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

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Exclusion Vis: A visual Interactive System for Exclusion analysis in Life Insurance Using Machine Learning Techniques 11

Exclusion Vis: A Visual Interactive System for Exclusion Ana...

引用

11th IEEE International conference on Behavioural and Social Computing, BESC 2024

作者： Islam, Md Rafiqul Thanh Thao, Lam Nguyen Prome, Shanjita Akter Hossain Sakib, Md. Kowsar Sanin, Cesar Xu, Guandong Huang, Weidong Information Systems Australian Institute of Higher Education Sydney Australia College of Business & Management Uin University Viet Nam School of Computer Science Taylors University Malaysia Data Science Institute University of Technology Sydney Australia TD School University of Technology Sydney Australia

ISBN: (纸本)9798331531904

Exclusion analysis in life insurance stands out as a crucial task in minimizing customer risks. Numerous insur-ance companies have implemented artificial intelligence (AI) solutions to streamline processes and mitigate customer-related risks, specifically leveraging machine learning (ML) technolo-gies. While some studies have acknowledged its significance, there needs to be more to deliver comprehensive research on exclusion analysis utilizing advanced techniques. Thus, this paper focuses on exclusion classification blending with visual interactive systems (Vis), aiming to comprehend data obtained from customer disclosure information. To tackle this challenge, 1) we initiate the investigation by employing various multi-label classifiers, 2) apply several ML techniques, utilizing data from a prominent Australian insurance company, where support vector classification (SVC) and random forest (RF) emerges as the superior performer, and 3) we introduce a visual interactive system called Exclusion Vis, meticulously crafted to facilitate in-depth exploration of the obtained results. The evaluation of Exclusion Vis further underscores its superiority compared to existing systems. © 2024 IEEE.

关键词： Sales

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Social Media User Behavior Mining and Sentiment analysis based on Computer Big data 4

Social Media User Behavior Mining and Sentiment Analysis bas...

引用

4th IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Zhang, Qiang Hou, Yutong School of Artificial Intelligence and Big Data Chongqing Metropolitan College of Science and Technology Chongqing China

ISBN: (纸本)9798350352931

Faced with the rapid development of social networks and the enormous business opportunities they contain, data mining and analysis based on social networks has become an inevitable trend. By utilizing various technologies such as visual C++6.0 and Pajek, data collection and analysis were achieved. The user ID (Identification) was added to the Sina server and relevant user information was requested through the API (Application Programming Interface). Through the data analysis module, the stored data was processed appropriately for different research objects, and network analysis tools can be used to visually display certain results. Using this model, sentiment classification was performed on Weibo samples with existing labels, and the accuracy, recall and F1 value of manual labeling were evaluated. The positive accuracy was 0.85;recall was 0.82;F1 value was 0.84. This article helps to improve user experience and explore more user value. © 2024 IEEE.

关键词： data accuracy

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Demo: CaseFinder: Automated visual and Quantitative analysis Tool for Police Narrative

Demo: CaseFinder: Automated Visual and Quantitative Analysis...

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9th IEEE/ACM International conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Brown, Martin Thomas, Dominic Khan, Md Abdullah Al Hafiz Azmee, Abm Adnan Nandan, Monica Pei, Yong Kennesaw State Univ Dept Data Sci & Analyt Marietta GA USA Kennesaw State Univ Comp Sci Marietta GA USA Kennesaw State Univ Informat Syst Marietta GA USA Kennesaw State Univ Social Work & Human Serv Marietta GA USA

ISBN: (纸本)9798350345025;9798350345018

This demonstration introduces the CaseFinder tool, a GUI designed to automate visualizations and quantitatively analyze 911 police reports. The tool allows for efficient data exploration for non-technical users integrating behavioral health-related keywords. By amalgamating diverse keyword category inputs, CaseFinder provides a holistic approach to analyzing reports by uncovering indications of behavioral health problems while including perspectives from multiple professional domains. Ultimately, CaseFinder aims to improve the understanding of the magnitude of the problem of behavioral health issues involved in 911 crises to mitigate reoccurrence.

关键词： mental health behavioral health natural language processing

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Spatial exploration Indicators in the Remote Assessment of visual Neglect 22nd

Spatial Exploration Indicators in the Remote Assessment of V...

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22nd International conference on Image analysis and Processing (ICIAP)

作者： Ferraro, Federica Iaconi, Giulia Genesio, Giulia Truffelli, Romina Amella, Roberta Simonini, Marina Dellepiane, Silvana Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit Genoa Italy RAISE Ecosyst Genoa Italy La Colletta Hosp Struttura Complessa & Recupero Rieducaz Funz ASL3 Sistema Sanit Reg Liguria Genoa Italy

ISBN: (纸本)9783031431524;9783031431531

The visual neglect is a visuo-spatial attention disorder associated with stroke events and its presence is considered a negative prognostic factor of functional recovery. The specific assessment tests that are most frequently administered to evaluate the presence of this disorder are "paper and pencil tests", such as barrage tests. The current work presents the digital version of the Albert's barrage test for the evaluation of the Unilateral Spatial Neglect (USN) as it has been integrated into the ReMoVES tele-rehabilitation system. The data captured by this activity is broader and more complete than the information extractable from the paper version, including the order of the exploration sequence, work trajectories, punctual speed and other indicators. The procedure is preliminarily validated both on a control group of healthy subjects and on some patients. Several sessions are examined with the aim of observing which parameter of the digital Albert Test is statistically more significant for verifying the similarity in behavior between non-pathological subjects and the possible improvement over time of two pathological case studies.

关键词： Spatial exploration USN assessment test

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EmoGeoCity: Interactive visual exploration of City's Historical and Cultural Evolution Based on Emotional Geography 17

EmoGeoCity: Interactive Visual Exploration of City's Histori...

引用

17th Pacific visualization conference (PacificVis)

作者： Yang, Yutong Liu, Yinuo Bai, Qishuo Zhou, Tianyi Ye, Ziduo Dong, Xiaoju Shanghai Jiao Tong Univ SJTU Paris Elite Inst Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

ISBN: (纸本)9798350393811;9798350393804

A city's history and culture studies involves understanding the literary works and historical events that have shaped the city's identity. Increased availability of quantitative historical data has provided new opportunities. Taking Nanjing as an example, this paper proposes EmoGeoCity, a visual analytics system to study a city's cultural and historical evolution, through the use of digital humanities methods and emotional geography. The system incorporates sentiment analysis into historical research to quantify the emotional content of works and synthesize an overall emotion trend within a specific location. A dynamic emotional map, integrating locations, works, and events, enables a macroscopic observation of city emotions over time. An emotional polyline is designed to provide a microscopic interpretation of the emotion trend of a single location. exploration through the system reveals the evolution of the city from an emotional geographic perspective, which gives insights for humanities researchers studying a city's history and literature, as well as for the general public interested in gaining knowledge on historical sites. Case and user studies illustrate the effectiveness and usability of our system.

关键词： Human-centered computing-visualization-visual Analytics

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Engineering Annotations to Support Analytical Provenance in visual exploration Processes 22nd

Engineering Annotations to Support Analytical Provenance in ...

引用

22nd International conference on Web Engineering (ICWE)

作者： Tikat, Maroua Menin, Aline Buffa, Michel Winckler, Marco Univ Cote dAzur CNRS INRIA I3SSPARKS Wimm Team Nice France

ISBN: (纸本)9783031099175;9783031099168

This paper focuses on the fundamental role played by annotations to support provenance analysis in visual exploration processes of large datasets. Particularly, we investigate the use of annotations during the visual exploration of semantic datasets assisted by chained visualization techniques. In this paper, we identify three potential uses of annotations: (i) documenting findings (including errors in the dataset), (ii) supporting collaborative reasoning among teammates, and (iii) analysing provenance during the exploratory process. To demonstrate the feasibility of our approach, we implemented it as a tool support, while illustrating its usage and effectiveness through a series of use case scenarios. We identify the attributes and meta-data that describe the dependencies between annotations and visual representations, and we illustrate these dependencies through a domain-specific model.

关键词： Annotations Provenance analysis visual exploration Information visualization data quality

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Exploring Tabular data Through Networks 45th

Exploring Tabular Data Through Networks

引用

45th European conference on Information Retrieval (ECIR)

作者： Bobic, Aleksandar Le Goff, Jean-Marie Gutl, Christian CERN IPT Dept Geneva Switzerland Graz Univ Technol CoDiS Lab ISDS Graz Austria

ISBN: (纸本)9783031282409;9783031282416

Representing and visualizing data as networks is a widely spread approach to analyzing highly connected data in domains such as medicine, social sciences, and information retrieval. Investigating data as networks requires pre-processing, retrieval or filtering, conversion of data into networks, and application of various network analysis approaches. These processes are usually complex and hard to perform without some programming knowledge and resources. To the best of our knowledge, most solutions attempting to make these functionalities accessible to users focus on particular processes in isolation without exploring how these processes could be further abstracted or combined in a real-world application to assist users in their data exploration and knowledge extraction journey. Furthermore, most applications focusing on such approaches tend to be closed-source. This paper introduces a solution that combines the approaches above as part of Collaboration Spotting X (CSX), an open-source network-based visual analytics tool for retrieving, modeling, and exploring or analyzing data as networks. It abstracts the concepts above through the use of multiple interactive visualizations. In addition to being an easily accessible open-source platform for data exploration and analysis, CSX can also serve as a real-world evaluation platform for researchers in related computer science areas who wish to test their solutions and approaches to machine learning, visualizations, interactions, and more in a real-world system.

关键词： Information retrieval Network modeling Network visual analytics

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Complex Networks exploration with Triangles

Complex Networks Exploration with Triangles

引用

2023 IEEE International conference on Big data, Bigdata 2023

作者： Hossain, Shahadat Mir, Raheem Hossain, Emam University of Northern British Columbia Department of Computer Science Prince George Canada University of Lethbridge Mathematics and Computer Science LethbridgeAB Canada

ISBN: (纸本)9798350324457

Triangles are an essential structure in complex network analysis representing metrics such as clustering coefficient and transitivity. In this work, we employ the recently introduced 'Triangle Centrality' in exploring cohesive sub-networks of interest within the maximum k -core of complex networks. Motivated by the recent interest in higher-order connectivity patterns to understand fundamental structures that control behaviour of complex systems, we employ triangles as 'higher-order' structural units in complex systems. Each triangle in the network is attributed with a numeric score, the so called k -count, to indicate its 'influence' calculated based on the number of other triangles it is associated with. We demonstrate our proposed method to perform visual exploration of cohesive sub-networks of interest on a well-studied benchmark social network 'Lusseau's Dolphin Social Network'. © 2023 IEEE.

关键词： Cohesive Sub-Networks Higher-Order Connectivity Scalable Network analysis. Triangle Centrality visual Analytics

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VisStoryMaker: supporting non-expert analysts in visually exploring datasets and communicating insights with visual annotations and data stories 24

VisStoryMaker: supporting non-expert analysts in visually ex...

引用

23rd Brazilian Symposium on Human Factors in Computing Systems, IHC 2024

作者： Braga, Dieinison Jack Freire da Silva, Marisa Carmo Lima, Raul de Araújo Barbosa, Gabriel Diniz Junqueira Barbosa, Simone Diniz Junqueira IDEIAS-SERG PUC-Rio RJ Rio de Janeiro Brazil Datamint Datamint Systems and Services RJ Rio de Janeiro Brazil

ISBN: (纸本)9798400712241

Due to data production and availability growth, professionals in several disciplines have been facing an increasing need to explore and understand data, obtain insights, and communicate them effectively. Many visualization systems have been developed commercially and within the research community to support non-expert analysts. We can consider at least three challenges these tools aim to face: support the selection of appropriate visualizations and decide on the visual mappings, extract and communicate factual information from the visualizations, and use visualizations in data-rich narratives. In response to these challenges, we developed VisStoryMaker, a visualization tool that supports both exploration and communication about data. To aid users in exploring and understanding data, VisStoryMaker recommends visualizations through system-generated questions and data facts. To support communicating about data, the system recommends visual annotations of data facts and provides a story-building module, allowing analysts to use the generated charts and facts as a blueprint for a data story. We have conducted empirical studies to compare VisStoryMaker’s features with existing applications: chart recommendations with Voyager 2, storytelling construction with Flourish, and data facts and chart annotations with Tableau. Our findings indicate that the system-generated questions and data facts supported non-expert analysts in exploratory analysis. They perceived visual data facts annotations as useful and supported them in raising hypotheses about the data, understanding data, and leading to insights, thus enhancing data analysis. Participants perceived the visual annotations and StoryMaker as helpful in organizing the system-generated pieces of information and incorporating them into comprehensive narratives and presentations. © 2024 Copyright held by the owner/author(s).

关键词： data assimilation

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A LayoutLMv3-Based Model for Enhanced Relation Extraction in visually-Rich Documents 18th

A LayoutLMv3-Based Model for Enhanced Relation Extraction in...

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18th International conference on Document analysis and Recognition (ICDAR)

作者： Adnan, Wiam Tang, Joel Zouggari, Yassine Bel Khayat Laatiri, Seif Edinne Lame, Laurent Caspani, Fabien Ecole Polytech Palaiseau France BNP Paribas CIB Paris France

ISBN: (纸本)9783031705458;9783031705465

Document Understanding is an evolving field in Natural Language Processing (NLP). In particular, visual and spatial features are essential in addition to the raw text itself and hence, several multimodal models were developed in the field of visual Document Understanding (VDU). However, while research is mainly focused on Key Information Extraction (KIE), Relation Extraction (RE) between identified entities is still under-studied. For instance, RE is crucial to regroup entities or obtain a comprehensive hierarchy of data in a document. In this paper, we present a model that, initialized from LayoutLMv3, can match or outperform the current state-of-the-art results in RE applied to visually-Rich Documents (VRD) on FUNSD and CORD datasets, without any specific pre-training and with fewer parameters. We also report an extensive ablation study performed on FUNSD, highlighting the great impact of certain features and modelization choices on the performances.

关键词： Relation Extraction visual Document Understanding Document Intelligence

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