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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Dominik Vietinghoff Christian Heine Michael Böttinger Gerik Scheuermann Leipzig University Deutsches Klimarechenzentrum

To assess the reliability of weather forecasts and climate simulations, common practice is to generate large ensembles of numerical simulations. Analyzing such data is challenging and requires pattern and feature detection. For single time-dependent scalar fields, empirical orthogonal functions (EOFs) are a proven means to identify the main variation. In this paper, we present an extension of that concept to time-dependent ensemble data. We applied our methods to two ensemble data sets from climate research in order to investigate the North Atlantic Oscillation (NAO) and East Atlantic (EA) pattern.

关键词： Feature detection Weather forecasting data visualization Predictive models Numerical simulation Numerical models Reliability

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Sentiment analysis on Online Learning for Higher Education During Covid-19 3

Sentiment Analysis on Online Learning for Higher Education D...

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3rd International Conference on Artificial Intelligence and data Sciences, AiDAS 2022

作者： Remali, Nor Ain Syafiqah Shamsuddin, Mohd Razif Abdul-Rahman, Shuzlina Faculty of Computer and Mathematical Sciences UiTM Shah Alam Selangor Malaysia Research Initiative Group of Intelligent Systems Faculty of Computer and Mathematical Sciences UiTM Shah Alam Selangor Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Distance learning has become a necessary option to the education system for almost every nation because of the COVID-19 outbreak. Due to the closure of educational institutions, which creates obstacles to students' learning in the current COVID 19 pandemic environment, the role of information technology has gained momentum. With the current technology advancements, online learning is made possible for everyone. Although this method appears to be helpful to students and instructors, the effectiveness depended on several factors. For instance, the availability of internet services and the economic capacity of the users may affect the user experience of online learning. the student's reaction is desperately needed to enhance the university's effectiveness and acquire insight into student wants. However, it is difficult to collect and evaluate all the text data on social media, particularly Twitter. In this research, the analysis regarding perception students towards online learning is done through the Natural Language Processing technique which is sentiment analysis. the aim for this study is to determine the terms or keywords used for online learning and identify which machine learning classifiers work best with a large dataset. this research used a Twitter dataset that consisted of 38,602 tweets posted between 23rd July and 14th August 2020. Firstly, pre-processed the data to remove irrelevant tweets. Second, classify the tweet into three classes namely positive, negative, and neutral using rapid miner. Consequently, several machine learning classifiers are trained using different techniques which are Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DCT), and Random Forest (RF). Support Vector Machine classifier with a percentage split 80:20 using VADER Lexicon managed to get the highest accuracy which is approximately 90.41%. Finally, the result of the sentiment analysis has been shown using Power BI data visualization for better understanding. For fut

关键词： Students

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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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Research on Cross-modal image-text Retrieval Method based on multi-module graph structure

Research on Cross-modal image-text Retrieval Method based on...

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IEEE International symposium on Information (IT) in Medicine and Education, ITME

作者： Fuling Wang Li Liu Hua Ji Xinchen Dai Shandong Normal University School of Information Science and Engineering Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the rapid development of artificial intelligence technology and the widespread application of big data, the amount of media data in the real world is showing an explosive growth trend, and a large number of heterogeneous data have emerged in various industries. Because the scale of data is continuously expanding, the semantic correspondence between images and text has become increasingly important. How to efficiently retrieve in the vast amount of image-text data has become a highly concerned issue. there are still two limitations in the existing cross-modal image-text retrieval methods: 1) Different modal data have different distributions and are uneven. 2) Semantics are complex and abstract, and there is still an insurmountable semantic gap between vision and language. In this paper, we have done the following research: A cross-modal retrieval method based on multi-module graph structure is proposed, including visual, word, and phrase graph structure modules. Due to the complexity of data and the interaction between images and text not only in semantic information but also in rich spatial location information, we propose a visual module that combines image features with location features to learn the relationship between semantics and space using GCN. the diversity of text descriptions makes it difficult to learn consistent text feature representations. For this reason, we first use GCN in the word graph structure module to capture the associations between words based on semantic dependency relationships. then, in the phrase graph structure module, GCN is used to infer the relationships between noun phrases. Finally, a gated fusion layer is used to filter and reduce redundant information interference. this method has performed well on the Mirflickr25K and NUS-WIDE datasets.

关键词： visualization Graph convolutional networks Semantics Process control Interference Logic gates Media Information filters Complexity theory Cross modal retrieval

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A large-scale analysis of Hundreds of In-memory Key-value Cache Clusters at Twitter

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ACM TRANSACTIONS ON STORAGE 2021年第3期17卷 1–35页

作者： Yang, Juncheng Yue, Yao Rashmi, K., V Carnegie Mellon Univ 5000 Forbes Ave Pittsburgh PA 15213 USA Twitter 1355 Market St San Francisco CA 94103 USA

Modern web services use in-memory caching extensively to increase throughput and reduce latency. there have been several workload analyses of production systems that have fueled research in improving the effectiveness of in-memory caching systems. However, the coverage is still sparse considering the wide spectrum of industrial cache use cases. In this work, we significantly further the understanding of real-world cache workloads by collecting production traces from 153 in-memory cache clusters at Twitter, sifting through over 80 TB of data, and sometimes interpreting the workloads in the context of the business logic behind them. We perform a comprehensive analysis to characterize cache workloads based on traffic pattern, time-to-live (TTL), popularity distribution, and size distribution. A fine-grained view of different workloads uncover the diversity of use cases: many are far more write-heavy or more skewed than previously shown and some display unique temporal patterns. We also observe that TTL is an important and sometimes defining parameter of cache working sets. Our simulations show that ideal replacement strategy in production caches can be surprising, for example, FIFO works the best for a large number of workloads.

关键词： Cache in-memory key-value cache key-value store workload analysis datasets Twitter

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Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional data

Mapper Interactive: A Scalable, Extendable, and Interactive ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Youjia Zhou Nithin Chalapathi Archit Rathore Yaodong Zhao Bei Wang Scientific Computing and Imaging (SCI) Institute University of Utah

ISBN: (纸本)9781665439312;9781665439329

the mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactive, a web-based framework for the interactive analysis and visualization of high-dimensional point cloud data. It implements the mapper algorithm in an interactive, scalable, and easily extendable way, thus supporting practical data analysis. In particular, its command-line API can compute mapper graphs for 1 million points of 256 dimensions in about 3 minutes (4 times faster than the vanilla implementation). Its visual interface allows on-the-fly computation and manipulation of the mapper graph based on user-specified parameters and supports the addition of new analysis modules with a few lines of code. Mapper Interactive makes the mapper algorithm accessible to nonspecialists and accelerates topological analytics workflows.

关键词： visualization data analysis three-dimensional displays data visualization Tools data mining

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Visual analysis of Spatio-Temporal Trends in Time-Dependent Ensemble data Sets on the Example of the North Atlantic Oscillation

Visual Analysis of Spatio-Temporal Trends in Time-Dependent ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Dominik Vietinghoff Christian Heine Michael Böttinger Nicola Maher Johann Jungclaus Gerik Scheuermann Leipzig University Deutsches Klimarechenzentrum Max Planck Institute for Meteorology

A driving factor of the winter weather in Western Europe is the North Atlantic Oscillation (NAO), manifested by fluctuations in the difference of sea level pressure between the Icelandic Low and the Azores High. Different methods have been developed that describe the strength of this oscillation, but they rely on certain assumptions, e.g., fixed positions of these two pressure systems. It is possible that climate change affects the mean location of both the Low and the High and thus the validity of these descriptive methods. this study is the first to visually analyze large ensemble climate change simulations (the MPI Grand Ensemble) to robustly assess shifts of the drivers of the NAO phenomenon using the uncertain northern hemispheric surface pressure fields. For this, we use a sliding window approach and compute empirical orthogonal functions (EOFs) for each window and ensemble member, then compare the uncertainty of local extrema in the results as well as their temporal evolution across different CO₂ scenarios. We find systematic northeastward shifts in the location of the pressure systems that correlate with the simulated warming. Applying visualization techniques for this analysis was not straightforward; we reflect and give some lessons learned for the field of visualization.

关键词： Climate change visualization Sea surface Uncertainty Systematics data visualization Market research

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Bibliometric visualization analysis of Sustainable Design from 2001 to 2021

Bibliometric Visualization Analysis of Sustainable Design fr...

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International symposium on Computational Intelligence and Design, ISCID

作者： Ruiyi Cai Yudi Xiang Modern Industrial Design Institute Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665438575

Based on the data of 1,430 sustainable design literature included in the Web of Science database from 2001 to 2021, this paper integrates the analytical advantages of CiteSpace and VOSviewer, and conducts field-specific bibliometric analysis and visualization presentation of results. the article analyzes the distribution and sources of the literature at the micro level, combs the research hotspots, branches and development frontiers at the macro level, providing a comprehensive and clear context combing of the sustainable design field.

关键词： Green design Bibliometrics data visualization Visual databases Computational intelligence

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Unravelling the Human Perspective and Considerations for Urban data visualization

Unravelling the Human Perspective and Considerations for Urb...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Sarah Goodwin Sebastian Meier Lyn Bartram Alex Godwin Till Nagel Marian Dörk Monash University HafenCity University Simon Fraser University American University Mannheim University of Applied Sciences Potsdam University of Applied Sciences

ISBN: (纸本)9781665439312;9781665439329

Effective use of data is an essential asset to modern cities. visualization as a tool for analysis, exploration, and communication has become a driving force in the task of unravelling our complex urban fabrics. this paper outlines the findings from a series of three workshops from 2018-2020 bringing together experts in urban data visualization with the aim of exploring multidisciplinary perspectives from the human-centric lens. Based on the rich and detailed workshop discussions identifying challenges and opportunities for urban data visualization research, we outline major human-centric themes and considerations fundamental for CityVis design and introduce a framework for an urban visualization design space.

关键词： Design methodology Conferences Urban areas Force data visualization Tools Fabrics

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Business Intelligence Architecture to Improve Decision Making

Business Intelligence Architecture to Improve Decision Makin...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Cristhian Villasante Moreno Ciro Rodriguez Favio Guevara Puente Ivan Petrlik Pedro Lezama Yuri Pomachagua Software Engineering Department Universidad Nacional Mayor de San MarcosUNMSM Lima Peru Department of Electronic Engineering and Informatics Universidad Nacional Federico Villarreal UNFV Lima Peru Systems Engineering Department Universidad Nacional Federico Villarreal UNFV Lima Peru

ISBN: (纸本)9781665487726

Summary - Currently in different organizations and public entities, have large amounts of data and many times the tools or technologies that achieve the analysis of such data are unknown. in this context, business intelligence emerged, which allows data to be analyzed and provided information to supports decision- making for senior executives of public entities. the objective of this research is to propose a business intelligence architecture that provides a solution to manage large amount of information. the methodology used was based on scrum as part of a good organization, structure and time control by the implementation of the proposed architecture in any system or tool of the public entities. as part of the review and part of the results, the research of various articles was carried out, and 20 were selected for sample and proof that the proposed architecture is the one indicated for decision making. in the results of part of the research, was found evidence that a business intelligence architecture is not only the most used, but it provides various response mechanisms for the large amount of information possessed and, in many cases, not used in the best way.

关键词： Decision making data visualization Memory Computer architecture Organizations User experience Software

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