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24th ieee International Conference on Mobile data Management, MDM 2023

作者： Chan, Tsz Nam Leong Hou, U. Choi, Byron Xu, Jianliang Cheng, Reynold Hong Kong Baptist University Department of Computer Science Hong Kong University of Macau Department of Computer and Information Science China The University of Hong Kong Department of Computer Science Hong Kong Guangdong-Hong Kong-Macau Joint Laboratory Hong Kong

ISBN: (纸本)9798350341010

The use of Kernel Density visualization (KDV) has become widespread in a number of disciplines, including geography, crime science, transportation science, and ecology, for analyzing geospatial data. However, the growing scale of massive geospatial data has rendered many commonly used software tools unable of generating high-resolution KDVs, leading to concerns about the inefficiency of KDV. This 90-minute tutorial aims to raise awareness among database researchers about this important, emerging, database-related, and interdisciplinary topic. It is structured into four parts: a thorough discussion of the background of KDV, a review of state-of-the-art methods for generating KDVs, a discussion of key variants of KDV, including network kernel density visualization (NKDV) and spatiotemporal kernel density visualization (STKDV), and an outline of future directions for this topic. © 2023 ieee.

关键词： visualization

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Molecumentary: Adaptable Narrated Documentaries Using Molecular visualization

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第3期29卷 1733-1747页

作者： Kouril, David Strnad, Ondrej Mindek, Peter Halladjian, Sarkis Isenberg, Tobias Groller, M. Eduard Viola, Ivan Masaryk Univ Brno 60177 Czech Republic TU Wien A-040 Vienna Austria King Abdullah Univ Sci & Technol KAUST Thuwal 23955 Saudi Arabia TU Wien A-1040 Vienna Austria Nanograph GmbH A-1040 Vienna Austria Univ Paris Saclay CNRS Inria LISN F-91190 Gif Sur Yvette France TU Wien A-1220 Vienna Austria VRVis Res Ctr A-1220 Vienna Austria

We present a method for producing documentary-style content using real-time scientific visualization. We introduce molecumentaries, i.e., molecular documentaries featuring structural models from molecular biology, created through adaptable methods instead of the rigid traditional production pipeline. Our work is motivated by the rapid evolution of scientific visualization and it potential in science dissemination. Without some form of explanation or guidance, however, novices and lay-persons often find it difficult to gain insights from the visualization itself. We integrate such knowledge using the verbal channel and provide it along an engaging visual presentation. To realize the synthesis of a molecumentary, we provide technical solutions along two major production steps: (1) preparing a story structure and (2) turning the story into a concrete narrative. In the first step, we compile information about the model from heterogeneous sources into a story graph. We combine local knowledge with external sources to complete the story graph and enrich the final result. In the second step, we synthesize a narrative, i.e., story elements presented in sequence, using the story graph. We then traverse the story graph and generate a virtual tour, using automated camera and visualization transitions. We turn texts written by domain experts into verbal representations using text-to-speech functionality and provide them as a commentary. Using the described framework, we synthesize fly-throughs with descriptions: automatic ones that mimic a manually authored documentary or semi-automatic ones which guide the documentary narrative solely through curated textual input.

关键词： data visualization visualization Cameras Three-dimensional displays Animation Real-time systems Solid modeling Virtual tour audio biological data storytelling illustrative visualization

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DRCmpVis: Visual Comparison of Physical Targets in Mobile Diminished and Mixed Reality

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第12期30卷 7672-7686页

作者： Liu, Richen Ye, Shunlong Ding, Zhifei Yang, Guang Cheng, Shenghui Mueller, Klaus Nanjing Normal Univ Sch Comp & Elect Informat Sch Artificial Intelligence Nanjing 210098 Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Westlake Univ Sch Engn Hangzhou 310024 Peoples R China SUNY Stony Brook Comp Sci Dept Stony Brook NY 11794 USA

Numerous physical objects in our daily lives are grouped or ranked according to a stereotyped presentation style. For example, in a library, books are typically grouped and ranked based on classification numbers. However, for better comparison, we often need to re-group or re-rank the books using additional attributes such as ratings, publishers, comments, publication years, keywords, prices, etc., or a combination of these factors. In this article, we propose a novel mobile DR/MR-based application framework named DRCmpVis to achieve in-context multi-attribute comparisons of physical objects with text labels or textual information. The physical objects are scanned in the real world using mobile cameras. All scanned objects are then segmented and labeled by a convolutional neural network and replaced (diminished) by their virtual avatars in a DR environment. We formulate three visual comparison strategies, including filtering, re-grouping, and re-ranking, which can be intuitively, flexibly, and seamlessly performed on their avatars. This approach avoids breaking the original layouts of the physical objects. The computation resources in virtual space can be fully utilized to support efficient object searching and multi-attribute visual comparisons. We demonstrate the usability, expressiveness, and efficiency of DRCmpVis through a user study, NASA TLX assessment, quantitative evaluation, and case studies involving different scenarios.

关键词： visualization data visualization Avatars Libraries Filtering History Geography Diminished reality visual comparison mixed reality mobile environment

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Creating Tangible VR Spaces for Exploring Algorithm Complexity and data Structures 53

Creating Tangible VR Spaces for Exploring Algorithm Complexi...

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53rd ieee ASEE Frontiers in Education International Conference, FIE 2023

作者： Green, Nathan School of Technology and Innovation Marymount University ArlingtonVA United States

ISBN: (纸本)9798350336429

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. © 2023 ieee.

关键词： Algorithm Analysis Computer science data Structures Virtual Reality XR

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Canonical Correlation Analysis and visualization for Big data in Smart Grid

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ieee JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2023年第3期13卷 702-711页

作者： Jiang, Zigui Yuan, Qihao Lin, Rongheng Yang, Fangchun Sun Yat Sen Univ Sch Software Engn Zhuhai 519082 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Electricity consumption behaviors are influenced by various external and internal factors such as climate, location, building type, consumer characteristics and even other energy consumption. In order to investigate the electricity consumption behaviors of diverse consumers, we propose a methodology based on canonical correlation analysis to explore the correlation among electricity consumption, gas consumption and climate change under different circumstances. We first preprocess three multivariable datasets that contain 24-value daily data in a one-year period, and conduct consumer segmentation based on climate zones, locations and building types. Then an optimized canonical correlation analysis model with an optimal result selection mechanism is adopted to calculate the canonical correlations and weights of every set of daily data. Finally, we propose a post-processing analysis for further comparison on the calculated results. We investigate three research questions to present and discuss the analysis results, including canonical correlation and weights overview, typical patterns analysis, and comparison on climate zones and locations.

关键词： Smart grid electricity consumption climate zone canonical correlation analysis visualization

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Visual Analysis of High Dam Based on Digital Twin Technology 7

Visual Analysis of High Dam Based on Digital Twin Technology

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2023 7th International Symposium on Computer science and Intelligent Control, ISCSIC 2023

作者： Jiang, Kunhong Tang, Rong Chai, Xinyao Liu, Simin Liu, Zhigui Li, Li Southwest University of Science and Technology Sichuan Eng. Technology Research Center of Industrial Self-Supporting and Artificial Intelligence Mianyang China

ISBN: (纸本)9798350342987

To solve the problem that high dams have high data dimension, high collection density and it is difficult for traditional data analysis methods to obtain effective information from large amounts of data in the construction of water conservancy projects, this paper puts forward a visualization analysis of high dams based on digital twin technology, which realizes the construction of virtual high dams and real-time feedback of virtual high dams to real high dams through the modeling and data reuse of Physical high dams. © 2023 ieee.

关键词： visualization

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Network Big data visualization Information System

Network Big Data Visualization Information System

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2023 International Conference on Electronics and Devices, Computational science, ICEDCS 2023

作者： Wu, Xiaoling Ji, Lijian Liu, Zhixiong Xu, Dan Xue, Hanxiao Guangzhou College of Commerce School of Information Technology and Engineering Guangzhou511363 China

ISBN: (纸本)9798350343038

This article discusses a network Big data visualization information system based on a three-tier architecture, namely database system layer, data exchange layer, and operation layer. Among them, the database system layer is responsible for collecting, integrating, managing, maintaining, updating, etc., and can effectively protect, transmit, share, query, etc;The data exchange layer, on the other hand, is a web server that enables fast communication between multiple servers, making communication between servers more convenient and orderly. The WEB front-end is a complex platform that consists of underlying logical processing, information system transmission, communication, information storage, user permission settings, system configuration, WEB interface layout, visual display of information, programming of various functions, and program execution. Together, they form the core of the entire platform. © 2023 ieee.

关键词： visualization

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Tensor Completion Network for Visual data

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ieee TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 386-400页

作者： Wang, Xiang-Yu Li, Xiao-Peng Sidiropoulos, Nicholas D. So, Hing Cheung City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Peoples R China Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22904 USA

Tensor completion aims at filling in the missing elements of an incomplete tensor based on its partial observations, which is a popular approach for image inpainting. Most existing methods for visual data recovery can be categorized into traditional optimization-based and neural network-based methods. The former usually adopt a low-rank assumption to handle this ill-posed problem, enjoying good interpretability and generalization. However, as visual data are only approximately low rank, handcrafted low-rank priors may not capture the complex details properly, limiting the recovery performance. For neural network-based methods, despite their impressive performance in image inpainting, sufficient training data are required for parameter learning, and their generalization ability on the unseen data is a concern. In this paper, combining the advantages of these two distinct approaches, we propose a tensor Completion neural Network (CNet) for visual data completion. The CNet is comprised of two parts, namely, the encoder and decoder. The encoder is designed by exploiting the CANDECOMP/PARAFAC decomposition to produce a low-rank embedding of the target tensor, whose mechanism is interpretable. To compensate the drawback of the low-rank constraint, a decoder consisting of several convolutional layers is introduced to refine the low-rank embedding. The CNet only uses the observations of the incomplete tensor to recover its missing entries and thus is free from large training datasets. Extensive experiments in inpainting color images, grayscale video sequences, hyperspectral images, color video sequences, and light field images are conducted to showcase the superiority of CNet over state-of-the-art methods in terms of restoration performance.

关键词： Tensors visualization Neural networks Vectors Matrix decomposition Image restoration Decoding Videos Signal processing algorithms Matrix converters Tensor completion CP decomposition unrolling encoder-decoder neural network image inpainting visual data recovery

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Securing and Visualizing Sensor data on Private Blockchain

Securing and Visualizing Sensor Data on Private Blockchain

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2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023

作者： Pal, Pawan Kumar Khanna, Siddhant Shukla, Siddharth Shukla, Virat KIET Group of Institutions Ghaziabad Department of Computer Science India

ISBN: (纸本)9798350396485

In this technological era, technologies like 5G, Blockchain and IoT are growing rapidly which gives birth to problems like data securing of IoT devices. This paper aims to design and develop a secure architectural environment that will provide automation with a security layer of Blockchain technology, that introduces high Security and Privacy. The Overall abstract of this research paper is based on IoT automation System, subdivided into two parts: Real-Time data visualization & Blockchain Network. We use the open-source IoT platform(s) 'Hyperledger Explorer' or 'ThingsBoard' for real-Time data visualization. And use the Hyperledger Fabric blockchain network for providing a private blockchain network. © 2023 ieee.

关键词： data visualization

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Infrared and Visible Image Fusion: From data Compatibility to Task Adaption

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ieee TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第4期47卷 2349-2369页

作者： Liu, Jinyuan Wu, Guanyao Liu, Zhu Wang, Di Jiang, Zhiying Ma, Long Zhong, Wei Fan, Xin Liu, Risheng Dalian Univ Technol Sch Software Technol Dalian 116024 Peoples R China

Infrared-visible image fusion (IVIF) is a fundamental and critical task in the field of computer vision. Its aim is to integrate the unique characteristics of both infrared and visible spectra into a holistic representation. Since 2018, growing amount and diversity IVIF approaches step into a deep-learning era, encompassing introduced a broad spectrum of networks or loss functions for improving visual enhancement. As research deepens and practical demands grow, several intricate issues like data compatibility, perception accuracy, and efficiency cannot be ignored. Regrettably, there is a lack of recent surveys that comprehensively introduce and organize this expanding domain of knowledge. Given the current rapid development, this paper aims to fill the existing gap by providing a comprehensive survey that covers a wide array of aspects. Initially, we introduce a multi-dimensional framework to elucidate the prevalent learning-based IVIF methodologies, spanning topics from basic visual enhancement strategies to data compatibility, task adaptability, and further extensions. Subsequently, we delve into a profound analysis of these new approaches, offering a detailed lookup table to clarify their core ideas. Last but not the least, We also summarize performance comparisons quantitatively and qualitatively, covering registration, fusion and follow-up high-level tasks. Beyond delving into the technical nuances of these learning-based fusion approaches, we also explore potential future directions and open issues that warrant further exploration by the community.

关键词： Image fusion visualization Surveys Meters Object detection Feature extraction Taxonomy Semantic segmentation Intelligent systems Gamma-rays infrared and visible image registration object detection

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