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4th International conference on E-Business, Information Management and Computer Science, EBIMCS 2021

作者： Zhang, Yuanjing Cultural And Tourism Inustry Research Center Of Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9781450395687

In this paper, Knowledge Map is used to make a visual analysis of 802 Chinese papers related to smart scenic spot research collected by CNKI, discuss the hot spots and development trends of smart scenic spot research in China. The research finds that the promotion of smart tourism policy plays a positive role in promoting the national smart tourism reform plan and realizing the establishment and implementation of smart tourism scenic spots in various regions, thus driving the growth of literature quantity. Nowdays the hotspots of smart tourism research in China focus on "smart scenic spots""smart tourism""scenic spot management"and "tourist attractions", with increasing heat. However, core authors and institutions published fewer papers, and their cooperation with each other was scattered, so no representative academic group was formed. At the same time, the cooperation of most research institutions in China is divided by regional scope, and trans-regional, cross-disciplinary and cross-institutional cooperation needs to be further strengthened. At present, domestic researches on smart scenic spots have gone deep into the practical application level, at the present stage, the development results are uneven, and the efficiency of production, education and research institutes is not obvious. In the future, the high-quality development of smart scenic spots still needs the exploration and deepening of scientific researchers. © 2021 ACM.

关键词： data visualization

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Knowledge Map of Chinese Geriatric Medical Care Research Field: Research Status and Trend—data visualization analysis Based on Cooc Software

Knowledge Map of Chinese Geriatric Medical Care Research Fie...

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HBDSS 2022;2nd International conference on Health Big data and Smart Sports

作者： Hongrui Bao Yanyin Cui Hui Yue

In the long-term context of an ageing population, geriatric medical care is an important safeguard for the Chinese response to the problem of aging. The study aimed to explore the research hotspots and process of geriatric medical care. We conducted a visual analysis of the literatures from 1986 to 2021 in CNKI, Wanfang and VIP database by Louvain algorithm and bibliometric method. We found that the research of geriatric medical care in China lags behind that in developed countries, and the development mainly presents three stages: germination, development and adjustment. The number of publications presents a steady growth trend; Medical and Health journals are the main publication journals; The core research teams are mainly composed of universities and research institutes in Shanghai and Beijing; The clustering topics focus on exploring the elderly medical security system, the feasibility of the medical service model, the management of chronic diseases, the development of the silver industry, medical disputes and health education for the elderly. Finally, we come to the conclusion that the health management of chronic diseases has become a new research hotspot and the exploration of new medical service models is an important trend.

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Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive data

Matrix Profile XXVI: Mplots: Scaling Time Series Similarity ...

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IEEE International conference on data Mining (ICDM)

作者： Maryam Shahcheraghi Ryan Mercer João Manuel De Almeida Rodrigues Audrey Der Hugo Filipe Silveira Gamboa Zachary Zimmerman Eamonn Keogh University of California Riverside Libphys-UNL NOVA Faculty of Science and Technology

Time series similarity matrices (informally, recurrence plots), are useful tools for time series data mining. They can be used to guide data exploration, and various useful features can be derived from them and then fed into downstream analytics. However, time series similarity matrices suffer from very poor scalability, taxing both time and memory requirements. In this work, we introduce novel ideas that allow us to scale the largest time series similarity matrices that can be examined by several orders of magnitude. The first idea is a novel algorithm to compute the matrices in a way that removes dependency on the subsequence length. This algorithm is so fast that it allows us to now address datasets where the memory limitations begin to dominate. Our second novel contribution is a multiscale algorithm that computes an approximation of the matrix appropriate for the limitations of the user’s memory/screen-resolution, then performs a local, just-in-time recomputation of any region that the user wishes to zoom-in on. Given that we can largely remove time and space barriers, human visual attention then becomes the bottleneck. We further introduce algorithms that search massive matrices with quadrillions of cells and then prioritize regions for later attention by either humans or algorithms. We will demonstrate the utility of our ideas for data exploration, segmentation, and classification in diverse domains.

关键词： visualization Scalability Time series analysis Memory management Approximation algorithms Classification algorithms data mining

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Geovisualization: Multidimensional exploration of the Territory 15

Geovisualization: Multidimensional Exploration of the Territ...

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15th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 11th International conference on Information visualization Theory and Applications (ivAPP)

作者： Christophe, Sidonie Paris Est Univ LaSTIG IGN ENSG Paris France

ISBN: (纸本)9789897584022

The purpose of this position paper is to emphasize the remaining challenges for geovisualization in an evolutive context of data, users and spatio-temporal problems to solve in an interdisciplinary approach. Geovisualization is the visualization of spatio-temporal data, phenomena and dynamics on earth, based on the user interaction with heterogeneous data, and their capacities of perception and cognition. This implies to bring closer together knowledge, concepts and models from related scientific visualization domains, for a better understanding, interpretation and analysis of spatio-temporal phenomena on earth. We currently face and cross several types of complexities, regarding spaces, data, models and tools. Our position here, based on past and on-going works, as first proofs of concept, is to model a multidimensional exploration of the territory, because integrating explorations of uses, styles, interaction and immersion capacities, until various 'points of view' on the represented spatio-temporal phenomenon.

关键词： Geovisualization visual analysis Style Interaction Immersion

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exploration on Optimization of Business Talent Training in Digital Economy Era 22

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

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Proceedings of the 6th International conference on Digital Technology in Education

作者： Yijun Zhang Dan Yuan School of Finance and Economics Guangdong University of Science and Technology China School of Management 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.

关键词： digital economy new business talent training business

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Self-Supervised visual Representation Learning Framework for Road Sign Classification

Self-Supervised Visual Representation Learning Framework for...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Godson Michael Dsilva Vinayak Ashok Information Technology Department Finolex Academy of Management and Technology Ratnagiri India

In the landscape of machine learning, the dominance of supervised learning in crafting specialized models for specific tasks is evident, yet its confinement in terms of broader generalization has underscored the quest for more versatile models capable of acquiring multiple skills without an insurmountable reliance on labelled data. A pivotal avenue toward this goal lies in self-supervised learning (SSL), which promises to instill foundational knowledge and a semblance of common sense into AI systems. This investigation undertakes a comprehensive exploration of diverse SSL strategies, elucidating their inherent constraints on the road sign detection and classification usecase. Through an intricate comparative analysis spotlighting Context Autoencoders (CAE) and MoCoV3 - two prominent state-of-the-art methodologies leveraging masked image modelling and contrastive learning, respectively - we unravel their distinct attributes and limitations. Furthermore, we introduce an innovative end-to-end framework meticulously tailored for self-supervised road sign classification. Our roadmap encompasses the infusion of domain-specific expertise in road signs into this framework, followed by rigorous benchmarking against state-of-the-art SSL models across diverse road sign datasets, employing a spectrum of evaluation metrics. This study emerges as a stepping stone, propelling AI closer to comprehesive intelligence by harnessing the potency of self-supervised paradigms.

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iConViz: Interactive visual exploration of the Default Contagion Risk of Networked-Guarantee Loans 15

iConViz: Interactive Visual Exploration of the Default Conta...

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15th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Niu, Zhibin Li, Runlin Wu, Junqi Cheng, Dawei Zhang, Jiawan Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728180090

Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk, a pivotal issue in developing preventive measures, and established iConViz, an interactive visual analysis tool that facilitates the closed-loop analysis process. A novel financial metric, the contagion effect, was formulated to quantify the infectious consequences of guarantee chains in this type of network. Based on this metric, we designed and implemented a series of novel and coordinated views that address the analysis of financial problems. Experts evaluated the system using real-world financial data. The proposed approach grants practitioners the ability to avoid previous ad hoc analysis methodologies and extend coverage of the conventional Capital Accord to the banking industry.

关键词： visualization analytics Regulatory visualization

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Evaluating Query Strategies for Different Feedback Types in Interactive View Recommendation 24

Evaluating Query Strategies for Different Feedback Types in ...

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24th International conference Information visualisation (iv)

作者： Zhang, Xiaozhong Ge, Xiaoyu Chrysanthis, Panos K. Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781728191348

Existing visualization recommendation approaches introduced an overwhelmingly large number of utility functions (UFs) to rank the visualizations (i.e., views). In order to discover the ideal UF and their tunable parameters that are most suited for a particular analysis context, recent works have proposed Interactive View Recommendation (ivR). ivR identifies the ideal UF by learning from the interactions with the user during the analysis process. We claim that the user is usually unable to provide an accurate real number between 0 and 1, indicating the view interestingness, and in this work, propose three alternative feedback types: binary, Likert scale, and pairwise comparison. We further developed ranking algorithms for the three feedback types. Finally, we propose different example selection strategies, and experimentally evaluate them on the three feedback types. We found that uncertainty-based and hybrid-based strategies usually outperforms the random strategy in terms of recommendation accuracy across all feedback types, while the interestingness-based strategy usually performs worse than the random strategy.

关键词： data exploration, data visualization, Relevance Feedback, View Utility Function, Personalized Recommendation

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Offshore Oil Slick Detection: From Photo-Interpreter to Explainable Multi-Modal Deep Learning Models Using SAR Images and Contextual data

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REMOTE SENSING 2022年第15期14卷 3565-3565页

作者： Amri, Emna Dardouillet, Pierre Benoit, Alexandre Courteille, Hermann Bolon, Philippe Dubucq, Dominique Credoz, Anthony Univ Savoie Mt Blanc Polytech Annecy Chambery LISTIC Lab F-74944 Annecy Le Vieux France TotalEnergies SE Ave Larribau F-64018 Pau France

Ocean surface monitoring, emphasizing oil slick detection, has become essential due to its importance for oil exploration and ecosystem risk prevention. Automation is now mandatory since the manual annotation process of oil by photo-interpreters is time-consuming and cannot process the data collected continuously by the available spaceborne sensors. Studies on automatic detection methods mainly focus on Synthetic Aperture Radar (SAR) data exclusively to detect anthropogenic (spills) or natural (seeps) oil slicks, all using limited datasets. The main goal is to maximize the detection of oil slicks of both natures while being robust to other phenomena that generate false alarms, called "lookalikes". To this end, this paper presents the automation of offshore oil slick detection on an extensive database of real and recent oil slick monitoring scenarios, including both types of slicks. It relies on slick annotations performed by expert photo-interpreters on Sentinel-1 SAR data over four years and three areas worldwide. In addition, contextual data such as wind estimates and infrastructure positions are included in the database as they are relevant data for oil detection. The contributions of this paper are: (i) A comparative study of deep learning approaches using SAR data. A semantic and instance segmentation analysis via FC-DenseNet and Mask R-CNN, respectively. (ii) A proposal for Fuse-FC-DenseNet, an extension of FC-DenseNet that fuses heterogeneous SAR and wind speed data for enhanced oil slick segmentation. (iii) An improved set of evaluation metrics dedicated to the task that considers contextual information. (iv) A visual explanation of deep learning predictions based on the SHapley Additive exPlanation (SHAP) method adapted to semantic segmentation. The proposed approach yields a detection performance of up to 94% of good detection with a false alarm reduction ranging from 14% to 34% compared to mono-modal models. These results provide new solutions to improve

关键词： oil slicks data fusion offshore detection SAR images meteorological data deep learning AI explanation

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visualization of similarity queries with trajectory estimation in complex data 24

Visualization of similarity queries with trajectory estimati...

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24th International conference Information visualisation (iv)

作者： Paiva, Claudio Eduardo Malaquias Junior, Roseval Donisete Bueno, Renato Sao Paulo State Technol Coll Franca SP Brazil Univ Fed Sao Carlos Sao Carlos SP Brazil

ISBN: (纸本)9781728191348

The information in a database can change over time and requires specific support for operations that process the temporal characteristics of the data. New instances of objects present in the database are inserted frequently, requiring management methods capable of handling temporal information. This paper aims to extend the delimitation techniques for visualization of similarity queries of images using estimates of their trajectories. The study of temporal evolution in complex data can benefit several areas of knowledge, such as agriculture, medicine, civil engineering and others. The use of visualization to understand data changes over time improves the knowledge of the distribution of the dataset, enables the recognition of data patterns more quickly and favors the acquisition of new useful information. In this sense, the visualization of queries with trajectory estimation in a three-dimensional space allows to study how the data are organized in the estimated regions. To perform this visual analysis, we use images at different time points of the same object in the database to estimate what an image of that object would look like at a different time.

关键词： Similarity query Complex data Temporal databases Trajectory-based query Time information

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