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检索条件"任意字段=14th Symposium on Large Data Analysis and Visualization"

共 540 条记录，以下是411-420 订阅

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Method of Constructing a visualization of threat Model of Information Security

Method of Constructing a Visualization of Threat Model of In...

引用

International Conference on Application of Information and Communication Technologies (AICT)

作者： A.V. Manzhosov I.P. Bolodurina Orenburg State University Orenburg Russia

ISBN: (数字)9781728173863

ISBN: (纸本)9781728173870

One of the most pressing tasks in the field of information security (IS) is the reduction of data processing time. Accurate and detailed data visualization optimizes the analysis process. the article proposes a method for visualizing the IS threat model, which reduce the amount of time to process the IS threat model. the classic way to display the IS threat model is a table. Processing the classical model of IS threats is quite laborious and takes a long time for IS analysts. In addition, there remains the risk of missing part of the information due to the large amount of data. the visualization method proposed in the article includes a two- and three-dimensional representation of the threat model. Such visualization reduces the data processing time for the analyst and can be applied in the field of information security management. the result of this work is an approach to visualization of the IS threat model that takes into account the spatio-temporal factor.

关键词： Analytical models data visualization Information security Tools data processing Acceleration Task analysis

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Axisymmetric structural simulation of the central solenoid for the Burning Plasma Experiment (BPX)

Axisymmetric structural simulation of the central solenoid f...

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IEEE/NPSS symposium on Fusion Engineering

作者： R.L. Myatt Massachusetts Institute of Technology Cambridge MA USA

the structural design and analysis of the BPX central solenoid are presented. the large-scale structural behavior of the solenoid is simulated with a 2D axisymmetric ANSYS finite element model. the computer model is used to determine the time-dependent stress history in the coils and tie rods, and to perform various structural performance studies. Some of these studies are included, such as tie-rod preload optimization, relative movement at low-friction surfaces and alternate material and transient cooldown effects. the results of these simulations are presented and discussed. A fatigue analysis of the conductor stresses is presented based on the BPX design criteria and preliminary material property data. the relationships between the 2D solenoid model and more detailed 3D models of subcomponents are also discussed.< >

关键词： Solenoids Computational modeling Stress Conducting materials large-scale systems Finite element methods History Coils Structural rods Fatigue

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Enhancing mobile device security with process mining

Enhancing mobile device security with process mining

引用

International symposium on Intelligent Systems and Informatics (SISY)

作者： Ladislav Hluchý Ondrej Habala Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia

We present part of a research project aimed at mobile device security. During the project we have tested several methods of processing of data from mobile devices in order to detect attacks and suspicious activity in general. One method we have tested was also mining of processes found in mobile device activity logs and analysis of those processes. We have used several devices with the Android TM OS, collected a large amount of activity data, including (simulated) harmful activity, and then modeled processes found in the data. Results of the comparison of these processes to the harmful activity suggest that this application of process mining shows promise, but also requires a lot of further research.

关键词： data mining Mobile handsets Security Performance evaluation Business Petri nets Browsers

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Web-based visualization and Analytics of Petascale data: Equity as a Tide that Lifts All Boats

Web-based Visualization and Analytics of Petascale data: Equ...

引用

IEEE symposium on large data analysis and visualization (LDAV)

作者： Aashish Panta Xuan Huang Nina McCurdy David Ellsworth Amy A. Gooch Giorgio Scorzelli Hector Torres Patrice Klein Gustavo A. Ovando-Montejo Valerio Pascucci University of Utah NASA Ames Research Center ViSOAR LLC NASA Jet Propulsion Lab Caltech Utah State University Blanding

ISBN: (数字)9798331516925

ISBN: (纸本)9798331516932

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. this paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We also optimize network utilization while streaming from petas-cale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any internet connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA's use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. these dashboards, released in simpl

关键词： Training Cloud computing visualization Prevention and mitigation NASA data visualization Boats Supercomputers Fabrics Tides

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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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Efficient visualization of encoded Fourier transform infrared microscopic data of osteoporotic bone

Efficient visualization of encoded Fourier transform infrare...

引用

Annual IEEE symposium on Computer-Based Medical Systems

作者： G. Zamora S. Yang S. Mitra M. Peterson E.P. Paschalis Department of Electrical and Computer Engineering Texas Tech University Lubbock Texas USA The Hospital for Special Surgery New York NY USA Hospital for Special Surgery New York NY USA

Fourier transform IR microspectroscopy (FTIRM) of bone slices has become a major research tool to analyze the chemical constitution of calcified structures. Qualitative as well quantitative measures of bone components using this technique have been shown to be useful in the characterization of normal bone in both animal and human specimens. Furthermore, the technique can be used to characterize bone diseases like osteoporosis and the effects of different chemical constitutions on bone structures. However, the ever-increasing number of FTIRM studies is yielding an amount of data that is becoming unmanageable in terms of size for the purpose of storage. We address this problem by applying two different lossless coding techniques to the images generated by the studies. Compression ratios as high as 100:1 are obtained by coding the lower bit levels of the image while retaining the information that is used by the specialists.

关键词： visualization Fourier transforms Image coding Chemical analysis Constitution Animal structures Humans Bone diseases Osteoporosis Image generation

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Big data Framework for Analyzing Patents to Support Strategic R&D Planning

Big Data Framework for Analyzing Patents to Support Strategi...

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IEEE International symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Wonchul Seo Namhyoung Kim Sungchul Choi Division of Systems Management and Engineering Pukyong National University Busan Republic of Korea Department of Industrial and Management Engineering Gachon University Republic of Korea

Patents are important technical documents that are widely used to support strategic research and development (R&D) planning. With the rapid evolution of Information Technology (IT), many researchers have attempted to analyze patent data using IT-based methods such as data mining. More recently, advancements in big data technologies have increased their suitability for analyzing large-scale patent data. In this paper, we propose a big data framework to process and analyze large-scale patent data. the proposed framework consists of four layers: an aggregator layer, a storage layer, an analysis layer, and an application layer. these layers are designed to collect patent data, store the collected data, analyze the data, and present the results. the primary objectives of the proposed framework are to provide a patent analysis service platform based on big data technologies, and to support strategic R&D planning for organizations. To demonstrate the feasibility of the proposed approach, we implement a Hadoop cluster based on the proposed framework and conduct a case study to analyze patents in the Quantum Dot technology field.

关键词： Patents Big data Planning Distributed databases Text mining

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EMC Field analysis in the Vicinity of a large Array Antenna

EMC Field Analysis in the Vicinity of a Large Array Antenna

引用

14th International Zurich symposium and Technical Exposition on Electromagnetic Compatibility

作者： Harm-Friedrich Harms thyssen Nordseewerke Emden Germany

ISBN: (纸本)9783952119952

In naval shipbuilding modern high power radar systems with phased array antennas are used for different tasks. To improve the performance, especially the range of the radar systems, the pulse power of new development systems has been increasing for many years. Nevertheless the ship yard has to provide personal and ammunition safety against electromagnetic radiation as well as the Electromagnetic Compatibility (EMC) for the above deck equipment. Field analysis has to be done during definition phase and building phase of the ship to estimate the field density in the above deck region. Usually less data can be found in the specification of the antenna system to do an exact field analysis. Especially this is true if the radar antenna system is still under development. Far field calculations can be done easily. But calculations in the near field of a large phased array antenna are much harder to do.

关键词： Antenna arrays Antenna measurements

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Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images 38

Point Transformer with Federated Learning for Predicting Bre...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Li, Bao Liu, Zhenyu Shao, Lizhi Qiu, Bensheng Bu, Hong Tian, Jie Univ Sci & Technol China Ctr Biomed Imaging Hefei Peoples R China Chinese Acad Sci Beijing Key Lab Mol Imaging Inst Automat CAS Key Lab Mol Imaging Beijing Peoples R China Sichuan Univ West China Hosp Dept Pathol Chengdu Peoples R China Beihang Univ Sch Engn Med Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing Peoples R China

ISBN: (纸本)1577358872

Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables collaborative training of these WSIs without gigabyte-size WSIs transportation and data privacy concerns. However, federated learning encounters challenges in addressing label imbalance in multi-site WSIs from the real world. Moreover, existing WSI classification methods cannot simultaneously exploit local context information and long-range dependencies in the site-end feature representation of federated learning. To address these issues, we present a point transformer with federated learning for multi-site HER2 status prediction from HE-stained WSIs. Our approach incorporates two novel designs. We propose a dynamic label distribution strategy and an auxiliary classifier, which helps to establish a well-initialized model and mitigate label distribution variations across sites. Additionally, we propose a farthest cosine sampling based on cosine distance. It can sample the most distinctive features and capture the long-range dependencies. Extensive experiments and analysis show that our method achieves state-of-the-art performance at four sites with a total of 2687 WSIs. Furthermore, we demonstrate that our model can generalize to two unseen sites with 229 WSIs. Code is available at: https://***/boyden/PointTransformerFL

关键词： data privacy

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CellHeap: A Workflow for Optimizing COVID-19 Single-Cell RNA-Seq data Processing in the Santos Dumont Supercomputer 14th

CellHeap: A Workflow for Optimizing COVID-19 Single-Cell RNA...

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14th Brazilian symposium on Bioinformatics (BSB)

作者： Silva, Vanessa S. Costa, Maiana O. C. Castro, Maria Clicia S. Silva, Helena S. Walter, Maria Emilia M. T. Melo, Alba C. M. A. Ocana, Kary A. C. dos Santos, Marcelo T. Nicolas, Marisa F. Carvalho, Anna Cristina C. Henriques-Pons, Andrea Silva, Fabricio A. B. Fundacao Oswaldo Cruz Rio De Janeiro Brazil Natl Lab Sci Comp Petropolis RJ Brazil Univ Brasilia Brasilia DF Brazil Univ Estado Rio De Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9783030918132;9783030918149

Currently, several hundreds of Terabytes of COVID-19 single-cell RNA-seq (scRNA-seq) data are available in public repositories. this data refers to multiple tissues, comorbidities, and conditions. We expect this trend to continue, and it is realistic to predict amounts of COVID-19 scRNA-seq data increasing to several Petabytes in the coming years. However, thoughtful analysis of this data requires large-scale computing infrastructures, and software systems optimized for such platforms to generate biological knowledge. this paper presents CellHeap, a portable and robust workflow for scRNA-seq customizable analyses, with quality control throughout the execution steps and deployable on supercomputers. Furthermore, we present the deployment of CellHeap in the Santos Dumont supercomputer for analyzing COVID-19 scRNA-seq datasets, and discuss a case study that processed dozens of Terabytes of COVID-19 scRNA-seq raw data.

关键词： Single-cell RNA-seq Bioinformatics workflow COVID-19 High-performance computing

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