In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online lear...In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online learning resources face numerous challenges.The emergence of AIGC(Artificial Intelligence-Generated Content)technology offers innovative solutions to these issues.This paper explores the application of AIGC technology in enhancing the“new quality productive forces”of visual design for online learning resources.It emphasizes the need to balance technological innovation with humanistic care and highlights the importance of human intervention in the design process.展开更多
This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online ide...This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.展开更多
The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a p...The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a prompt way. In this paper, we apply non-negative matrix factorization and visualization to clustering message data, in order to provide a summary view of messages that disclose their deep semantic relationships. In particular, the NMF is able to find the underlying issues hidden in the messages about which most of the students are concerned. Visualization is employed to estimate the initial number of clusters, showing the relation communities. The experiments and comparison on a real dataset have been reported to demonstrate the effectiveness of the approaches.展开更多
Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for rese...Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.展开更多
Large-scale software systems,which are the most sophisticated human-designed objects,play more and more important role in our daily life.Consequently effective analysis for large-scale software has become an urgent pr...Large-scale software systems,which are the most sophisticated human-designed objects,play more and more important role in our daily life.Consequently effective analysis for large-scale software has become an urgent problem to be solved with the increasing issues of software security and the continuous expansion of software applications scope.For the characteristics of large scale and complex structure in large-scale software,the traditional software analysis techniques are difficult to be used.With the problem of difficulty in presentation,storage and low efficiency in the process of large-scale software analysis,the visualization analysis framework for large-scale software based on software network,named SoNet,is proposed with the combination of complex network theory and program slicing technique.Constraint logic attributes of the programs will be obtained through source code parsing.Then we will construct a global view by the theory of complex network after extracting software structure and behavior,improving user’s perception of software architecture in a macro perspective.Use case slicing will be realized combined with Redis cluster,and accessibility analysis when given a keyword to be analyzed.We evaluate our prototype implementation on an open source software project named SoundSea in Github,and the results suggest that our approach can realize the analysis for large-scale software.展开更多
Online question and answer(Q&A)communities,which allow users to exchange knowledge by asking and answering questions,have become increasingly popular.As a result of user active participation,these communities stor...Online question and answer(Q&A)communities,which allow users to exchange knowledge by asking and answering questions,have become increasingly popular.As a result of user active participation,these communities store overwhelming volumes of information.However,existing related methods are unable to meet community operators’needs for analyzing multi-dimensional Q&A sequences and understanding user behavior.In this paper,collaborating with domain experts in online community,we present a system,VisQAC,which explores the patterns of Q&A sequence and user behavior.In the system,a novel visual design is proposed,which is combined with flexible mapping measures for analyzing critical characteristics of sequence data.Moreover,a timeline visualization method is designed to visualize data with categorical attributes and its correlation can be displayed flexibly by choosing time mode and time granularity.The usefulness and effectiveness of the system are demonstrated with several case studies of VisQAC with community operators based on the Zhihu dataset.Our evaluation shows that VisQAC is beneficial to the understanding of Q&A sequence and associated user behavior.展开更多
In Hello Jiuzhaigou and Hello Huanglong,a set of high-definition lens presented the unique charm of Jiuzhaigou and Huanglong,the famous National 5A tourist attractions in Sichuan Province.The strong visual impact of t...In Hello Jiuzhaigou and Hello Huanglong,a set of high-definition lens presented the unique charm of Jiuzhaigou and Huanglong,the famous National 5A tourist attractions in Sichuan Province.The strong visual impact of the images aroused great interest of foreign friends who had never set foot in Bashu area.展开更多
3D terrain visualization of geographic information systems(GIS)data has become an important issue in recent years.This is due to the emergence of new geo-browsers such as Google Earth,widely popular among users.The av...3D terrain visualization of geographic information systems(GIS)data has become an important issue in recent years.This is due to the emergence of new geo-browsers such as Google Earth,widely popular among users.The availability of 3D representation tools has increased the demand for 3D terrain visualization.The aim of this paper is to review the literature related to the 3D terrain visualization of GIS data from the first map produced until the online mapping era.The reviews are divided into four different sections:manual visualization of 3D terrain,automated visualization of 3D terrain,online visualization of 3D terrain,and software for visualizing 3D terrain.Then,the paper compares between the different types of systems developed by various authors based on the capabilities and the limitations of the system.Some of the techniques have their own strengths and limitations which solve the problem in 3D terrain visualization.However,the research on improving 3D terrain visualization is still ongoing.This is due to the popularity of online environments and mobile devices that render 3D terrain.This review paper will help interested users understand the current state of 3D terrain visualization of GIS data in a better way.展开更多
With the popularity of online learning in recent decades,MOOCs(Massive Open Online Courses)are increasingly pervasive and widely used in many areas.Visualizing online learning is particularly important because it help...With the popularity of online learning in recent decades,MOOCs(Massive Open Online Courses)are increasingly pervasive and widely used in many areas.Visualizing online learning is particularly important because it helps to analyze learner performance,evaluate the effectiveness of online learning platforms,and predict dropout risks.Due to the large-scale,high-dimensional,and heterogeneous characteristics of the data obtained from online learning,it is difficult to find hidden information.In this paper,we review and classify the existing literature for online learning to better understand the role of visualization in online learning.Our taxonomy is based on four categorizations of online learning tasks:behavior analysis,behavior prediction,learning pattern exploration and assisted learning.Based on our review of relevant literature over the past decade,we also identify several remaining research challenges and future research work.展开更多
Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic m...Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic models, supervised Latent Dirichlet Allocation （sLDA） is acknowledged as a popular and competitive supervised topic model. How- ever, the gradual increase of the scale of datasets makes sLDA more and more inefficient and time-consuming, and limits its applications in a very narrow range. To solve it, a parallel online sLDA, named PO-sLDA （Parallel and Online sLDA）, is proposed in this study. It uses the stochastic variational inference as the learning method to make the training procedure more rapid and efficient, and a parallel computing mechanism implemented via the MapReduce framework is proposed to promote the capacity of cloud computing and big data processing. The online training capacity supported by PO-sLDA expands the application scope of this approach, making it instrumental for real-life applications with high real-time demand. The validation using two datasets with different sizes shows that the proposed approach has the comparative accuracy as the sLDA and can efficiently accelerate the training procedure. Moreover, its good convergence and online training capacity make it lucrative for the large-scale text data analyzing and processing.展开更多
Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this conte...Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.展开更多
Large-scale public buildings have high energy density, which on average consume 5 to 15 times more electricity than residential buildings. In Beijing, those public buildings account for about ten percent of the total ...Large-scale public buildings have high energy density, which on average consume 5 to 15 times more electricity than residential buildings. In Beijing, those public buildings account for about ten percent of the total building area, but their energy consumption （except heating） amounts to more than thirty percent of the total. Few electric meters are installed in those public buildings, however, making it more difficult to monitor how the energy is used.展开更多
文摘In the current trend of educational digitization,online learning platforms have proliferated,making the visual design of digital learning resources increasingly critical.However,existing visual designs for online learning resources face numerous challenges.The emergence of AIGC(Artificial Intelligence-Generated Content)technology offers innovative solutions to these issues.This paper explores the application of AIGC technology in enhancing the“new quality productive forces”of visual design for online learning resources.It emphasizes the need to balance technological innovation with humanistic care and highlights the importance of human intervention in the design process.
基金supported by the State Grid Science&Technology Project(5100-202114296A-0-0-00).
文摘This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.
文摘The use of online discussion forum can?effectively engage students in their studies. As the number of messages posted on the forum is increasing, it is more difficult for instructors to read and respond to them in a prompt way. In this paper, we apply non-negative matrix factorization and visualization to clustering message data, in order to provide a summary view of messages that disclose their deep semantic relationships. In particular, the NMF is able to find the underlying issues hidden in the messages about which most of the students are concerned. Visualization is employed to estimate the initial number of clusters, showing the relation communities. The experiments and comparison on a real dataset have been reported to demonstrate the effectiveness of the approaches.
文摘Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.
文摘Large-scale software systems,which are the most sophisticated human-designed objects,play more and more important role in our daily life.Consequently effective analysis for large-scale software has become an urgent problem to be solved with the increasing issues of software security and the continuous expansion of software applications scope.For the characteristics of large scale and complex structure in large-scale software,the traditional software analysis techniques are difficult to be used.With the problem of difficulty in presentation,storage and low efficiency in the process of large-scale software analysis,the visualization analysis framework for large-scale software based on software network,named SoNet,is proposed with the combination of complex network theory and program slicing technique.Constraint logic attributes of the programs will be obtained through source code parsing.Then we will construct a global view by the theory of complex network after extracting software structure and behavior,improving user’s perception of software architecture in a macro perspective.Use case slicing will be realized combined with Redis cluster,and accessibility analysis when given a keyword to be analyzed.We evaluate our prototype implementation on an open source software project named SoundSea in Github,and the results suggest that our approach can realize the analysis for large-scale software.
基金Supported by Major Development Program of Sichuan Province(18ZDYF1790)Key Technology R&D Program of Chengdu City(2015-HM01-00484-SF)the National Science and Technology Major Project(2018ZX100201AA-002-004)
文摘Online question and answer(Q&A)communities,which allow users to exchange knowledge by asking and answering questions,have become increasingly popular.As a result of user active participation,these communities store overwhelming volumes of information.However,existing related methods are unable to meet community operators’needs for analyzing multi-dimensional Q&A sequences and understanding user behavior.In this paper,collaborating with domain experts in online community,we present a system,VisQAC,which explores the patterns of Q&A sequence and user behavior.In the system,a novel visual design is proposed,which is combined with flexible mapping measures for analyzing critical characteristics of sequence data.Moreover,a timeline visualization method is designed to visualize data with categorical attributes and its correlation can be displayed flexibly by choosing time mode and time granularity.The usefulness and effectiveness of the system are demonstrated with several case studies of VisQAC with community operators based on the Zhihu dataset.Our evaluation shows that VisQAC is beneficial to the understanding of Q&A sequence and associated user behavior.
文摘In Hello Jiuzhaigou and Hello Huanglong,a set of high-definition lens presented the unique charm of Jiuzhaigou and Huanglong,the famous National 5A tourist attractions in Sichuan Province.The strong visual impact of the images aroused great interest of foreign friends who had never set foot in Bashu area.
文摘3D terrain visualization of geographic information systems(GIS)data has become an important issue in recent years.This is due to the emergence of new geo-browsers such as Google Earth,widely popular among users.The availability of 3D representation tools has increased the demand for 3D terrain visualization.The aim of this paper is to review the literature related to the 3D terrain visualization of GIS data from the first map produced until the online mapping era.The reviews are divided into four different sections:manual visualization of 3D terrain,automated visualization of 3D terrain,online visualization of 3D terrain,and software for visualizing 3D terrain.Then,the paper compares between the different types of systems developed by various authors based on the capabilities and the limitations of the system.Some of the techniques have their own strengths and limitations which solve the problem in 3D terrain visualization.However,the research on improving 3D terrain visualization is still ongoing.This is due to the popularity of online environments and mobile devices that render 3D terrain.This review paper will help interested users understand the current state of 3D terrain visualization of GIS data in a better way.
基金supported by the National Natural Science Foundation of China(61972356,62036009).
文摘With the popularity of online learning in recent decades,MOOCs(Massive Open Online Courses)are increasingly pervasive and widely used in many areas.Visualizing online learning is particularly important because it helps to analyze learner performance,evaluate the effectiveness of online learning platforms,and predict dropout risks.Due to the large-scale,high-dimensional,and heterogeneous characteristics of the data obtained from online learning,it is difficult to find hidden information.In this paper,we review and classify the existing literature for online learning to better understand the role of visualization in online learning.Our taxonomy is based on four categorizations of online learning tasks:behavior analysis,behavior prediction,learning pattern exploration and assisted learning.Based on our review of relevant literature over the past decade,we also identify several remaining research challenges and future research work.
基金This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61572226 and 61876069, and the Key Scientific and Technological Research and Development Project of Jilin Province of China under Grant Nos. 20180201067GX and 20180201044GX.
文摘Topic modeling is a mainstream and effective technology to deal with text data, with wide applications in text analysis, natural language, personalized recommendation, computer vision, etc. Among all the known topic models, supervised Latent Dirichlet Allocation （sLDA） is acknowledged as a popular and competitive supervised topic model. How- ever, the gradual increase of the scale of datasets makes sLDA more and more inefficient and time-consuming, and limits its applications in a very narrow range. To solve it, a parallel online sLDA, named PO-sLDA （Parallel and Online sLDA）, is proposed in this study. It uses the stochastic variational inference as the learning method to make the training procedure more rapid and efficient, and a parallel computing mechanism implemented via the MapReduce framework is proposed to promote the capacity of cloud computing and big data processing. The online training capacity supported by PO-sLDA expands the application scope of this approach, making it instrumental for real-life applications with high real-time demand. The validation using two datasets with different sizes shows that the proposed approach has the comparative accuracy as the sLDA and can efficiently accelerate the training procedure. Moreover, its good convergence and online training capacity make it lucrative for the large-scale text data analyzing and processing.
文摘Political discussions are characterized by conflicts of interest, and decisions are made based on negotiations. In general, participants need to reinforce their opinions and influence other participants. In this context, it is important to know how allies and opponents are positioned, in order to understand the discussion dynamics and plan adequate actions. This paper suggests the use of social network visualizations to explicit oppositions and alliances in order to support the understanding and following of political discussions. A system which supports these visualizations was built. An experiment performed to test the proposed visualizations showed to which extent they can be more efficient in identifying information about clashes and alliances than an online discussion system can.
文摘Large-scale public buildings have high energy density, which on average consume 5 to 15 times more electricity than residential buildings. In Beijing, those public buildings account for about ten percent of the total building area, but their energy consumption （except heating） amounts to more than thirty percent of the total. Few electric meters are installed in those public buildings, however, making it more difficult to monitor how the energy is used.
