狠狠撸shows by User: hangdong14

狠狠撸shows by User: hangdong14 / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: hangdong14 / Sat, 01 Sep 2018 13:17:03 GMT 狠狠撸Share feed for 狠狠撸shows by User: hangdong14 Learning Relations from Social Tagging Data /slideshow/learning-relations-from-social-tagging-data/112564920 pricai2018relationlearningslides-180901131703
An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.]]>
An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.]]> Sat, 01 Sep 2018 13:17:03 GMT /slideshow/learning-relations-from-social-tagging-data/112564920 hangdong14@slideshare.net(hangdong14) Learning Relations from Social Tagging Data hangdong14 An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pricai2018relationlearningslides-180901131703-thumbnail.jpg?width=120&height=120&fit=bounds" /> An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distri- butions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.

Learning Relations from Social Tagging Data from Hang Dong

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0 Rules for inducing hierarchies from social tagging data /slideshow/rules-for-inducing-hierarchies-from-social-tagging-data/92292756 rulesforinducinghierarchiesfromsocialtaggingdata-180329084645
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.]]>
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.]]> Thu, 29 Mar 2018 08:46:45 GMT /slideshow/rules-for-inducing-hierarchies-from-social-tagging-data/92292756 hangdong14@slideshare.net(hangdong14) Rules for inducing hierarchies from social tagging data hangdong14 Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rulesforinducinghierarchiesfromsocialtaggingdata-180329084645-thumbnail.jpg?width=120&height=120&fit=bounds" /> Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.

Rules for inducing hierarchies from social tagging data from Hang Dong

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0 Excel VBA programming basics /hangdong14/excel-vba-programming-basics excelvbaprogrammingbasicsforxjtlunew-170821000151
Excel VBA programming basics: a 2-hour tutorial to learn from scratch (presented in May 2017 in XJTLU)]]>
Excel VBA programming basics: a 2-hour tutorial to learn from scratch (presented in May 2017 in XJTLU)]]> Mon, 21 Aug 2017 00:01:51 GMT /hangdong14/excel-vba-programming-basics hangdong14@slideshare.net(hangdong14) Excel VBA programming basics hangdong14 Excel VBA programming basics: a 2-hour tutorial to learn from scratch (presented in May 2017 in XJTLU) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/excelvbaprogrammingbasicsforxjtlunew-170821000151-thumbnail.jpg?width=120&height=120&fit=bounds" /> Excel VBA programming basics: a 2-hour tutorial to learn from scratch (presented in May 2017 in XJTLU)

Excel VBA programming basics from Hang Dong

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0 语义沙龙：如何自动建构社会标签中的语义关系 /slideshow/ss-79003550/79003550 random-170820235632
how to automatically generate semantic trees from social tags? (a brief presentation in Chinese)]]>
how to automatically generate semantic trees from social tags? (a brief presentation in Chinese)]]> Sun, 20 Aug 2017 23:56:32 GMT /slideshow/ss-79003550/79003550 hangdong14@slideshare.net(hangdong14) 语义沙龙：如何自动建构社会标签中的语义关系 hangdong14 how to automatically generate semantic trees from social tags? (a brief presentation in Chinese) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/random-170820235632-thumbnail.jpg?width=120&height=120&fit=bounds" /> how to automatically generate semantic trees from social tags? (a brief presentation in Chinese)

语义沙龙：如何自动建构社会标签中的语义关系 from Hang Dong

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0 Enrichment of Cross-Lingual Information on Chinese Genealogical Linked Data /slideshow/enrichment-of-crosslingual-information-on-chinese-genealogical-linked-data/73593157 presentationiconfenrichmentofcross-lingualinformationonchinesegenealogicallinked1-170324141630
With the emergence of non-English Linked Datasets, discrepancy in language has become a major obstacle for cross-lingual access of resources in the Semantic Web. To prevent non-English monolingual Linked Datasets to form “islands” in the Web of Data, it is suggested to enrich a further layer of multilingual information on the Linked Open Data cloud. In the domain of culture heritage, enriching cross-lingual information can enhance the multilingual retrieval of cultural heritage resources, and promote international communication in the field. In this article, methods to enrich cross-lingual information for Linked Data are summarized, with a review on the cultural heritage domain. The mobile App Demo, Learn Chinese Surnames, winning the Shanghai Library Open Data Application Development Contest on 2016, is then introduced as a case study, to present the practice of enriching English-described information on a Chinese genealogical Linked Dataset, through consuming multilingual sources in the Linked Open Data cloud. Further in the data validation and conclusion, the issues of data quality and experience of consuming Linked Data are summarized.]]>
With the emergence of non-English Linked Datasets, discrepancy in language has become a major obstacle for cross-lingual access of resources in the Semantic Web. To prevent non-English monolingual Linked Datasets to form “islands” in the Web of Data, it is suggested to enrich a further layer of multilingual information on the Linked Open Data cloud. In the domain of culture heritage, enriching cross-lingual information can enhance the multilingual retrieval of cultural heritage resources, and promote international communication in the field. In this article, methods to enrich cross-lingual information for Linked Data are summarized, with a review on the cultural heritage domain. The mobile App Demo, Learn Chinese Surnames, winning the Shanghai Library Open Data Application Development Contest on 2016, is then introduced as a case study, to present the practice of enriching English-described information on a Chinese genealogical Linked Dataset, through consuming multilingual sources in the Linked Open Data cloud. Further in the data validation and conclusion, the issues of data quality and experience of consuming Linked Data are summarized.]]> Fri, 24 Mar 2017 14:16:30 GMT /slideshow/enrichment-of-crosslingual-information-on-chinese-genealogical-linked-data/73593157 hangdong14@slideshare.net(hangdong14) Enrichment of Cross-Lingual Information on Chinese Genealogical Linked Data hangdong14 With the emergence of non-English Linked Datasets, discrepancy in language has become a major obstacle for cross-lingual access of resources in the Semantic Web. To prevent non-English monolingual Linked Datasets to form “islands” in the Web of Data, it is suggested to enrich a further layer of multilingual information on the Linked Open Data cloud. In the domain of culture heritage, enriching cross-lingual information can enhance the multilingual retrieval of cultural heritage resources, and promote international communication in the field. In this article, methods to enrich cross-lingual information for Linked Data are summarized, with a review on the cultural heritage domain. The mobile App Demo, Learn Chinese Surnames, winning the Shanghai Library Open Data Application Development Contest on 2016, is then introduced as a case study, to present the practice of enriching English-described information on a Chinese genealogical Linked Dataset, through consuming multilingual sources in the Linked Open Data cloud. Further in the data validation and conclusion, the issues of data quality and experience of consuming Linked Data are summarized. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentationiconfenrichmentofcross-lingualinformationonchinesegenealogicallinked1-170324141630-thumbnail.jpg?width=120&height=120&fit=bounds" /> With the emergence of non-English Linked Datasets, discrepancy in language has become a major obstacle for cross-lingual access of resources in the Semantic Web. To prevent non-English monolingual Linked Datasets to form “islands” in the Web of Data, it is suggested to enrich a further layer of multilingual information on the Linked Open Data cloud. In the domain of culture heritage, enriching cross-lingual information can enhance the multilingual retrieval of cultural heritage resources, and promote international communication in the field. In this article, methods to enrich cross-lingual information for Linked Data are summarized, with a review on the cultural heritage domain. The mobile App Demo, Learn Chinese Surnames, winning the Shanghai Library Open Data Application Development Contest on 2016, is then introduced as a case study, to present the practice of enriching English-described information on a Chinese genealogical Linked Dataset, through consuming multilingual sources in the Linked Open Data cloud. Further in the data validation and conclusion, the issues of data quality and experience of consuming Linked Data are summarized.

Enrichment of Cross-Lingual Information on Chinese Genealogical Linked Data from Hang Dong

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0 关联数据的消费与应用构建: 以上海图书馆家谱开放数据为例 /hangdong14/ss-69935989 2-161208014849
presentation at adls 2016, 第十三届数字图书馆前沿问题高级研讨班（ADLS2016）, Dec 5-6 2016, http://society.library.sh.cn/adls2016 Title: Consuming Linked Data and Application Development: a case study on Shanghai Library Genealogical Open Data ]]>
presentation at adls 2016, 第十三届数字图书馆前沿问题高级研讨班（ADLS2016）, Dec 5-6 2016, http://society.library.sh.cn/adls2016 Title: Consuming Linked Data and Application Development: a case study on Shanghai Library Genealogical Open Data ]]> Thu, 08 Dec 2016 01:48:48 GMT /hangdong14/ss-69935989 hangdong14@slideshare.net(hangdong14) 关联数据的消费与应用构建: 以上海图书馆家谱开放数据为例 hangdong14 presentation at adls 2016, 第十三届数字图书馆前沿问题高级研讨班（ADLS2016）, Dec 5-6 2016, http://society.library.sh.cn/adls2016 Title: Consuming Linked Data and Application Development: a case study on Shanghai Library Genealogical Open Data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2-161208014849-thumbnail.jpg?width=120&height=120&fit=bounds" /> presentation at adls 2016, 第十三届数字图书馆前沿问题高级研讨班（ADLS2016）, Dec 5-6 2016, http://society.library.sh.cn/adls2016 Title: Consuming Linked Data and Application Development: a case study on Shanghai Library Genealogical Open Data

关联数据的消费与应用构建: 以上海图书馆家谱开放数据为例 from Hang Dong

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0 Learning structured knowledge from social tagging data: a critical review of methods and techniques /slideshow/learning-structured-knowledge-from-social-tagging-data-a-critical-review-of-methods-and-techniques/56940904 learningstructuredknowledgefromsocialtaggingdata11-160112065419
For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.]]>
For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.]]> Tue, 12 Jan 2016 06:54:19 GMT /slideshow/learning-structured-knowledge-from-social-tagging-data-a-critical-review-of-methods-and-techniques/56940904 hangdong14@slideshare.net(hangdong14) Learning structured knowledge from social tagging data: a critical review of methods and techniques hangdong14 For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/learningstructuredknowledgefromsocialtaggingdata11-160112065419-thumbnail.jpg?width=120&height=120&fit=bounds" /> For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.

Learning structured knowledge from social tagging data: a critical review of methods and techniques from Hang Dong

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0 Modeling health related topics in an online forum designed for the deaf & hard of hearing /slideshow/modeling-health-related-topics-in-an-online-forum-designed-for-the-deaf-amp-hard-of-hearing-with-some-hidden-slides-last-update-9th-jan-2016-copy/56849498 modelinghealth-relatedtopicsinanonlineforumdesignedforthedeafhardofhearingwithsomehiddenslideslastup-160109072115
The main objective of this presentation is to elicit some discussions about whether a computational or a semi-computational method is suitable for understanding health related topics. The slides were presented at the 1st?XJTLU Research Symposium on Healthy Ageing & Society, Xi'an Jiaotong-Liverpool University, Suzhou, on 14 Dec, 2015. For uploading online, the slides were last revised on 9th Jan 2016. All websites referenced in the slides were retrieved on 5th Jan 2016.]]>
The main objective of this presentation is to elicit some discussions about whether a computational or a semi-computational method is suitable for understanding health related topics. The slides were presented at the 1st?XJTLU Research Symposium on Healthy Ageing & Society, Xi'an Jiaotong-Liverpool University, Suzhou, on 14 Dec, 2015. For uploading online, the slides were last revised on 9th Jan 2016. All websites referenced in the slides were retrieved on 5th Jan 2016.]]> Sat, 09 Jan 2016 07:21:15 GMT /slideshow/modeling-health-related-topics-in-an-online-forum-designed-for-the-deaf-amp-hard-of-hearing-with-some-hidden-slides-last-update-9th-jan-2016-copy/56849498 hangdong14@slideshare.net(hangdong14) Modeling health related topics in an online forum designed for the deaf & hard of hearing hangdong14 The main objective of this presentation is to elicit some discussions about whether a computational or a semi-computational method is suitable for understanding health related topics. The slides were presented at the 1st?XJTLU Research Symposium on Healthy Ageing & Society, Xi'an Jiaotong-Liverpool University, Suzhou, on 14 Dec, 2015. For uploading online, the slides were last revised on 9th Jan 2016. All websites referenced in the slides were retrieved on 5th Jan 2016. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/modelinghealth-relatedtopicsinanonlineforumdesignedforthedeafhardofhearingwithsomehiddenslideslastup-160109072115-thumbnail.jpg?width=120&height=120&fit=bounds" /> The main objective of this presentation is to elicit some discussions about whether a computational or a semi-computational method is suitable for understanding health related topics. The slides were presented at the 1st?XJTLU Research Symposium on Healthy Ageing & Society, Xi'an Jiaotong-Liverpool University, Suzhou, on 14 Dec, 2015. For uploading online, the slides were last revised on 9th Jan 2016. All websites referenced in the slides were retrieved on 5th Jan 2016.

Modeling health related topics in an online forum designed for the deaf & hard of hearing from Hang Dong

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0 Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies /slideshow/oil-ts-evaluation-revised-3th-april-2014/33100438 oiltsevaluationrevised3thapril2014-140403151438-phpapp01
Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies Hang Dong Presented in 3th September 2013 in Summer School 2013 University of Sheffield]]>
Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies Hang Dong Presented in 3th September 2013 in Summer School 2013 University of Sheffield]]> Thu, 03 Apr 2014 15:14:38 GMT /slideshow/oil-ts-evaluation-revised-3th-april-2014/33100438 hangdong14@slideshare.net(hangdong14) Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies hangdong14 Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies Hang Dong Presented in 3th September 2013 in Summer School 2013 University of Sheffield <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oiltsevaluationrevised3thapril2014-140403151438-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies Hang Dong Presented in 3th September 2013 in Summer School 2013 University of Sheffield

Identifying Evaluation Standards for Online Information Literacy Tutorials (OILTs): A Review of Existing OILTs Evaluation Studies from Hang Dong

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0 My hometown -- Wuhan /slideshow/hometown-1/25213837 hometown1-130813124710-phpapp01
Last edit time: 13-08-2013]]>
Last edit time: 13-08-2013]]> Tue, 13 Aug 2013 12:47:10 GMT /slideshow/hometown-1/25213837 hangdong14@slideshare.net(hangdong14) My hometown -- Wuhan hangdong14 Last edit time: 13-08-2013 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hometown1-130813124710-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Last edit time: 13-08-2013

My hometown -- Wuhan from Hang Dong

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0 https://cdn.slidesharecdn.com/profile-photo-hangdong14-48x48.jpg?cb=1687937309 https://cdn.slidesharecdn.com/ss_thumbnails/pricai2018relationlearningslides-180901131703-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/learning-relations-from-social-tagging-data/112564920 Learning Relations fro... https://cdn.slidesharecdn.com/ss_thumbnails/rulesforinducinghierarchiesfromsocialtaggingdata-180329084645-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/rules-for-inducing-hierarchies-from-social-tagging-data/92292756 Rules for inducing hie... https://cdn.slidesharecdn.com/ss_thumbnails/excelvbaprogrammingbasicsforxjtlunew-170821000151-thumbnail.jpg?width=320&height=320&fit=bounds hangdong14/excel-vba-programming-basics Excel VBA programming ...