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检索条件"任意字段=Conference on Visual Data Exploration and Analysis"

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visual exploration and analysis of Bank Performance Using Self Organizing Map 8th

Visual Exploration and Analysis of Bank Performance Using Se...

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8th International conference on Sciences of Electronics, Technologies of Information and Telecommunication, SETIT 2018

作者： Kessentini, Mouna Jeffers, Esther University of Picardie Jules Verne 10 Placette Lafleur Amiens Cédex80027 France

ISBN: (纸本)9783030210045

The visualization of high dimensional data has an important role to play as an artifact supporting exploratory data analysis. There is growing evidence of the effectiveness of information visualization as it provides help in understanding data, increases the level of cognitive functioning and performs pattern recognition. This paper deals with the usefulness of Self-Organizing Map (SOM) neural network in the area of the banking sector. We want to show how SOM can be useful to convert huge amounts of financial data into valuable information used to speed up the decision-making process and facilitate data analysis for deeper understanding © 2020, Springer Nature Switzerland AG.

关键词： Self organizing maps

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Toward Improving the Evaluation of visual Attention Models: a Crowdsourcing Approach

Toward Improving the Evaluation of Visual Attention Models: ...

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International Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Zanca, Dario Melacci, Stefano Gori, Marco Univ Siena Siena Italy

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

Human visual attention is a complex phenomenon. A computational modeling of this phenomenon must take into account where people look in order to evaluate which are the salient locations (spatial distribution of the fixations), when they look in those locations to understand the temporal development of the exploration (temporal order of the fixations), and how they move from one location to another with respect to the dynamics of the scene and the mechanics of the eyes (dynamics). State-of-the-art models focus on learning saliency maps from human data, a process that only takes into account the spatial component of the phenomenon and ignore its temporal and dynamical counterparts. In this work we focus on the evaluation methodology of models of human visual attention. We underline the limits of the current metrics for saliency prediction and scanpath similarity, and we introduce a statistical measure for the evaluation of the dynamics of the simulated eye movements. While deep learning models achieve astonishing performance in saliency prediction, our analysis shows their limitations in capturing the dynamics of the process. We find that unsupervised gravitational models, despite of their simplicity, outperform all competitors. Finally, exploiting a crowd-sourcing platform, we present a study aimed at evaluating how strongly the scanpaths generated with the unsupervised gravitational models appear plausible to naive and expert human observers.

关键词： visual attention models evaluation scanpath fixations saliency crowd-sourcing

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Collaboration Spotting X - A visual Network exploration Tool

Collaboration Spotting X - A Visual Network Exploration Tool

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International conference on Social Networks analysis, Management and Security (SNAMS)

作者： Aleksandar Bobic Jean–Mariele Goff Christian Guetl IPT Department CERN GenevaSwitzerland CoDiS Lab ISDS Graz University of Technology Graz Austria

ISBN: (纸本)9781665494960

Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.

关键词： Computer science Industries Social networking (online) visual analytics Prototypes Collaboration Security

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

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

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

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

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

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.

关键词： COVID-19 Epidemics visualization Market research data visualization Hospitals Urban areas

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Designing a Semi-automatic Map Construction Process for the Effective visualisation of Business Geodata 20th

Designing a Semi-automatic Map Construction Process for the ...

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20th International conference on Computational Science and Its Applications (ICCSA)

作者： Simon, Marion Asche, Hartmut Univ Potsdam Inst Geosci Potsdam Germany Univ Potsdam Hasso Plattner Inst Comp Graph Syst Grp Potsdam Germany

ISBN: (纸本)9783030588113;9783030588106

This paper proposes a map construction process for the semiautomatic construction of thematic maps from business information data. Addressing a non-specialist user audience, the map construction process will allow a correct, at the same time effective cartographic visualisation for further (geo)visual analysis. Utilising the frequently disregarded geospatial component of existing business mass data, quality map representations facilitate the visual exploration, detection, and analysis of relevant spatial data distributions and structures hitherto unseen in the data. Presently, neither operational procedures nor appropriate software systems, such as BIS, DDS or GIS, are available in the industry for an effective map representation of the geocoded data. To put economic and business experts into a position to make full use of the geo coordinates present in the data, an easy-to-handle map construction process is required to exploit the full semantic and spatial potential of business data. Exemplified for an area diagram map, the map construction process discussed here provides the relevant tools and methods for the targeted audience.

关键词： Geocoded business data Geovisualisation Map construction Quality map representation Geovisual analysis Modular process design

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Efficient visual monitoring of offshore windmill installations with online image annotation and deep learning computer vision

Efficient visual monitoring of offshore windmill installatio...

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Global OCEANS Singapore - U.S. Gulf Coast conference

作者： Langenkaemper, Daniel Moeller, Torben Bruen, Daniel Nattkemper, Tim Wilhelm Bielefeld Univ Biodata Min Grp Bielefeld Germany Saltat GmbH & Ko KG Bielefeld Germany

ISBN: (纸本)9781728154466

The number of offshore windmills keeps growing around the world since they are an essential part of any strategy to produce energy without using nuclear power plants or burning fossil resources. Due to the growing number of installations new monitoring and inspection strategies and routines need to be developed that are not only effective but efficient. In this work we will present first results from a joint project of industry partners and academia that aims at the development of new approaches making use of modern imaging technologies and new methods from artificial intelligence and computer vision research. We investigate three different strategies for collecting digital photos or video and posterior computational analysis using Convolutional Neural Networks (CNN). Our results indicate, that patterns of interest like rust or coating damage can be detected and classified with F-1 scores between 0.80 and 0.94 in photos collected by inspectors. In videos collected with unmanned aerial vehicles (UAV) rust patterns, oils spills and coating damage were detected with F-1 = 0.91. To support users in the time-consuming task of visual data exploration, we present a new visualization method referred to as "virtual twin". Using hand recorded image collections, a data-driven 3D model of a windmill is generated, that can be used to investigate damage patterns in the full context of the windmill and in full detail. Altogether our results indicate, that although each single approach has its limitations, a combination of different imaging methods, deep learning computer vision algorithms and sophisticated visual data exploration by experienced users appears to have potential to overcome the bottleneck in data analysis, interpretation and decision making in the context of the future inspections of offshore windmills, platforms or other constructions.

关键词： Inspection Offshore windmill Offshore wind turbine Unmanned Aerial Vehicles Computer vision Deep Learning Artificial Intelligence

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Drawn threads: Drawing as a visual methodology to enhance qualitative studies

Drawn threads: Drawing as a visual methodology to enhance qu...

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2013 International conference of the Design Research Society Special Interest Group on Experiential Knowledge: Knowing Inside Out -Experiential Knowledge, Expertise and Connoisseurship, EKSIG 2013

作者： Lerpiniere, Claire De Montfort University United Kingdom

ISBN: (纸本)9781907382727

This paper is an analysis of the value that drawing can bring to a formal research methodology. It is based on a series of drawings that were produced to extend and develop a form of qualitative enquiry, an Interpretative Phenomenological analysis (IPA). This analysis was conducted as part of a study of personal textiles that individuals retain and value beyond their practice use, solely for their sentimental or family historical value, termed, the domestic textile archive. Interpretative Phenomenological analysis was used for analysing the individual experience of these textiles, and was found to be a methodologically sound, yet flexible and creative method of uncovering the data. Phenomenological research methods are established as valid means to investigate subjective human experience, across a range of different subject disciplines (Smith, Flowers & Larkin, 2009). Such an interpretative approach was found to be an effective method to discern and illustrate the themes that arise through the individual's engagement with their own archived personal textiles. However, a visual rather than a text-based method is investigated for the supplementary value and illumination such an approach could bring to a qualitative study. In this respect, drawing is explored as a practice-based method of visual inquiry to supplement and support the initial research analysis. Within this model of thinking, drawing is a means of embodied, visual enquiry, which can be used to produce an analytical and evaluative practice that offers further insights to the text-based analysis. Drawing from the final and completed artefacts is a method for making implicit aspects of the experience of their making explicit. The drawn exploration of the material qualities of a textile design enabled an increased understanding of the tacit expertise of the designer or crafts-person, through applied drawing expertise. Copyright © 2013. The copyright rests with the authors and editors.

关键词： Textiles

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2020 1st International conference on Big data Analytics and Practices, IBDAP 2020

2020 1st International Conference on Big Data Analytics and ...

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1st International conference on Big data Analytics and Practices, IBDAP 2020

ISBN: (纸本)9781728181066

The proceedings contain 22 papers. The topics discussed include: research topic modeling: a use case for data analytics on research project data;visual representations of different family structures using an interactive data exploration technique;benefit recommendation system: a use case for pension fund data analytics;BoydCut: bidirectional LSTM-CNN model for Thai sentence segmenter;Sakdas: a python package for data profiling and data quality auditing;digital transformation in business and management research: bibliometric and co-word network analysis;student dropout prediction: a KMUTT case study;and the relations between implementation date of policies and the spreading of COVID-19.

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visual analysis of Biomechanical of lower limbs in 2001–2020 based on VOSviewer

Visual analysis of Biomechanical of lower limbs in 2001–202...

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Health Big data and Smart Sports (HBDSS), International conference on

作者： Xinyi Wei Qian Chen Sijie Yang Ruhao Huang Nanjing Sport Institute Nanjing China SuqianZeda Vocational College Suqian China Hunan Normal University Changsha China

ISBN: (纸本)9781665421898

Taking WOS (web of Science) as the retrieval platform, the retrieval time is from 2001 to 2021. 2180 English core journals on lower limbs biomechanics in recent 20 years are visually included by using “vosviewer 1.6.17”. By analyzing the existing research literature, the number of papers, research countries and key words related to lower limb biomechanics are sorted and summarized hope to understand the current research situation and judge the future research trend, so as to provide reference for further research. VOSviewer is a commonly used visual analysis software, but there are few related analyses on lower limb biomechanics. It is found that in the past two decades, the exploration in related fields has been deepening, among which the United States, Britain and Australia are the active countries with more research in this field, and coordination, speed, model are the most popular words, which reflect the current research trend.

关键词： Biomechanics visualization Biological system modeling Big data Market research Software Australia

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New Analytic Framework of Public Mental Health Prediction Using data Science 1

New Analytic Framework of Public Mental Health Prediction Us...

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International conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)

作者： Marrapu, Hari Krishna Maram, Balajee Reddi, Prasadu GMR Inst Technol Dept IT Rajam Andhra Pradesh India GMR Inst Technol Dept CSE Rajam Andhra Pradesh India Anil Neerukonda Inst Technol & Sci Dept IT Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781665421119;9781665421126

Nowadays the human-being is suffering from many issues in the society. In the current situation mental health problem is a prevalence issue. Usage of sensor data and dataset is an interesting issue dealing with prediction of mental health of the people living in society. The data science process can be carried out by collecting datasets from various health sector domains and differentiated into three categories such as low, medium, high. This is helpful in detecting weather the person is suffering from any mental issue or not. A sample test is conducted to assess the health conditions of healthy groups and people having mental illness with the help of data mining in data science field. This paper explains about a novel framework for prediction of public mental health condition which are helpful to handle the challenges in the field of data science. Evaluation of data science surveys is different from traditional data analysis due to the scientific approach method used during data classification and filtration. Here the role of "data Science" framework is for promoting the usage of framework by the healthcare professionals to face challenges of mental health.

关键词： data Science Public Health Mental Health Predictive modeling Pervasive computing visual data exploration data Mining

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