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Building Robust Information Systems For Remote Sensing data Management

Building Robust Information Systems For Remote Sensing Data ...

International Scientific Conference on Information Technology and Management science of Riga Technical University (ITMS)

作者： P.A. Osipovs M. Fraimans D. Bliznuks M. Kovalovs Riga Technical University Institute of Smart Computer Technologies Riga Latvia

The article describes the experience of building an information system for collecting and processing data from remote sensors. The specificity of the organization of a local system that provides storage, visualization [1] and processing of data from a certain set of external sensors is considered. Both the general architecture of the system and its modules are described, which provide a local graphical user interface, asynchronous data collection, visualization, notifications about reaching unacceptable values by the sensor, and processing of sensor data. The structure of interaction of the system with an external WEB-site through the implementation of the REST API [2] is shown. The issues of software emulation of sensors and risk management were also touched upon.

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An Empirical Guide for visualization Consistency in Multiple Coordinated Views

An Empirical Guide for Visualization Consistency in Multiple...

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Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Shaocong Tan Chufan Lai Xiaolong Luke Zhang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China College of Information Sciences and Technology Pennsylvania State University USA National Engineering Laboratory for Big Data Analysis and Application Peking University China

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

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System Construction of Artificial Intelligence Dependent Cloud Service Mode based on Deep Learning Core Algorithms

System Construction of Artificial Intelligence Dependent Clo...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Wu, Yanhong Zhanjiang University of Science and Technology School of Accounting Zhanjiang China Ucsi University Ucsi Graduate Business School Kuala Lumpur Malaysia

ISBN: (纸本)9798350360660

In recent years, with the rapid development of computer technology, artificial intelligence, an important branch of the discipline, has been undergoing constant changes. The essence of artificial intelligence is the use of computer technology to simulate the human way of thinking and a series of intelligent behaviors, such as co-ordination, thinking, reasoning and so on. In intelligent robots, AI (artificial intelligence) language processing systems, image recognition systems and various types of expert systems, complex computer algorithms are used to simulate certain logical reasoning of the human brain, so as to achieve the intelligence of equipment. Against the background of global economic integration and accelerated informationization, artificial intelligence technology is developing rapidly. This paper presents a distributed model cloud platform based on Kubernetes architecture for data transfer, distributed concurrent multitasking and functional business requirements through virtualization and model componentization techniques. The platform adopts a three-tier structure of IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service), and combines with ArcGIS and modern Web technologies to realize efficient visualization display. We evaluate the performance of AWS, Google Cloud, Azure, and IBM Cloud in terms of deep learning model training and inference performance, and the experimental results show that Google Cloud and Azure are the best performers in terms of training time and accuracy as well as inference speed, while AWS and IBM Cloud excel in terms of scalability and security. Ultimately, this paper provides an important reference for organizations when choosing a cloud service provider to support deep learning applications. © 2024 ieee.

关键词： Cloud platforms

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Using Extended Reality Technology in science Education

Using Extended Reality Technology in Science Education

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ieee Long Island Systems, Applications and Technology Conference (LISAT)

作者： Iman Cumberbatch James Olatunji Stefan A. Robila School of Computing Montclair State University Montclair NJ 07043

Across all scientific fields, the amount of data generated continues to grow exponentially, which creates both opportunities and challenges. Increased availability allows for new insights into phenomena and stimulates discovery. While traditional ways of interacting with data have focused mostly on flat displays, Extended Reality (XR) technologies have the potential to transform the way both research and education are done. They provide an immersive and interactive experience that allows participants to visualize and interact with data while also working collaboratively. In this paper, the research and development of immersive educational 3D visualization tools aligned with several science drivers including environmental science and sustainability and life sciences is described. Specifically, the development of two XR applications is discussed with emphasis on the innovative mechanisms introduced using head-mounted display technologies. Preliminary results from the prototype applications indicate that the use of the XR technologies significantly expands the visualization ability by integrating additional data sources and improving the user’s experience.

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Creating Tangible VR Spaces for Exploring Algorithm Complexity and data Structures

Creating Tangible VR Spaces for Exploring Algorithm Complexi...

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Frontiers in Education (FIE) Conference

作者： Nathan Green School of Technology and Innovation Marymount University Arlington VA USA

This research explores the use of virtual reality (VR) in Computer science education to enhance the visualization and understanding of algorithm complexity and data structures. The proposed VR system aims to provide undergraduate students with a more tangible and interactive learning experience, allowing them to interact with algorithms and data structures in a virtual environment. Through the implementation of immersive techniques and real-time visualization, students can manipulate and observe the behavior of these abstract concepts. The VR application covers topics such as algorithm analysis, queues, stacks, tree structures, and algorithmic complexity. The use of VR in Computer science education has the potential to transform teaching methods and provide instructors with valuable supplementary materials. Future studies will evaluate the impact of VR on student learning outcomes, and the VR application will be made available for further research and instruction in the field.

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Counterfactual Samples Synthesizing and Training for Robust Visual Question Answering

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13218-13234页

作者： Chen, Long Zheng, Yuhang Niu, Yulei Zhang, Hanwang Xiao, Jun Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

Today's VQA models still tend to capture superficial linguistic correlations in the training set and fail to generalize to the test set with different QA distributions. To reduce these language biases, recent VQA works introduce an auxiliary question-only model to regularize the training of targeted VQA model, and achieve dominating performance on diagnostic benchmarks for out-of-distribution testing. However, due to the complex model design, ensemble-based methods are unable to equip themselves with two indispensable characteristics of an ideal VQA model: 1) Visual-explainable: The model should rely on the right visual regions when making decisions. 2) Question-sensitive: The model should be sensitive to the linguistic variations in questions. To this end, we propose a novel model-agnostic Counterfactual Samples Synthesizing and Training (CSST) strategy. After training with CSST, VQA models are forced to focus on all critical objects and words, which significantly improves both visual-explainable and question-sensitive abilities. Specifically, CSST is composed of two parts: Counterfactual Samples Synthesizing (CSS) and Counterfactual Samples Training (CST). CSS generates counterfactual samples by carefully masking critical objects in images or words in questions and assigning pseudo ground-truth answers. CST not only trains the VQA models with both complementary samples to predict respective ground-truth answers, but also urges the VQA models to further distinguish the original samples and superficially similar counterfactual ones. To facilitate the CST training, we propose two variants of supervised contrastive loss for VQA, and design an effective positive and negative sample selection mechanism based on CSS. Extensive experiments have shown the effectiveness of CSST. Particularly, by building on top of model LMH+SAR (Clark et al. 2019), (Si et al. 2021), we achieve record-breaking performance on all out-of-distribution benchmarks (e.g., VQA-CP v2, VQA-CP v1, and

关键词： Training Xenon visualization Predictive models Image color analysis Benchmark testing Question answering (information retrieval) Contrastive learning counterfactual thinking data augmentation language biases visual question answering

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Design of visualization Platform for Aviation Cultural and Creative IP Based on Computer Virtual Technology

Design of Visualization Platform for Aviation Cultural and C...

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Electronic Communications, Internet of Things and Big data (ICEIB), International Conference on

作者： Yilei Yuan Xuan Zhou College of Art and Design Guangdong Polytechnic of Science and Technology Zhuhai China

With the development of the aviation industry, the visualization of aviation culture IP is becoming increasingly popular. visualization of aviation culture IP enables people to tend to enjoy visual beauty and is an important bridge for information transmission and communication between the aviation industry and people. However, due to the complexity and variability of the aviation environment, as well as the uncertainty of aviation information, the design of the platform for aviation cultural creativity IP visualization faces enormous challenges. In disseminating information about the product, the platform needs to show the cultural added value of the product. In order to enhance consumers' visual impression and improve the application effect of visualization platforms, we conduct in-depth research on the design of the platform through online surveys on their application effects using combined computer virtual technology. The survey results showed that 34.2% of the people were very satisfied with the visualization effect of the platform, and 48.6% were very satisfied with the body feeling in using the platform. The results imply that the platform for aviation culture creative IP visualization based on computer virtual technology provides users with an immersive and ultimate sensory experience, attracting users' interest in aviation culture.

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Tomo2Mesh: Fast Porosity Mapping and visualization for Synchrotron Tomography

Tomo2Mesh: Fast Porosity Mapping and Visualization for Synch...

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ieee International Conference on e-science and Grid Computing

作者： Aniket Tekawade Viktor Nikitin Yashas Satapathy Zhengchun Liu Xuan Zhang Peter Kenesei Weijian Zheng Francesco De Carlo Ian Foster Rajkumar Kettimuthu Data Science and Learning Division Argonne National Laboratory X-ray Science Division Argonne National Laboratory Nuclear Science Division Argonne National Laboratory

Applications of X-ray computed tomography (CT) in porosity characterization of engineering materials often involve an extensive data analysis workflow. This workflow includes CT reconstruction of raw projection data, binarization, labeling, and mesh extraction. Mapping porosity in larger samples presents a significant computational challenge, as it requires processing gigabytes of raw data to extract porosity information, which becomes a critical bottleneck in the analysis. In this study, we present algorithms and an implementation of an end-to-end porosity mapping framework. Our framework processes raw projection data obtained from a synchrotron CT instrument, generating a porosity map and a visualization in the form of a triangular face mesh. To achieve this objective, we introduce a novel subset reconstruction scheme for X-ray CT, combining filtered back-projection and a convolutional neural network. This scheme allows us to reconstruct subsets of a tomography object with arbitrary shapes. Building upon this scheme, we have developed a fast and efficient framework for porosity mapping. Initially, our framework detects potential voids by performing a coarse reconstruction on down-sampled projections. Subsequently, we enhance the shape of these voids by reconstructing selected subsets from the original raw data, providing higher detail. To evaluate the performance of our framework, we measured the processing time from raw data to a triangular face mesh across multiple visualization scenarios. Our experiments were conducted on a single high-performance workstation equipped with a GPU. The results demonstrate that our framework enables the visualization of local porosity within an 8-gigavoxel CT volume (12 gigabytes raw data) in just 1 to 2 minutes. Moreover, for a larger 64-gigavoxel CT volume (100 gigabytes of raw data), the visualization can be generated within 3 to 7 minutes, showcasing the efficiency of our approach.

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PyGWalker: On-the-fly Assistant for Exploratory Visual data Analysis

PyGWalker: On-the-fly Assistant for Exploratory Visual Data ...

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International Conference on Visualisation, VIS

作者： Yue Yu Leixian Shen Fei Long Huamin Qu Hao Chen Hong Kong University of Science and Technology Kanaries Data

ISBN: (数字)9798350354850

ISBN: (纸本)9798350354867

Exploratory visual data analysis tools empower data analysts to efficiently and intuitively explore data insights throughout the entire analysis cycle. However, the gap between common programmatic analysis (e.g., within computational notebooks) and exploratory visual analysis leads to a disjointed and inefficient data analysis experience. To bridge this gap, we developed PyGWalker, a Python library that offers on-the-fly assistance for exploratory visual data analysis. It features a lightweight and intuitive GUI with a shelf builder modality. Its loosely coupled architecture supports multiple computational environments to accommodate varying data sizes. Since its release in February 2023, PyGWalker has gained much attention, with 612k downloads on PyPI and over 10.5k stars on GitHub as of June 2024. This demonstrates its value to the data science and visualization community, with researchers and developers integrating it into their own applications and studies.

关键词： Visual analytics data visualization Stars Computer architecture Tutorials data science Libraries Graphical user interfaces Software development management Python

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Reflections on Teaching ‘data Exploration and Visualisation’ in Multiple Modes

Reflections on Teaching ‘Data Exploration and Visualisation...

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visualization Education, Literacy, and Activities (EduVis), ieee VIS Workshop on

作者： Michael Niemann Sarah Goodwin Kim Marriott Monash University

Whilst the visual arts is a very practical and tangible field, data visualisation is a combination of programming skills, data science and design theory. Each of these can be taught in a variety of ways, from traditional methods of lectures to technical programming classes or even asynchronous worksheets. The challenge is how to cover all of the required content within the constraints of the teaching period, environment, delivery method and ensuring the appropriate activities and assessment tasks. In this paper we reflect on the past 8 years of delivering the postgraduate unit ‘data Exploration and Visualisation’ at Monash University. We present the four different ways that the same content has been taught to different cohorts, through a combination of on-campus lectures, large-format workshops, small group tutorials and online or hybrid tutorials and workshops, as well as various readings, videos and asynchronous activities. We explain the different external and internal requirements set for the teaching and describe the adaption of the teaching methods and assessments in order to meet these conditions. We reflect on our experiences of teaching multiple methods across different formats from very small to very large student cohorts.

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