentinel-1 SWOT OSSE

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Visual Cascade Analytics of large-Scale Spatiotemporal data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第6期28卷 2486-2499页

作者： Deng, Zikun Weng, Di Liang, Yuxuan Bao, Jie Zheng, Yu Schreck, Tobias Xu, Mingliang Wu, Yingcai Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Natl Univ Singapore Sch Comp Singapore 119077 Singapore JD Technol JD Intelligent Cities Res Beijing 100176 Peoples R China Graz Univ Technol A-8010 Graz Austria Zhengzhou Univ Sch Informat Engn Zhengzhou 450052 Peoples R China Zhengzhou Univ Henan Inst Adv Technol Zhengzhou 450052 Peoples R China

Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges 1) generalized pattern inference;2) implicit influence visualization;and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.

关键词： Spatial cascade pattern mining spatiotemporal data

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RSIS: A Secure and Reliable Secret Image Sharing System Based on Extended Hamming Codes in Industrial Internet of Things

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ieee INTERNET OF THINGS JOURNAL 2023年第3期10卷 1933-1945页

作者： Xiong, Lizhi Han, Xiao Zhong, Xinwei Yang, Ching-Nung Xiong, Neal N. Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien 97401 Taiwan Northeastern State Univ Dept Math & Comp Sci Tahlequah OK 74464 USA

In the industrial Internet of Things (IoT), large amounts of image data are collected, stored, and transmitted by low-power cooperative sensors and heterogeneous devices, such as monitoring pictures taken by surveillance cameras in intelligent transportation. Thus, the increasing image data would be outsourced and shared between different distributed servers. Recently, some secret image sharing with authentication (SISA) schemes were proposed, which can provide image sharing in the distributed IoT system. However, if the IoT data are tampered with by malicious attackers, the error cannot be corrected, which causes unreliability and data loss. Therefore, in order to solve the above issues, we propose a secure and reliable secret image-sharing system based on extended Hamming codes (RSIS) in IoT. In RSIS, a novel distributed IoT architecture-based secret sharing is proposed. A secret image is shared into a series of stego-images. These stego-images can be distributed among multiple edge servers in IoT, which is not easy to attract attentions of attackers. In RSIS, check bits generated by authentication bits and hamming code are embedded into the stego-images to identify the tampered places with a high probability. Theoretical analysis and experiments prove that the proposed scheme achieves a high authentication capability, and the bit errors of data can be accurately detected and corrected. Generally, the scheme is effective and practical.

关键词： Internet of Things Authentication Image reconstruction Servers Image edge detection Codes visualization error correction capability Hamming code Internet of Things (IoT) secret image sharing (SIS)

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Revisiting Reliability in large-Scale Machine Learning Research Clusters 31

Revisiting Reliability in Large-Scale Machine Learning Resea...

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31st ieee International symposium on High Performance Computer Architecture, HPCA 2025

作者： Kokolis, Apostolos Kuchnik, Michael Hoffman, John Kumar, Adithya Malani, Parth Ma, Faye Devito, Zachary Sengupta, Shubho Saladi, Kalyan Wu, Carole-Jean FAIR at Meta United States

ISBN: (纸本)9798331506476

Reliability is a fundamental challenge in operating large-scale machine learning (ML) infrastructures, particularly as the scale of ML models and training clusters continues to grow. Despite decades of research on infrastructure failures, the impact of job failures across different scales remains unclear. This paper presents a view of managing two large, multi-tenant ML clusters, providing quantitative analysis, operational experience, and our own perspective in understanding and addressing reliability concerns at scale. Our analysis reveals that while large jobs are most vulnerable to failures, smaller jobs make up the majority of jobs in the clusters and should be incorporated into optimization objectives. We identify key workload properties, compare them across clusters, and demonstrate essential reliability requirements for pushing the boundaries of ML training at *** hereby introduce a taxonomy of failures and key reliability metrics, analyze 11 months of data from two state-of-the-art ML environments with 4 million jobs and over 150 million A100 GPU hours. Building on our data, we fit a failure model to project Mean Time to Failure for various GPU scales. We further propose a method to estimate a related metric, Effective Training Time Ratio, as a function of job parameters, and we use this model to gauge the efficacy of potential software mitigations at scale. Our work provides valuable insights and future research directions for improving the reliability of AI supercomputer clusters, emphasizing the need for flexible, workload-agnostic, and reliability-aware infrastructure, system software, and algorithms. © 2025 ieee.

关键词： Program processors

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Shape-analysis Driven Memory Graph visualization 30

Shape-Analysis Driven Memory Graph Visualization

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30th ieee/ACM International Conference on Program Comprehension (ICPC)

作者： Boockmann, Jan H. Luettgen, Gerald Univ Bamberg Software Technol Res Grp Bamberg Germany

ISBN: (纸本)9781450392983

Analyzing heap dumps containing complex dynamic data structures is essential when debugging modern software systems. However, existing tools for visualizing memory graphs can neither deal with corrupt structures such as binary trees exhibiting cycles, nor do they offer adequate abstractions when being confronted with large heaps. This paper presents MGE (Memory Graph Explorer), a memory analyzer and visualizer that combines a novel memory graph abstraction with an interactive visualization. MGE borrows ideas from separation logic and shape analysis to reveal relationships between memory nodes, name recognized structures such as doubly-linked lists and binary trees, and summarize complex structures. This summarization works for corrupt data structures, too, and is particularly powerful for large, nested structures due to its support for interactive (un)folding. MGE's utility for aiding program comprehension is illustrated by real-world and textbook examples and contrasted with existing debuggers.

关键词： Shape Navigation Memory management data visualization Debugging Binary trees Programming

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A Comparative Study of Intersection-Based Triangle Counting Algorithms on GPUs 38

A Comparative Study of Intersection-Based Triangle Counting ...

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International Parallel and Distributed Processing symposium (IPDPS)

作者： Li, Jiangbo Xu, Zichen Pham, Minh Tu, Yicheng Zhou, Qihe Nanchang Univ Sch MCS Nanchang Jiangxi Peoples R China Univ S Florida Dept CSE Tampa FL USA City Univ Macau Fac Data Sci Macau Peoples R China

ISBN: (纸本)9798350387117;9798350387124

Counting triangles in large graphs, being a crucial problem in graph computing, has attracted significant attention from research communities. There is a large body of work dedicated to algorithmic design and efficient implementation on parallel platforms such as GPUs. Among them, the intersection-based triangle counting algorithm is found to be the most efficient approach and a few GPU implementations have been proposed following this algorithm. However, there remains a gap in understanding how these algorithms perform when confronted with diverse real-world graph datasets. It is a well-established fact that the performance of GPU code is heavily influenced by data characteristics, including graph size and node degree, often leading to issues such as workload imbalances and inefficient memory access. The goal of this study is to systematically evaluate the performance and analyze the behavior of eight recently published intersection-based triangle counting implementations. For that, we developed a unified testing framework that facilitates fast performance assessment of any triangle counting algorithm. Our experiments show that the TRUST algorithm outperforms competitors in most cases. To our surprise, the Polak algorithm, with a simple design, has displayed commendable performance across the board. Notably, it even surpasses the TRUST algorithm when processing small datasets. We conducted an in-depth analysis on the resource consumption patterns of these implementations in relation to their performance and identified key factors that contributed to their behaviors. Based on insights gained from such analysis, we proposed a novel algorithm named GroupTC that delivers outstanding performance under all types of datasets.

关键词： GPU Triangle Counting

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MoneyVis: Open Bank Transaction data for visualization and Beyond

MoneyVis: Open Bank Transaction Data for Visualization and B...

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25th Eurographics Conference on visualization, EuroVis 2023

作者： Firat, Elif E. Vytla, Dharmateja Singh, Navya Vasudeva Jiang, Zhuoqun Laramee, Robert S. The Department of Computer Engineering Cukurova University Turkey The School of Computer Science The University of Nottingham United Kingdom

ISBN: (纸本)9783038682196

With the rapid evolution of financial technology (FinTech) the importance of analyzing financial transactions is growing in importance. As the prevalence and number of financial transactions grow, so does the necessity of visual analysis tools to study the behavior represented by these transactions. However, real bank transaction data is generally private for security and confidentiality reasons, thus preventing its use for visual analysis and research. We present Moneydata, an anonymized open bank data set spanning seven years worth of transactions for research and analysis purposes. To our knowledge, this is the first real-world retail bank transaction data that has been anonymized and made public for visualization and analysis by other researchers. We describe the data set, its characteristics, and the anonymization process and present some preliminary analysis and images as a starting point for future research. The transactions are also categorized to facilitate understanding. We believe the availability of this open data will greatly benefit the research community and facilitate further study of finance. © 2023 The Authors.

关键词： Finance

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