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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

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Top data analysis performance – case study

Top data analysis performance – case study

引用

International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Michal Kvet Marek Kvet Department of Informatics Faculty of Management Science and Informatics University of Žilina Žilina Slovakia

Obtaining top data based on defined criteria requires sorting the data, which can be applied to all data in one group or to individual data partitions specified in analytical functions. The limitation of the database system performance is associated with the cardinality of the data set processed in the individual evaluation steps, especially regarding data warehouses and large sets that need to be joined. However, most of the data are refused while maintaining only top data. Many solutions are available, but the significant aspect is related to the performance from the system resources and time consumption point of view. This paper aims to analyze individual solutions and techniques and provide a methodology for processing top data not only in the data warehouse but also in an online transaction processing environment limiting the index number range.

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Radargrams as Sequences: A Method for The Semantic Segmentation of Radar Sounder data

Radargrams as Sequences: A Method for The Semantic Segmentat...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jordy Dal Corso Lorenzo Bruzzone University of Trento Trento Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Radar sounders (RS) are electromagnetic sensors employed for subsurface investigation on Earth and other celestial bodies. The data acquired by RS are represented as radargrams and they are traditionally analyzed through visual interpretation or manual feature engineering to detect subsurface layers and relevant features. The emergence of deep learning has prompted exploration into deep automated radargram analysis, frequently treating them as conventional images. Here we present a novel method for the semantic segmentation of radar-grams inspired to video object segmentation (VOS). It consists in an initial phase of self-supervised VOS-based learning of features from sequences of radargram patches, and a second phase of label propagation from an initial reference patch provided by experts. The effectiveness of the proposed method is confirmed by validation on a dataset of radargrams generated by the airborne radar sounder MCoRDS1 in Antarctica.

关键词： Representation learning Radar remote sensing visualization Semantic segmentation Semantics Object segmentation Manuals

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analysis of VF-TLP ESD Measurements Using Machine Learning

Analysis of VF-TLP ESD Measurements Using Machine Learning

引用

ieee International symposium on Quality Electronic Design

作者： Seyed Hossein Hosseini Mehrdad Nourani Theo Smedes Charvaka Duvvury Dept. of ECE Univ. of Texas at Dallas Dallas USA NXP Semiconductors Nijmegen Netherlands iT2 Technologies Dallas USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

In this paper, we propose a machine learning-powered approach to explore analysis of electrostatic discharge (ESD) protection device measurements taken with high-current pulse method. Specifically, the data analyzed was collected under a very fast transmission line pulse (VF - TLP) on a grounded gate NMOS (ggNMOS) protection device. The analysis is done by applying K-means clustering in two complementary steps. First, we use the raw data for a holistic pattern-based clustering to filter out the possible measurement errors. In the second step, the subset that seems error-free is further analyzed through a feature-based clustering that provides insight into parametric variations and stability of the protection design. We also employ visualization techniques, i.e. t-SNE dimensionality reduction and statistical boxplots, to make interpretation of results easier for ESD test and quality engineers. We have validated the proof of concept using measurement data obtained in Kelvin setup for 18 devices.

关键词： Dimensionality reduction Measurement errors Power transmission lines Statistical analysis Pulse measurements Statistical distributions Power system stability Transmission line measurements Electrostatic discharges Stability analysis

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AppMAIS Simple data visualization App

AppMAIS Simple Data Visualization App

引用

ieee Southeastcon

作者： Logan Richardson Christopher Campell Rahman Tashakkori Will O’Brien Sarah Elizabeth Davis Appalachian State University Boone United States of America

Due to the fact that a large number of honey bee hives have been lost in recent years, there have been some efforts to collect various data to assist with monitoring the hives and determine their health status. The Appalachian Multi-purpose Apiary Informatics Systems (AppMAIS) was funded by the North Carolina General Assembly in 2020 to establish a collaborative research project to monitor the health of honey bee hives. The AppMAIS Simple data visualization App greatly increases our ability to analyze data, detect patterns, and train our ML swarm-like detection models. The app utilizes tooltips, hyperlinking, and highlight functionalities to significantly improve the efficiency of data analysis.

关键词： Analytical models data analysis data visualization Collaboration data models Informatics Monitoring

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An Integrated Questionnaire Survey Method Utilizing Cloud Services and Communication Platforms: Enhancing Research Efficiency in Healthcare Settings

An Integrated Questionnaire Survey Method Utilizing Cloud Se...

引用

International Computer symposium (ICS)

作者： Chao-Ying Chang Shu-Min Chien I-Ching Hou Department of Nursing National Yang Ming Chiao Tung University Taipei City Taiwan Head Nurse Taipei Hospital Ministry of Health and Welfare New Taipei City Taiwan Efficient Smart Care Research Center College of Nursing National Yang Ming Chiao Tung University Taipei City Taiwan

ISBN: (数字)9798331509071

ISBN: (纸本)9798331509088

Questionnaire surveys are a common data collection method in nursing research, but traditional research methods face challenges such as low efficiency and inconsistent data quality. This study proposes a Platform-integrated Real-time Intelligent Survey Model (PRISM) based on cloud technology and instant messaging platforms. PRISM integrates Google Workspace, LINE Messaging API, and Google Cloud, achieving efficient questionnaire management, real-time data validation, and visualization analysis. In an application at a regional hospital, PRISM reduced data collection time from 30-45 days to 14 days, data processing time from 13.33 hours to 0.5 hours, and research costs from 15,000-20,000 NTD to approximately 10 NTD. PRISM improved data quality through automatic contradiction detection and real-time feedback mechanisms, providing an efficient, low-cost, and high-quality solution for large-scale, multi-stage questionnaire surveys.

关键词： Surveys Costs Hospitals data integrity data visualization Instant messaging data collection Drives Real-time systems Faces

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Adaptive Spatial and Multi-Variable Generalization of 4dvarnet in Ocean Colour Remote Sensing

Adaptive Spatial and Multi-Variable Generalization of 4dvarn...

引用

ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Clément Dorffer Thi Thuy Nga Nguyen Ronan Fablet Frédéric Jourdin IMT-Atlantique UMR CNRS Lab-STICC INRIA Team Odyssey Brest France Service Hydrographique et Océanographique de la Marine (SHOM) Brest France

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study presents an enhanced approach to ocean colour L4 product generation through the Adaptive Spatial and Multi-Variable Generalization of 4DVarNet - an innovative integration of deep neural networks with variational data assimilation proposed in [1]. We explore the model’s capabilities in generalizing across various geographical regions and bio-optical variables using datasets of the North Sea and the Mediterranean Sea. Our analysis and visualization show that 4DVarNet demonstrates a notable ability to adapt and scale, reducing significantly training cost thanks to this generalization ability.

关键词： Training Adaptation models Costs Image color analysis Biological system modeling Oceans Atmospheric modeling

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Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in large data Sets 51

Hierarchical Subspace Learning for Dimensionality Reduction ...

引用

51st ieee International symposium on Multiple-Valued Logic (ISMVL)

作者： Poorheravi, Parisa Abdolrahim Gaudet, Vincent Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781728192246

Manifold learning is used for dimensionality reduction, with the goal of finding a projection subspace to increase and decrease the inter- and intraclass variances, respectively. However, a bottleneck for subspace learning methods often arises from the high dimensionality of datasets. In this paper, a hierarchical approach is proposed to scale subspace learning methods, with the goal of improving classification in large datasets by a range of 3% to 10%. Different combinations of methods are studied. We assess the proposed method on five publicly available large datasets, for different eigen-value based subspace learning methods such as linear discriminant analysis, principal component analysis, generalized discriminant analysis, and reconstruction independent component analysis. To further examine the effect of the proposed method on various classification methods, we fed the generated result to linear discriminant analysis, quadratic linear analysis, k-nearest neighbor, and random forest classifiers. The resulting classification accuracies are compared to show the effectiveness of the hierarchical approach, reporting results of an average of 5% increase in classification accuracy.

关键词： Learning systems Independent component analysis Linear discriminant analysis Manifold learning Random forests Principal component analysis

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An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmonic Centrality 34

An MPI-Parallel Algorithm for Static and Dynamic Top-k Harmo...

引用

34th ieee International symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： van der Grinten, Alexander Custers, Geert Duy Le Thanh Meyerhenke, Henning Humboldt Univ Berlin Germany Delft Univ Technol Delft Netherlands

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Analyzing large graphs in parallel has received considerable attention recently due to ever increasing data set sizes. Centrality measures indicate the importance of vertices (or edges) and belong to the most widely used analytic kernels. Harmonic centrality is a popular vertex centrality measure with many desirable properties. Since most of the applications of vertex centrality rely only on the ranking of vertices and not on their exact centrality scores, previous research has considered various algorithms that can quickly determine a ranking of the top-k vertices with highest harmonic centrality. Such algorithms are available for many types of real-world graphs, including dynamic graphs. Yet, no attempts have been made to efficiently parallelize these top-k algorithms (besides naive implementations based on a global lock). In this paper, we propose an MPI-distributed algorithm for (dynamic) top-k harmonic centrality. Our algorithm exploits an algebraic BFS technique and batching to parallelize the approximation of centrality scores of multiple vertices. Likewise, we use algebraic techniques to compute various bounds and heuristics that are necessary to obtain a fast top-k algorithm. Experiments demonstrate that our MPI-parallel algorithm outperforms existing implementations. Consequently, our new approach allows the computation of top-k harmonic centrality on graphs that are substantially larger.

关键词： algebraic graph algorithm network analysis harmonic centrality top-k ranking MPI parallelism

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Zero-Shot Text-Guided Object Generation with Dream Fields

Zero-Shot Text-Guided Object Generation with Dream Fields

引用

ieee/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jain, Ajay Mildenhall, Ben Barron, Jonathan T. Abbeel, Pieter Poole, Ben Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. Due to the scarcity of diverse, captioned 3D data, prior methods only generate objects from a handful of categories, such as ShapeNet. Instead, we guide generation with image-text models pre-trained on large datasets of captioned images from the web. Our method optimizes a Neural Radiance Field from many camera views so that rendered images score highly with a target caption according to a pre-trained CLIP model. To improve fidelity and visual quality, we introduce simple geometric priors, including sparsity-inducing transmittance regularization, scene bounds, and new MLP architectures. In experiments, Dream Fields produce realistic, multi-view consistent object geometry and color from a variety of natural language captions.

关键词： Geometry visualization Solid modeling Computer vision Three-dimensional displays Image color analysis Shape

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Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound

Weakly Supervised Contrastive Learning for Better Severity S...

引用

ieee International symposium on Biomedical Imaging

作者： Gautam Gare Hai Tran Bennett DeBoisblanc Ricardo Rodriguez John Galeotti Carnegie Mellon University Pittsburgh USA LSUHSC Internal Medicine New Orleans USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our contrastive learning method treats the video clip severity labels as noisy weak severity labels for individual frames, thus requiring only video-level labels. We show that it performs better than the conventional cross-entropy loss based training. We combine frame severity predictions to come up with video severity predictions and show that the frame based model achieves comparable performance to a video based TSM model, on a large dataset combining public and private sources.

关键词： COVID-19 visualization Ultrasonic imaging Lungs Biological system modeling Contrastive learning Predictive models Noise measurement Labeling Artificial intelligence

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