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

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large Scale Online Vocabulary Classes for prediction of College Student Learning Patterns 2

Large Scale Online Vocabulary Classes for prediction of Coll...

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2nd ieee International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Alabdeli, Haider Obaid, Mohammed Kadhim Krebat, Muammer K. Al-Ani, Mahmood Muhsin, Abdulhussain The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ahl Al Bayt University College of Arts Karbala Iraq Kut University College Law Department Kut Iraq Al-Farahidi University Department of Dental Industry Techniques Baghdad10011 Iraq Al-Kunooze University College Computer Engineering Department Basra Iraq

ISBN: (纸本)9798331534950

One of the biggest challenges for college students learning a second language is vocabulary. Despite the growing number of research projects on technology and vocabulary learning, others have shown how technology improves college students' vocabulary learning. However, authentic learning context behavioral performance pattern language is unclear. Traceability studies of college students' online vocabulary learning are rare. It emphasizes basic life learning interactions in college student learning patterns. Learning vocabulary affects classroom practice. Language teachers use idea mapping to improve reading comprehension. Analytical learning methods must also track learning processes, language use, and behavior. This study uses multimodal learning analysis to improve vocabulary and reading skills using idea mapping. Student learning pattern logs using computational tools validate college students' behavioral learning habits for data collection. data is also analyzed using content analysis and visualization. Three college student compartmental learning subgroups have been identified and studied. The proposed VR-IMA achieves a high-performance ratio of 92.1%, learning rate of 94.2%, enhanced probability ratio of 96.6%, vocabulary rate of 95.3%, behavior rate of 91.7%, engagement rate of 93.8%, and usability level of 97.3% compared to other © 2024 ieee.

关键词： Contrastive Learning

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Architecture for Web-Based visualization of large-Scale Energy Domains

Architecture for Web-Based Visualization of Large-Scale Ener...

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Energy data visualization (EnergyVis), Workshop on

作者： Graham Johnson Sam Molnar Nicholas Brunhart-Lupo Kenny Gruchalla NREL

ISBN: (数字)9798350379235

ISBN: (纸本)9798350379242

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

关键词： Navigation Scalability Conferences data visualization Focusing Electrification Electric vehicle charging Systems support Power systems Distributed power generation

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Aggregation and Evaluation of Communication data of large Telepresentation Infrastructure based on Computing Nodes on Critical Endpoints 20

Aggregation and Evaluation of Communication Data of Large Te...

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20th ieee International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Jakab, F. Molitorisova, J. Vapenik, R. Cymbalak, D. Kovacova, J. Slovak Centre of Scientific and Technical Information Košice Slovakia Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Computers and Informatics Košice Slovakia Slovak Centre of Scientific and Technical Information Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

This paper introduces an architecture of model solution for aggregation, evaluation and in the last step also visualization extended data related to videoconferences held by heterogenous environment of Slovak's National Telepresence Infrastructure (NTI). The main aim of this system is to properly process, and aggregate multiple attributes of videoconferences obtained on computing nodes located on critical endpoints and display them graphically for a straightforward overview. Different types of graphs are used to visualize basic videoconferences properties, while for overall statistics table visualization is used. Processed historical data is subsequently used to predict the upcoming number of videoconferences in near future and the number of videoconferences related to the particular organization for the modeled period. © 2022 ieee.

关键词： visualization

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Design of Soil Pollution Prevention and Control Evaluation and analysis Platform based on WebGIS

Design of Soil Pollution Prevention and Control Evaluation a...

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Computer Technology and Information Science (ISCTIS), International symposium on

作者： Derong Liu Hong Liang Kun Gao School of Information Science and Technology Yunnan University Kunming China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

This study focuses on heavy metal pollution in Yunnan Province and designed and developed a data informatization platform for soil environmental quality monitoring and data informatization for pollution control. The platform integrates a large amount of existing heterogeneous data resources from multiple sources, enabling WebGIS visualization of soil environmental quality and pollution, data analysis and graph presentation, thematic mapping and evaluation analysis report generation, and functions for assessing soil environmental quality and determining the causes of agricultural pollution and building areas [1] . By adopting the latest technological architecture such as NodeJS, VueJS and Cesium, the platform achieves efficient data integration, transmission, analysis and visualization, improving the efficiency of analysis, monitoring and control of soil pollution data. The platform uses the single-factor method, the comprehensive Nemerow index method and the analytical hierarchy process as soil pollution assessment methods, combined with coordinated monitoring of heavy metal content in agricultural products, making the assessment results more comprehensive and accurate. This system will help improve soil environmental quality monitoring and pollution control capabilities in Yunnan Province by providing comprehensive and accurate baseline data and scientific decision support to government agencies.

关键词： Prevention and mitigation data visualization Pollution control Metals Soil Cesium Agricultural products Soil pollution Indexes Monitoring

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The Categorical data Map: A Multidimensional Scaling-Based Approach

The Categorical Data Map: A Multidimensional Scaling-Based A...

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ieee visualization in data Science (VDS)

作者： Frederik L. Dennig Lucas Joos Patrick Paetzold Daniela Blumberg Oliver Deussen Daniel A. Keim Maximilian T. Fischer University of Konstanz Germany

ISBN: (数字)9798331528423

ISBN: (纸本)9798331528430

Categorical data does not have an intrinsic definition of distance or order, and therefore, established visualization techniques for categorical data only allow for a set-based or frequency-based analysis, e.g., through Euler diagrams or Parallel Sets, and do not support a similarity-based analysis. We present a novel dimensionality reduction-based visualization for categorical data, which is based on defining the distance of two data items as the number of varying attributes. Our technique enables users to pre-attentively detect groups of similar data items and observe the properties of the projection, such as attributes strongly influencing the embedding. Our prototype visually encodes data properties in an enhanced scatterplot-like visualization, visualizing attributes in the background to show the distribution of categories. In addition, we propose two graph-based measures to quantify the plot’s visual quality, which rank attributes according to their contribution to cluster cohesion. To demonstrate the capabilities of our similarity-based projection method, we compare it to Euler diagrams and Parallel Sets regarding visual scalability and evaluate it quantitatively on seven real-world datasets using a range of common quality measures. Further, we validate the benefits of our approach through an expert study with five data scientists analyzing the Titanic and Mushroom dataset with up to 23 attributes and 8124 category combinations. Our results indicate that our Categorical data Map offers an effective analysis method for large datasets with a high number of category combinations.

关键词： visualization Scalability Layout data visualization Prototypes data science Topology

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The Art of Managing Flaky Tests at Scale

The Art of Managing Flaky Tests at Scale

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ieee International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Oleg Andreyev Okta Inc. USA

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

Test flakiness, characterized by non-deterministic test outcomes despite unchanged code, poses a significant challenge to continuous integration system efficiency and developer productivity. We comprehensively study a year-long initiative to mitigate test flakiness within a large-scale engineering organization. We adjust test failure sensitivity thresholds to account for alert fatigue and resolution quality, refocus data visualization for prioritization and pattern analysis, and introduce incentives to improve the rate of solutions contributing to test stabilization. Key findings reveal the importance of balancing technical efficiency gains with organizational dynamics.

关键词： Software testing Productivity Sensitivity Conferences data visualization Organizations Continuous integration Fatigue Software Pattern analysis

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Tracking the Evolution of Water Flow Patterns Based on Spatio-Temporal Particle Flow Clusters 23

Tracking the Evolution of Water Flow Patterns Based on Spati...

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23rd ieee International Conference on Mobile data Management (ieee MDM)

作者： de Sousa, Nelson Tavares Trahms, Carola Kroeger, Peer Renz, Matthias Schubert, Rene Biastoch, Arne Univ Kiel Dept Comp Sci Kiel Germany GEOMAR Helmholtz Ctr Ocean Res Kiel Kiel Germany Univ Kiel Kiel Germany Univ Brest IUEM Lab Oceanog Phys & Spatiale LOPS CNRSIRDIfremer Brest France

ISBN: (数字)9781665451765

ISBN: (纸本)9781665451765

Marine scientists investigate the movement of oceanic water particles with floating measurement devices released in the real ocean, as well as with virtual particles released in numerical model simulations. The detection, visualization, and evolution of clustered particles is key for gaining a comprehensive understanding of the underlying processes in the oceans. Thereby, vast amounts of mobility data (3D coordinates of these particles over time) need to be analyzed using mobility data science methods. In this paper, we describe the application of data science techniques to detect particle clusters and, more importantly, to track the evolution of these clusters over time in order to support the analysis of oceanic flows. In particular, we apply a well-known concept for tracking the cluster evolution from the data mining community that relies on pair-counting and, thus, is rather inefficient. In order to be applicable to large amounts of particles, we further elaborate two heuristic solutions to compute the cluster transitions based on spatial approximations. Experiments on real world data show a considerable speed-up while sacrificing marginal accuracy drops. Our prototype is used by domain experts for the analysis of the large-scale ocean by virtual particle release experiments in ocean simulations.

关键词： data science clustering cluster evolution spatiotemporal data

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Visual Analytics for Multivariate Time-Series data Using Interactive Dimensionality Reduction Methods

Visual Analytics for Multivariate Time-Series Data Using Int...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Mizuki Emmei Naoki Okami Takanori Fujiwara Naohisa Sakamoto Jorji Nonaka Kobe University Linköping University RIKEN R-CCS

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

One advancing machine-learning-based analysis approach for multivariate time-series data is representing data as a third-order tensor and then applying dimensionality reduction (DR) methods. In this work, we introduce a visual analytics method that employs multiple interactive DR methods to support both extraction and interpretation of latent patterns of multivariate time-series data. Our method first allows analysts to select an analysis focus from three axes: instance, variable, and time axes. Then, the method applies a multi-step DR method to produce a 2D scatterplot that depicts latent patterns of the selected axis’s elements (e.g., time points). Afterward, the analysts interactively investigate data groups that appeared in the plot with a DR method designed for comparative analysis. The method can be further applied iteratively to perform more precise and detailed analyses. We implement a prototype system and demonstrate the effectiveness of our method by analyzing supercomputer log data.

关键词： Dimensionality reduction Tensors Reviews Visual analytics data visualization Prototypes Supercomputers Iterative methods data mining Pattern analysis

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Blue Danube: A large-Scale, End-to-End Synchronous, Distributed data Stream Processing Architecture for Time-Sensitive Applications 26

Blue Danube: A Large-Scale, End-to-End Synchronous, Distribu...

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26th ieee/ACM International symposium on Distributed Simulation and Real Time Applications (DS-RT)

作者： Michael, Panayiotis A. Tsanakas, Panayiotis D. Parker, D. S. Univ Calif Los Angeles Los Angeles CA 90024 USA Natl Tech Univ Athens Athens Greece Natl Tech Univ Athens Elect Engn Comp Engn Dept Athens Greece Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

ISBN: (数字)9781665497992

ISBN: (纸本)9781665497992

An extensive list of time-sensitive applications requiring ultra-low latency ranging from a few microseconds to a few milliseconds are presented in recent publications and ieee standards. Time-sensitive applications, include industrial, critical healthcare and transportation applications as also applications for Smart Grids and the Internet of Vehicles -- one of the most active research fields of Intelligent Transportation Systems of Smart Cities. In this work, we mainly set our focus on the suite of safety applications which attracts strong interest from the research community, as it aims to avoid road accidents and save lives. The ieee Time-Sensitive Networking (TSN) set of standards specifies fundamental real-time characteristics. Nevertheless, as TSN works on data Link layer (Layer 2 of the OSI model) the benefits of these characteristics fade away when other layers are crossed from the Application layer (Layer 7). Indicatively, recent research works report latencies on the order of tens of seconds when benchmarking data Stream Processing and IoT platforms, and thus they are not suited for time-critical applications. Such platforms mainly use loosely coupled components with asynchronous communication. On Application layer, we propose a novel End-to-End Synchronous, Distributed Architecture for large-Scale, High-Bandwidth, Ultra-Low Latency data Stream Processing. Through our Big data Stream analysis experiments (4.7 Gbit/s total average aggregated throughput, 1 Terabyte in-memory distributed database, 4 milliseconds average query latency) we have demonstrated the suitability of our architecture for time-sensitive applications such as accident avoidance for the Internet of Vehicles.

关键词： synchronous data stream processing end-to-end synchronous time-sensitive applications time-critical applications ultra-low latency Internet-of-Things Intelligent Transportation Systems Internet of Vehicles Smart Cities

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analysis of the Effect of Off-chip Memory Access on the Performance of an NPU System 23

Analysis of the Effect of Off-chip Memory Access on the Perf...

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23rd International symposium on Quality Electronic Design (ISQED)

作者： Lee, Keonjoo Kang, Donghyun Kang, Duseok Ha, Soonhoi Seoul Natl Univ 1 Gwanak Ro Seoul South Korea

ISBN: (纸本)9781665494663

Numerous CNN accelerators, called neural processing units (NPUs), have been proposed and developed recently to accelerate CNN computation with a customized chip. To minimize the DRAM access volume, NPUs commonly have a large on-chip memory and try to reuse the fetched data from the off-chip DRAM maximally While extensive researches have been conducted to minimize the effect of off-chip DRAM access on the performance in the NPU design, little attention is paid to the detailed analysis of the off-chip DRAM access overhead on the NPU performance. In this paper, we analyze the effects of off-chip DRAM access latency on the NPU performance and how the off-chip SRAM changes the DRAM access latency based on a cycle-accurate system simulation environment.

关键词： Off-chip memory access NPU performance

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