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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

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Experiences from the first delivery of a new immersive software engineering course: mathematical foundations and data analytics

Experiences from the first delivery of a new immersive softw...

引用

ieee Annual International Computer Software and Applications Conference (COMPSAC)

作者： Florenc Demrozi Marina Marchisio Tiziana Margaria Matteo Sacchet Department of Electrical Engineering and Computer Science University of Stavanger Norway Department of Computer Science and Information Systems University of Limerick Ireland Department of Molecular Biotechnology and Health Sciences University of Turin Italy

According to data from the US Bureau of Labor Statistics, the number of job postings for software engineers has steadily increased over the past few years and is expected to grow by 22% from 2019 to 2029. This paper presents the pedagogical experience within the new Immersive Software Engineering (ISE) program concerning mathematical foundations and data analytics topics. These topics were designed to cover essential mathematical concepts such as calculus, linear algebra, probability, and statistics and their integration within data analytic tools and techniques such as time-series forecasting, data cleaning, data visualization, and introduction to pattern recognition. In addition, hands-on projects and real-world applications were incorporated throughout the course to provide students with practical experience in these areas. We reflect on the first delivery of the ISE course, which provided students with a new innovative blended learning environment, and how it will be further developed towards Open Educational Resources (OER) components and refined to respond to the rapidly evolving needs of the software engineering industry.

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Body Cosmos: An Immersive Experience Driven by Real-Time Bio-data

Body Cosmos: An Immersive Experience Driven by Real-Time Bio...

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ieee VIS Arts Program (VISAP)

作者： Rem RunGu Lin Yongen Ke Kang Zhang Computational Media and Art HongKong University of Science and Technology (Guangzhou) Co-Founder BeFun/Funtheory Studio

This paper presents “Body Cosmos”, an artwork that creates a symbiotic relationship between the human body and a simulated cosmic environment through volumetric rendering and particle system. Drawing from DICOM data to simulate the human body and nebulae, we create an interactive and dynamic virtual environment. The real-time bio-data of users, collected via heart rate sensors and EEG devices, is integrated into the visualization, fostering a personal engagement and unity within this ‘cosmos.’ Body Cosmos provokes curiosity and expands users’ imagination, and deepens their understanding of life’s macrocosm and microcosm. This exploratory project redefines traditional perceptions of the human body in relation to the universe, creating a unique lens to view selfhood, embodiment, and identity. As we look to the future, the system’s evolution will include incorporation of more bio-data sensors, an investigation into its potential psychological and physiological benefits, and the development of social interactive features through multi-user capabilities.

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Acoustic Measurements for Hexagonal Pipe Jet Flow

Acoustic Measurements for Hexagonal Pipe Jet Flow

引用

Engineering Informatics, ieee

作者： Annu Sharma Rajesh Kumar S B T Kannan Department of Aerospace Engineering College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338539

The research paper aims to understand the acoustic behavior of hexagonal pipe jet flow. The length to diameter ratio of pipe is 6. The circular equivalent diameter (D e ) is 5 mm. The Nozzle Pressure Ratio is from 2 to 6. The emission angles (ϕ) considered are 60° and 90° measured from downstream direction (jet axis). The Sound Pressure Level (SPL) and Over All Sound Pressure Level (OASPL) are compared for the two emission angles. The 90° emission angle has more harmonic peaks comparatively. The number of screech harmonics increases as NPR increases from 4 to 6. The maximum OASPL difference among the emission angles are also made and it is found to decrease with increase in NPR. The Schlieren flow visualization is done to support the acoustic data qualitatively.

关键词： Electric shock data visualization Harmonic analysis Acoustic measurements Acoustics Informatics

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Research on the Application of Intelligent Management Accounting Platform

Research on the Application of Intelligent Management Accoun...

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International Conference on Big data and Artificial Intelligence (BDAI)

作者： Yong Wen Department of finance Guangdong University of Science and Technology Dongguan China Nueva Ecija University of Science and Technology Graduate School Cabanatuan Philippines

5G technology, RPA technology, intelligent recognition technology, machine learning technology, business intelligence and other technologies have led to the birth of intelligent management accounting. The intelligent management accounting platform highly integrates business intelligence, artificial intelligence, and management accounting, forming a new management accounting model. This article constructs an intelligent management accounting platform, explores the architecture of the intelligent management accounting platform, analyzes the processing process of the intelligent management accounting platform, summarizes the main functions of the intelligent management accounting platform, and takes Guangdong JL Company as an example to introduce the application of the company’s intelligent management accounting platform. Practice has proven that the intelligent management accounting platform has achieved automation, visualization, and intelligence in management accounting, improved management decision-making efficiency, and provided support for enterprise risk prevention.

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Predictive Analytics System for Stock data: methodology, data pre-processing and case studies

Predictive Analytics System for Stock Data: methodology, dat...

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Computer Applications (ICCA), ieee Conference on

作者： Khin Nyein Myint Yi Yi Hlaing FCS Department UCSM Myanmar

Predictive analytics system is a pillar of science for forecasting future trends in vast amount of data. The aim of this research is to make decision buying or selling trend the stocks based on the calculated value of Moving Average (MA) method and Long Short Term Memory (LSTM) model for historical data. This paper presents a big data analytics framework which contains data collection, pre-processing and describe a correlation between various stock market datasets. Moreover, the proposed architecture overcomes multiple challenges including extracting large amount of data, collecting, transformation, storing, analytics and visualization in stock data using machine learning tools, tensor flow and pyspark framework. The system achieves the prediction accuracy with 95.82 % by using LSTM model and it also gives the correlation 54% between Monarch staffing (MSTF) stock and Alphabet (GOOG) stocks by using MA method. However, this work establishes a fundamental concepts for students, researchers and industrial practitioners on how to analysis and predictive stock data movement using technical analysis and neural network in trading algorithm.

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Towards sensor agnostic artificial intelligence for underwater imagery

Towards sensor agnostic artificial intelligence for underwat...

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International Symposium on Underwater Technology

作者： Miquel Massot-Campos Takaki Yamada Blair Thornton Centre for In Situ and Remote Intelligent Sensing Faculty of Engineering and Physical Science University of Southampton Southampton United Kingdom Institute of Industrial Science The University of Tokyo Tokyo Japan

Underwater images in global datasets are gathered by different cameras and under different lighting and altitude conditions. Image formation models can potentially compensate for these known differences and improve machine learning (ML) performance. In this paper we will investigate how ML trained on images from one system can classify images taken by another. We will train two ML classification models based on two different underwater camera systems. We are going to evaluate the performance of each model with data from both platforms and will demonstrate the improvement of using image formation models to make an image look-like it has been gathered with a different system and how ML performance is affected.

关键词： visualization Satellites Lighting Machine learning Cameras Attenuation data models

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Deep Learning Assisted Cyber Criminal Profiling

Deep Learning Assisted Cyber Criminal Profiling

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International Conference on Big data and Artificial Intelligence (BDAI)

作者： Biodoumoye George Bokolo Lei Chen Qingzhong Liu Dept. of Computer Science Sam Houston State University Huntsville Texas USA Dept. of Info. Tech. Georgia Southern University Statesboro Georgia

This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution.

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iTrace-Visualize: Visualizing Eye-Tracking data for Software Engineering Studies

iTrace-Visualize: Visualizing Eye-Tracking Data for Software...

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ieee International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT)

作者： Joshua Behler Gino Chiudioni Alex Ely Julia Pangonis Bonita Sharif Jonathan I. Maletic Department of Computer Science Kent State University Kent Ohio USA School of Computing University of Nebraska-Lincoln Lincoln Nebraska USA

iTrace is community infrastructure that allows software engineering researchers to conduct eye-tracking studies on large realistic code bases. The iTrace infrastructure consists of a set of tools that assist with gathering, processing, and evaluating eye-tracking data on large software projects within an Integrated Development Environment (IDE). A typical eye-tracking study results in millions of raw gazes that are overwhelming to view and sort through. To help researchers view and comprehend this data, iTrace-Visualize is presented. This tool integrates information produced by the iTrace infrastructure into a dynamic video recording of the eye-tracking session. Eye fixations and the scan path between fixations are overlayed on the video. Additionally, the line being examined can be highlighted in the video. iTrace-Visualize lets a researcher replay eye fixations via a video overlay immediately after a study. This serves as quick validation of what was done during the study and can also provide quick insights into what the participants looked at. To illustrate iTrace-Visualize's capabilities, a small preliminary study is performed. Demo Video-https://***/c1hUFDmBM50

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Recommendation System for Journals based on ELMo and Deep Learning

Recommendation System for Journals based on ELMo and Deep Le...

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Swiss Conference on data science (SDS)

作者： Mahmoud Hemila Heiko Rölke ETH Library ETH Zurich Zurich Switzerland Center for Data Analysis Visualization and Simulation (DAViS) University of Applied Sciences of the Grisons Chur Switzerland

Choosing the right journal to publish research studies is critical for researchers. Despite its importance, the task of determining the suitable and high-ranking journal for publishing can be challenging due to several factors such as the growing number of the available journals and the fact that every journal has its specific area of expertise. In this paper we investigate content-based journal recommendation systems that rely on using NLP to analyze features of existing journals and use those to pre-select a particular number of suitable journals for a new paper. Our experiments are based on the ELMo feature engineering mechanism and use different deep learning neural network architectures (CNN, RNN). We used datasets from the disciplines physics, chemistry and biology, with each containing data of more than 750000 publications. The data source consists of the abstracts of the papers. The experiments show promising results with the accuracy of our models outperforming existing models. Specffically, our RNN model can achieve 83% accuracy when using data from physics by considering top-20 journals.

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Indy Survey Tool: A Framework to Unearth Correlations in Survey data

Indy Survey Tool: A Framework to Unearth Correlations in Sur...

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International Conference on Visualisation, VIS

作者： Tarik Crnovrsanin Sara Di Bartolomeo Connor Wilson Cody Dunne Northeastern University

Survey companion websites allow users to explore collected survey information more deeply, as well as update or add entries for papers. These sites can help information stay relevant past the original release date of the survey paper. However, creating and maintaining a website can be laborious and difficult, especially when authors might not be experienced with programming. We introduce Indy Survey Tool to help authors develop companion websites for survey papers across diverse fields of study. The tool’s core aim is to identify correlations between categorizations of papers. To accomplish this, the tool offers multiple combined filters and correlation matrix visualizations that enable users to explore the data from diverse perspectives. The tool’s visualizations, list of papers, and filters are harmoniously integrated and highly responsive, providing users with feedback based on their selections. Identifying correlations in survey papers is a pivotal aspect of research, as it can enable the recognition of common combinations of categorizations within the papers—as well as highlight any omissions. The versatility of Indy Survey Tool enables researchers to delve into the correlations between categorizations in survey data, an essential aspect of research that can reveal gaps in the literature and highlight promising areas for future exploration. A preprint and supplemental material for the paper can be found at ***/tdhqn.

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