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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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A Critical Assessment of visual Sound Source Localization Models Including Negative Audio

A Critical Assessment of Visual Sound Source Localization Mo...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xavier Juanola Gloria Haro Magdalena Fuentes Universitat Pompeu Fabra Barcelona Spain MARL-IDM New York University New York USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The task of visual Sound Source Localization (VSSL) involves identifying the location of sound sources in visual scenes, integrating audio-visual data for enhanced scene understanding. Despite advancements in state-of-the-art (SOTA) models, we observe three critical flaws: i) The evaluation of the models is mainly focused in sounds produced by objects that are visible in the image, ii) The evaluation often assumes a prior knowledge of the size of the sounding object, and iii) No universal threshold for localization in real-world scenarios is established, as previous approaches only consider positive examples without accounting for both positive and negative cases. In this paper, we introduce extended test sets and new metrics designed to complete the current standard evaluation of VSSL models by testing them in scenarios where none of the objects in the image corresponds to the audio input, i.e. a negative audio. We consider three types of negative audio: silence, noise and offscreen. Our analysis reveals that numerous SOTA models fail to appropriately adjust their predictions based on audio input, suggesting that these models may not be leveraging audio information as intended. Additionally, we provide a comprehensive analysis of the range of maximum values in the estimated audio-visual similarity maps, in both positive and negative audio cases, and show that most of the models are not discriminative enough, making them unfit to choose a universal threshold appropriate to perform sound localization without any a priori information of the sounding object, that is, object size and visibility.

关键词： Location awareness Measurement visualization Analytical models Noise Predictive models Speech processing Standards Testing Videos

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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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Development of the HySpex hyperspectral drill core scanner: Case study on exploration core from the Au-rich LaRonde-Penna volcanogenic massive sulfide deposit, Quebec, Canada 2

Development of the HySpex hyperspectral drill core scanner: ...

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conference on Hyperspectral Imaging and Applications ii

作者： Korting, Friederike Hernandez, Julio Koirala, Pesal Lehman, Miranda Monecke, Thomas Lindblom, David Norsk Elektro Opt AS Hyspex Ostensjoveien 34 N-0667 Oslo Norway Colorado Sch Mines Dept Geol & Geol Engn Ctr Adv Sci Explorat Reclamat Min 1516 Illinois St Golden CO 80401 USA Prediktera AB Tvistevagen 48A S-90736 Umea Sweden

ISBN: (纸本)9781510657489;9781510657496

HySpex is presenting an integrated solution for hyperspectral drill core imaging. The system's mineral mapping capabilities are presented in close cooperation with renowned academic and industrial partners through the Center to Advance the Science of exploration to Reclamation in Mining (CASERM) led by the Colorado School of Mines and Virginia Tech. Utilizing HySpex cameras covering the spectral range between 400 and 2500 nm, the system is capable of scanning full core boxes in seconds. Using Prediktera's new Breeze-GEO Software, real-time mineral mapping of the highest quality is achieved. Apart from different interactive qualitative and quantitative data analysis tools offered by Breeze, the platform includes the publicly available USGS Material Identification and Classification Algorithm (MICA) for mineral identification, as well as the minimum wavelength mapping (MWL) algorithm. The scanner's capabilities are demonstrated using drill cores from the LaRonde-Penna deposit. The deposit is located within the Archean Abitibi greenstone belt of Ontario and Quebec, Canada, which is home to numerous volcanogenic massive sulfide (VMS) deposits. LaRonde-Penna contains an endowment of 71 Mt of ore grading on average 3.9 g/t Au and economic grades of Zn, Cu and Pd. Because the deposits has been studied extensively over the past decades, cores from the deposit open up a unique opportunity for research and development.

关键词： hyperspectral imaging digital core shed core scanner mineral mapping Au-rich volcanogenic massive sulfides

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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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PREVIS: Predictive visual analytics of anatomical variability for radiotherapy decision support

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COMPUTERS & GRAPHICS-UK 2021年 97卷 126-138页

作者： Furmanova, Katarina Muren, Ludvig P. Casares-Magaz, Oscar Moiseenko, Vitali Einck, John P. Pilskog, Sara Raidou, Renata G. Masaryk Univ Brno Czech Republic Aarhus Univ Hosp Danish Ctr Particle Therapy Aarhus Denmark Univ Calif San Diego Dept Radiat Med & Appl Sci San Diego CA USA Univ Bergen Dept Phys & Technol Bergen Norway TU Wien Vienna Austria Univ Groningen Groningen Netherlands

Radiotherapy (RT) requires meticulous planning prior to treatment, where the RT plan is optimized with organ delineations on a pre-treatment Computed Tomography (CT) scan of the patient. The conventionally fractionated treatment usually lasts several weeks. Random changes (e.g., rectal and bladder filling in prostate cancer patients) and systematic changes (e.g., weight loss) occur while the patient is being treated. Therefore, the delivered dose distribution may deviate from the planned. Modern technology, in particular image guidance, allows to minimize these deviations, but risks for the patient remain. We present PREVIS: a visual analytics tool for (i) the exploration and prediction of changes in patient anatomy during the upcoming treatment, and (ii) the assessment of treatment strategies, with respect to the anticipated changes. Records of during-treatment changes from a retrospective imaging cohort with complete data are employed in PREVIS, to infer expected anatomical changes of new incoming patients with incomplete data, using a generative model. Abstracted representations of the retrospective cohort partitioning provide insight into an underlying automated clustering, showing main modes of variation for past patients. Interactive similarity representations support an informed selection of matching between new incoming patients and past patients. A Principal Component analysis (PCA)-based generative model describes the predicted spatial probability distributions of the incoming patient's organs in the upcoming weeks of treatment, based on observations of past patients. The generative model is interactively linked to treatment plan evaluation, supporting the selection of the optimal treatment strategy. We present a usage scenario, demonstrating the applicability of PREVIS in a clinical research setting, and we evaluate our visual analytics tool with eight clinical researchers. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Medical visualization visual Analytics Comparative visualization Ensemble visualization Radiotherapy Planning Cohort Study

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

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

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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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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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