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

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Three Dimensions of Science: A Web Tool for 3D visualization of Scientific Literature 21

Three Dimensions of Science: A Web Tool for 3D Visualization...

引用

Proceedings of the 2021 ACM/ieee Joint Conference on Digital Libraries

作者： Jakub Swacha Dept. of IT in Management University of Szczecin Szczecin Poland

ISBN: (纸本)9781665417709

Graphical analysis is one of the primary methods in the study of networks. While the traditional approach uses a two-dimensional (2D) visualization, once the networks become complex, obtaining anything but superficial observations from 2D graphs becomes very difficult, mainly due to the so-called hairball effect, caused by a large number of overlapping nodes and edges. This problem can be effectively addressed with three-dimensional (3D) visualization. The power of modern web browsers' scripting engines can be utilized to provide 3D visualization without a hassle of installing platform-specific software. Consequently, a number of tools serving this purpose were developed, dedicated to the analysis of various types of networks in domains such as biology, social sciences, or engineering. Quite surprisingly, till now there were no free open-source tools of this kind dedicated to the analysis of networks representing bibliographic data. This paper introduces 3dSciLi, a web tool capable of 3D visualization of five types of such networks (work citations and co-citations, author citations and co-authorship, as well as keyword co-occurrence). The tool requires only an input of a set of bibliographic database search results, freeing the researchers from using a pipeline of programs and manual processing of data for the sake of their 3D visualization.

关键词： bibliographic data analysis

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MARTA: Multi-configuration Assembly pRofiler and Toolkit for performance analysis

MARTA: Multi-configuration Assembly pRofiler and Toolkit for...

引用

ieee International symposium on Performance analysis of Systems and Software (ISPASS)

作者： Horro, Marcos Pouchet, Louis-Noel Rodriguez, Gabriel Tourino, Juan Univ A Coruna CITIC La Coruna Spain Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

ISBN: (纸本)9781665459549

Benchmarking to characterize specific software or hardware features is an error-prone, arduous and repetitive task. Designing a specialized experimental setup frequently requires writing new scripts or ad-hoc programs in order to properly exhibit interesting performance effects, using code changes and hardware events measurements. These artifacts may have limited reusability for subsequent experiments, since they are dependent on specific problems and, in some cases, platforms. To improve productivity and reproducibility of such experiments, which are often investigative in nature, we introduce MARTA: a fully customizable toolkit that aims to increase productivity by generating benchmark templates, compiling them, and profiling the regions of interest (RoI) specified using hardware events, and performing static code analysis. MARTA can also be applied on existing code regions of interest, it only requires to write a simple configuration file. In an orthogonal dimension, the system is able to run various statistical analyses on the measurements collected. MARTA uses data mining and machine learning or AI-based techniques for classification and regression, automatically extracting the features of the experimental setup which have the most impact on performance or whichever other metric of interest, given a large set of experiments and dimensions to consider. These post-processing tasks are valuable for deriving knowledge from experiments and are not included in most profiling tools. We also provide a set of cases of study to illustrate the ability of MARTA to conveniently create a reliable and reproducible setup for high-performance computing experiments, investigating three vastly different performance effects on modern processors.

关键词： profiling benchmarking micro-benchmarking performance analysis compiler characterization

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End-to-End Model-Based Gait Recognition with Matching Module Based on Graph Neural Networks 6

End-to-End Model-Based Gait Recognition with Matching Module...

引用

6th International symposium on Autonomous Systems, ISAS 2023

作者： Xu, Yixin Wang, Zhihao Nanjing University of Science and Technology School of Automation Nanjing China Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China

ISBN: (纸本)9798350316155

Existing gait recognition systems have achieved success by extracting robust gait features from silhouette images, but gait can be sensitive to appearance features such as clothing and carried items. As a more promising alternative, model-based gait recognition is robust against some variations, such as clothing and baggage carried. With the recent development of human pose estimation, the difficulty of implementing model-based methods has been mitigated. This paper introduces an end-to-end model-based gait recognition method designed for large-scale and uncontrolled datasets. The proposed method takes 3D data as input and utilizes the ST-GCN as the embedding module. We have replaced the simple nearest neighbor algorithm for better performance. Specifically, the 3D skeletons are embedded into the Graph Neural Network to represent similarities. The proposed method is evaluated on the GREW dataset, showing state-of-the-art (SOTA) results in model-based gait recognition. © 2023 ieee.

关键词： Gait analysis

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data Mining and visualization to Understand Employee Attrition and Work Performance 3

Data Mining and Visualization to Understand Employee Attriti...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Gollapalli, Mohammed Rahman, Ata-Ur Osama, Abdurrahman Alfaify, Ahmad Yassin, Muhammad Alabdullah, Ahmad Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Information Systems Dammam31441 Saudi Arabia Imam Abdulrahman Bin Faisal University College of Computer Science & Information Technology Department of Computer Science Dammam31441 Saudi Arabia

ISBN: (纸本)9798350321487

Throughout the course of careers, a large number of employees encounter a variety of normal life events that can have an impact on their performance and job environment satisfaction. Under these circumstances, some employees are compelled to leave their positions, not because of issues with their work environment, but due to personal circumstances. This is what we refer to as Employee Attrition, a pervasive phenomenon affecting numerous businesses and work environments in the present day. Employee attrition could occur in a variety of ways and be determined by several entities. Therefore, we look into various conditions, causes, indicators, and entities in order to study the attrition problem and determine its answers and enhancements. This study investigated the attrition of employees based on IBM data repository, together with the machine learning based experimental analysis and knowledge discoveries, to identify the most effective approaches to the attrition problem. The experimental data contained 1,471 records with 35 distinct characteristics. We formally employed Nave Bayes Classifier in this study using WEKA datamining software, and adequate knowledge has been extracted to better comprehend employee turnover and their work performance facets. © 2023 ieee.

关键词： data mining

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data Parallel Hypersweeps for in Situ Topological analysis 10

Data Parallel Hypersweeps for in Situ Topological Analysis

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Hristov, Petar Weber, Gunther H. Carr, Hamish A. Rubel, Oliver Ahrens, James P. Univ Leeds Leeds W Yorkshire England Lawrence Berkeley Natl Lab Berkeley CA USA Univ Calif Davis Davis CA 95616 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

The contour tree is a tool for understanding the topological structure of a scalar field. Recent work has built efficient contour tree algorithms for shared memory parallel computation, driven by the need to analyze large data sets in situ while the simulation is running. Unfortunately, methods for using the contour tree for practical data analysis are still primarily serial, including single isocontour extraction, branch decomposition and simplification. We report data parallel methods for these tasks using a data structure called the hyperstructure and a general purpose approach called a hypersweep. We implement and integrate these methods with a Cinema database that stores features as depth images and with a web server that reconstructs the features for direct visualization.

关键词： branch decomposition contour tree geometric measures in situ scalar field

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Augmented Reality and Virtual Reality for Ice-Sheet data analysis

Augmented Reality and Virtual Reality for Ice-Sheet Data Ana...

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： Alexandra Boghosian S. Isabel Cordero Carmine Elvezio Sofia Sanchez-Zarate Ben Yang Shengyue Guo Qazi Ashikin Joel Salzman Kirsty Tinto Steven Feiner Robin Bell Lamont-Doherty Earth Observatory Columbia University

Three-dimensional geospatial thinking is an important skillset used by earth scientists and students to analyze and interpret data [1]. This method of inquiry is useful in glaciology, where traditional geophysical survey techniques have been adapted to map three-dimensional (3D) ice sheet structures and inform studies of ice flow, mass change, and history in both Greenland and Antarctica. Ice-penetrating radar images the ice in two-dimensional (2D) cross-sections from the surface to the base. analysis of this data often requires visual inspection and 3D interpretation, but is hindered by data visualization tools and techniques that rarely transcend the two-dimensionality of the computer screen [2]. Recent advances in Augmented Reality (AR) and Virtual Reality (VR), together referred to as Extended Reality (XR), offer a glimpse into the future of 3D ice-sheet data analysis [3]. These technologies offer users an immersive experience where 3D geospatial datasets can be understood more immediately than with 2D maps, and gestural user interfaces can enhance understanding. Here we present Pol-XR, an XR application that supports both visualization and interpretation of ice-penetrating radar in Antarctica and Greenland.

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Interactive visualization of Terascale data in the Browser: Fact or Fiction? 10

Interactive Visualization of Terascale Data in the Browser: ...

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Usher, Will Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9781728184685

Information visualization applications have become ubiquitous, in no small part thanks to the ease of wide distribution and deployment to users enabled by the web browser. Scientific visualization applications, relying on native code libraries and parallel processing, have been less suited to such widespread distribution, as browsers do not provide the required libraries or compute capabilities. In this paper, we revisit this gap in visualization technologies and explore how new web technologies, WebAssembly and WebGPU, can be used to deploy powerful visualization solutions for large-scale scientific data in the browser. In particular, we evaluate the programming effort required to bring scientific visualization applications to the browser through these technologies and assess their competitiveness against classic native solutions. As a main example, we present a new GPU-driven isosurface extraction method for block-compressed data sets, that is suitable for interactive isosurface computation on large volumes in resource-constrained environments, such as the browser. We conclude that web browsers are on the verge of becoming a competitive platform for even the most demanding scientific visualization tasks, such as interactive visualization of isosurfaces from a 1TB DNS simulation. We call on researchers and developers to consider investing in a community software stack to ease use of these upcoming browser features to bring accessible scientific visualization to the browser.

关键词： Web Applications Parallel Isosurface Extraction

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A Robust Warranty data analysis Method Using data Science Techniques

A Robust Warranty Data Analysis Method Using Data Science Te...

引用

Annual symposium on Reliability and Maintainability (RAMS)

作者： Navaneethakannan Annadurai HCL Technologies Limited

SUMMARY & CONCLUSIONSWarranty data analysis (WDA) is generally performed through life data analysis using warranty claims and sales data [1]. While we can automate most of the activities involved in WDA, the warranty analyst performs the remaining steps manually, making the analysis time intensive. In today’s competitive environment, there is an inevitable need to efficiently analyze a large number of products in a shorter time. This paper explains how each step involved in WDA can be effectively automated using data science techniques and derive meaningful insights in real time.

关键词： Warranties Random access memory data visualization data science Real-time systems Reliability

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visualization and analysis of technological systems experimental operating results 16

Visualization and analysis of technological systems experime...

引用

16th ieee International Conference on Computer Science and Information Technologies, CSIT 2021

作者： Aksonov, Yevhen Kombarov, Volodymyr Tsegelnyk, Yevgen Plankovskyy, Sergiy Fojtu, Olena Piddubna, Lidiia O.M. Beketov National University of Urban Economy in Kharkiv Department of Automation and Computer Integrated Technologies Kharkiv Ukraine Czech Technical University in Prague Research Center of Manufacturing Technology Prague Czech Republic

ISBN: (纸本)9781665442572

Improving the accuracy, reliability and performance of high-speed technological systems is one of the most pressing challenges in modern industry. These technologies are implemented at high speeds of tool movement and require a clear interaction of a large number of the technological system or robotic complex elements. Experimental investigations of such systems functioning lead to the receipt of a large amount of data, the display, and analysis of which with the help of universal software means certain difficulties. The paper proposes the creation of specialized software for solving problems of visualization and analysis of experimental data with the ability to simultaneously display several hundred graphs. The proposed algorithm for layer-by-layer graph formation, which provides the task of increasing the speed of data conversion for display and increasing the speed of rendering an image. The proposed algorithm is implemented in specialized software for the visualization and analysis of experimental data obtained in the CNC system. © 2021 ieee.

关键词： data visualization

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Decomposing Complementary and Substitutable Relations for Intercorporate Investment Recommendation 22

Decomposing Complementary and Substitutable Relations for In...

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22nd ieee International Conference on data Mining (ICDM)

作者： Dai, Le Yin, Yu Qin, Chuan Chen, Enhong Xiong, Hui Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Univ Sci & Technol China Sch Data Sci Hefei Peoples R China Baidu Inc Talent Intelligence Ctr Beijing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Guangzhou HKUST Fok Ying Tung Res Inst Guangzhou Peoples R China

ISBN: (纸本)9781665450997

Intercorporate investment has a large impact in financial performance and long-term development of a corporate. Among all the concerns for a company's investment strategy, complementary and substitutable investments are two fundamental factors. However, these two relations are implicit and entangled in the complex corporate network, requiring extra caution before investment. To this end, in this paper, we proposed a novel graph convolutional network called Series-Parallel decomposed Graph Convolutional Network (SPGCN). We first decompose the complementary and substitutable relations as two information propagating directions in company dependency graph, producing multifaceted node features. Then, with an Attentive Aggregation Module, we are able to further measure the impact of both features to the final investment decision making, producing an interpretable analysis for investment strategy. Finally, we conduct experiments on a real-world dataset, to show the effectiveness of decomposing two concerns on investment recommendation task. With visualization and case studies, our method also shows great potential to help understand and conduct complementary and substitutable investment decisions. We open source our code to support future research: https://***/lem0nle/SPGCN.

关键词： complementary and substitutable investment recommender system graph neural network

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