The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people an...The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people and the environment in which we live.Such datasets are often high dimensional and heterogeneous in nature,with complex geospatial relations.Analysing such data can be challenging,especially when there is a need to maintain spatial awareness as the non-spatial attributes are studied.Geo-Coordinated Parallel Coordinates(GCPC)is a geovisual analytics approach designed to support exploration and analysis within complex geospatial environmental data.Parallel coordinates are tightly coupled with a geospatial representation and an investigative scatterplot,all of which can be used to show,reorganize,filter,and highlight the high dimensional,heterogeneous,and geospatial aspects of the data.Two sets of field trials were conducted with expert data analysts to validate the real-world benefits of the approach for studying environmental data.The results of these evaluations were positive,providing real-world evidence and new insights regarding the value of using GCPC to explore among environmental datasets when there is a need to remain aware of the geospatial aspects of the data as the non-spatial elements are studied.展开更多
Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping,rotation and scaling.These attacks are the easiest yet most successful in rendering the survival of watermark dif...Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping,rotation and scaling.These attacks are the easiest yet most successful in rendering the survival of watermark difficult.Such geometric operations alter the pixel orientation in the cover thereby rendering the watermark difficult to locate and extract.However,if the alterations produced by the geometric attacks such as scaling,cropping and rotation can be modeled in terms of the change in the image geometry,it is possible to relocate the watermark even after the original cover has suffered an attack.This paper contributes to the state of the art by proposing an image watermarking technique that attempts to model the attacks like cropping,scaling and rotation in terms of the image geometry.The proposed scheme is acceptably resistant to common geometric attacks and common image processing attacks.The watermark embedding is also done efficiently to offer resistance to image processing attacks.The watermark detection procedure is blind and key based,also not requiring the original cover work for watermark extraction.Efforts have been given to ensure that the proposed scheme conforms to robustness against attacks and exhibits high visual fidelity of the watermarked cover.展开更多
Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as electrocardiograms.TBSS has similarities to Principal Component Analysis(PCA)as it se...Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as electrocardiograms.TBSS has similarities to Principal Component Analysis(PCA)as it separates the input data into univariate components and is applicable to suitable datasets from various domains,such as medicine,finance,or civil engineering.Despite TBSS’s broad applicability,the involved tasks are not well supported in current tools,which offer only text-based interactions and single static images.Analysts are limited in analyzing and comparing obtained results,which consist of diverse data such as matrices and sets of time series.Additionally,parameter settings have a big impact on separation performance,but as a consequence of improper tooling,analysts currently do not consider the whole parameter space.We propose to solve these problems by applying visual analytics(VA)principles.Our primary contribution is a design study for TBSS,which so far has not been explored by the visualization community.We developed a task abstraction and visualization design in a user-centered design process.Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution.We present TBSSvis,an interactive web-based VA prototype,which we evaluated extensively in two interviews with five TBSS experts.Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.展开更多
Bus travel time is uncertain due to the dynamic change in the environment.Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks.To ...Bus travel time is uncertain due to the dynamic change in the environment.Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks.To quantify the uncertainty of the bus travel time prediction model,a visual analysis method about the bus travel time uncertainty is proposed in this paper,which can intuitively obtain uncertain information of bus travel time through visual graphs.Firstly,a Bayesian encoder–decoder deep neural network(BEDDNN)model is proposed to predict the bus travel time.The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty.Second,an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines.The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties.Finally,the model evaluation results based on actual bus data illustrate the effectiveness of the model.The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.展开更多
This article introduces the Visualization Laboratory at the Department of Computer Science&Engineering,the University of Notre Dame,including the lab’s overview,current research directions,facilities,and interna...This article introduces the Visualization Laboratory at the Department of Computer Science&Engineering,the University of Notre Dame,including the lab’s overview,current research directions,facilities,and international collaborations.展开更多
Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always ...Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.展开更多
The authors regret that they missed to add the fourth author Abdallah Altahan Alnuaimi who contributed to this paper in formalizing the research methodology.His affiliation details and email address are as below。
Maritime transports play a critical role in international trade and commerce.Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel...Maritime transports play a critical role in international trade and commerce.Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel navigations.Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management.As essential techniques in complex data analysis and understanding,visualization and visual analysis have been widely used in vessel trajectory data analysis.This paper presents a literature review on the visualization and visual analysis of vessel trajectory data.First,we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing.Then,we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details.Finally,we expound on the prospects of the remaining challenges and directions for future research.展开更多
Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interest...Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.展开更多
This paper proposes a generative approach for the automatic typesetting of books in desktop publishing.The presented system consists in a computer script that operates inside a widely used design software tool and imp...This paper proposes a generative approach for the automatic typesetting of books in desktop publishing.The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules,styles and principles which have been identified in the literature.The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people.The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.展开更多
Digital learning is becoming increasingly important in the crisis COVID-19 and is widespread in most countries.The proliferation of smart devices and 5G telecommunications systems are contributing to the development o...Digital learning is becoming increasingly important in the crisis COVID-19 and is widespread in most countries.The proliferation of smart devices and 5G telecommunications systems are contributing to the development of digital learning systems as an alternative to traditional learning systems.Digital learning includes blended learning,online learning,and personalized learning which mainly depends on the use of new technologies and strategies,so digital learning is widely developed to improve education and combat emerging disasters such as COVID-19 diseases.Despite the tremendous benefits of digital learning,there are many obstacles related to the lack of digitized curriculum and collaboration between teachers and students.Therefore,many attempts have been made to improve the learning outcomes through the following strategies:collaboration,teacher convenience,personalized learning,cost and time savings through professional development,and modeling.In this study,facial expressions and heart rates are used to measure the effectiveness of digital learning systems and the level of learners’engagement in learning environments.The results showed that the proposed approach outperformed the known related works in terms of learning effectiveness.The results of this research can be used to develop a digital learning environment.展开更多
Chinese calligraphy,as a well-known performing art form,occupies an important role in the intangible cultural heritage of China.Previous studies focused on the psychophysiological benefits of Chinese calligraphy.Littl...Chinese calligraphy,as a well-known performing art form,occupies an important role in the intangible cultural heritage of China.Previous studies focused on the psychophysiological benefits of Chinese calligraphy.Little attention has been paid to its aesthetic attributes and effectiveness on the cognitive process.To complement our understanding of Chinese calligraphy,this study investigated the aesthetic experience of Chinese cursive-style calligraphy using brain functional network analysis.Subjects stayed on the coach and rested for several minutes.Then,they were requested to appreciate artwork of cursive-style calligraphy.Results showed that(1)changes in functional connectivity between frontooccipital,fronto-parietal,bilateral parietal,and central–occipital areas are prominent for calligraphy condition,(2)brain functional network showed an increased normalized cluster coefficient for calligraphy condition in alpha2 and gamma bands.These results demonstrate that the brain functional network undergoes a dynamic reconfiguration during the aesthetic experience of Chinese calligraphy.Providing evidence that the aesthetic experience of Chinese calligraphy has several similarities with western art while retaining its unique characters as an eastern traditional art form.展开更多
A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics(IA).We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D...A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics(IA).We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D analysis in expressing and perceiving data.This innovative visual method enables users to interact directly with data through non-contact aerial gestures(NCAG).Referring to the“What you see is what you get”concept in scientific visualization,we proposed a novel approach for the visual exploration of meteorological data that aims to immerse users in the analysis process.We hope this approach can inspire immersive visualization techniques for other types of geographic data as well.Finally,we conducted a user questionnaire to evaluate our system and work.The evaluation results demonstrate that our system effectively reduces cognitive burden,alleviates mental workload,and enhances users’retention of analysis findings.展开更多
Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills.Although existing libraries and tools reduce the difficulty of generating...Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills.Although existing libraries and tools reduce the difficulty of generating graph visualization,there are still many challenges.We work closely with developers and formulate several design goals,then design and implement G6,a web-based library for graph visualization.It combines template-based configuration for high usability and flexible customization for high expressiveness.To enhance development efficiency,G6 proposes a range of optimizations,including state management and interaction modes.We demonstrate its capabilities through an extensive gallery,a quantitative performance evaluation,and an expert interview.G6 was first released in 2017 and has been iterated for 317 versions.It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.展开更多
Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallengin...Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance,and tiny-size carving motifs. This research aims to improve carving motif detection performance onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese carving dataset through data collection, augmentation, and annotation. To our knowledge,it is the first Balinese carving dataset for the object detection task. Based on experimental results,CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.展开更多
It is our great pleasure to announce the launch of a new journal,Visual Informatics,which will publish original articles and survey papers on theories and algorithms for visual information modeling,synthesis and proce...It is our great pleasure to announce the launch of a new journal,Visual Informatics,which will publish original articles and survey papers on theories and algorithms for visual information modeling,synthesis and processing.Visual information is the major channel for human and ma-chines to perceive the surrounding world,whereas images,videos,graphics and animations are the most popular visual media.Be-sides real-time generation and transmission of visual information,efficient acquisition and perception of the features and semantics behind visual information have long been the grand challenges of multiple areas in computer science.展开更多
With the growing demand of automatic emotion recognition system,emotion recognition is becoming more and more crucial for human-computer interaction(HCI)research.Recently,there is a continuous improvement in the perfo...With the growing demand of automatic emotion recognition system,emotion recognition is becoming more and more crucial for human-computer interaction(HCI)research.Recently,there is a continuous improvement in the performance of automatic emotion recognition due to the development of both hardware and deep learning methods.However,because of the abstract concept and multiple expressions of emotion,automatic emotion recognition is still a challenging task.In this paper,we propose a novel Multi-modal Correlated Network for emotion recognition aiming at exploiting the information from both audio and visual channels to achieve more robust and accurate detection.In the proposed method,the audio signals and visual signals are first preprocessed for the feature extraction.After preprocessing,we obtain the Mel-spectrograms,which can be treated as images,and the representative frames from visual segments.Then the Mel-spectrograms are fed to the convolutional neural network(CNN)to get the audio features and the representative frames are fed to the CNN and LSTM to get features.Specially,we employ the triplet loss to increase the differentiation of inter-class.Meanwhile,we propose a novel correlated loss to reduce the differentiation of intra-class.Finally,we apply the feature fusion method to fuse the audio and visual feature for emotion recognition classification.The experimental result on AEFW dataset demonstrates the correlation information of multiple modals is crucial for automatic emotion recognition and the proposed method can achieve the state-of-the-art performance on the classification task.展开更多
Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions.These methods and st...Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions.These methods and strategies should not only present scientific findings as images in a concise way but also need to be effective and expressive,which often remain untested.Here,we present Versus,a tool to enable easy image quality assessment and image ranking,utilizing a two-alternative forced choice methodology(2AFC)and an efficient ranking algorithm based on a binary search.The tool provides a systematic way of setting up evaluation experiments via the web without the necessity to install any additional software or require any programming skills.Furthermore,Versus can easily interface with crowdsourcing platforms,such as Amazon’s Mechanical Turk,or can be used as a stand-alone system to carry out evaluations with experts.We demonstrate the use of Versus by means of an image evaluation study,aiming to determine if hue,saturation,brightness,and texture are good indicators of uncertainty in three-dimensional protein structures.Drawing from the power of crowdsourcing,we argue that there is demand and also great potential for this tool to become a standard for simple and fast image evaluations,with the aim to test the effectiveness and expressiveness of scientific visualizations.展开更多
Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive ...Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive a finding.It is thus beneficial to capture and summarize the important steps in an analysis.This provenance would then effectively support recalling,reusing,reproducing,and sharing the analysis process and results.However,the provenance of analyzing a large,complex network would often be a long interaction record.To automatically compose a concise visual summarization of network analysis provenance,we introduce a ranking model together with a reduction algorithm.The model identifies and orders important interactions used in the network analysis.Based on this model,our algorithm is able to minimize the provenance,while still preserving all the essential steps for recalling and sharing the analysis process and results.We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.展开更多
基金This work was supported in part by grant from Social Sciences and Humanities Research Council of Canada(SSHRC)(895-2011-1011)held by the second author.
文摘The large number of environmental problems faced by society in recent years has driven researchers to collect and study massive amounts of data in order to understand the complex relations that exist between people and the environment in which we live.Such datasets are often high dimensional and heterogeneous in nature,with complex geospatial relations.Analysing such data can be challenging,especially when there is a need to maintain spatial awareness as the non-spatial attributes are studied.Geo-Coordinated Parallel Coordinates(GCPC)is a geovisual analytics approach designed to support exploration and analysis within complex geospatial environmental data.Parallel coordinates are tightly coupled with a geospatial representation and an investigative scatterplot,all of which can be used to show,reorganize,filter,and highlight the high dimensional,heterogeneous,and geospatial aspects of the data.Two sets of field trials were conducted with expert data analysts to validate the real-world benefits of the approach for studying environmental data.The results of these evaluations were positive,providing real-world evidence and new insights regarding the value of using GCPC to explore among environmental datasets when there is a need to remain aware of the geospatial aspects of the data as the non-spatial elements are studied.
文摘Most of the digital image watermarking techniques are susceptible to geometric attacks such as cropping,rotation and scaling.These attacks are the easiest yet most successful in rendering the survival of watermark difficult.Such geometric operations alter the pixel orientation in the cover thereby rendering the watermark difficult to locate and extract.However,if the alterations produced by the geometric attacks such as scaling,cropping and rotation can be modeled in terms of the change in the image geometry,it is possible to relocate the watermark even after the original cover has suffered an attack.This paper contributes to the state of the art by proposing an image watermarking technique that attempts to model the attacks like cropping,scaling and rotation in terms of the image geometry.The proposed scheme is acceptably resistant to common geometric attacks and common image processing attacks.The watermark embedding is also done efficiently to offer resistance to image processing attacks.The watermark detection procedure is blind and key based,also not requiring the original cover work for watermark extraction.Efforts have been given to ensure that the proposed scheme conforms to robustness against attacks and exhibits high visual fidelity of the watermarked cover.
基金supported by the Austrian Science Fund(FWF)under grant P31881-N32.
文摘Temporal Blind Source Separation(TBSS)is used to obtain the true underlying processes from noisy temporal multivariate data,such as electrocardiograms.TBSS has similarities to Principal Component Analysis(PCA)as it separates the input data into univariate components and is applicable to suitable datasets from various domains,such as medicine,finance,or civil engineering.Despite TBSS’s broad applicability,the involved tasks are not well supported in current tools,which offer only text-based interactions and single static images.Analysts are limited in analyzing and comparing obtained results,which consist of diverse data such as matrices and sets of time series.Additionally,parameter settings have a big impact on separation performance,but as a consequence of improper tooling,analysts currently do not consider the whole parameter space.We propose to solve these problems by applying visual analytics(VA)principles.Our primary contribution is a design study for TBSS,which so far has not been explored by the visualization community.We developed a task abstraction and visualization design in a user-centered design process.Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution.We present TBSSvis,an interactive web-based VA prototype,which we evaluated extensively in two interviews with five TBSS experts.Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.
基金supported by National Natural Science Foundation of China(Grant No.61872304,No.61802320)Excellent Youth Foundation of Si’chuan(Grant No.19JCQN0108).
文摘Bus travel time is uncertain due to the dynamic change in the environment.Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks.To quantify the uncertainty of the bus travel time prediction model,a visual analysis method about the bus travel time uncertainty is proposed in this paper,which can intuitively obtain uncertain information of bus travel time through visual graphs.Firstly,a Bayesian encoder–decoder deep neural network(BEDDNN)model is proposed to predict the bus travel time.The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty.Second,an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines.The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties.Finally,the model evaluation results based on actual bus data illustrate the effectiveness of the model.The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.
基金the U.S.National Science Foundation through grants IIS-1017935,CNS-1229297,IIS-1456763,IIS-1455886,CNS-1629914,DUE-1833129,and IIS-1955395.
文摘This article introduces the Visualization Laboratory at the Department of Computer Science&Engineering,the University of Notre Dame,including the lab’s overview,current research directions,facilities,and international collaborations.
基金the Ministry of National Education,Turkey for financially supporting the first author’s PhD study at Newcastle University,UK.
文摘Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.
文摘The authors regret that they missed to add the fourth author Abdallah Altahan Alnuaimi who contributed to this paper in formalizing the research methodology.His affiliation details and email address are as below。
基金supported in part by the National Natural Science Foundation of China(No.41801313,41901397,and 61872388).
文摘Maritime transports play a critical role in international trade and commerce.Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel navigations.Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management.As essential techniques in complex data analysis and understanding,visualization and visual analysis have been widely used in vessel trajectory data analysis.This paper presents a literature review on the visualization and visual analysis of vessel trajectory data.First,we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing.Then,we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details.Finally,we expound on the prospects of the remaining challenges and directions for future research.
文摘Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.
基金This work is partially supported by the Foundation for Science and Technology,I.P./MCTES(Portugal)through national funds(PIDDAC),within the scope of project UIDB/00326/2020 or project code UIDP/00326/2020Sérgio M.Rebelo was funded by FCT under the grant SFRH/BD/132728/2017 and COVID/BD/151969/2021.
文摘This paper proposes a generative approach for the automatic typesetting of books in desktop publishing.The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules,styles and principles which have been identified in the literature.The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people.The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.
文摘Digital learning is becoming increasingly important in the crisis COVID-19 and is widespread in most countries.The proliferation of smart devices and 5G telecommunications systems are contributing to the development of digital learning systems as an alternative to traditional learning systems.Digital learning includes blended learning,online learning,and personalized learning which mainly depends on the use of new technologies and strategies,so digital learning is widely developed to improve education and combat emerging disasters such as COVID-19 diseases.Despite the tremendous benefits of digital learning,there are many obstacles related to the lack of digitized curriculum and collaboration between teachers and students.Therefore,many attempts have been made to improve the learning outcomes through the following strategies:collaboration,teacher convenience,personalized learning,cost and time savings through professional development,and modeling.In this study,facial expressions and heart rates are used to measure the effectiveness of digital learning systems and the level of learners’engagement in learning environments.The results showed that the proposed approach outperformed the known related works in terms of learning effectiveness.The results of this research can be used to develop a digital learning environment.
基金the National Natural Science Foundation of China(Grant No.61772440,Grant No.62007016).
文摘Chinese calligraphy,as a well-known performing art form,occupies an important role in the intangible cultural heritage of China.Previous studies focused on the psychophysiological benefits of Chinese calligraphy.Little attention has been paid to its aesthetic attributes and effectiveness on the cognitive process.To complement our understanding of Chinese calligraphy,this study investigated the aesthetic experience of Chinese cursive-style calligraphy using brain functional network analysis.Subjects stayed on the coach and rested for several minutes.Then,they were requested to appreciate artwork of cursive-style calligraphy.Results showed that(1)changes in functional connectivity between frontooccipital,fronto-parietal,bilateral parietal,and central–occipital areas are prominent for calligraphy condition,(2)brain functional network showed an increased normalized cluster coefficient for calligraphy condition in alpha2 and gamma bands.These results demonstrate that the brain functional network undergoes a dynamic reconfiguration during the aesthetic experience of Chinese calligraphy.Providing evidence that the aesthetic experience of Chinese calligraphy has several similarities with western art while retaining its unique characters as an eastern traditional art form.
基金supported by Natural Science Foundation of Sichuan Province(Grant No.2022NSFSC0961)the Ph.D.Research Foundation of Southwest University of Science and Technology(Grant No.19zx7144)the Special Research Foundation of China(Mianyang)Science and Technology City Network Emergency Management Research Center(Grant No.WLYJGL2023ZD04).
文摘A novel approach to visually represent meteorological data has emerged with the maturation of Immersive Analytics(IA).We have proposed an immersive meteorological virtual sandbox as a solution to the limitations of 2D analysis in expressing and perceiving data.This innovative visual method enables users to interact directly with data through non-contact aerial gestures(NCAG).Referring to the“What you see is what you get”concept in scientific visualization,we proposed a novel approach for the visual exploration of meteorological data that aims to immerse users in the analysis process.We hope this approach can inspire immersive visualization techniques for other types of geographic data as well.Finally,we conducted a user questionnaire to evaluate our system and work.The evaluation results demonstrate that our system effectively reduces cognitive burden,alleviates mental workload,and enhances users’retention of analysis findings.
基金supported by National Natural Science Foundation of China(61772456).
文摘Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills.Although existing libraries and tools reduce the difficulty of generating graph visualization,there are still many challenges.We work closely with developers and formulate several design goals,then design and implement G6,a web-based library for graph visualization.It combines template-based configuration for high usability and flexible customization for high expressiveness.To enhance development efficiency,G6 proposes a range of optimizations,including state management and interaction modes.We demonstrate its capabilities through an extensive gallery,a quantitative performance evaluation,and an expert interview.G6 was first released in 2017 and has been iterated for 317 versions.It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.
基金the Directorate General of Higher Education,Research,and Technology,Republic of Indonesia under the grand number 3/E1/KP.PTNBH/2021.
文摘Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance,and tiny-size carving motifs. This research aims to improve carving motif detection performance onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese carving dataset through data collection, augmentation, and annotation. To our knowledge,it is the first Balinese carving dataset for the object detection task. Based on experimental results,CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.
文摘It is our great pleasure to announce the launch of a new journal,Visual Informatics,which will publish original articles and survey papers on theories and algorithms for visual information modeling,synthesis and processing.Visual information is the major channel for human and ma-chines to perceive the surrounding world,whereas images,videos,graphics and animations are the most popular visual media.Be-sides real-time generation and transmission of visual information,efficient acquisition and perception of the features and semantics behind visual information have long been the grand challenges of multiple areas in computer science.
文摘With the growing demand of automatic emotion recognition system,emotion recognition is becoming more and more crucial for human-computer interaction(HCI)research.Recently,there is a continuous improvement in the performance of automatic emotion recognition due to the development of both hardware and deep learning methods.However,because of the abstract concept and multiple expressions of emotion,automatic emotion recognition is still a challenging task.In this paper,we propose a novel Multi-modal Correlated Network for emotion recognition aiming at exploiting the information from both audio and visual channels to achieve more robust and accurate detection.In the proposed method,the audio signals and visual signals are first preprocessed for the feature extraction.After preprocessing,we obtain the Mel-spectrograms,which can be treated as images,and the representative frames from visual segments.Then the Mel-spectrograms are fed to the convolutional neural network(CNN)to get the audio features and the representative frames are fed to the CNN and LSTM to get features.Specially,we employ the triplet loss to increase the differentiation of inter-class.Meanwhile,we propose a novel correlated loss to reduce the differentiation of intra-class.Finally,we apply the feature fusion method to fuse the audio and visual feature for emotion recognition classification.The experimental result on AEFW dataset demonstrates the correlation information of multiple modals is crucial for automatic emotion recognition and the proposed method can achieve the state-of-the-art performance on the classification task.
基金This work was supported by CSIRO’s OCE Science Leader programme and Computational and Simulation Sciences platformpartly by the Australian Research Council under Linkage Project LP140100574。
文摘Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions.These methods and strategies should not only present scientific findings as images in a concise way but also need to be effective and expressive,which often remain untested.Here,we present Versus,a tool to enable easy image quality assessment and image ranking,utilizing a two-alternative forced choice methodology(2AFC)and an efficient ranking algorithm based on a binary search.The tool provides a systematic way of setting up evaluation experiments via the web without the necessity to install any additional software or require any programming skills.Furthermore,Versus can easily interface with crowdsourcing platforms,such as Amazon’s Mechanical Turk,or can be used as a stand-alone system to carry out evaluations with experts.We demonstrate the use of Versus by means of an image evaluation study,aiming to determine if hue,saturation,brightness,and texture are good indicators of uncertainty in three-dimensional protein structures.Drawing from the power of crowdsourcing,we argue that there is demand and also great potential for this tool to become a standard for simple and fast image evaluations,with the aim to test the effectiveness and expressiveness of scientific visualizations.
基金This research is sponsored in part by the U.S.National Science Foundation through grants IIS-1528203 and IIS-1741536.
文摘Large,complex networks are commonly found in many application domains,such as sociology,biology,and software engineering.Analyzing such networks can be a non-trivial task,as it often takes many interactions to derive a finding.It is thus beneficial to capture and summarize the important steps in an analysis.This provenance would then effectively support recalling,reusing,reproducing,and sharing the analysis process and results.However,the provenance of analyzing a large,complex network would often be a long interaction record.To automatically compose a concise visual summarization of network analysis provenance,we introduce a ranking model together with a reduction algorithm.The model identifies and orders important interactions used in the network analysis.Based on this model,our algorithm is able to minimize the provenance,while still preserving all the essential steps for recalling and sharing the analysis process and results.We create a prototype system demonstrating the effectiveness of our model and algorithm with two usage scenarios.
