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2023 ieee World Conference on Applied Intelligence and Computing, AIC 2023

作者： Li, Jiayu School of Marxism Hohai University Nanjing China

ISBN: (纸本)9798350310061

In the digital era, it is necessary to promote talent cultivation and education reform with the help of big data. In order to understand the development status of education big data and AI education, this paper takes Web of science (WoS) 2010-2023 as the data source, and uses the visualization software CiteSpace and VOSviewer to conduct quantitative analysis and visualization of relevant literature. Through co-citation analysis and co-occurrence network analysis, cluster analysis is conducted on authors, publishing institutions and high-frequency keywords to explore and analyze the hot spots, evolution paths and development trends of education big data and AI education research. At the same time, the highly cited literature and representative scholars are analyzed to further understand the trend of the field. Conclusions from the study(1) In the past decade, the scope of research in this field has been expanding and the research content has been gradually deepening.(2) Although researchers in this field have different focuses, they have formed stable cooperative relationships.(3) Most papers in this field have been published in journals related to educational technology and computational intelligence, and open source journals have promoted research in this field. (4) Research in this field has prompted some scholars to pay attention to data privacy, data quality issues, and ethical issues. © 2023 ieee.

关键词： Big data

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Research on Time-series visualization Methods for Scientific and Technical Consulting Graph data

Research on Time-series Visualization Methods for Scientific...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： Hu, Xinyang Wang, Lei Wei, Qiushi Shang, Jin Haihong, E. Beijing University of Posts and Telecommunications School of Computing Beijing China China Mobile Information Technology Co. Ltd Beijing China Jinze Building No.2 Guangningbo Street Xicheng District Beijing China

ISBN: (纸本)9798350301113

The existing open-source knowledge visualization components do not support knowledge graph temporal visualization exploration capabilities and cannot meet the demand of analyzing the dynamic evolutionary changes of industries in science and technology consulting scenarios. In this paper, to address the above problems, we propose a knowledge graph-based temporal visualization component construction and optimization method to build a graph visualization component that supports temporal dynamic exploration based on existing open-source knowledge visualization components to address the demand for industry insight analysis in science and technology consulting graph analysis services. At the same time, a data processing and graph rendering optimization strategy is proposed to improve the overall task execution efficiency in response to the problems of low rendering efficiency and high computational resource occupation rate in the time-series visualization exploration scenario. © 2023 ieee.

关键词： Time series

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HR Dashboard Analytics 5

HR Dashboard Analytics

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5th ieee International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Gupta, Anushka Ali, Faiz Singh, Brijesh Kumar Janarthanan, S. Galgotia's University School Of Computing Science And Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350330861

Every company wants to get full information and hidden pattern about the rate of hiring and attrition of their employees. Most of the companies have structured or unstructured data related to the hiring and attrition of their employees but there is no easy way to visualize and extract hidden patterns from the data. They need to hire resources for work as simple as visualization. This not only increases their cost but also creates a risk of a data *** dashboard allows users to instantly create a dynamic dashboard full of visualizations instantly without the need of having deep knowledge of programming languages. An HR Dashboard is a visualization dashboard created using R's Flex Dashboard framework which provides us a way to create dynamic dashboards. The goal of the HR dashboard is to allow visualization and understanding of company data such as Hiring's, Expenses, Employee leaving rate, etc. The HR dashboard package will allow users to install and visualize any company's data as long as they match a particular data structure. The HR dashboard consists of different graphs such as line graph, bar graph to visualize temporal (time-related data), and a leaflet map for spatial (Geography related data). The whole dashboard is divided into different tabs for easy navigation and to avoid mixing of different categories of information. © 2023 ieee.

关键词： visualization

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AutoEDA: Iterative data Focusing and Exploratory Analysis Based on Attribute Frequency

AutoEDA: Iterative Data Focusing and Exploratory Analysis Ba...

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2024 ieee International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wu, Tong Wang, Song Peng, Xin School of Computer Science and Technology Southwest University of Science and Technology Department of Computer Science Mianyang China

ISBN: (纸本)9781665410205

The paper proposes an automated data exploration and analysis method based on Attribute Frequency Statistical Feature Ratio (AFSFR). It integrates AutoVis and data Preprocessing Methods to design and develop AutoEDA-Segment. Addressing the Concentrate on Field Sequences (CFS) problem in data exploration and analysis, this study employs various classification models and combines AFSFR with field type and the Elbow Inflection Point (EIP) of index features to design a field type identification and field value assessment method. For evaluating the effectiveness of focus analysis, the approach provides clustering visualization effects and an analysis scheme based on Field Type Search Tree (FST) and cluster comparison profiles, using a custom CFS approach. Additionally, to enhance the value of focused subset analysis data, the approach introduces a Parallel Coordinates-Based data Filter (PCF), forming an EDA feedback loop to achieve Iterative Exploratory data Analysis for User-inferred Cognition (IEDA-UC). Finally, we engaged graduate students with varying levels of experience in visualization research for collaboration and discussion, validating the effectiveness and feasibility of the approach using structured data from Kaggle. © 2024 ieee.

关键词： visualization

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Development of a Visual Analysis Tool for Seabird Behavior 1

Development of a Visual Analysis Tool for Seabird Behavior

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Onuki, Tomoya Misue, Kazuo University of Tsukuba Graduate School of Science and Technology Ibaraki Japan University of Tsukuba Department of Computer Science Ibaraki Japan

ISBN: (纸本)9798350335569

Biologging data are used to analyze animal behavior. visualization of such data facilitates analysis;however, the data are often large, and designing visual representations and interactions is challenging. Another problem is that few tools can comprehensively support analysis. The authors aim to develop an integrated analysis tool to support seabird researchers in data analysis. To support the analysis of large datasets, they developed an automatic behavior classification function and visual representation of seabird behavior in a spatiotemporal manner. The usefulness of the tool was confirmed through interviews with seabird researchers. © 2023 ieee.

关键词： data visualization

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Predict Tactile Grasp Outcomes Based on Attention and Low-Rank Fusion Network

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ieee SENSORS JOURNAL 2024年第24期24卷 42500-42510页

作者： Wu, Peng Chu, Chiawei Liu, Chengliang Fang, Senlin Wang, Jingnan Liu, Jiashu Yi, Zhengkun City Univ Macau Fac Data Sci Macau 999078 Peoples R China Xinyang Agr & Forestry Univ Sch Informat Engn Xinyang 464000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen 518055 Peoples R China

Tactile measurement endows robots with the ability to interact with the environment, which is crucial for accurately predicting grasp outcomes. However, this field has some areas for improvement, particularly regarding the key feature extraction and efficient fusion of multiple tactile modality features. To address these issues, we propose an advanced measurement technique that uses a tactile attention (TacAtt) module and a tactile low-rank tensor fusion (TLRTF) module to enhance the measurement and evaluation capabilities of multiple tactile sensors. By integrating the TacAtt module into the convolutional neural network (CNN), our model enhances the feature extraction capabilities for multiple tactile signals, focusing more on the contact area of the object, which provides highly targeted tactile input features for subsequent feature fusion. Moreover, the TLRTF module successfully addresses the challenges of insufficient fusion and redundant information in traditional concatenation methods when integrating features from multiple tactile sensors. The combination of the two proposed modules forms a strong system for tactile feature extraction and fusion. Our model achieves an accuracy of 78.31% on the public tactile dataset, which represents a significant improvement of 3.16% over the baseline model, thus validating the effectiveness and superiority of our model.

关键词： Feature extraction Robot sensing systems Tactile sensors Sensors Attention mechanisms Tensors Convolutional neural networks visualization Stability analysis Grasping Grasp outcomes low-rank tensor fusion multiple tactile sensors robotic tactile attention (TacAtt)

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On Construction of Precise Positioning System via ieee 802.11ax

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ieee Internet of Things Journal 2023年第14期10卷 12951-12960页

作者： Tsung, Chen-Kun Yang, Chao-Tung Liu, Jung-Chun Hsiung, Chun Chang, Shih-Kuang Hsu, Ming-Shang National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung41170 Taiwan Tunghai University Department of Computer Science and Research Center for Smart Sustainable Circular Economy Taichung407224 Taiwan Tunghai University Department of Computer Science Taichung40704 Taiwan Krystal Technology Company Ltd. Network Department Taichung42756 Taiwan

Wireless network stability is critical for organizations. Most companies rely on a solid Internet connection for at least part of their day-to-day activities. It is essential to show the fast and high capacity of Wi-Fi. Wireless network management also significantly supplies the user with quality service and helps the system administrator manage and track the network infrastructure. The user must be able to stay connected. The Wi-Fi connection engine was applied in this article using the analysis and locations engine (ALE). ALE provides the information, including MAC address, location, floor information, and the building where the device belongs. From this data, we presented the Web visualization of the Wi-Fi network monitoring system on the map using Cesium. Except for the common information about Wi-Fi users, this work presents the vertical position visualization in the buildings to provide more details about user usage. This work analyzes the user experience of using the proposed system, and latency is the appropriate metric to measure the user experience. The experiment results show that the optimized number of cluster partitions is four, and the latency is less than 1.3 s for 4-MB data. Therefore, the system provides a visualization platform with acceptable system response, and Internet managers would consider the proposed system as the management tool. © 2014 ieee.

关键词： Engines

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Reverse Engineering Piping and Instrumentation Drawings: A Deep Learning Approach

Reverse Engineering Piping and Instrumentation Drawings: A D...

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2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Ekanayake, Indrajith Warnakula, Jayani Viswakula, Sameera School of Computing Robert Gordon University Scotland Aberdeen United Kingdom Uva Wellassa University Department of Science and Technology Badulla Sri Lanka University of Colombo Department of Statistics Colombo Sri Lanka

ISBN: (纸本)9798350334463

data visualization is an easy way to communicate knowledge ever since human evolution. There are many visualization techniques including different types of plots, graphs, charts, and many more. Despite its usefulness, there is one common problem with visualization. The static image that generates the visualization is separated from the raw data. When a research study requires raw data it's hard to reverse engineer and take the raw data back. There are very limited tools available to reverse engineer and extract raw data from the above-mentioned static image. However, it's challenging to analyze all the data visualization techniques at a single glance. So, this research is focused on data extraction and summarization from Piping and Instrumental Drawings(P&ID). The dataset contains symbols taken from 2432 instances of piping and instrumental drawings. The goal of this study is to improve the accuracy of the prebuilt Convolutional Nural Network(CNN) model with hyperparameter optimization techniques and create a web application where users can extract and summarize text and symbols data from any piping and instrumentation drawing. As future directions, the applicability of other visualization techniques is also discussed at the conclusion. © 2023 ieee.

关键词： data visualization

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Higher-Order Truss Decomposition in Graphs

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第4期35卷 3966-3978页

作者： Chen, Zi Yuan, Long Han, Li Qian, Zhengping East China Normal Univ Software Engn Inst Shanghai 200050 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Alibaba Grp Hangzhou 310052 Peoples R China

kappa-truss model is a typical cohesive subgraph model and has been received considerable attention recently. However, the k-truss model only considers the direct common neighbors of an edge, which restricts its ability to reveal fine-grained structure information of the graph. Motivated by this, in this paper, we propose a new model named (k, tau)-truss that considers the higher-order neighborhood (tau hop) information of an edge. Based on the (k, tau)-truss model, we study the higher-order truss decomposition problem which computes the (k, tau)-trusses for all possible k values regarding a given tau. Higher-order truss decomposition can be used in the applications such as community detection and search, hierarchical structure analysis, and graph visualization. To address this problem, we first propose a bottom-up decomposition paradigm in the increasing order of k values to compute the corresponding (k,tau)-truss. Based on the bottom-up decomposition paradigm, we further devise three optimization strategies to reduce the unnecessary computation. We evaluate our proposed algorithms on real datasets and synthetic datasets, the experimental results demonstrate the efficiency, effectiveness and scalability of our proposed algorithms.

关键词： Computational modeling Image edge detection Optimization Scalability data models Collaboration visualization Higher-order neighborhood truss decomposition graph algorithm

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DOVAR: data-on-Object visualization with Virtual and Augmented Reality in Scientific Education 11

DOVAR: Data-on-Object Visualization with Virtual and Augment...

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11th International Conference on Information and Education Technology, ICIET 2023

作者： Wang, Xiaoyin Rivera, Maria Isais, Lisette Styles, Corbin The University of Texas at San Antonio Department of Computer Science San Antonio United States

ISBN: (纸本)9781665465489

Virtual and augmented reality is an emerging approach of visualization that provides immersive experience to users. The approach has been widely adopted in different domains such as training, remote conference, navigation, gaming, etc. In education, it has also been widely adopted. For example, the 3D virtual solar system scene has been implemented in multiple applications to help young kids and students to learn basic astronomical knowledge. However, most existing approaches focus on creating virtual objects that simulate real-world objects but on a different scale. In this paper, we propose DOVAR, a novel VR/AR-based visualization approach that visualizes real-Time and accumulated data directly on virtual objects. In particular, DOVAR adds data points as colored accessories at different surfaces (i.e. polygons) of the 3D models in a VR or AR environment. Our experiment study on different usage cases shows that visualization technique can be helpful in teaching scientific concepts. © 2023 ieee.

关键词： visualization

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