Word cloud visualization is a compelling graphical representation that visually depicts the frequency of words within a given text or dataset[1].Research on word clouds focuses on two main aspects.The first emphasizes...Word cloud visualization is a compelling graphical representation that visually depicts the frequency of words within a given text or dataset[1].Research on word clouds focuses on two main aspects.The first emphasizes processing words,such as using the latent Dirichlet allocation(LDA)algorithm to uncover topics in the documents[2],while the second involves visual impact through striking word arrangements[3,4].In the realm of extensive biomedical data,effectiveknowledge delivery to biologists is crucial.展开更多
The integration, analysis and visualization of the big omics data are critical for addressing a broad spectrum of biological questions. One of the most frequently conducted procedures is enrichment analysis, which sta...The integration, analysis and visualization of the big omics data are critical for addressing a broad spectrum of biological questions. One of the most frequently conducted procedures is enrichment analysis, which statistically tests whether individual functional an- notations of Gent Ontology （GO） or Kyoto Encyclopedia of Genes and Genomes （KEGG） are significantly over-or under-represented in an ＂interesting＂ gene or protein list against the reference set （Tavazoie et al., 1999）.展开更多
Emotion(e.g.,valence and arousal)is an important factor in literature(e.g.,poetry and prose),and has rich values for plotting the life and knowledge of historical figures and appreciating the aesthetics of literary wo...Emotion(e.g.,valence and arousal)is an important factor in literature(e.g.,poetry and prose),and has rich values for plotting the life and knowledge of historical figures and appreciating the aesthetics of literary works.Currently,digital humanities and computational literature apply data statistics extensively in emotion analysis but lack visual analytics for efficient exploration.To fill the gap,we propose a user-centric approach that integrates advanced machine learning models and intuitive visualization for emotion analysis in literature.We make three main contributions.First,we consolidate a new emotion dataset of literary works in different periods,literary genres,and language contexts,augmented with fine-grained valence and arousal labels.Next,we design an interactive visual analytic system named EmotionLens,which allows users to perform multi-granularity(e.g.,individual,group,society)and multi-faceted(e.g.,distribution,chronology,correlation)analyses of literary emotions,supporting both exploratory and confirmatory approaches in digital humanities.Specifically,we introduce a novel affective word cloud with augmented word weight,position,and color,to facilitate literary text analysis from an emotional perspective.To validate the usability and effectiveness of EmotionLens,we provide two consecutive case studies,two user studies,and interviews with experts from different domains.Our results show that EmotionLens bridges literary text,emotion,and various other attributes,enables efficient knowledge discovery in massive data,and facilitates raising and validating domain-specific hypotheses in literature.展开更多
The Guqin art form is one of China’s oldest musical traditions and is recognized as a significant part of the world’s intangible cultural heritage.Numerous ancient scores have survived to the present day,but only a ...The Guqin art form is one of China’s oldest musical traditions and is recognized as a significant part of the world’s intangible cultural heritage.Numerous ancient scores have survived to the present day,but only a select few have been adapted for contemporary music.While ancient Guqin scores can be translated into modern numbered or staff notation,their rhythmic elements cannot be accurately replicated.The process of converting these scores into a simplified Guqin notation presents challenges,particularly in selecting appropriate fingerings.To address the issues related to playing ancient scores and transferring new ones,this paper introduces a triple representation method for Guqin score knowledge using knowledge mapping technology.It transforms the simplified Guqin notation into a text format,exemplified by the Ming Dynasty’s“Magic Secret Score.”A computer-editable text corpus of Guqin is created,and various tags are applied to the text.A word cloud visualization of the“Magic Secret Score”text spectrum is generated using a word cloud tool.The frequency of different right-hand fingerings in the“Magic Secret Score”is analyzed.By examining the temporal characteristics of the music,the paper extracts the timing relationships of right-hand fingerings from the text corpus and identifies various performance templates using the KMP pattern matching algorithm.Specifically,it analyzes 64 different right-hand finger techniques.Additionally,the frequency of string combinations in the“Magic Secret Score”is recorded,providing essential guidelines for future intelligent music transfer reasoning.The experimental findings indicate that there are specific constraints on the timing of fingerings and string usage in musical tones,with the maximum length of reusable fingering timing templates being no more than 25.展开更多
基金supported by the National Key R&D Program of China(2022YFC2704304 and 2021YFF0702000)the National Natural Science Foundation of China(32341020 and 32341021)+1 种基金Hubei Innovation Group Project(2021CFA005)the Research Core Facilities for Life Science(HUST).
文摘Word cloud visualization is a compelling graphical representation that visually depicts the frequency of words within a given text or dataset[1].Research on word clouds focuses on two main aspects.The first emphasizes processing words,such as using the latent Dirichlet allocation(LDA)algorithm to uncover topics in the documents[2],while the second involves visual impact through striking word arrangements[3,4].In the realm of extensive biomedical data,effectiveknowledge delivery to biologists is crucial.
基金supported by the Special Project on Precision Medicine under the National Key R&D Program (2017YFC0906600)the Natural Science Foundation of China (No. 31671360)
文摘The integration, analysis and visualization of the big omics data are critical for addressing a broad spectrum of biological questions. One of the most frequently conducted procedures is enrichment analysis, which statistically tests whether individual functional an- notations of Gent Ontology （GO） or Kyoto Encyclopedia of Genes and Genomes （KEGG） are significantly over-or under-represented in an ＂interesting＂ gene or protein list against the reference set （Tavazoie et al., 1999）.
基金supported by Guangzhou-HKUST(GZ)Joint Funding#2023A03J0670Guangzhou Edu-cation Bureau Project#2024312075.
文摘Emotion(e.g.,valence and arousal)is an important factor in literature(e.g.,poetry and prose),and has rich values for plotting the life and knowledge of historical figures and appreciating the aesthetics of literary works.Currently,digital humanities and computational literature apply data statistics extensively in emotion analysis but lack visual analytics for efficient exploration.To fill the gap,we propose a user-centric approach that integrates advanced machine learning models and intuitive visualization for emotion analysis in literature.We make three main contributions.First,we consolidate a new emotion dataset of literary works in different periods,literary genres,and language contexts,augmented with fine-grained valence and arousal labels.Next,we design an interactive visual analytic system named EmotionLens,which allows users to perform multi-granularity(e.g.,individual,group,society)and multi-faceted(e.g.,distribution,chronology,correlation)analyses of literary emotions,supporting both exploratory and confirmatory approaches in digital humanities.Specifically,we introduce a novel affective word cloud with augmented word weight,position,and color,to facilitate literary text analysis from an emotional perspective.To validate the usability and effectiveness of EmotionLens,we provide two consecutive case studies,two user studies,and interviews with experts from different domains.Our results show that EmotionLens bridges literary text,emotion,and various other attributes,enables efficient knowledge discovery in massive data,and facilitates raising and validating domain-specific hypotheses in literature.
基金supported by the 2024 special project of the Zhejiang Provincial Higher Education Association,“Research on the Application of Artificial Intelligence in Educational Teaching”(No.KT2024441)in China.
文摘The Guqin art form is one of China’s oldest musical traditions and is recognized as a significant part of the world’s intangible cultural heritage.Numerous ancient scores have survived to the present day,but only a select few have been adapted for contemporary music.While ancient Guqin scores can be translated into modern numbered or staff notation,their rhythmic elements cannot be accurately replicated.The process of converting these scores into a simplified Guqin notation presents challenges,particularly in selecting appropriate fingerings.To address the issues related to playing ancient scores and transferring new ones,this paper introduces a triple representation method for Guqin score knowledge using knowledge mapping technology.It transforms the simplified Guqin notation into a text format,exemplified by the Ming Dynasty’s“Magic Secret Score.”A computer-editable text corpus of Guqin is created,and various tags are applied to the text.A word cloud visualization of the“Magic Secret Score”text spectrum is generated using a word cloud tool.The frequency of different right-hand fingerings in the“Magic Secret Score”is analyzed.By examining the temporal characteristics of the music,the paper extracts the timing relationships of right-hand fingerings from the text corpus and identifies various performance templates using the KMP pattern matching algorithm.Specifically,it analyzes 64 different right-hand finger techniques.Additionally,the frequency of string combinations in the“Magic Secret Score”is recorded,providing essential guidelines for future intelligent music transfer reasoning.The experimental findings indicate that there are specific constraints on the timing of fingerings and string usage in musical tones,with the maximum length of reusable fingering timing templates being no more than 25.
