index.json

[{"authors":["admin"],"categories":null,"content":"Hello, my name is Hancheng Wang (王瀚橙). I am a fourth-year Ph.D. candidate in the Department of Computer Science and Technology at Nanjing University, supervised by Professors Guihai Chen and Haipeng Dai. I am also a visiting Ph.D. Student at the School of Computer Science and Engineering at Nanyang Technological University, jointly supervised by Professors Dmitrii Ustiugov and Siqiang Luo. My research interests include database indexing and data mining.\nCurrently, I am working on improving the performance of approximate membership query data structures.\n","date":-62135596800,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":-62135596800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://wanghanchengchn.github.io/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"Hello, my name is Hancheng Wang (王瀚橙). I am a fourth-year Ph.D. candidate in the Department of Computer Science and Technology at Nanjing University, supervised by Professors Guihai Chen and Haipeng Dai. I am also a visiting Ph.D. Student at the School of Computer Science and Engineering at Nanyang Technological University, jointly supervised by Professors Dmitrii Ustiugov and Siqiang Luo. My research interests include database indexing and data mining.\nCurrently, I am working on improving the performance of approximate membership query data structures.","tags":null,"title":"Hancheng Wang","type":"authors"},{"authors":null,"categories":null,"content":"Flexibility This feature can be used for publishing content such as:\n Online courses Project or software documentation Tutorials  The courses folder may be renamed. For example, we can rename it to docs for software/project documentation or tutorials for creating an online course.\nDelete tutorials To remove these pages, delete the courses folder and see below to delete the associated menu link.\nUpdate site menu After renaming or deleting the courses folder, you may wish to update any [[main]] menu links to it by editing your menu configuration at config/_default/menus.toml.\nFor example, if you delete this folder, you can remove the following from your menu configuration:\n[[main]]\rname = \u0026quot;Courses\u0026quot;\rurl = \u0026quot;courses/\u0026quot;\rweight = 50\r Or, if you are creating a software documentation site, you can rename the courses folder to docs and update the associated Courses menu configuration to:\n[[main]]\rname = \u0026quot;Docs\u0026quot;\rurl = \u0026quot;docs/\u0026quot;\rweight = 50\r Update the docs menu If you use the docs layout, note that the name of the menu in the front matter should be in the form [menu.X] where X is the folder name. Hence, if you rename the courses/example/ folder, you should also rename the menu definitions in the front matter of files within courses/example/ from [menu.example] to [menu.\u0026lt;NewFolderName\u0026gt;].\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"59c3ce8e202293146a8a934d37a4070b","permalink":"https://wanghanchengchn.github.io/courses/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/courses/example/","section":"courses","summary":"Learn how to use Academic's docs layout for publishing online courses, software documentation, and tutorials.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":"In this tutorial, I'll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 2 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"74533bae41439377bd30f645c4677a27","permalink":"https://wanghanchengchn.github.io/courses/example/example1/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example1/","section":"courses","summary":"In this tutorial, I'll share my top 10 tips for getting started with Academic:\nTip 1 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim.","tags":null,"title":"Example Page 1","type":"docs"},{"authors":null,"categories":null,"content":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\nTip 4 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1557010800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557010800,"objectID":"1c2b5a11257c768c90d5050637d77d6a","permalink":"https://wanghanchengchn.github.io/courses/example/example2/","publishdate":"2019-05-05T00:00:00+01:00","relpermalink":"/courses/example/example2/","section":"courses","summary":"Here are some more tips for getting started with Academic:\nTip 3 Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus.","tags":null,"title":"Example Page 2","type":"docs"},{"authors":["Hancheng Wang","Haipeng Dai","Shusen Chen","Meng Li","Rong Gu","Huayi Chai","Jiaqi Zheng","Zhiyuan Chen","Shuaituan Li","Xianjun Deng","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1715990400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1715990400,"objectID":"a41dadb4c9c852235c5859419f185574","permalink":"https://wanghanchengchn.github.io/publication/16-bambooton/","publishdate":"2024-05-18T00:00:00Z","relpermalink":"/publication/16-bambooton/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Data Mining"],"title":"Bamboo Filters: Make Resizing Smooth and Adaptive","type":"publication"},{"authors":["Hancheng Wang","Haipeng Dai","Shusen Chen","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1715904000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1715904000,"objectID":"40a2cfcf78f94fbb705cf5c97187d4e1","permalink":"https://wanghanchengchn.github.io/publication/18-cxl/","publishdate":"2024-05-17T00:00:00Z","relpermalink":"/publication/18-cxl/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Database Indexing"],"title":"Rethinking Hash Tables: Challenges and Opportunities with Compute Express Link (CXL)","type":"publication"},{"authors":["Haipeng Dai","Meng Li","Hancheng Wang","Haihan Zhang"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1715817600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1715817600,"objectID":"a01889e12092b380c6d746c95f804365","permalink":"https://wanghanchengchn.github.io/publication/19-cocoon/","publishdate":"2024-05-16T00:00:00Z","relpermalink":"/publication/19-cocoon/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Database Indexing"],"title":"Course Design and Textbook Development for Introduction to Computer Systems Course in the Era of Concurrency","type":"publication"},{"authors":["Meng Li","Wenqi Luo","Haipeng Dai","Hancheng Wang","Rong Gu","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1715731200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1715731200,"objectID":"7e1f3011a738fac1d1d50a52c6a9fde3","permalink":"https://wanghanchengchn.github.io/publication/17-cost-sensitive-cf-jos/","publishdate":"2024-05-15T00:00:00Z","relpermalink":"/publication/17-cost-sensitive-cf-jos/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Data Mining"],"title":"Cost-sensitive Variable-hash Cuckoo Filter","type":"publication"},{"authors":["Hancheng Wang","Haipeng Dai","Rong Gu","Youyou Lu","Jiaqi Zheng","Jingsong Dai","Shusen Chen","Zhiyuan Chen","Shuaituan Li","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1713744000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1713744000,"objectID":"a91e5e080a546f1d980f7e8c7be6f899","permalink":"https://wanghanchengchn.github.io/publication/9-wormhole/","publishdate":"2024-04-22T00:00:00Z","relpermalink":"/publication/9-wormhole/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Database Indexing"],"title":"Wormhole Filters: Caching Your Hash on Persistent Memory","type":"publication"},{"authors":["Hancheng Wang","Zhipeng Chen","Haipeng Dai","Rong Gu","Chaewon Kim","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1713484800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1713484800,"objectID":"a55184a4b145ba788b746b7e3495aba0","permalink":"https://wanghanchengchn.github.io/publication/15-lockfree-cuckoo/","publishdate":"2024-04-19T00:00:00Z","relpermalink":"/publication/15-lockfree-cuckoo/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Data Mining"],"title":"Lock-free Concurrent Cuckoo Filter","type":"publication"},{"authors":["Mingxin Li","Hancheng Wang","Haipeng Dai","Meng Li","Chengliang Chai","Rong Gu","Feng Chen","Zhiyuan Chen","Shuaituan Li","Qizhi Liu","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1706572800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1706572800,"objectID":"d83f7220a4aa282f44924f0146a9d55a","permalink":"https://wanghanchengchn.github.io/publication/14-multi-dimensional/","publishdate":"2024-01-30T00:00:00Z","relpermalink":"/publication/14-multi-dimensional/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Data Mining"],"title":"A Survey of Multi-dimensional Indexes: Past and Future Trends","type":"publication"},{"authors":["Hancheng Wang","Haipeng Dai","Zhipeng Chen","Shusen Chen","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1694476800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694476800,"objectID":"a80b42a2f6601f9596004a37680bcc29","permalink":"https://wanghanchengchn.github.io/publication/12-mapreduce/","publishdate":"2023-09-12T00:00:00Z","relpermalink":"/publication/12-mapreduce/","section":"publication","summary":"TBD","tags":["Database Indexing"],"title":"Large-scale Network Community Detection Algorithm Based on MapReduce","type":"publication"},{"authors":["Hancheng Wang","Haipeng Dai","Shusen Chen","Zhipeng Chen","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1694390400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694390400,"objectID":"662145f94488f31776a4a9b97c595fb4","permalink":"https://wanghanchengchn.github.io/publication/10-survey/","publishdate":"2023-09-11T00:00:00Z","relpermalink":"/publication/10-survey/","section":"publication","summary":"Filter data structures can approximately determine whether an element exists in a given set.Typical filter data structures,such as Bloom filters,cuckoo filters,and quotient filters,sacrifice query accuracy for lower memory space consumption and lower query time overhead.Due to their spatial and temporal efficiency,filter data structures are now widely used in approximate membership query operations in computer networks,the Internet of Things,database systems,file systems,bioinformatics,machine learning,and other fields.Since the 1970s,filters have been extensively studied.Their research ideas are constantly changing.This paper compiles the classic studies on filter data structures in the past fifty years,summarizes existing studies based on the mechanism of filter data structures and analyze the relationship between different studies.Finally,future research directions in filter data structures are discussed.","tags":["Database Indexing"],"title":"Filter Data Structures: A Survey","type":"publication"},{"authors":["Haipeng Dai","Hancheng Wang","Zhipeng Chen","Jiaqi Zheng","Meng Li","Rong Gu","Chen Tian","Wanchun Dou"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1693526400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1693526400,"objectID":"a95fd159e8e1903f8e43c4216138e446","permalink":"https://wanghanchengchn.github.io/publication/11-vle/","publishdate":"2023-09-01T00:00:00Z","relpermalink":"/publication/11-vle/","section":"publication","summary":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","tags":["Database Indexing"],"title":"Variable-length Encoding Framework: A Generic Framework for Enhancing the Accuracy of Approximate Membership Queries","type":"publication"},{"authors":["Rong Gu","Simian Li","Haipeng Dai","Hancheng Wang","Yili Luo","Bin Fan","Ran Ben Basat","Ke Wang","Zhenyu Song","Shouwei Chen","Beinan Wang","Yihua Huang","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1688169600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688169600,"objectID":"8e19a54b1078d145cc556c9e18c194db","permalink":"https://wanghanchengchn.github.io/publication/8-cuki/","publishdate":"2023-07-01T00:00:00Z","relpermalink":"/publication/8-cuki/","section":"publication","summary":"Big data applications extensively use cache techniques to accelerate data access. A key challenge for improving cache utilization is provisioning a suitable cache size to fit the dynamic working set size (WSS) and understanding the related item repetition ratio (IRR) of the trace. We propose Cuki, an approximate data structure for efficiently estimating online WSS and IRR for variable-size item access with proven accuracy guarantee. Our solution is cache-friendly, thread-safe, and light-weighted in design. Based on that, we design an adaptive online cache capacity tuning mechanism. Moreover, Cuki can also be adapted to accurately estimate the cache miss ratio curve (MRC) online. We built Cuki as a lightweight plugin of the widely-used distributed file caching system Alluxio. Evaluation results show that Cuki has higher accuracy than four state-of-the-art algorithms by over an order of magnitude and with better stability in performance. The end-to-end data access experiments show that the adaptive cache tuning framework using Cuki reduces the table querying latency by 79% and improves the file reading throughput by 29% on average. Compared with the cutting-edge MRC approach, Cuki uses less memory and improves accuracy by around 73% on average. Cuki is deployed on one of the world’s largest social platforms to run the Presto query workloads.","tags":["Database Indexing"],"title":"Adaptive Online Cache Capacity Optimization via Lightweight Working Set Size Estimation at Scale","type":"publication"},{"authors":["Haipeng Dai","Lei Meng","Hancheng Wang","Rong Gu","Siwen Chen","Feng Chen","Wei Hu"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1681689600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1681689600,"objectID":"9b123d1d6b91f64257aecb9e45ecc20b","permalink":"https://wanghanchengchn.github.io/publication/7-entitylinking/","publishdate":"2023-04-17T00:00:00Z","relpermalink":"/publication/7-entitylinking/","section":"publication","summary":"Entity linking (EL) aims to find entities that the textual mentions refer to from a knowledge base (KB). The performance of current distantly supervised EL methods is not satisfactory under the condition of low-quality candidate generation. In this paper, we consider the scenario where multiple KBs are available, and for each KB, there is an EL model corresponding to it. We propose the selection consistency constraint (SCC), that is, for one sample, the entities selected from multiple KBs should be consistent if these selections are all correct. In this work, we aim to utilize the SCC to improve the performance of each EL model (not the combination of multiple EL models) under low-quality candidate generation. Specifically, we define an SCC model from two different aspects minimizing probability and upper bound, which are used to introduce the SCC into the training of EL models. The experimental results show that our method, jointly training multiple EL models with the SCC model, outperforms the baseline which trains multiple EL models separately, and it has low cost.","tags":["Entity Linking"],"title":"Distantly Supervised Entity Linking with Selection Consistency Constraint","type":"publication"},{"authors":null,"categories":null,"content":" Please send \u0026ldquo;A1-\u0026lt;your ID\u0026gt;-\u0026lt;your name\u0026gt;.pdf\u0026rdquo; (e.g., \u0026ldquo;A1-DZ20330026-王瀚橙.pdf\u0026rdquo;) to hanchengwang2022@163.com.\nI have received the following submissions by 2022-08-25 23:59.\n   学号 姓名     DZ20330026 王瀚橙     ","date":1661385600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661385600,"objectID":"c59721fc9452b4a7c1096507b1b23491","permalink":"https://wanghanchengchn.github.io/post/1-distributed-networks-a1/","publishdate":"2022-08-25T00:00:00Z","relpermalink":"/post/1-distributed-networks-a1/","section":"post","summary":" Please send \u0026ldquo;A1-\u0026lt;your ID\u0026gt;-\u0026lt;your name\u0026gt;.pdf\u0026rdquo; (e.g., \u0026ldquo;A1-DZ20330026-王瀚橙.pdf\u0026rdquo;) to hanchengwang2022@163.com.\nI have received the following submissions by 2022-08-25 23:59.\n   学号 姓名     DZ20330026 王瀚橙     ","tags":null,"title":"Distributed Networks: Assignment #1","type":"post"},{"authors":null,"categories":null,"content":" Please send \u0026ldquo;A2-\u0026lt;your ID\u0026gt;-\u0026lt;your name\u0026gt;.pdf\u0026rdquo; (e.g., \u0026ldquo;A2-DZ20330026-王瀚橙.pdf\u0026rdquo;) to hanchengwang2022@163.com.\nI have received the following submissions by 2022-08-25 23:59.\n   学号 姓名     DZ20330026 王瀚橙     ","date":1661385600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661385600,"objectID":"f4fe1b0a333491ebec58f32a4d285893","permalink":"https://wanghanchengchn.github.io/post/2-distributed-networks-a2/","publishdate":"2022-08-25T00:00:00Z","relpermalink":"/post/2-distributed-networks-a2/","section":"post","summary":" Please send \u0026ldquo;A2-\u0026lt;your ID\u0026gt;-\u0026lt;your name\u0026gt;.pdf\u0026rdquo; (e.g., \u0026ldquo;A2-DZ20330026-王瀚橙.pdf\u0026rdquo;) to hanchengwang2022@163.com.\nI have received the following submissions by 2022-08-25 23:59.\n   学号 姓名     DZ20330026 王瀚橙     ","tags":null,"title":"Distributed Networks: Assignment #2","type":"post"},{"authors":null,"categories":null,"content":" Please send \u0026ldquo;A3-\u0026lt;your ID\u0026gt;-\u0026lt;your name\u0026gt;.pdf\u0026rdquo; (e.g., \u0026ldquo;A3-DZ20330026-王瀚橙.pdf\u0026rdquo;) to hanchengwang2022@163.com.\nI have received the following submissions by 2022-08-25 23:59.\n   学号 姓名     DZ20330026 王瀚橙     ","date":1661385600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661385600,"objectID":"af787fadf56ef8f43f9351c3e548abe8","permalink":"https://wanghanchengchn.github.io/post/3-distributed-networks-a3/","publishdate":"2022-08-25T00:00:00Z","relpermalink":"/post/3-distributed-networks-a3/","section":"post","summary":" Please send \u0026ldquo;A3-\u0026lt;your ID\u0026gt;-\u0026lt;your name\u0026gt;.pdf\u0026rdquo; (e.g., \u0026ldquo;A3-DZ20330026-王瀚橙.pdf\u0026rdquo;) to hanchengwang2022@163.com.\nI have received the following submissions by 2022-08-25 23:59.\n   学号 姓名     DZ20330026 王瀚橙     ","tags":null,"title":"Distributed Networks: Assignment #3","type":"post"},{"authors":null,"categories":null,"content":"2022-2023, \u0026ldquo;Research on Adaptive Filters\u0026rdquo;, granted by the Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province, No. KYCX22_0152.\n","date":1654041600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1654041600,"objectID":"8f66d660a9a2edc2d08e68cc30f701f7","permalink":"https://wanghanchengchn.github.io/project/internal-project/","publishdate":"2022-06-01T00:00:00Z","relpermalink":"/project/internal-project/","section":"project","summary":"Postgraduate Research \u0026 Practice Innovation Program of Jiangsu Province (No. KYCX22_0152).","tags":null,"title":"Research on Adaptive Filters","type":"project"},{"authors":["Hancheng Wang","Haipeng Dai","Meng Li","Jun Yu","Rong Gu","Jiaqi Zheng","Guihai Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1652054400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1652054400,"objectID":"fd9b7a605221eb68e149b51e0d734292","permalink":"https://wanghanchengchn.github.io/publication/1-bamboofilters/","publishdate":"2021-11-24T00:00:00Z","relpermalink":"/publication/1-bamboofilters/","section":"publication","summary":"The approximate membership query (AMQ) data structure is a kind of space-efficient probabilistic data structure. It can approximately indicate whether an element exists in a set. The AMQ data structure has been widely used in database indexing, network security, IoT applications, etc. Resizing is an extensively utilized operation of the AMQ data structure, but it can lead to system performance degradation. We summarize two main problems that lead to such degradation. Specifically, one of them is that the resizing operation can block other operations, while the other is that the performance of AMQ structures will deteriorate after multiple resizing operations. However, existing related work cannot alleviate both of them. Therefore, we propose a novel AMQ data structure called bamboo filter, which can alleviate the two problems simultaneously. Bamboo filters can insert, search and delete an element in constant time. Moreover, bamboo filters can dynamically resize in a fine-grained way according to the number of contained elements. Experimental results show that bamboo filters significantly outperform state-ofthe- art resizable AMQ data structures in insertion, lookup, and deletion operations. For example, bamboo filters achieve 2.46× lookup throughput of the dynamic cuckoo filter, on average.","tags":["Database Indexing"],"title":"Bamboo Filters: Make Resizing Smooth","type":"publication"},{"authors":["Shuang Yu","Xiongfei Li","Siru Sun","Hancheng Wang","Xiaoli Zhang","Shiping Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1644451200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1644451200,"objectID":"6a85bd71edbb5c5c510f2151104d6c79","permalink":"https://wanghanchengchn.github.io/publication/13-ibmvsvm/","publishdate":"2022-02-10T00:00:00Z","relpermalink":"/publication/13-ibmvsvm/","section":"publication","summary":"As an emerging research direction of machine learning, the multi-view learning (MVL) pays attention to the tasks that learn from datasets with several distinct views to achieve better generalization performance. Recently, various Support Vector Machine (SVM)-based algorithms with solid theoretical foundation have been proposed for MVL. However, there is a constraining assumption for these algorithms, i.e., in the learning process, different views are important equally for an instance in a data set, and the same view is important equally for all instances in a data set. In fact, an instance generally has different adaptability to different views, namely, the degree to which the information from different views accurately describes the instance varies. And naturally, different instances in a data set also have different adaptability to the same view. In this paper, the concept of view vector of each instance is proposed first, which quantitatively describes the adaptability of a specific instance to different views. It also reflects the characteristics of different instances that some instances are more suitable to be represented by a view, while others tend to be better represented by another view. Then, a new instance-based multi-view SVM algorithm, named IBMvSVM, is proposed by building the view vector of each instance into the multi-view SVM learning. IBMvSVM focuses on characteristics of each instance itself in different views rather than treating them equally. Experiments performed on 48 multi-view datasets reveal the superiority of IBMvSVM algorithm on generalization against several recently state-of-the-art MVL algorithms.","tags":["Data Mining"],"title":"IBMvSVM: An Instance-Based Multi-View SVM Algorithm for Classification","type":"publication"},{"authors":["Shuang Yu","Xiongfei Li","Hancheng Wang","Xiaoli Zhang","Shiping Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1625788800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1625788800,"objectID":"726a6be9135be0456b616847616c2cfa","permalink":"https://wanghanchengchn.github.io/publication/3-ccart/","publishdate":"2020-08-25T00:00:00Z","relpermalink":"/publication/3-ccart/","section":"publication","summary":"In classification, a decision tree is a common model due to its simple structure and easy understanding. Most of decision tree algorithms assume all instances in a dataset have the same degree of confidence, so they use the same generation and pruning strategies for all training instances. In fact, the instances with greater degree of confidence are more useful than the ones with lower degree of confidence in the same dataset. Therefore, the instances should be treated discriminately according to their corresponding confidence degrees when training classifiers. In this paper, we investigate the impact and significance of degree of confidence of instances on the classification performance of decision tree algorithms, taking the classification and regression tree (CART) algorithm as an example. First, the degree of confidence of instances is quantified from a statistical perspective. Then, a developed CART algorithm named C_CART is proposed by introducing the confidence of instances into the generation and pruning processes of CART algorithm. Finally, we conduct experiments to evaluate the performance of C_CART algorithm. The experimental results show that our C_CART algorithm can significantly improve the generalization performance as well as avoiding the over-fitting problem to a certain extend.","tags":["Data Mining"],"title":"C_CART: An Instance Confidence-Based Decision Tree Algorithm for Classification","type":"publication"},{"authors":["Shuang Yu","Xiongfei Li","Hancheng Wang","Xiaoli Zhang","Shiping Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1614556800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1614556800,"objectID":"4c764ef5de77bc6eabf853bb32264d72","permalink":"https://wanghanchengchn.github.io/publication/2-bidi/","publishdate":"2020-08-28T00:00:00Z","relpermalink":"/publication/2-bidi/","section":"publication","summary":"In artificial intelligence, an expert/intelligent systems can emulate the decision-making ability of human experts. A good classification algorithm can provide significant assistance to expert/intelligent systems in solving a variety of practical problems. In classification, the “hard” instances may be outliers or noisy instances that are difficult to learn, which may confuse the classifier and induce the overfitting problem in the case of placing much emphasis on them. In fact, the difficulty of instances is crucial for improving the generalization and credibility of classification. Unfortunately, nearly all the existing classifiers ignore this important information. In this paper, the classification difficulty of each instance is introduced from a statistical perspective, which is an inherent characteristic of the instance itself. Then, a new classification algorithm named “boosting with instance difficulty invariance (BIDI)” is proposed by incorporating the classification difficulty of instances. The BIDI conforms to the human cognition that easy instances are misclassified with a lower probability than difficult ones, and performs better with respect to generalization. The key insight of BIDI can provide relevant guidance for researchers to improve the generalization and credibility of classifiers in the expert systems of decision support systems. Experimental results demonstrate the effectiveness of BIDI in real-world data sets, indicating that it has great potential for solving many classification tasks of expert systems such as disease diagnosis and credit card fraud detection. Although the classification difficulty has strong statistical significance, its implementation remains computationally expensive. A fast method demonstrating rationality and feasibility is also proposed to approximate instances’ classification difficulty.","tags":["Data Mining"],"title":"BIDI: A Classification Algorithm with Instance Difficulty Invariance","type":"publication"},{"authors":["Shuang Yu","Xiongfei Li","Hancheng Wang","Xiaoli Zhang","Shiping Chen"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1605139200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1605139200,"objectID":"9bd4611ec7f85aeb52da17bdc55c8cb0","permalink":"https://wanghanchengchn.github.io/publication/5-fabricgene/","publishdate":"2020-11-12T00:00:00Z","relpermalink":"/publication/5-fabricgene/","section":"publication","summary":"Nationality fabric classification is a significant work to promote the protection work of fabric patterns and further reveal its unique connotation and inheritance rules in big data era. Thus, how to ascertain the feature representation of fabric patterns becomes a primary problem. This paper presents a high-level feature representation for fabric patterns for nationality classification, called FabricGene, which improves the semantic expression ability of the fabric pattern features. In fabric patterns, each FabricGene represents a complete abstract concept including the external shape and connotation characteristics. We evaluate the performance of FabricGenes and basic geometric primitives to illustrate the effectiveness of FabricGenes in nationality classification. Five widely used classification algorithms are applied to classify the fabric patterns by learning from training data with 12 groups of FabricGenes and 11 groups of basic geometric primitives respectively. The results demonstrate that the FabricGenes perform more effectively and stably in nationality classification than the basic geometric primitives. Namely, the FabricGenes can express the fabric patterns’ nationality features more accurately.","tags":["Data Mining"],"title":"FabricGene: A Higher-Level Feature Representation of Fabric Patterns for Nationality Classification","type":"publication"},{"authors":["Shuang Yu","Xiongfei Li","Xiaoli Zhang","Hancheng Wang"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1565049600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1565049600,"objectID":"3535e4609ce84fb0f2907b3a78971945","permalink":"https://wanghanchengchn.github.io/publication/4-ocs-svm/","publishdate":"2019-08-02T00:00:00Z","relpermalink":"/publication/4-ocs-svm/","section":"publication","summary":"Studies on the traditional support vector machine (SVM) implicitly assume that the costs of different types of mistakes are the same and minimize the error rate. On the one hand, it is not enough for many practical applications to rely solely on the error rate, which reflects only the average classification ability of a classifier. It is also of great significance to consider the performance of classifiers from the perspective of each sample. On the other hand, many real-world problems, such as credit card fraud detection, intrusion detection, oil-spill detection and cancer diagnosis, usually involve substantially unequal misclassification costs. To solve this problem, many works on the cost-sensitive SVM (CS-SVM) have emerged. The misclassification costs for this model are generally provided by domain experts. Inspired by the concept of the CS-SVM, we propose a new SVM with sample-based misclassification cost invariance with the aim of constructing a relatively reliable classifier. The relatively reliable classifier is defined as the one with low probabilities of finding a classifier that correctly classifies each misclassified sample. Note that the cost is determined by the inherent characteristics of each sample rather than being subjectively assigned, so we denote the proposed classifier as the objective-cost-sensitive SVM (OCS-SVM). The experimental results demonstrate the superiority of the proposed method compared with nine other commonly used classifiers.","tags":["Data Mining"],"title":"The OCS-SVM: An Objective-Cost-Sensitive SVM with Sample-Based Misclassification Cost Invariance","type":"publication"},{"authors":["Zeyu Wang","Xiongfei Li","Haoran Duan","Xiaoli Zhang","Hancheng Wang"],"categories":null,"content":"\rClick the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.\r\r\r\rClick the Slides button above to demo Academic's Markdown slides feature.\r\r\rSupplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --\r","date":1564704000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564704000,"objectID":"11cc0327d03e8136ba23d3c321ed03be","permalink":"https://wanghanchengchn.github.io/publication/6-cnn/","publishdate":"2019-08-02T00:00:00Z","relpermalink":"/publication/6-cnn/","section":"publication","summary":"In this paper, a novel multifocus image fusion algorithm based on the convolutional neural network (CNN) in the discrete wavelet transform (DWT) domain is proposed. The algorithm combines the advantages of spatial domain- and transform domain-based methods. The CNN is used to amplify features and generate different decision maps for different frequency subbands instead of image blocks or source images. In addition, the CNN, which can be seen as an adaptive fusion rule, replaces the traditional fusion rules. The proposed algorithm includes the following steps: first, we decompose each source image into one low frequency subband and several high frequency subbands using the DWT; second, these frequency subbands are used as input to the CNN to generate weight maps. To obtain a more accurate decision map, it is refined by a series of postprocessing operations, including the sum-modified-Laplacian (SML) and guided filter (GF). According to their decision maps, the frequency subbands are fused; finally, the fused image can be obtained using the inverse DWT. The experimental results show that our algorithm is superior to other algorithms.","tags":null,"title":"Multifocus Image Fusion Using Convolutional Neural Networks in the Discrete Wavelet Transform Domain","type":"publication"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Academic Academic | Documentation\n Features  Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides   Controls  Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E   Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot;\rif porridge == \u0026quot;blueberry\u0026quot;:\rprint(\u0026quot;Eating...\u0026quot;)\r  Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\n Fragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}}\r{{% fragment %}} **Two** {{% /fragment %}}\r{{% fragment %}} Three {{% /fragment %}}\r Press Space to play!\nOne  Two  Three \n A fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears   Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}}\r- Only the speaker can read these notes\r- Press `S` key to view\r{{% /speaker_note %}}\r Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view \r  Themes  black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links    night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links   Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}}\r{{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}}\r{{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}}\r  Custom CSS Example Let's make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1,\r.reveal section h2,\r.reveal section h3 {\rcolor: navy;\r}\r  Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://wanghanchengchn.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Academic's Slides feature.","tags":[],"title":"Slides","type":"slides"}]