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Teaching innovation results based on double helix teaching strategy and ADDIE model- Take the Big data and visualization course for example

Teaching innovation results based on double helix teaching s...

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

作者： Jia Luo Mingming Li Guiyang College of Humanities and Technology&Information Engineering Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

"Big data analysis and visualization" course focuses on innovative teaching concepts and content, integrating key skills of data science and ideological and political education. This course not only focuses on teaching professional knowledge of big data processing, analysis and visualization, but also emphasizes the importance of ideological and political education to cultivate students' social responsibility and ethical concepts in the field of data science. According to the teaching pain points of students' weak data thinking ability, lack of practical project training and ideological and political thinking in this course, this paper proposes to reconstruct the "internal spectrum" and "external spectrum" of course design based on the double helix teaching strategy and ADDIE teaching mode, and the teaching objectives of the course clearly show its content structure and its connection with other fields of data science. Guided by the needs of enterprise talents, with the purpose of applying what we have learned and scenario-based case teaching, we will raise our teaching objectives and thinking to a higher level. Through online and offline mixed teaching, we will conduct reality training, practical projects and practical tests to create a composite, application-oriented and innovative talent training model that Adapts to the new situation and has the latest thinking and skills. Through practical teaching and case studies, students' engineering practice innovation ability has been significantly improved, and they have achieved excellent results in various national and provincial data science competitions, demonstrating the remarkable effect of the course in cultivating students' synthesis ability,Cultivate data science application talents with innovative thinking and cross-border integration.

关键词： Training Technological innovation Analytical models Pain Education Employment data visualization data science Big data data models

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MuchiSim: A Simulation Framework for Design Exploration of Multi-Chip Manycore Systems

MuchiSim: A Simulation Framework for Design Exploration of M...

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ieee International symposium on Performance analysis of Systems and Software

作者： Marcelo Orenes-Vera Esin Tureci Margaret Martonosi David Wentzlaff Princeton University

ISBN: (数字)9798350376388

ISBN: (纸本)9798350376395

The design space exploration of scaled-out manycores for communication-intensive applications (e.g., graph analytics and sparse linear algebra) is hampered due to either lack of scalability or accuracy of existing frameworks at simulating data-dependent execution patterns. This paper presents MuchiSim, a novel parallel simulator designed to address these challenges when exploring the design space of distributed multi-chiplet manycore architectures. We evaluate MuchiSim at simulating systems with up to a million interconnected processing units (PUs) while modeling data movement and communication cycle by cycle. In addition to performance, MuchiSim reports the energy, area, and cost of the simulated system. It also comes with a benchmark application suite and two data visualization tools. MuchiSim supports various parallelization strategies and communication primitives such as task-based parallelization and message passing, making it highly relevant for architectures with software-managed coherence and distributed memory. Via a case study, we show that MuchiSim helps users explore the balance between memory and computation units and the constraints related to chiplet integration and inter-chip communication. MuchiSim enables evaluating new techniques or design parameters for systems at scales that are more realistic for modern parallel systems, opening the gate for further research in this area.

关键词： Measurement Scalability Linear algebra Tutorials Throughput data models Software

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Exploring Demographic Variations of Freshmen to Online Learning Anxiety: A data visualization analysis Based Approach

Exploring Demographic Variations of Freshmen to Online Learn...

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2021 International Research symposium On Advanced Engineering and Vocational Education, IRSAEVE 2021

作者： Citra, Kurniawan Wahyuni, Fitri Universitas Negeri Malang Faculty of Education Malang Indonesia

ISBN: (纸本)9781665442237

Digital developments have an impact on the ability to read and construct visual data as important as how someone reads a text. The presentation of data was of course very important in the learning process. New experiences in the learning process provide the potential for freshmen anxiety. The existence of variance in demographic data as an attribute of freshmen characteristics can be a consideration for the measurement results of online learning anxiety. This research aims to explore data on freshmens' anxiety in dealing with online learning. This research uses data visualization analysis methods and descriptive statistics to reinforce the meaning of the data presented. This research involved 384 freshmen consisting of female freshmen (n = 270) and male freshmen (n = 114). The finding of this research was that gender variance does not provide a significant correlation with the maj ority of aspects of online learning anxiety. Whereas in the study program variance, different findings were obtained where the variance of the study program influenced the major aspects of online learning anxiety. Overall, this research re-emphasizes attention to anxiety factors in online learning so that freshmen can prepare themselves better. © 2021 ieee.

关键词： data visualization

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Navigating Color Constraints in Multi-View visualizations with MVcolor

Navigating Color Constraints in Multi-View Visualizations wi...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Yun-Rou Lin Fu-Yin Cherng Yi-Chieh Lee Zhu-Ying Tian Wen-Chieh Lin National Yang Ming Chiao Tung University National Chung Cheng University National University of Singapore

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Multi-view visualizations have gained prominence for their ability to simultaneously present multiple perspectives in a single display, aiding users in making informed decisions. Although several tools have been developed to facilitate the design of multi-view visualizations, there is a lack of support for effective color design in these systems. Thoughtful color design can enhance readability and provide cues, while careless color choices may lead to confusion. Moreover, human short-term memory imposes constraints on the number of colors that can be effectively employed in a multi-view visualization. To address these challenges, we introduce MVcolor, a color encoding recommendation system designed to maintain color encoding consistency and ensure adequate color discriminability within the constraints of human short-term memory. Our approach employs a unified color scheme across the entire multi-view visualization and groups views and visual objects based on their visual and semantic similarities. We conducted user studies to evaluate the effectiveness of our system and demonstrate its ability to improve color encoding in multi-view visualizations.

关键词： Human computer interaction visualization Image color analysis Navigation Semantics Memory management data visualization Encoding User experience Recommender systems

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Application of LLM Techniques for data Insights in DHP

Application of LLM Techniques for Data Insights in DHP

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Digital Twins and Parallel Intelligence (DTPI), ieee International Conference on

作者： Jun Luo Chao Ouyang Yinggang Jing Huajian Fang Yunlong Xiao Qing Zhang Rongjun Li China Nuclear Power Operation Technology Corporation Ltd. Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

With the rapid development of China's nuclear power industry, data insights play an increasingly crucial role in enhancing the safety and operational efficiency of nuclear power plants. Traditional data analysis methods face challenges in handling massive, complex, and heterogeneous industrial data from multiple sources. The emergence of LLM (large language model) offers new solutions for industrial data insights. This article proposes a digital intelligent platform architecture for nuclear power LLM and thoroughly explores its applications in the nuclear power industry. The architecture includes data layer, model layer, analysis layer, and application layer, responsible respectively for data collection, storage, processing, analysis, and application. Finally, based on the architecture proposed in this article, prototypes for applications such as nuclear power industry statement generation and intelligent analysis assistant for patrol inspection data were developed, with a view towards future development directions.

关键词： data analysis Decision making data visualization Inspection data collection Power industry Vectors data models Visual databases Power generation

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Exploring Transparency and AI Assessment in LLM-Assisted Research Applications

Exploring Transparency and AI Assessment in LLM-Assisted Res...

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2025 ieee SoutheastCon, SoutheastCon 2025

作者： Bacon, Greg Menon, Vineetha The University of Alabama in Huntsville Dept. of Computer Science HuntsvilleAL United States

ISBN: (纸本)9798331504847

In this work, we further investigate the utility of using a large language model as a research assistant to identify research grant funding opportunities that are best suited for a user-defined natural language set of capabilities. The use case is a United States Department of Defense Small Business Innovation Research Broad Agency Announcement. To explore principles of responsible/ethical artificial intelligence and accountability in the context of large language model-driven applications, we perform clustering on embeddings and apply a suite of metrics to compare cluster quality against Latent Semantic Indexing. Further, we use visualization techniques to depict the contents of funding opportunities that lie at the intersection of multiple capabilities. Finally, we show the importance of maintaining the human in the loop for vetted data quality. © 2025 ieee.

关键词： Cluster analysis

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An fMRI-Based Centrality analysis of Brain Connectivity in Autism Spectrum Disorder

An fMRI-Based Centrality Analysis of Brain Connectivity in A...

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ieee Region 10 symposium

作者： Nayana Uday Judy K George Elizabeth Sherly SoCSE Digital University Kerala Thiruvananthapuram Kerala India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Recent studies have probed Autism Spectral Disorder's (ASD's) neural substrates, analyzing brain connectivity patterns through techniques like centrality using the resting-state functional Magnetic Resonance imaging (rs-fMRI) data, revealing deficits in social communication, repetitive behaviors, and sensory processing. This research aims to observe alterations in information flow and communication networks by assessing the centrality of key brain regions, distinguishing individuals with ASD from neurotypical controls. Evaluating centrality measures' accuracy using a Graph Attention Network (GAT) model classifier and model interpretability with the attention score visualization unveils significant contributors to the model's performance, providing insights into ASD's neural mechanisms and enhancing our understanding of brain connectivity in this disorder.

关键词： Measurement Neuroimaging Autism Accuracy Organizations Machine learning Functional magnetic resonance imaging Brain modeling Substrates ieee Regions

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What We Talk About When We Talk About Logs: Understanding the Effects of dataset Quality on Endpoint Threat Detection Research

What We Talk About When We Talk About Logs: Understanding th...

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ieee symposium on Security and Privacy

作者： Jason Liu Muhammad Adil Inam Akul Goyal Andy Riddle Kim Westfall Adam Bates Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois USA

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Endpoint threat detection research hinges on the availability of worthwhile evaluation benchmarks, but experimenters' understanding of the contents of benchmark datasets is often limited. Typically, attention is only paid to the realism of attack behaviors, which comprises only a small percentage of the audit logs in the dataset, while other characteristics of the data are inscrutable and unknown. We propose a new set of questions for what to talk about when we talk about logs (i.e., datasets): What activities are in the dataset? We introduce a novel visualization that succinctly represents the totality of 100+ GB datasets by plotting the occurrence of provenance graph neighborhoods in a time series. How synthetic is the background activity? We perform autocorrelation analysis of provenance neighborhoods in the training split to identify process behaviors that occur at predictable intervals in the test split. Finally, How conspicuous is the malicious activity? We quantify the proportion of attack behaviors that are observed as benign neighborhoods in the training split as compared to previously-unseen attack neighborhoods. We then validate these questions by profiling the classification performance of state-of-the-art intrusion detection systems (R-CAID, FLASH, KAIROS, GNN) against a battery of public benchmark datasets (DARPA Transparent Computing and OpTC, ATLAS, ATLASv2). We demonstrate that synthetic background activities dramatically inflate True Negative Rates, while conspicuous malicious activities artificially boost True Positive Rates. Further, by explicitly controlling for these factors, we provide a more holistic picture of classifier performance. This work will elevate the dialogue surrounding threat detection datasets and will increase the rigor of threat detection experiments.

关键词： Training Privacy Time series analysis Intrusion detection data visualization Benchmark testing Fasteners Threat assessment Batteries Security

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A Visual Analytics Framework for Reviewing Streaming Performance data 13

A Visual Analytics Framework for Reviewing Streaming Perform...

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ieee Pacific visualization symposium (PacificVis)

作者： Kesavan, Suraj P. Fujiwara, Takanori Li, Jianping Kelvin Ross, Caitlin Mubarak, Misbah Carothers, Christopher D. Ross, Robert B. Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Rensselaer Polytech Inst Troy NY 12181 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (数字)9781728156972

ISBN: (纸本)9781728156972

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

关键词： Human-centered computing visualization visualization application domains Visual analytics

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IMAGING COMPLEX FAULT SLIP OF large EARTHQUAKES WITH SENTINEL-1 AND ALOS-2 SAR analysis AND OTHER GEODETIC AND SEISMIC data

IMAGING COMPLEX FAULT SLIP OF LARGE EARTHQUAKES WITH SENTINE...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Fielding, Eric J. Liang, Cunren Huang, Mong-Han Liu, Zhen Ragon, Thea Bekaert, David Simons, Mark CALTECH Jet Prop Lab Pasadena CA 91125 USA CALTECH Seismol Lab Pasadena CA 91125 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665403696

We study the distribution of slip on faults during earthquakes with integrated analysis of geodetic imaging and seismic data to learn about parameters that control how faults slip and potentially how damaging future earthquakes may be. We mapped complex fault ruptures for a number of large earthquakes in 2015-2020 using analysis of synthetic aperture radar (SAR) data from the Copernicus Sentinel-1A and Sentinel-1B satellites operated by the European Space Agency and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by the Japan Aerospace Exploration Agency (JAXA). We used regular SAR interferometry, along-track or multiple-aperture interferometry, and pixel offset tracking to measure surface displacements and combined this with other geodetic and seismic data to infer slip on faults at depth.

关键词： Earthquakes SAR interferometry pixel offsets fault slip

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