Text visualization is concerned with the representation of text in a graphicalform to facilitate comprehension of large textual data. Its aim is to improve the ability tounderstand and utilize the wealth of text-based...Text visualization is concerned with the representation of text in a graphicalform to facilitate comprehension of large textual data. Its aim is to improve the ability tounderstand and utilize the wealth of text-based information available. An essential task inany scientific research is the study and review of previous works in the specified domain,a process that is referred to as the literature survey process. This process involves theidentification of prior work and evaluating its relevance to the research question. With theenormous number of published studies available online in digital form, this becomes acumbersome task for the researcher. This paper presents the design and implementationof a tool that aims to facilitate this process by identifying relevant work and suggestingclusters of articles by conceptual modeling, thus providing different options that enablethe researcher to visualize a large number of articles in a graphical easy-to-analyze form.The tool helps the researcher in analyzing and synthesizing the literature and building aconceptual understanding of the designated research area. The evaluation of the toolshows that researchers have found it useful and that it supported the process of relevantwork analysis given a specific research question, and 70% of the evaluators of the toolfound it very useful.展开更多
In recent years, text visualization has been widely acknowledged as an effective approach for understanding the structure and patterns hidden in complicated textual information. In this paper, we propose a new visuali...In recent years, text visualization has been widely acknowledged as an effective approach for understanding the structure and patterns hidden in complicated textual information. In this paper, we propose a new visualization system called TextInsight with two of our contributions. Firstly, a textual entropy theory is introduced to encode the semantic importance distribution in the corpus. Based on the proposed multidimensional joint probability histogram in vector fields, the improved algorithm provides a novel way to position valuable information in massive short texts accurately. Secondly, a map-like metaphor is generated to visualize the textual topics and their relationships. For the problem of over-segmentation in the layout and clustering procedure, we propose an optimization algorithm combining Affinity Propagation(AP) and MultiDimensional Scaling(MDS), and the improved geographical representation is more comprehensible and aesthetically appealing. Our experimental results and initial user feedback suggest that this system is effective in aiding text analysis.展开更多
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.展开更多
With the development of cities and the explosion of infonnation,vast amounts of geo-tagged textural data about Points of Interests(POIs)have been generated.Extracting useful information and discovering text spatial di...With the development of cities and the explosion of infonnation,vast amounts of geo-tagged textural data about Points of Interests(POIs)have been generated.Extracting useful information and discovering text spatial distributions from the data are challenging and meaningful.Also,the huge numbers of POIs in modem cities make it important to have efficient approaches to retrieve and choose a destination.This paper provides a visual design combing metro map and wordles to meet the needs.In this visualization,metro lines serve as the divider lines splitting the city into several subareas and the boundaries to constrain wordles within each subarea.The wordles are generated from keywords extracted from the text about POIs(including reviews,descriptions,etc.)and embedded into the subareas based on their geographical locations.By generating intuitive results and providing an interactive visualization to support exploring text distribution patterns,our strategy can guide the users to explore urban spatial characteristics and retrieve a location efficiently.Finally,we implement a visual analysis of the restaurants data in Shanghai,China as a case study to evaluate our strategy.展开更多
With the rapid development of Internet technology,a rich set of e-government data are collected by the government departments.For example,a variety of feedback text data can be obtained quickly and efficiently through...With the rapid development of Internet technology,a rich set of e-government data are collected by the government departments.For example,a variety of feedback text data can be obtained quickly and efficiently through various channels such as the mayor’s mailbox.It is an effective way to improve the working efficiency of the government to extract hot topics from large-scale e-government text data,establish the correlation between topics and geographic space,and interactively explore the sources of public feedback problems.However,it is a difficult task to explore the large-scale e-government text data with traditional visualization methods such as word cloud,because too many words are hardly distributed in a limited space which will largely disturb the visual perception.In this paper,we propose a visual analytics system for large-scale e-government data exploration by means of simplified word cloud.Firstly,a representation learning model is used to embed the text data into high-dimensional space to quantitatively represent the semantic structure features of e-government text data.Then,the high-dimensional vectors are projected into a two-dimensional space where the coordinate distribution of points effectively expresses the semantic similarity of original words,which also presents geographic features that can be quantized by means of a similarity computing model.In order to simplify the understanding of large-scale e-government data and improve the cognitive efficiency of word could,we adopt the adaptive blue noise method to sample the topic words,which can simplify the visual expression of word cloud and improve the understanding efficiency of e-government data without losing the semantic structure features.Furthermore,an abstraction and visual analysis system for large-scale e-government text data is designed and implemented by integrating the above representation learning model,sampling-based abstraction model of word cloud,and topic and geographic correlation analysis model.This system provides convenient human-computer interaction modes and supports users to explore the analysis and extraction of the characteristics hidden in large-scale e-government data.It also helps government departments quickly locate the hot topics of public concern and their related regional distribution,and provides decision support to further improve the work efficiency of the government.Case studies based on real-world datasets further verify the effectiveness and practicability of our system.展开更多
Document collections do not only contain rich semantic content but also a diverse range of relationships.We propose WordleNet,an approach to supporting effective relationship exploration in document collections.Existi...Document collections do not only contain rich semantic content but also a diverse range of relationships.We propose WordleNet,an approach to supporting effective relationship exploration in document collections.Existing approaches mainly focus on semantic similarity or a single category of relationships.By constructing a general definition of document relationships,our approach enables the flexible and real-time generation of document relationships that may not otherwise occur to human researchers and may give rise to interesting patterns among documents.Multiple novel visual components are integrated in our approach,the effectiveness of which has been verified through a case study,a comparative study,and an eye-tracking experiment.展开更多
Microblogging services nformation and express opinions pro by vide a novel and popular communication scheme for Web users to share publishing short posts, which usually reflect the users＇ daily life. We can thus mode...Microblogging services nformation and express opinions pro by vide a novel and popular communication scheme for Web users to share publishing short posts, which usually reflect the users＇ daily life. We can thus model the users＇ daily status and interests according to their posts. Because of the high complexity and the large amount of the content of the microblog users＇ posts, it is necessary to provide a quick summary of the users＇ life status, both for personal users and commercial services. It is non-trivial to summarize the life status of microblog users, particularly when the summary is conducted over a long period. In this paper, we present a compact interactive visualization prototype, LifeCircle, as an efficient summary for exploring the long-term life status of microblog users. The radial visualization provides multiple views for a given microblog user, including annual topics, monthly keywords, monthly sentiments, and temporal trends of posts. We tightly integrate interactive visualization with novel and state-of-the-art microblogging analytics to maximize their advantages. We implement LifeCircle on Sina Weibo, the most popular microblogging service in China, and illustrate the effectiveness of our prototype with various case studies. Results show that our prototype makes users nostalgic and makes them reminiscent about past events, which helps them to better understand themselves and others展开更多
Effective analysis of large text collections remains a challenging problem given the growing volume of available text data.Recently,text mining techniques have been rapidly developed for automatically extracting key i...Effective analysis of large text collections remains a challenging problem given the growing volume of available text data.Recently,text mining techniques have been rapidly developed for automatically extracting key information from massive text data.Topic modeling,as one of the novel techniques that extracts a thematic structure from documents,is widely used to generate text summarization and foster an overall understanding of the corpus content.Although powerful,this technique may not be directly applicable for general analytics scenarios since the topics and topic-document relationship are often presented probabilistically in models.Moreover,information that plays an important role in knowledge discovery,for example,times and authors,is hardly reflected in topic modeling for comprehensive analysis.In this paper,we address this issue by presenting a visual analytics system,VISTopic,to help users make sense of large document collections based on topic modeling.VISTopic first extracts a set of hierarchical topics using a novel hierarchical latent tree model(HLTM)(Liu et al.,2014).In specific,a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic organization.To leverage multi-perspective information for visual analytics,VISTopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical documents.Three case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.展开更多
With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields.Facing a new research fi...With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields.Facing a new research field,researchers often hope to quickly learn what is being studied in this field,which research points are receiving high attention,which researchers are studying these research points,and then consider the possibility of collaborating with core researchers on these research points.In addition,students who are preparing for academic further education usually conduct research on mentors and mentors’research platforms,including academic connections,employment opportunities,etc.In order to satisfy these requirements,we(1)design a research point state map based on a science map to help researchers and students understand the development state of a new research field;(2)design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors;(3)designs citation pattern histogram to quickly discover research achievements with high research value,such as the Sleeping Beauty papers,recently hot papers,classic papers and so on.Finally,an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data,and its effectiveness is verified through case studies.展开更多
文摘Text visualization is concerned with the representation of text in a graphicalform to facilitate comprehension of large textual data. Its aim is to improve the ability tounderstand and utilize the wealth of text-based information available. An essential task inany scientific research is the study and review of previous works in the specified domain,a process that is referred to as the literature survey process. This process involves theidentification of prior work and evaluating its relevance to the research question. With theenormous number of published studies available online in digital form, this becomes acumbersome task for the researcher. This paper presents the design and implementationof a tool that aims to facilitate this process by identifying relevant work and suggestingclusters of articles by conceptual modeling, thus providing different options that enablethe researcher to visualize a large number of articles in a graphical easy-to-analyze form.The tool helps the researcher in analyzing and synthesizing the literature and building aconceptual understanding of the designated research area. The evaluation of the toolshows that researchers have found it useful and that it supported the process of relevantwork analysis given a specific research question, and 70% of the evaluators of the toolfound it very useful.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA7013033)
文摘In recent years, text visualization has been widely acknowledged as an effective approach for understanding the structure and patterns hidden in complicated textual information. In this paper, we propose a new visualization system called TextInsight with two of our contributions. Firstly, a textual entropy theory is introduced to encode the semantic importance distribution in the corpus. Based on the proposed multidimensional joint probability histogram in vector fields, the improved algorithm provides a novel way to position valuable information in massive short texts accurately. Secondly, a map-like metaphor is generated to visualize the textual topics and their relationships. For the problem of over-segmentation in the layout and clustering procedure, we propose an optimization algorithm combining Affinity Propagation(AP) and MultiDimensional Scaling(MDS), and the improved geographical representation is more comprehensible and aesthetically appealing. Our experimental results and initial user feedback suggest that this system is effective in aiding text analysis.
文摘A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
基金This work is supported by National Key Research and Development Program of China(Grant No.2017YFB0701900,2016QY02D0304)National Nature Science Foundation of China(Grant No.61100053,61572318,61772336,61672055)。
文摘With the development of cities and the explosion of infonnation,vast amounts of geo-tagged textural data about Points of Interests(POIs)have been generated.Extracting useful information and discovering text spatial distributions from the data are challenging and meaningful.Also,the huge numbers of POIs in modem cities make it important to have efficient approaches to retrieve and choose a destination.This paper provides a visual design combing metro map and wordles to meet the needs.In this visualization,metro lines serve as the divider lines splitting the city into several subareas and the boundaries to constrain wordles within each subarea.The wordles are generated from keywords extracted from the text about POIs(including reviews,descriptions,etc.)and embedded into the subareas based on their geographical locations.By generating intuitive results and providing an interactive visualization to support exploring text distribution patterns,our strategy can guide the users to explore urban spatial characteristics and retrieve a location efficiently.Finally,we implement a visual analysis of the restaurants data in Shanghai,China as a case study to evaluate our strategy.
基金the National Natural Science Foundation of China(No.61872314,No.61802339)the Natural Science Foundation of Zhejiang Province(No.LY18F020024)+2 种基金the Humanities and Social Sciences Foundation of Ministry of Education in China(No.18YJC910017)the Major Humanities and Social Sciences Research Project in Zhejiang Province(2018QN021)the Open Project Program of the State Key Lab of CAD&CG of Zhejiang University(No.A2001)
文摘With the rapid development of Internet technology,a rich set of e-government data are collected by the government departments.For example,a variety of feedback text data can be obtained quickly and efficiently through various channels such as the mayor’s mailbox.It is an effective way to improve the working efficiency of the government to extract hot topics from large-scale e-government text data,establish the correlation between topics and geographic space,and interactively explore the sources of public feedback problems.However,it is a difficult task to explore the large-scale e-government text data with traditional visualization methods such as word cloud,because too many words are hardly distributed in a limited space which will largely disturb the visual perception.In this paper,we propose a visual analytics system for large-scale e-government data exploration by means of simplified word cloud.Firstly,a representation learning model is used to embed the text data into high-dimensional space to quantitatively represent the semantic structure features of e-government text data.Then,the high-dimensional vectors are projected into a two-dimensional space where the coordinate distribution of points effectively expresses the semantic similarity of original words,which also presents geographic features that can be quantized by means of a similarity computing model.In order to simplify the understanding of large-scale e-government data and improve the cognitive efficiency of word could,we adopt the adaptive blue noise method to sample the topic words,which can simplify the visual expression of word cloud and improve the understanding efficiency of e-government data without losing the semantic structure features.Furthermore,an abstraction and visual analysis system for large-scale e-government text data is designed and implemented by integrating the above representation learning model,sampling-based abstraction model of word cloud,and topic and geographic correlation analysis model.This system provides convenient human-computer interaction modes and supports users to explore the analysis and extraction of the characteristics hidden in large-scale e-government data.It also helps government departments quickly locate the hot topics of public concern and their related regional distribution,and provides decision support to further improve the work efficiency of the government.Case studies based on real-world datasets further verify the effectiveness and practicability of our system.
基金partially supported by the National Natural Science Foundation of China (Nos. 61602340 and 61572348)the National Key Research and Development Program of China (Nos. 2018YFC0831700 and 2018YFC0809800)
文摘Document collections do not only contain rich semantic content but also a diverse range of relationships.We propose WordleNet,an approach to supporting effective relationship exploration in document collections.Existing approaches mainly focus on semantic similarity or a single category of relationships.By constructing a general definition of document relationships,our approach enables the flexible and real-time generation of document relationships that may not otherwise occur to human researchers and may give rise to interesting patterns among documents.Multiple novel visual components are integrated in our approach,the effectiveness of which has been verified through a case study,a comparative study,and an eye-tracking experiment.
基金supported by the National Natural Science Foundation of China (Nos. 61170196 and 61202140)by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office
文摘Microblogging services nformation and express opinions pro by vide a novel and popular communication scheme for Web users to share publishing short posts, which usually reflect the users＇ daily life. We can thus model the users＇ daily status and interests according to their posts. Because of the high complexity and the large amount of the content of the microblog users＇ posts, it is necessary to provide a quick summary of the users＇ life status, both for personal users and commercial services. It is non-trivial to summarize the life status of microblog users, particularly when the summary is conducted over a long period. In this paper, we present a compact interactive visualization prototype, LifeCircle, as an efficient summary for exploring the long-term life status of microblog users. The radial visualization provides multiple views for a given microblog user, including annual topics, monthly keywords, monthly sentiments, and temporal trends of posts. We tightly integrate interactive visualization with novel and state-of-the-art microblogging analytics to maximize their advantages. We implement LifeCircle on Sina Weibo, the most popular microblogging service in China, and illustrate the effectiveness of our prototype with various case studies. Results show that our prototype makes users nostalgic and makes them reminiscent about past events, which helps them to better understand themselves and others
基金This project is funded by a grant proposal(Ref:YBCB2009041-44)of Huawei Technologies Noah’s Ark Lab.
文摘Effective analysis of large text collections remains a challenging problem given the growing volume of available text data.Recently,text mining techniques have been rapidly developed for automatically extracting key information from massive text data.Topic modeling,as one of the novel techniques that extracts a thematic structure from documents,is widely used to generate text summarization and foster an overall understanding of the corpus content.Although powerful,this technique may not be directly applicable for general analytics scenarios since the topics and topic-document relationship are often presented probabilistically in models.Moreover,information that plays an important role in knowledge discovery,for example,times and authors,is hardly reflected in topic modeling for comprehensive analysis.In this paper,we address this issue by presenting a visual analytics system,VISTopic,to help users make sense of large document collections based on topic modeling.VISTopic first extracts a set of hierarchical topics using a novel hierarchical latent tree model(HLTM)(Liu et al.,2014).In specific,a topic view accounting for the model features is designed for overall understanding and interactive exploration of the topic organization.To leverage multi-perspective information for visual analytics,VISTopic further provides an evolution view to reveal the trend of topics and a document view to show details of topical documents.Three case studies based on the dataset of IEEE VIS conference demonstrate the effectiveness of our system in gaining insights from large document collections.
基金the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDB38030300.
文摘With the intersection and convergence of multiple disciplines and technologies,more and more researchers are actively exploring interdisciplinary cooperation outside their main research fields.Facing a new research field,researchers often hope to quickly learn what is being studied in this field,which research points are receiving high attention,which researchers are studying these research points,and then consider the possibility of collaborating with core researchers on these research points.In addition,students who are preparing for academic further education usually conduct research on mentors and mentors’research platforms,including academic connections,employment opportunities,etc.In order to satisfy these requirements,we(1)design a research point state map based on a science map to help researchers and students understand the development state of a new research field;(2)design a bar-link author-affiliation information graph to help researchers and students clarify academic networks of scholars and find suitable collaborators or mentors;(3)designs citation pattern histogram to quickly discover research achievements with high research value,such as the Sleeping Beauty papers,recently hot papers,classic papers and so on.Finally,an interactive analytical system named PubExplorer was implemented with IEEE VIS publication data,and its effectiveness is verified through case studies.
