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Blockchain and Artificial Intelligence: Scientometric Analysis and visualization

ieee ACCESS 2023年 11卷 137911-137928页

作者： Adel, Kareem Elhakeem, Ahmed Marzouk, Mohamed Arab Acad Sci Technol & Maritime Transport AASTMT Coll Engn & Technol Construct & Bldg Engn Dept Cairo 2033 Egypt Cairo Univ Fac Engn Dept Struct Engn Giza 12613 Egypt

Integrating Artificial Intelligence (AI) with Blockchain Technology (BT) is deemed the fourth generation of BT applications (Blockchain 4.0). This generation has gained considerable attention from the research community. Such attention has led to a vast amount of scientific literature. However, a comprehensive quantitative analysis of this literature is still missing. The present study conducts a scientometric analysis to explore and characterize the development track and trends of BT-AI research. Using the Web of science (WoS) Core Collection database, a total of 2615 peer-reviewed journal articles were identified between 2017-2023 and extracted for analysis, while employing VOSviewer and Biblioshiny as software tools. First, the publication trend was analyzed, and the pivotal articles were identified. Second, the scientific collaboration networks were analyzed and mapped to identify the key researchers, countries, and organizations. Third, the sources' productivity and citation were analyzed and mapped to identify the dependable sources of information and the best-fit sources for publishing the BT-AI studies. Fourth, the conceptual structure for the BT-AI literature was analyzed and visualized using keywords co-occurrence and keywords thematic evolution to explore and identify the research hotspots and emerging themes. The findings of this study can help in further familiarizing new researchers with BT-AI literature and assist practitioners, policy-makers, and editors to focus on the promising and arising BT-AI trends for further development.

关键词： Artificial intelligence Bibliometrics bibliometrics blockchains data visualization reviews

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Research on the Application of Digital Twin Technology in the Field of Electric Energy Metering Popularization 7

Research on the Application of Digital Twin Technology in th...

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7th ieee International Conference on Energy Internet, ICEI 2023

作者： Li, Dan Wu, Tong Sun, Lina Zhao, Yudong Xue, Jiguang Wang, Jun State Grid Liaoning Marketing Service Center Shenyang China State Grid Liaoning Marketing Department Shenyang China

ISBN: (纸本)9798350386684

Electric energy metering is a crucial aspect in ensuring the quality of customer services in the new power system. The calibration/testing of legal measuring instruments directly affects the fairness and justice of power trade settlement. How to popularize and interpret electric energy metering has become an important challenge in improving the quality of customer services in the new power system. Therefore, this article applies digital twin technology to the field of electric energy metering popularization and verifies its practical value through pilot usage. The research results indicate that digital twin technology can achieve real-time data monitoring, provide data accuracy and precision, support energy management and optimization, facilitate fault detection and early warning, as well as enable data visualization and analysis. This study provides new ideas and methods for the popularization of electric energy metering. © 2023 ieee.

关键词： data visualization

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A Unified Optimal Transport Framework for Cross-Modal Retrieval With Noisy Labels

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Han, Haochen Luo, Minnan Liu, Huan Nan, Fang Liu, Jun Pengcheng Lab Shenzhen 518066 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Shaanxi Prov Key Lab Big Data Knowledge Engn Xian 710049 Peoples R China

Cross-modal retrieval (CMR) aims to establish interaction between different modalities, among which supervised CMR is emerging due to its flexibility in learning semantic category discrimination. Despite the remarkable performance of previous supervised CMR methods, much of their success can be attributed to the well-annotated data. However, even for unimodal data, precise annotation is expensive and time-consuming, and it becomes more challenging with the multimodal scenario. In practice, massive multimodal data are collected from the Internet with coarse annotation, which inevitably introduces noisy labels. Training with such misleading labels would bring two key challenges-enforcing the multimodal samples to align incorrect semantics and widen the heterogeneous gap, resulting in poor retrieval performance. To tackle these challenges, this work proposes UOT-RCL, a unified framework based on optimal transport (OT) for robust CMR. First, we propose a semantic alignment based on partial OT to progressively correct the noisy labels, where a novel cross-modal consistent cost function is designed to blend different modalities and provide precise transport cost. Second, to narrow the discrepancy in multimodal data, an OT-based relation alignment is proposed to infer the semantic-level cross-modal matching. Both of these components leverage the inherent correlation among multimodal data to facilitate effective cost function. The experiments on three widely used CMR datasets demonstrate that our UOT-RCL surpasses the state-of-the-art approaches and significantly improves the robustness against noisy labels.

关键词： Noise measurement Semantics Training Noise Cross modal retrieval Costs Cost function visualization Hands Annotations Noisy labels optimal transport (OT) supervised cross-modal retrieval (CMR)

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Forecasting Continuous Air Pollution with Cloud-Based Big data Platform using Spatial-Temporal visualization 8

Forecasting Continuous Air Pollution with Cloud-Based Big Da...

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8th ieee International Conference for Convergence in Technology, I2CT 2023

作者： Chang, Yue Shan Abimananan, Satheesh Shukla, Saurabh Satheesh, Dhivyadharsini National Taipei University Department of Computer Science and Information Engineering New Taipei City Taiwan Amity University Maharashtra Department of Computer Science and Engineering Mumbai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350334012

In today's era of rapidly growing data, having a well-designed platform for analyzing and visualizing big data is becoming increasingly important. This study presents a cloud-based big data platform for analyzing and visualizing continuous spatial-temporal air pollution. The platform combines a Spark Hadoop-based machine learning environment with a TensorFlow-based deep learning framework for forecasting and a map for demonstration and visualization of the results. The platform uses the Inverse Distance Weighted (IDW) method to estimate the concentration of particulate matter in the atmosphere and a cloud based non-SQL database to store the pre-calculated air quality images. The platform also features a sliding window mechanism, which allows users to easily visualize the results over time. This work provides an instant overview of air quality in Taiwan and evaluates the performance of the platform, concluding that it is efficient and capable of providing continuous spatial-temporal results. This platform represents a significant step forward in the field of air quality monitoring and forecasting. © 2023 ieee.

关键词： data visualization

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Design of visualization Platform for Aviation Cultural and Creative IP Based on Computer Virtual Technology 3

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

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3rd ieee International Conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

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

ISBN: (纸本)9798350333862

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. © 2023 ieee.

关键词： visualization

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An SVM-Based Anomaly Detection Method for Power System Security Analysis Using Particle Swarm Optimization and t-SNE for High-Dimensional data Classification

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PROCESSES 2025年第2期13卷 549-549页

作者： Tao, Ye Yan, Jiongcheng Niu, Enquan Zhai, Pengming Zhang, Shuolin Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Shandong Luruan Digital Technol Co Ltd Jinan 250101 Peoples R China Wenshan Power Supply Bur Yunnan Power Grid Co Ltd Wenshan 663000 Peoples R China

Research on the detection and identification of anomalies in electric power systems is crucial for ensuring their secure and stable operation. Anomaly detection models based on Support Vector Machines (SVMs) effectively process high-dimensional data while maintaining strong generalization capabilities. However, the performance of SVMs significantly depends on the choice of parameters, where improper parameter settings can lead to overfitting or underfitting, consequently decreasing the accuracy of anomaly detection. Furthermore, the dimensions of anomaly data in electric power systems are often unknown, making it difficult for existing methods to maintain a high precision in multidimensional data detection, and the segmentation of such data lacks intuitive display. In response, this article proposes an improved SVM model for electric power system anomaly detection, enhanced by parameter optimization algorithms, alongside a method for nonlinear dimension reduction and visualization using t-Distributed Stochastic Neighbor Embedding (t-SNE). Initially, traditional SVM parameters are optimized using the following four algorithms: Grid Search (GS), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Artificial Bee Colony (ABC) algorithms, in order to establish the optimized SVM model for electric power system anomaly detection. Finally, the effectiveness of the proposed method is verified through simulations. The simulation results indicate that, in the ieee-14 node system case study, the accuracy for normal data reaches 97.58%, the accuracy for load step change detection reaches 99.52%, the accuracy for bad data detection reaches 99.92%, and the accuracy under fault conditions reaches 100%.

关键词： anomaly data detection SVM particle swarm optimization parameter optimization t-SNE

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voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第10期29卷 4296-4311页

作者： He, Xiangyang Tao, Yubo Yang, Shuoliu Dai, Haoran Lin, Hai Zhejiang Univ State Key Lab CAD & CG Hangzhou 310027 Zhejiang Peoples R China

Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.

关键词： Graphical models Association analysis feature classification representation learning scientific data

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A Spherical Geometric B-Spline Model for Lunar Brightness Temperature data Approximation

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ieee TRANSACTIONS ON GEOscience AND REMOTE SENSING 2025年 63卷

作者： Li, Jiayang Cai, Zhanchuan Zhu, Mingwen Macau Univ Sci & Technol Sch Comp Sci & Engn Macau Peoples R China Macau Univ Sci & Technol Sch Comp Sci & Engn State Key Lab Lunar & Planetary Sci Macau Peoples R China Zhuhai MUST Sci & Technol Res Inst Zhuhai 519099 Peoples R China

Brightness temperature (TB) data from the Chinese Chang'E-2 (CE-2) microwave radiometer (MRM) are constrained by the limited quantity of the original dataset, which cannot express global TB distribution. To construct the lunar TB model with the TB data obtained by the MRM onboard CE-2, we propose a novel spherical geometric B-spline (SGB-spline) model. The model fully integrates the observed TB data with the lunar geometric features and determines optimal fitting parameters through a subdivision-based optimization process. More specifically, the establishment of the lunar TB model begins by employing spherical area coordinates (SACs) for CE-2 TB data representation across all four frequency channels, followed by applying geometric B-splines to refine the TB distribution. At the same time, it preserves the geometric integrity of the Moon. We observed that the SGB-spline model constructs the more comprehensive TB models during both lunar daytime and nighttime in the 3-D Euclidean space, providing a more detailed representation of the spherical spatial information and the effect of frequency channels. Experimental results demonstrated that the proposed SGB-spline model significantly outperforms representative interpolation approaches.

关键词： Moon Three-dimensional displays data models Splines (mathematics) Microwave radiometry Interpolation Aerospace electronics Microwave integrated circuits Microwave FET integrated circuits data visualization Brightness temperature (TB) interpolation least-squares microwave radiometer (MRM) data spherical data

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Kernel Density visualization for Big Geospatial data: Algorithms and Applications 24

Kernel Density Visualization for Big Geospatial Data: Algori...

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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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