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exploration on Optimization of Business Talent Training in Digital Economy Era 6

Exploration on Optimization of Business Talent Training in D...

引用

6th International conference on Digital Technology in Education, ICDTE 2022

作者： Zhang, Yijun Yuan, Dan Guangdong University of Science and Technology China

ISBN: (纸本)9781450398091

The booming development of China’s digital economy is of great significance to realizing high-quality employment and promoting the healthy and sustainable development of the economy. Meanwhile, it also brings new opportunities and challenges to business talent training. Based on the literature on "business talent training" in the core journals of Peking University from 2010 to 2021 as sample data, this paper makes a visual bibliometric analysis and finds that the number of literature on business talent training generally shows a trend of fluctuation and rise. In recent years, more and more literature are related to the concepts of "intelligent manufacturing", "enterprise digital transformation", "new era", "new business", etc. Therefore, it is concluded that the cultivation of business talents in the era of digital economy should fully combine information technology and conform to the background of the digital era. Based on this, this paper discusses the practical challenges of digital economy to business talent training, and accordingly explores the implementation path of business talent training in the era of digital economy. © 2022 Association for Computing Machinery.

关键词： E-learning

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visualization analysis of Metaverse Research Situation Based on CiteSpace

Visualization Analysis of Metaverse Research Situation Based...

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International Association of Institutes of Navigation World Congress (IAIN)

作者： Weiqiao Zhu Defang Li Guangzhou Maritime University Guangzhou China Guangzhou Vocational College of Technology &Business Guangzhou China

Using the CiteSpace visual knowledge map analysis method, this paper conducts a comprehensive quantitative analysis of the literature on metaverse research from 2000 to 2023, which was collected from the Web of Science database. The analysis focuses on the time distribution, spatial distribution, research hotspots, and frontier trends. Currently, both domestic and international research in the field of metaverse is experiencing a prosperous period of theoretical deliberation and practical exploration. The cutting-edge research topics are primarily centered around virtual reality, digital twinning, blockchain, and other related fields. Additionally, there is still a need for further development in terms of technical layout, standards, and regulations.

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Proceedings - 2021 IEEE visualization conference - Short Papers, VIS 2021

Proceedings - 2021 IEEE Visualization Conference - Short Pap...

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2021 IEEE visualization conference, VIS 2021

ISBN: (纸本)9781665433358

The proceedings contain 41 papers. The topics discussed include: understanding the effects of visualizing missing values on visual data exploration;screen parsing: towards reverse engineering of UI models from screenshots;an empirical evaluation of the T-SNE algorithm for data visualization in structural engineering;KNN learning techniques for proportional myocontrol in prosthetics;segmentation driven peeling for visual analysis of electronic transitions;hybrid data augmentation and deep attention-based dilated convolutional-recurrent neural networks for speech emotion recognition;and wellbeing supportive design - research-based guidelines for supporting psychological wellbeing in user experience.

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Hybrid Line-Based and Region-Based Interactive Set data visualization

Hybrid Line-Based and Region-Based Interactive Set Data Visu...

引用

CHI conference on Human Factors in Computing Systems

作者： Wang, Xiaohan Zhang, Chuyu Zhu, Yu Chen, Xueyi Shen, Liming Liu, Richen Qian, Rongtao Nanjing Normal Univ Sch Artificial Intelligence Sch Comp & Elect Informat Nanjing Peoples R China

ISBN: (纸本)9781450380959

A generic step in data analysis is to group data items into multiple sets based on specific attribute values. In this paper, we propose an interactive set-data exploration tool named BalloonVis, to make nested balloons as a visual metaphor to group set data over a timeline and visualize the set overlapping information while preserving the original layout. We employ a hybrid region-based and line-based scheme, which allows placing a representative image of each set data item at its region position and helps reduce visual clutter by line connection design. Energy optimization is exploited to compute the layouts of region-based set data items (balloons) and connected lines. The case study and the user study suggest the BalloonVis can visualize set data with more information. Besides, the proposed hierarchical scheme is more scalable on set data item size.

关键词： set visualization region-based visualization line-based visualization

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visual analysis and exploration of COVID-19 Based on Multi-source Heterogeneous data 13

Visual Analysis and Exploration of COVID-19 Based on Multi-s...

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2020 IEEE Congress on Cybermatics: 13th IEEE International conferences on Internet of Things, iThings 2020, 16th IEEE International conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International conference on Smart data, Smartdata 2020

作者： Zhou, Yun He, Hu Rong, Jieqi Cheng, Yun Li, YongChang Zhong, Wei Jiang, Fu Hu Nan University of Finance and Economics Changsha410205 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9781728176475

The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world. In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on. The increasingly complex data poses a significant challenge to understand. A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage. Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation. Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified. © 2020 IEEE.

关键词： COVID-19

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Universal visual Decomposer: Long-Horizon Manipulation Made Easy

Universal Visual Decomposer: Long-Horizon Manipulation Made ...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Zichen Zhang Yunshuang Li Osbert Bastani Abhishek Gupta Dinesh Jayaraman Yecheng Jason Ma Luca Weihs Allen Institute for AI University of Pennsylvania University of Washington

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands decomposing the overarching task into several manageable subtasks to facilitate policy learning and generalization to unseen tasks. Prior task decomposition methods require task-specific knowledge, are computationally intensive, and cannot readily be applied to new tasks. To address these shortcomings, we propose Universal visual Decomposer (UVD), an off-the-shelf task decomposition method for visual long-horizon manipulation using pre-trained visual representations designed for robotic control. At a high level, UVD discovers subgoals by detecting phase shifts in the embedding space of the pre-trained representation. Operating purely on visual demonstrations without auxiliary information, UVD can effectively extract visual subgoals embedded in the videos, while incurring zero additional training cost on top of standard visuomotor policy training. Goal-conditioned policies learned with UVD-discovered subgoals exhibit significantly improved compositional generalization at test time to unseen tasks. Furthermore, UVD-discovered subgoals can be used to construct goal-based reward shaping that jump-starts temporally extended exploration for reinforcement learning. We extensively evaluate UVD on both simulation and real-world tasks, and in all cases, UVD substantially outperforms baselines across imitation and reinforcement learning settings on in-domain and out-of-domain task sequences alike, validating the clear advantage of automated visual task decomposition within the simple, compact UVD framework.

关键词： Training visualization Costs Reinforcement learning Aerospace electronics data mining Task analysis

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Exploratory and statistical data analysis of Indian economy

Exploratory and statistical data analysis of Indian economy

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Bhawna Singh Vandana Sandhya Umrao Sanjay Katiyar Vanshika Sharma Department of CSE G.L. Bajaj Institute of Technology and Management Greater Noida India Department of CSE Noida Institute of Engineering and Technology Greater Noida India Department of CSE Greater Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Exploratory data analysis provides visual exploration of data and also allows for the identification of trends and relationships between variables. The aim of this research is to study about different factors involved in the economy of India. The dataset has been collected from year 1960 to 2020 and is analyzed by looking at the factors such as GDP, import and export value of goods and services, total reserves(include gold, current US Dollar) and health status of nation’s citizens. Thus, it suggests that all factors are important for a stable economy and it also provides different ways to improve these factors.

关键词： Gold visualization data analysis Oils Linear regression Production Market research

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Improving Short-Term Weather Predictions Using GANs

Improving Short-Term Weather Predictions Using GANs

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Progressive Innovations in Intelligent Systems and data Science (ICPIDS), International conference on

作者： Sanjay Singla Neeraj Kumar Kulwinder Singh Kavya Rawat Vedant Singh Abhinav Sachdeva Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798331534691

ISBN: (纸本)9798331534707

Short term weather forecast is very important to different fields like agriculture, disaster management and urban planning. Inherent complexity and non-linearity of atmospheric data presents a challenge for traditional forecasting methods. In a paper written this week, we propose a new way to improve short-term weather predictions using Generative Adversarial Networks (GANs), which are effective at generating high dimensional data. To increase the prediction accuracy, we propose a GAN architecture tailored for time series weather data that utilizes historical weather patterns. In the model are trained on extensive datasets of temperature, humidity, and precipitation and able to approximate the nature of weather phenomena. We show that the GAN based scheme outperforms conventional forecasting techniques in RMSE and MAE metrics. Additionally, visual comparisons show that GAN generated forecasts are closer to observed weather conditions. This work demonstrates the potential for use of GANs in transforming weather forecasting methodologies, the foundation for future exploration of their use in meteorology.

关键词： visualization Accuracy Urban planning Time series analysis Weather forecasting Computer architecture Generative adversarial networks Reliability Forecasting Meteorology

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UNet-Based YOLO for Underwater Object Detection

UNet-Based YOLO for Underwater Object Detection

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data Intelligence and Innovative Application (DIIA), IEEE International conference on

作者： Haigui Zhu Xiaonan Luo Zhongyi Zhai Ji Li Fang Li Xiaoshu Zhu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

Vision-guided autonomous underwater vehicles (AUVs) are becoming crucial tools for deep-sea exploration, resource assessment, and mariculture, among other underwater tasks. However, the challenging underwater environment often leads to image distortion and quality degradation, which negatively impacts the accuracy of vision systems and hinders effective target detection. Recently, the advancement of deep learning, particularly through Transformer-based architectures, has opened new avenues for improving underwater image enhancement. However, applying Transformer models directly for this task faces challenges, including the difficulty of capturing global information at the pixel level while preserving local details. In this paper, we introduce an innovative approach that combines the U-Net network with YOLOv5 for enhanced underwater target detection. Firstly, we construct an improved U-Net model, which can better correct color distortion in global and local features, and overcome the limitations of Transformer in extracting details and textures through convolutional neural networks. Next, YOLOv5 is used to detect the target of the enhanced image, and the quality of information extraction at high and low resolutions is improved through multi-level feature fusion, thus strengthening the attention and correction of severely degraded areas in the image. The experimental results show that our method has obvious advantages in subjective visual perception and comprehensive performance evaluation compared with the existing technology, and shows excellent learning and generalization ability.

关键词： YOLO Image color analysis Feature extraction Distortion visual effects Transformers Real-time systems Circuit faults Image enhancement visual perception

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visual Communication Design of Multi-dimensional Image visualization Fusion System Based on Wavelet Transform

Visual Communication Design of Multi-dimensional Image Visua...

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Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Wenjie Li Art Education Teaching and Research Section of Marxism College Changchun University of Chinese Medicine Changchun China

With the rapid development of remote sensing technology, multi-source remote sensing image fusion has become one of the hot issues in remote sensing field in recent years. Aiming at the poor edge registration of images obtained by conventional remote sensing image fusion system, a multidimensional visual fusion system of remote sensing images based on visual communication is designed. CABAC encoder is selected to make data flow and control flow cooperate with each other to obtain feedback data. However, the rapid increase of data also increases the difficulty of comprehensive observation, because the image data obtained by different sensors have their own differences and limitations in spectral information and spatial details, so we want to bring the fusion technology into this field, expand the working range of the system, improve the reliability of the system, obtain a more efficient representation of information and improve the cost performance of the system. Image fusion technology plays an extremely important role in remote sensing, medicine, natural resources exploration, marine resources management, topography analysis, biology and other fields. The left and right focused images, infrared images and visible images are used for simulation experiments, and the objective evaluation indexes of images are used to verify the feasibility of this method.

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