index.json

[{"authors":["HuaXu"],"categories":null,"content":"","date":1669880609,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1669880609,"objectID":"ea83c3d4ee291b52424d8790b81f3023","permalink":"https://thuiar.github.io/author/hua-xu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/hua-xu/","section":"authors","summary":"","tags":null,"title":"Hua Xu","type":"authors"},{"authors":["Shaojie Zhao"],"categories":null,"content":"","date":1665217444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1665217444,"objectID":"479d4a8ab459290f5a804534662d3b28","permalink":"https://thuiar.github.io/author/shaojie-zhao/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/shaojie-zhao/","section":"authors","summary":"","tags":null,"title":"Shaojie Zhao","type":"authors"},{"authors":["Xin Wang"],"categories":null,"content":"","date":1665217444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1665217444,"objectID":"bcf064d3fb101b3ae8d5fc2d2644aaf0","permalink":"https://thuiar.github.io/author/xin-wang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/xin-wang/","section":"authors","summary":"","tags":null,"title":"Xin Wang","type":"authors"},{"authors":["JingLiangFang"],"categories":null,"content":"","date":1658219044,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1658219044,"objectID":"104f003d8345f6aecd11c4b79ed99fec","permalink":"https://thuiar.github.io/author/jingliang-fang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jingliang-fang/","section":"authors","summary":"","tags":null,"title":"JingLiang Fang","type":"authors"},{"authors":["Ziqi Yuan"],"categories":null,"content":"","date":1644913444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1644913444,"objectID":"f4d1961f6c0b2e857821d67f19067d83","permalink":"https://thuiar.github.io/author/ziqi-yuan/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/ziqi-yuan/","section":"authors","summary":"","tags":null,"title":"Ziqi Yuan","type":"authors"},{"authors":["HongyanWang"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523729,"objectID":"4f873441743a07d1a87f73f3f295378b","permalink":"https://thuiar.github.io/author/hongyan-wang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/hongyan-wang/","section":"authors","summary":"","tags":null,"title":"Hongyan Wang","type":"authors"},{"authors":["JiadongYang"],"categories":null,"content":"Joined the team in 2007, obtained Ph.D in 2012. First-class scholarship of Tsinghua University in the 2010-2011 academic year. Nominated for the 2010 ACM SIGEVO / Gecco Best Paper Award.\n","date":1356998400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523752,"objectID":"53e4157cfbc018364679a81056f42d6c","permalink":"https://thuiar.github.io/author/jiadong-yang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jiadong-yang/","section":"authors","summary":"Joined the team in 2007, obtained Ph.D in 2012. First-class scholarship of Tsinghua University in the 2010-2011 academic year. Nominated for the 2010 ACM SIGEVO / Gecco Best Paper Award.","tags":null,"title":"Jiadong Yang","type":"authors"},{"authors":["BaozhengZhang"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"288b6f643e7e4035dfa12da72dde255e","permalink":"https://thuiar.github.io/author/baozheng-zhang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/baozheng-zhang/","section":"authors","summary":"","tags":null,"title":"Baozheng Zhang","type":"authors"},{"authors":["Jiangong Yang"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"1d02e195564b5e722a9301ec0c6d134b","permalink":"https://thuiar.github.io/author/jiangong-yang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jiangong-yang/","section":"authors","summary":"","tags":null,"title":"Jiangong Yang","type":"authors"},{"authors":["JianhuaSu"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"5c43def9d5de0a9960c7bd52046becdd","permalink":"https://thuiar.github.io/author/jianhua-su/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jianhua-su/","section":"authors","summary":"","tags":null,"title":"Jianhua Su","type":"authors"},{"authors":["JinyueZhao"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"3d9a7a4833b022c3b1b462c3a50a3e1e","permalink":"https://thuiar.github.io/author/jinyue-zhao/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jinyue-zhao/","section":"authors","summary":"","tags":null,"title":"Jinyue Zhao","type":"authors"},{"authors":["WeilongLiu"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"aa9c7e9fcb6a9d6e1da6b3faa5ef8cd5","permalink":"https://thuiar.github.io/author/weilong-liu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/weilong-liu/","section":"authors","summary":"","tags":null,"title":"Weilong Liu","type":"authors"},{"authors":["Xiaofei Chen"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"2cf8d800d7be5de9685420b651d6f7cf","permalink":"https://thuiar.github.io/author/xiaofei-chen/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/xiaofei-chen/","section":"authors","summary":"","tags":null,"title":"Xiaofei Chen","type":"authors"},{"authors":["Yihe Liu"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"9cc45ce7ac53316bf0e410b2fa6eb5bc","permalink":"https://thuiar.github.io/author/yihe-liu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/yihe-liu/","section":"authors","summary":"","tags":null,"title":"Yihe Liu","type":"authors"},{"authors":["YuanzheQiu"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"d0c5cb56804073c6d2d4641363b45ea2","permalink":"https://thuiar.github.io/author/yuanzhe-qiu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/yuanzhe-qiu/","section":"authors","summary":"","tags":null,"title":"Yuanzhe Qiu","type":"authors"},{"authors":["YuanYuan"],"categories":null,"content":"Joined the team in 2010, obtained Ph.D in 2015.\n","date":1672561444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1672561444,"objectID":"44942e0c679dcc5762ab8f476d9f50a8","permalink":"https://thuiar.github.io/author/yuan-yuan/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/yuan-yuan/","section":"authors","summary":"Joined the team in 2010, obtained Ph.D in 2015.","tags":null,"title":"Yuan Yuan","type":"authors"},{"authors":["ZhijingWu"],"categories":null,"content":"","date":1658219044,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1658219044,"objectID":"03f89d60a903f6fdab07d38ff7c198f8","permalink":"https://thuiar.github.io/author/zhijing-wu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/zhijing-wu/","section":"authors","summary":"","tags":null,"title":"Zhijing Wu","type":"authors"},{"authors":["HuadongLi"],"categories":null,"content":"Joined the team in 2019, obtained Master\u0026rsquo;s Degree in 2021.\n","date":1598858644,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1598858644,"objectID":"a12f19b3699cb39de6d8ea2964efd6a4","permalink":"https://thuiar.github.io/author/huadong-li/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/huadong-li/","section":"authors","summary":"Joined the team in 2019, obtained Master\u0026rsquo;s Degree in 2021.","tags":null,"title":"Huadong Li","type":"authors"},{"authors":["DengjunHui"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523732,"objectID":"fd45fe441c47644dadc47c6e3365cfa9","permalink":"https://thuiar.github.io/author/junhui-deng/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/junhui-deng/","section":"authors","summary":"","tags":null,"title":"Junhui Deng","type":"authors"},{"authors":["Li Chen"],"categories":null,"content":"","date":1658219044,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1658219044,"objectID":"f6ec6d6d5a8c3c4c3a4fd699ae08a70e","permalink":"https://thuiar.github.io/author/li-chen/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/li-chen/","section":"authors","summary":"","tags":null,"title":"Li Chen","type":"authors"},{"authors":["KangZhao"],"categories":null,"content":"","date":1644913444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1644913444,"objectID":"76d0454d9d9ee26b6db1c95e6db40f55","permalink":"https://thuiar.github.io/author/kang-zhao/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/kang-zhao/","section":"authors","summary":"","tags":null,"title":"Kang Zhao","type":"authors"},{"authors":["XiaominSun"],"categories":null,"content":"Xiaomin Sun, born in 1963, is currently an associate professor at the State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science, Tsinghua University. He graduated from Harbin Engineering University in July 1984, and graduated from Beijing University of Aeronautics and Astronautics in January 1987 with a master\u0026rsquo;s degree in engineering. Since working in 1987, the main research direction is intelligent robot control, embedded computer system and intelligent control application technology; successively completed a number of engineering application projects and the \u0026ldquo;863\u0026rdquo; plan, the \u0026ldquo;Eighth Five-Year Plan\u0026rdquo; and the \u0026ldquo;Ninth Five-Year Plan\u0026rdquo; Research and development of key projects. The book \u0026ldquo;MCS-51 Series Single Chip Microcomputer Practical Interface Technology\u0026rdquo; (co-authored), as a science and technology monograph, won the third prize of Beijing Municipal Science and Technology Progress Award in 1999.\n","date":1588926244,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523755,"objectID":"4d4b53a9e6bd8ad6bfd1cbb50719620b","permalink":"https://thuiar.github.io/author/xiaomin-sun/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/xiaomin-sun/","section":"authors","summary":"Xiaomin Sun, born in 1963, is currently an associate professor at the State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science, Tsinghua University. He graduated from Harbin Engineering University in July 1984, and graduated from Beijing University of Aeronautics and Astronautics in January 1987 with a master\u0026rsquo;s degree in engineering.","tags":null,"title":"Xiaomin Sun","type":"authors"},{"authors":["CongfengYin"],"categories":null,"content":"Joined the team in 2019, obtained Master\u0026rsquo;s Degree in 2021.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"375b2c70171639464f068ac3f2e876ba","permalink":"https://thuiar.github.io/author/congfeng-yin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/congfeng-yin/","section":"authors","summary":"Joined the team in 2019, obtained Master\u0026rsquo;s Degree in 2021.","tags":null,"title":"Congfeng Yin","type":"authors"},{"authors":["HanleiZhang"],"categories":null,"content":"More detailed information can be seen on https://hanleizhang.github.io/\n","date":1665217444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1665217444,"objectID":"3aa9bc82010c2dbac4719c7b95e8a230","permalink":"https://thuiar.github.io/author/hanlei-zhang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/hanlei-zhang/","section":"authors","summary":"More detailed information can be seen on https://hanleizhang.github.io/","tags":null,"title":"Hanlei Zhang","type":"authors"},{"authors":["QianruiZhou"],"categories":null,"content":"","date":1665217444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1665217444,"objectID":"2e63efe2371772ca2cd3e7634ca832ed","permalink":"https://thuiar.github.io/author/qianrui-zhou/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/qianrui-zhou/","section":"authors","summary":"","tags":null,"title":"Qianrui Zhou","type":"authors"},{"authors":["XiaotengLi"],"categories":null,"content":"","date":1620462244,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1620462244,"objectID":"ba31996bc1d02597153d6830ccec75e0","permalink":"https://thuiar.github.io/author/xiaoteng-li/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/xiaoteng-li/","section":"authors","summary":"","tags":null,"title":"Xiaoteng Li","type":"authors"},{"authors":["HuigenYe"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"b7c5d96df0990481b836a2ece7fc5816","permalink":"https://thuiar.github.io/author/huigen-ye/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/huigen-ye/","section":"authors","summary":"","tags":null,"title":"Huigen Ye","type":"authors"},{"authors":["AnLiu"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"a1ad79a3a2a523f940eabc594d792cb0","permalink":"https://thuiar.github.io/author/an-liu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/an-liu/","section":"authors","summary":"","tags":null,"title":"An Liu","type":"authors"},{"authors":["HuishengMao"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"7eb8c8b0635d45f2accf81d4a610c428","permalink":"https://thuiar.github.io/author/huisheng-mao/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/huisheng-mao/","section":"authors","summary":"","tags":null,"title":"Huisheng Mao","type":"authors"},{"authors":["LunsongHuang"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"81aac8fbdec5980783c1bd0037110575","permalink":"https://thuiar.github.io/author/lunsong-huang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/lunsong-huang/","section":"authors","summary":"","tags":null,"title":"Lunsong Huang","type":"authors"},{"authors":["RobertoEvans"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"60f7295f7815d60c625b895bb716ee46","permalink":"https://thuiar.github.io/author/roberto-evans/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/roberto-evans/","section":"authors","summary":"","tags":null,"title":"Roberto Evans","type":"authors"},{"authors":["WeiYaWang"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"b46587539d6cebc7fc2feffcc327cced","permalink":"https://thuiar.github.io/author/weiya-wang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/weiya-wang/","section":"authors","summary":"","tags":null,"title":"WeiYa Wang","type":"authors"},{"authors":["WeiWan"],"categories":null,"content":"Joined the team in 2009, obtained Master\u0026rsquo;s Degree in 2012.\n","date":1356998400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600522727,"objectID":"6b448a48863cfc0603a02d6d7ab0bb6d","permalink":"https://thuiar.github.io/author/wei-wan/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/wei-wan/","section":"authors","summary":"Joined the team in 2009, obtained Master\u0026rsquo;s Degree in 2012.","tags":null,"title":"Wei Wan","type":"authors"},{"authors":["YunWen"],"categories":null,"content":"Joined the team in 2008, obtained Master\u0026rsquo;s Degree in 2011.\n","date":1293840000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523752,"objectID":"d8224dcbc7a5452ef1967454705df962","permalink":"https://thuiar.github.io/author/yun-wen/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/yun-wen/","section":"authors","summary":"Joined the team in 2008, obtained Master\u0026rsquo;s Degree in 2011.","tags":null,"title":"Yun Wen","type":"authors"},{"authors":["BoWang"],"categories":null,"content":"Joined the team in 2012, obtained Master\u0026rsquo;s Degree in 2015.\n","date":1420070400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523744,"objectID":"eca609a410ebcb1e5a447095b2237896","permalink":"https://thuiar.github.io/author/bo-wang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/bo-wang/","section":"authors","summary":"Joined the team in 2012, obtained Master\u0026rsquo;s Degree in 2015.","tags":null,"title":"Bo Wang","type":"authors"},{"authors":["JiaLi"],"categories":null,"content":"Joined the team in 2014, obtained Master\u0026rsquo;s Degree in 2017.\n","date":1598858644,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600523739,"objectID":"a03fff056354808323a63d17aa97e22f","permalink":"https://thuiar.github.io/author/jia-li/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jia-li/","section":"authors","summary":"Joined the team in 2014, obtained Master\u0026rsquo;s Degree in 2017.","tags":null,"title":"Jia Li","type":"authors"},{"authors":["XingweiHe"],"categories":null,"content":"Joined the team in 2015, obtained Master\u0026rsquo;s Degree in 2018.\n","date":1598858644,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1598858644,"objectID":"7646769138f76a7750e8c4791118fa02","permalink":"https://thuiar.github.io/author/xingwei-he/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/xingwei-he/","section":"authors","summary":"Joined the team in 2015, obtained Master\u0026rsquo;s Degree in 2018.","tags":null,"title":"Xingwei He","type":"authors"},{"authors":["JiyunZou"],"categories":null,"content":"","date":1644913444,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1644913444,"objectID":"66a0d18714990be03c802571a247ce22","permalink":"https://thuiar.github.io/author/jiyun-zou/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jiyun-zou/","section":"authors","summary":"","tags":null,"title":"Jiyun Zou","type":"authors"},{"authors":["WenmengYu"],"categories":null,"content":"","date":1635755044,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1635755044,"objectID":"877b3304f98bdd0c13734e9e7294a5cd","permalink":"https://thuiar.github.io/author/wenmeng-yu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/wenmeng-yu/","section":"authors","summary":"","tags":null,"title":"Wenmeng Yu","type":"authors"},{"authors":["TingenLin"],"categories":null,"content":"Joined the team in 2017, obtained a master\u0026rsquo;s degree in 2020, and an outstanding master\u0026rsquo;s degree graduate of the Department of Computer Science,Tsinghua University.\n","date":1613558326,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1613558326,"objectID":"61d3d513aa34dfbfbb229366fa18e9d9","permalink":"https://thuiar.github.io/author/tingen-lin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/tingen-lin/","section":"authors","summary":"Joined the team in 2017, obtained a master\u0026rsquo;s degree in 2020, and an outstanding master\u0026rsquo;s degree graduate of the Department of Computer Science,Tsinghua University.","tags":null,"title":"Tingen Lin","type":"authors"},{"authors":["KaichengYang"],"categories":null,"content":"","date":1602491044,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1602491044,"objectID":"800a24a91d07d612a641ef5040ae83c8","permalink":"https://thuiar.github.io/author/kaicheng-yang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/kaicheng-yang/","section":"authors","summary":"","tags":null,"title":"Kaicheng Yang","type":"authors"},{"authors":["YuxiangXie"],"categories":null,"content":"Joined the team in 2016, obtained Master\u0026rsquo;s Degree in 2019.\n","date":1577836800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600522715,"objectID":"4ea7b87ed00559775e8aeaa5666191ce","permalink":"https://thuiar.github.io/author/yuxiang-xie/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/yuxiang-xie/","section":"authors","summary":"Joined the team in 2016, obtained Master\u0026rsquo;s Degree in 2019.","tags":null,"title":"Yuxiang Xie","type":"authors"},{"authors":["TianqiWu"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"ff0a54b7503cee89c942152f70c6ec87","permalink":"https://thuiar.github.io/author/tianqi-wu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/tianqi-wu/","section":"authors","summary":"","tags":null,"title":"Tianqi Wu","type":"authors"},{"authors":["CongcongYang"],"categories":null,"content":"Joined the team in 2016, obtained Master\u0026rsquo;s Degree in 2019.\n","date":1577836800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1600522715,"objectID":"8792eeaf61be15ebed533947da301007","permalink":"https://thuiar.github.io/author/congcong-yang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/congcong-yang/","section":"authors","summary":"Joined the team in 2016, obtained Master\u0026rsquo;s Degree in 2019.","tags":null,"title":"Congcong Yang","type":"authors"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1669880609,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1669880609,"objectID":"49c71ee59356e28cecb4860aea67f640","permalink":"https://thuiar.github.io/monograph/natural-interaction-for-tri-co-robots-2-sentiment-analysis-of-multimodal-interaction-information/","publishdate":"2022-12-01T15:43:29+08:00","relpermalink":"/monograph/natural-interaction-for-tri-co-robots-2-sentiment-analysis-of-multimodal-interaction-information/","section":"monograph","summary":"The natural interaction ability between human and machine mainly involves human-machine dialogue ability, multi-modal sentiment analysis ability, human-machine cooperation ability and so on. In order to realize the multi-modal sentiment analysis ability of intelligent computer, it is necessary to make the computer own strong multi-modal sentiment analysis ability in the process of human-computer interaction. This is one of the key technologies to realize efficient and intelligent human-computer interaction. The research and practical application of multi-modal sentiment analysis oriented to human-computer natural interaction, this book mainly discusses the following levels of hot research content: Multi-modal Information Feature Representation, Feature Fusion and Sentiment Classification. Multi-modal sentiment analysis oriented to natural interaction is a comprehensive research field involving the integration of natural language processing, computer vision, machine learning, pattern recognition, algorithm, robot intelligent system, human-computer interaction, etc. In recent years, our research team from State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science, Tsinghua University, has conducted a lot of pioneering research and applied work, which have been carried out in the field of multi-modal sentiment analysis for natural interaction, especially in the field of sentiment feature representation, feature fusion, robust sentiment analysis based on deep learning model. Related achievements have also been published in the top academic international conferences in the field of artificial intelligence in recent years, such as ACL, AAAI, ACM MM, COLING and well-known international journals, such as Pattern Recognition, Knowledge based Systems, IEEE Intelligent Systems and Expert Systems with Applications. In order to systematically present the latest achievements in multi-modal sentiment analysis in academia in recent years, the relevant work achievements are systematically sorted out and presented to readers in the form of a complete systematic discussion. Currently, the research on multi-modal sentiment analysis in natural interaction develops fastly. The author's research team will timely sort out and summarize the latest achievements and share them with readers in the form of a series of books currenlty. This book can not only be used as a professional textbook in the fields of natural interaction, intelligent question answering (customer service), natural language processing, human-computer interaction, etc., but also as an important reference book for the research and development of systems and products in intelligent robots, natural language processing, human-computer interaction, etc. As the natural interaction is a new and rapidly developing research field, limited by the author's knowledge and cognitive scope, mistakes and shortcomings in the book are inevitable. We sincerely hope that you can give us valuable comments and suggestions for our book. Please contact (xuhua@mail.tsinghua.edu.cn) or a third party in the open source system platform https://thuiar.github.io/ to give us a message. All of the related source codes and datasets for this book have also been shared on the following websites https://github.com/thuiar/Books . The research work and writing of this book were supported by the National Natural Science Foundation of China (Project No. 62173195). We deeply appreciate the following students from State Key laboratory of Intelligent Technology and Systems, Department of Computer Science, Tsinghua University for their hard preparing work: Xiaofei Chen, Yuanzhe Qiu and Jiayu Huang. We also deeply appreciate the following students for the related research directions of cooperative innovation work: Zhongwu Zhai, Wenmeng Yu, Kaicheng Yang, Jiyun Zou, Ziqi Yuan, Huisheng Mao, Wei Li，Baozheng Zhang and Yihe Liu . Without the efforts of the members of our team, the book could not be presented in a structured form in front of every reader.","tags":["Aritificial Intelligence"],"title":"Natural Interaction for Tri-Co Robots (2) Sentiment Analysis of Multimodal Interaction Information","type":"monograph"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1647762209,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1647762209,"objectID":"3172463e7faeaa3bfd8dd580751177d6","permalink":"https://thuiar.github.io/monograph/natural-interaction-for-tri-co-robots-1-human-machine-dialogue-intention-understanding/","publishdate":"2022-03-20T15:43:29+08:00","relpermalink":"/monograph/natural-interaction-for-tri-co-robots-1-human-machine-dialogue-intention-understanding/","section":"monograph","summary":"Inclusive robots can interact naturally with the work environment, humans, and other robots, adapt to complex dynamic environments autonomously, and work collaboratively. [Keen and considerate] natural interaction is one of the hot research issues of inclusive service robots. At present, there is an urgent need for robots and humans to have the ability to understand the intention of interactive dialogue. This book is based on the field of human-computer understanding based on deep learning methods. Starting from the knowledge of human-computer dialogue intentions, it systematically introduces intention recognition, unknown intention detection, and new intention discovery in human-computer dialogue. This book is the first domestic professional book to present interactive dialogue intention analysis in inclusive robot natural interaction. It can help readers master the key technologies and basic knowledge of human-machine dialogue intention analysis in inclusive robot research and track the development frontiers in this field. Provide meaningful learning and research references.","tags":[],"title":"Natural Interaction for Tri-Co Robots (1) Human-machine Dialogue Intention Understanding","type":"monograph"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1646207009,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1646207009,"objectID":"ac3ff13e61fb460d5b7457abbbb13f72","permalink":"https://thuiar.github.io/textbook/datamining-methodandapplication2/","publishdate":"2022-03-02T15:43:29+08:00","relpermalink":"/textbook/datamining-methodandapplication2/","section":"textbook","summary":"Mainly based on the teaching practice and accumulation of the Data Mining Methods and Applications course set up by Tsinghua University, referring to the teaching system of relevant courses of famous foreign universities in recent years, systematically introducing the basic concepts and basic principles of data mining; combining some typical applications Examples show general patterns and ideas for solving problems with data mining thinking methods.","tags":["Data Mining"],"title":"Data Mining: Methodology and Applications(2nd edition)","type":"textbook"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1409557409,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1409557409,"objectID":"aa585aa49362c42e9f2830d01bcf8f37","permalink":"https://thuiar.github.io/textbook/datamining-methodandapplication/","publishdate":"2014-09-01T15:43:29+08:00","relpermalink":"/textbook/datamining-methodandapplication/","section":"textbook","summary":"Mainly based on the teaching practice and accumulation of the Data Mining Methods and Applications course set up by Tsinghua University, referring to the teaching system of relevant courses of famous foreign universities in recent years, systematically introducing the basic concepts and basic principles of data mining; combining some typical applications Examples show general patterns and ideas for solving problems with data mining thinking methods.","tags":["Data Mining"],"title":"Data Mining: Methodology and Applications","type":"textbook"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1409557409,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1409557409,"objectID":"1d2e51d6cd32618b7ac91b67270b41af","permalink":"https://thuiar.github.io/monograph/evolutionary-machine-learning/","publishdate":"2014-09-01T15:43:29+08:00","relpermalink":"/monograph/evolutionary-machine-learning/","section":"monograph","summary":"Mainly discusses the content of this book: it is a learning classifier and feature selection method, the key is to do both of the integration of research, will study the classification of the classifier model building process and feature selection of feature subset unified integration based on the genetic search process of machine learning framework, at the same time improve the prediction performance of classification algorithm and operation efficiency; Secondly, a learning classifier based on distribution estimation algorithm is introduced to improve the searching quality of rule space. 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How to apply big data? What is big data thinking? How to learn big data? How to build a big data platform? How to apply big data in the industry? This series of problems are very confusing problems in the current era of big data boom. Big Data Technology and Industry Application faces these questions directly, answers the above questions from the perspective of practitioners, and hopes to provide some help to beginners in the big data industry.","tags":["Big Data"],"title":"Big Data Technology and Industry Applications","type":"textbook"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1454312609,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1454312609,"objectID":"4949ddee0a08346cb8809beaaa742b23","permalink":"https://thuiar.github.io/monograph/sentimentanalysisandontologyengineering/","publishdate":"2016-02-01T15:43:29+08:00","relpermalink":"/monograph/sentimentanalysisandontologyengineering/","section":"monograph","summary":"Chapter 10: Chinese Micro-Blog Emotion Classification by Exploiting Linguistic Features and SVMperf ), Springer International Publishing, 2016, pp. 221-236, ISNN:978-3-319-30317-8 (Hua Hu participated in the writing, published in February 2016)","tags":[],"title":"Sentiment Analysis and Ontology Engineering","type":"monograph"},{"authors":["Hua Xu"],"categories":[],"content":"","date":1201851809,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1201851809,"objectID":"2c5c9a0907335dbc37fce7e6ceebe45b","permalink":"https://thuiar.github.io/monograph/recentadvancesinmulti-robotsystems/","publishdate":"2008-02-01T15:43:29+08:00","relpermalink":"/monograph/recentadvancesinmulti-robotsystems/","section":"monograph","summary":"Chapter 13: A Novel Modeling Method for Cooperative Multi-robot Systems Using Fuzzy Timed Agent Based Petri Nets ), I-Tech Education and Publishing, Vienna, Austria, 2008, pp.249-262, ISNN:978-3-902613-24-0 (Hua Hu participated in the writing, published in February 2008)","tags":[],"title":"Recent Advances in Multi-robot Systems","type":"monograph"},{"authors":["Junhui Deng"],"categories":[],"content":"","date":1107243809,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1107243809,"objectID":"30837e0151d041aff588987056d3a925","permalink":"https://thuiar.github.io/textbook/%E8%AE%A1%E7%AE%97%E5%87%A0%E4%BD%95%E7%AE%97%E6%B3%95%E4%B8%8E%E5%BA%94%E7%94%A8/","publishdate":"2005-02-01T15:43:29+08:00","relpermalink":"/textbook/%E8%AE%A1%E7%AE%97%E5%87%A0%E4%BD%95%E7%AE%97%E6%B3%95%E4%B8%8E%E5%BA%94%E7%94%A8/","section":"textbook","summary":"The first four chapters of \"Computational Geometry: Algorithms and Applications (3rd Edition)\" discuss geometric algorithms, including geometric intersection, triangulation, linear programming, etc. The random algorithm involved is also \"Computational Geometry: Algorithms and Applications (Third Edition)\" a distinctive feature. 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Yuan","ZhangZeqiu"],"categories":[],"content":"","date":1672561444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672561444,"objectID":"6f8664637f2d79d6e04d825b2d5692ba","permalink":"https://thuiar.github.io/publication/an-adaptive-batch-bayesian-optimization-approach-for-expensive-multi-objective-optimization-problems/","publishdate":"2022-09-01T16:24:04+08:00","relpermalink":"/publication/an-adaptive-batch-bayesian-optimization-approach-for-expensive-multi-objective-optimization-problems/","section":"publication","summary":"This paper presents Adaptive Batch-ParEGO, an adaptive batch Bayesian optimization method for expensive multi-objective problems. This method extends the classical multi-objective Bayesian optimization method, sequential ParEGO, to the batch mode. Specifically, the proposed method exploits a newly proposed bi-objective acquisition func- tion to recommend and evaluate multiple solutions. The bi-objective acquisition function takes exploitation and exploration as two optimization objectives, which are traded off by a multi-objective evolutionary algorithm. Since there’s usually a certain number of lim- ited hardware resources available in reality, we further propose an adaptive solution selec- tion criterion to fix the number of candidate solutions in each iteration. This strategy dynamically balances exploitation and exploration by tuning the hyper-parameter in the exploitation-exploration fitness function. In addition, the expected improvement is exploited to select another candidate solution to ensure convergence and make the algo- rithm more robust. We verify the effectiveness of Adaptive Batch-ParEGO on three multi-objective benchmarks and a hyperparameter tuning task of neural networks com- pared with the state-of-the-art multi-objective approaches. Our analysis demonstrates that the bi-objective acquisition function with the adaptive recommendation strategy can bal- ance exploitation and exploration well in batch mode for expensive multi-objective prob- lems. All our source codes will be published at https://github.com/thuiar/Adaptive-Batch-ParEGO.","tags":[],"title":"An Adaptive Batch Bayesian Optimization Approach for Expensive Multi-Objective Optimization problems","type":"publication"},{"authors":["Yihe Liu","Ziqi Yuan","Huisheng Mao","Zhiyun Liang","Wanqiuyue Yang","Yuanzhe Qiu","Tie Cheng","Xiaoteng Li","Hua Xu","Kai Gao"],"categories":[],"content":"","date":1667287444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1667287444,"objectID":"a0da352a6f6f1133ff876c4a297148bb","permalink":"https://thuiar.github.io/publication/make-acoustic-and-visual-cues-matter_/","publishdate":"2022-11-01T15:24:04+08:00","relpermalink":"/publication/make-acoustic-and-visual-cues-matter_/","section":"publication","summary":"Multimodal sentiment analysis (MSA), which supposes to improve text-based sentiment analysis with associated acoustic and visual modalities, is an emerging research area due to its potential applica- tions in Human-Computer Interaction (HCI). However, existing re- searches observe that the acoustic and visual modalities contribute much less than the textual modality, termed as text-predominant. Under such circumstances, in this work, we emphasize making non-verbal cues matter for the MSA task. Firstly, from the resource perspective, we present the CH-SIMS v2.0 dataset, an extension and enhancement of the CH-SIMS. Compared with the original dataset, the CH-SIMS v2.0 doubles its size with another 2121 refined video segments containing both unimodal and multimodal annotations and collects 10161 unlabelled raw video segments with rich acoustic and visual emotion-bearing context to highlight non-verbal cues for sentiment prediction. Secondly, from the model perspective, bene- fiting from the unimodal annotations and the unsupervised data in the CH-SIMS v2.0, the Acoustic Visual Mixup Consistent (AV-MC) framework is proposed. The designed modality mixup module can be regarded as an augmentation, which mixes the acoustic and vi- sual modalities from different videos. Through drawing unobserved multimodal context along with the text, the model can learn to be aware of different non-verbal contexts for sentiment prediction. Our evaluations demonstrate that both CH-SIMS v2.0 and AV-MC framework enable further research for discovering emotion-bearing acoustic and visual cues and pave the path to interpretable end-to- end HCI applications for real-world scenarios. The full dataset and code are available for use at https://github.com/thuiar/ch-sims-v2.","tags":[],"title":"Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module","type":"publication"},{"authors":["ChenXiaoFei","XuHua","QianPeng","XuYunFeng","LiFuFeng","LiShengWang"],"categories":[],"content":"","date":1667287444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1667287444,"objectID":"a97b5d74eb2c9642b5ee6c3e82a2518f","permalink":"https://thuiar.github.io/publication/multi-kernel-convolutional-neural-network-for-wrist-pulse-signal-classification/","publishdate":"2022-11-01T15:24:04+08:00","relpermalink":"/publication/multi-kernel-convolutional-neural-network-for-wrist-pulse-signal-classification/","section":"publication","summary":"Wrist pulse is one kind of biomedical signals, it is affected not only by the heart beatings, but also by the conditions of nerves, organs, muscles, skin, etc. Therefore, wrist pulse signals can reflect a person's physical state and it has been widely used in health status analysis. However, previous works mainly use traditional machine learning methods to analyze wrist pulse signal. Because wrist pulse signal is high-dimensional and complex, it is difficult for traditional machine learning methods to learn effective information from them. This study aims to explore the utilizing of deep learning methods on wrist pulse signal analysis. We propose a novel multi-kernel Convolutional Neural Network for wrist pulse signal classification. Our model can handle multiple kinds of input features and each of them will pass through a convolutional neural network that has three different sizes of convolution kernel to capture multi-scale information in different time steps. We compare our method with traditional machine learning methods on two tasks: Coronary Atherosclerotic Heart Disease Classification and Traditional Chinese Medicine Constitution yin deficiency and yang deficiency Classification. Besides, we also research the influence of different input features and different channels on wrist pulse signal analysis. The results show that our model significantly improves the performance on the two tasks, which proves the deep learning method is more suitable to deal with complex wrist pulse data.","tags":[],"title":"Multi-kernel Convolutional Neural Network for Wrist Pulse Signal Classification","type":"publication"},{"authors":["Zhijing Wu","Jingliang Fang","Hua Xu","Kai Gao"],"categories":[],"content":"","date":1665991444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1665991444,"objectID":"8f098ab084f29b346f2b3e5c00882cda","permalink":"https://thuiar.github.io/publication/an-in-depth-interactive-and-visualized-platform-for-evaluating-and-analyzing-mrc-models/","publishdate":"2022-10-17T15:24:04+08:00","relpermalink":"/publication/an-in-depth-interactive-and-visualized-platform-for-evaluating-and-analyzing-mrc-models/","section":"publication","summary":"Machine Reading Comprehension (MRC) has made leaps and bounds when focusing on answering questions. However, since the ex- isting accuracy-based evaluation metrics are agnostic to the nu- ances of neural networks, the true understanding and inferenc- ing abilities of MRC models remain largely unknown. To address the above limitations, InDepth-Eva-MRC, an interactive and vi- sualized platform, is proposed to provide analysis from cognitive fine-grained for MRC models. Concretely, the platform makes post- hoc systems to explain the behavior of MRC models. On the one hand, it analyzes the linguistic bias via performances with different linguistic properties. On the other hand, it performs skill-based analysis methods based on the modified test samples and semi- automatically generated test samples. Furthermore, through its detailed and interactive visualizations, the platform offers in-depth results analysis and model comparison from cognitive fine-grained. A screencast video and additional external material are available on https://github.com/thuiar/InDepth-Eva-MRC.","tags":[],"title":"An In-depth Interactive and Visualized Platform for Evaluating and Analyzing MRC Models","type":"publication"},{"authors":["Hanlei Zhang","Hua Xu","Xin Wang","Qianrui Zhou","Shaojie Zhao","JiayanTeng"],"categories":[],"content":"","date":1665217444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1665217444,"objectID":"6a62884c65681be0612c8f71e61c3e3a","permalink":"https://thuiar.github.io/publication/mintrec/","publishdate":"2022-10-08T16:24:04+08:00","relpermalink":"/publication/mintrec/","section":"publication","summary":"Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec.","tags":[],"title":"MIntRec: A New Dataset for Multimodal Intent Recognition","type":"publication"},{"authors":["WuZhiJing","XuHua"],"categories":[],"content":"","date":1664699044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664699044,"objectID":"a4a27e1b826342745c751f35e4f5c272","permalink":"https://thuiar.github.io/publication/trustworthy-machine-reading-comprehension-with-conditional-adversarial-calibration/","publishdate":"2022-10-02T16:24:04+08:00","relpermalink":"/publication/trustworthy-machine-reading-comprehension-with-conditional-adversarial-calibration/","section":"publication","summary":"Machine Reading Comprehension (MRC) has achieved impressive answer inference performance in recent years but rarely considers the trustworthiness and reliability of the deployed systems. However, it is crucial to estimate the predictive uncertainty in real-world applications to measure how likely the prediction is wrong. Hence it is possible to abstain from the uncertain prediction with low confidence and build a trustworthy system. Prior studies use post-processing ways to measure the predictive uncertainty, such as employing heuristic softmax probability or training a calibrator on top of a trained MRC model. However, they only calibrate the confidence without considering the domain adaptation relationship. To handle the limitations, this paper presents TrustMRC, a non-postprocessing trustworthy MRC system that leverages (1) conditional calibration strategy to get reliable uncertainty, and (2) conditional adversarial learning strategy to learn transfer representations under domain shift setting. On the one hand, to estimate the predictive uncertainty, a conditional calibration module is proposed to predict whether the output of the answer prediction module is correct, and it is combined with an additional ECE constraint to restrict the confidence more reliable. On the other hand, for domain shift, TrustMRC designs a conditional adversarial learning strategy to learn transfer representations through a domain discriminator with uncertainty constraints, which takes both input and uncertainty alignment into account. Besides, TrustMRC is a non-postprocessing model that completes the answer prediction and uncertainty prediction in an end-to-end framework, so that these two sub-tasks can benefit from each other via multi-task learning. Instead of traditional EM and F1 metrics, EM-coverage and F1-coverage curves are used, for the trustworthiness-aware MRC evaluation. The experimental results on SQuAD 1.1, Natural Questions, and NewsQA datasets indicate that TrustMRC can make reliable predictions under domain shift settings.","tags":[],"title":"Trustworthy Machine Reading Comprehension with Conditional Adversarial Calibration","type":"publication"},{"authors":["Wei Wang","Hua Xu","Weiwei Yang","Xiaoqiu Huang"],"categories":[],"content":"","date":1658219044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1658219044,"objectID":"82c559431cef96c2008a5dfa587e6094","permalink":"https://thuiar.github.io/publication/constrained-hlda-for-topic-discovery-in-chinese-microblogs/","publishdate":"2022-07-19T16:24:04+08:00","relpermalink":"/publication/constrained-hlda-for-topic-discovery-in-chinese-microblogs/","section":"publication","summary":"Since microblog service became information provider on web scale, research on microblog has begun to focus more on its content mining. Most research on microblog context is often based on topic models, such as: Latent Dirichlet Allocation(LDA) and its variations. However,there are some challenges in previous research. On one hand, the number of topics is fixed as a priori, but in real world, it is input by the users. On the other hand, it ignores the hierarchical information of topics and cannot grow structurally as more data are observed. In this paper, we propose a semi-supervised hierarchical topic model, which aims to explore more reasonable topics in the data space by incorporating some constraints into the modeling process that are extracted automatically. The new method is denoted as constrained hierarchical Latent Dirichlet Allocation (constrained-hLDA). We conduct experiments on Sina microblog, and evaluate the performance in terms of clustering and empirical likelihood. The experimental results show that constrained-hLDA has a significant improvement on the interpretability, and its predictive ability is also better than that of hLDA.","tags":["Hierarchical Topic Model","Constrained-hLDA","Topic Discovery"],"title":"Constrained-hLDA for Topic Discovery in Chinese Microblogs","type":"publication"},{"authors":["Zhijing Wu","Hua Xu","JingLiang Fang","KaiGao"],"categories":[],"content":"","date":1658219044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1658219044,"objectID":"ead9f2870e66b19f8d91ce94524fa012","permalink":"https://thuiar.github.io/publication/continual-machine-reading-comprehension-via-uncertainty-aware-fixed-memory-and-adversarial-domain-adaptation/","publishdate":"2022-07-19T16:24:04+08:00","relpermalink":"/publication/continual-machine-reading-comprehension-via-uncertainty-aware-fixed-memory-and-adversarial-domain-adaptation/","section":"publication","summary":"Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge. In this paper, MA-MRC, a continual MRC model with uncertainty-aware fixed Memory and Adversarial domain adaptation, is proposed. In MA-MRC, a fixed size memory stores a small number of samples in previous domain data along with an uncertainty-aware updating strategy when new domain data arrives. For incremental learning, MA-MRC not only keeps a stable understanding by learning both memory and new domain data, but also makes full use of the domain adaptation relationship between them by adversarial learning strategy. The experimental results show that MA-MRC is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings.","tags":[],"title":"Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation ","type":"publication"},{"authors":["Li Chen","Hua Xu"],"categories":[],"content":"","date":1658219044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1658219044,"objectID":"e609e769d5d1a86ae3e279650919b148","permalink":"https://thuiar.github.io/publication/mfenas/","publishdate":"2022-07-19T16:24:04+08:00","relpermalink":"/publication/mfenas/","section":"publication","summary":"Neural Architecture Search (NAS) aims to automatically find neural network architectures competitive with human-designed ones. Despite the remarkable progress achieved, existing NAS methods still suffer from vast computational resources cost. Inspired by MFEA, we model the NAS task as a two-factorial problem and propose a multifactorial evolutionary neural architecture search (MFENAS) algorithm to solve it. MFENAS divides a population into two subgroups according to factors, and then the factors influence the evolution and knowledge transfer between subgroups. Experimental results of NATS-Bench demonstrate the efficiency of the proposed MFENAS in finding optimal structures under resource constraints compared to other state-of-the-art methods.","tags":[],"title":"MFENAS: Multifactorial Evolution for Neural Architecture Search","type":"publication"},{"authors":["WangHongyan","XuHua","Yuan Yuan"],"categories":[],"content":"","date":1651393444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1651393444,"objectID":"505d3cfdcb4ae94233ab44aa73a9dfc5","permalink":"https://thuiar.github.io/publication/high-dimensional-expensive-multi-objective-optimization-via-additive-structure/","publishdate":"2022-05-01T16:24:04+08:00","relpermalink":"/publication/high-dimensional-expensive-multi-objective-optimization-via-additive-structure/","section":"publication","summary":"Expensive multi-objective problems (MOPs) are extremely challenging due to the high evaluation cost to find satisfying solutions with adequate precision, especially in high-dimensional cases. However, most of the current EGO-based algorithms for expensive MOPs are limited to low decision dimensions because of the exponential difficulty in high dimensional circumstances. This paper presents High-Dimensional Expensive Multi-objective Optimization with Additive structure (ADD-HDEMO) to solve high-dimensional expensive MOPs via additive structural kernel and identifies two key challenges in this endeavor. First, we integrate multiple sub-objectives in high-dimensional expensive MOPs into a single objective with the decision space unchanged. Then, we infer the dependence correlation between the decision and objective space of the augmented EMOP via an additive GP kernel structure where Gibbs sampling is used to learn the latent additive structure. Furthermore, we parallel the proposed algorithm by introducing a multi-point sampling mechanism when recommending infill points. The effectiveness of the proposed method is evaluated on ZDT and DTLZ benchmarks compared with three other EGO-based multi-objective optimization approaches, ParEGO, SMS-EGO and MOEA/D-EGO. Our analyses demonstrate that ADD-HDEMO is effective in solving high-dimensional expensive MOPs.","tags":[],"title":"High-dimensional expensive multi-objective optimization via additive structure","type":"publication"},{"authors":["Huisheng Mao","Ziqi Yuan","Hua Xu","Wenmeng Yu","Yihe Liu","Kai Gao"],"categories":[],"content":"","date":1647415444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1647415444,"objectID":"71f77290e373f601482fc2c9952ad45c","permalink":"https://thuiar.github.io/publication/m-sena/","publishdate":"2022-03-16T15:24:04+08:00","relpermalink":"/publication/m-sena/","section":"publication","summary":"M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. ","tags":[],"title":"M-SENA: An Integrated Platform for Multimodal Sentiment Analysis","type":"publication"},{"authors":["Kang Zhao","Hua Xu","Jiangong Yang","Kai Gao"],"categories":[],"content":"","date":1647329044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1647329044,"objectID":"3db2a0cc8fd381ca3c52729c03a25b8f","permalink":"https://thuiar.github.io/publication/consistent-representation-learning-for-continual-relation-extraction/","publishdate":"2022-03-15T15:24:04+08:00","relpermalink":"/publication/consistent-representation-learning-for-continual-relation-extraction/","section":"publication","summary":"Continual relation extraction (CRE) aims to continuously train a model on data with new relations while avoiding forgetting old ones. Some previous work has proved that storing a few typical samples of old relations and replaying them when learning new relations can effectively avoid forgetting. However, these memory-based methods tend to overfit the memory samples and perform poorly on imbalanced datasets. To solve these challenges, a consistent representation learning method is proposed, which maintains the stability of the relation embedding by adopting contrastive learning and knowledge distillation when replaying memory. Specifically, supervised contrastive learning based on a memory bank is first used to train each new task so that the model can effectively learn the relation representation. Then, contrastive replay is conducted of the samples in memory and makes the model retain the knowledge of historical relations through memory knowledge distillation to prevent the catastrophic forgetting of the old task. The proposed method can better learn consistent representations to alleviate forgetting effectively. Extensive experiments on FewRel and TACRED datasets show that our method significantly outperforms state-of-theart baselines and yield strong robustness on the imbalanced dataset.","tags":[],"title":"Consistent Representation Learning for Continual Relation Extraction","type":"publication"},{"authors":["Hua Xu","Ziqi Yuan","Kang Zhao","YunfengXu","Jiyun Zou","KaiGao"],"categories":[],"content":"","date":1644913444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1644913444,"objectID":"ba0f50233cdcfbb4b47d4b5b4a3bf639","permalink":"https://thuiar.github.io/publication/gar-net/","publishdate":"2022-02-15T16:24:04+08:00","relpermalink":"/publication/gar-net/","section":"publication","summary":"Conversation understanding, as a necessary step for many applications, including social media, education, and argumentation mining, has been gaining increasing attention from the research community. Reasoning over long-term dependent contextual information is the key to utterance-level conversation understanding. Aiming to emphasize the importance of contextual reasoning, an end-to-end graph attention reasoning network which takes both word-level and utterance-level context into concern is proposed. To be specific, a word-level encoder with a novel convolution gate is first built to filter out irrelevant contextual information. Based on the representation extracted by word-level encoder, a graph reasoning network is designed to utilize the context among utterance-level, where the entire conversation is treated as a fully connected graph, utterances as nodes, and attention scores between utterances as edges. The proposed model is a general framework for conversation understanding tasks, which can be flexibly applied on most conversation datasets without changing the network architecture. Furthermore, we revise the tensor fusion network by adding a residual connection to explore cross-modal conversation understanding. For uni-modal scene (text modality), experiments show that the proposed method surpasses current state-of-the-art methods on emotion recognition, intent classification, and dialogue act identification tasks. For cross-modal scenes (text modality and audio modality), experiments on IEMOCAP and MELD datasets show that the proposed method can use cross-modal information to improve model performance.","tags":[],"title":"GAR-Net: A Graph Attention Reasoning Network for Conversation Understanding","type":"publication"},{"authors":["Huisheng Mao","Baozheng Zhang","Hua Xu","Kai Gao"],"categories":[],"content":"","date":1641972244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1641972244,"objectID":"750b1ac20c18e626cd734ed9133c4929","permalink":"https://thuiar.github.io/publication/an-end-to-end-traditional-chinese-medicine-constitution-assessment-system-based-on-multimodal-clinical-feature-representation-and-fusion/","publishdate":"2022-01-31T15:24:04+08:00","relpermalink":"/publication/an-end-to-end-traditional-chinese-medicine-constitution-assessment-system-based-on-multimodal-clinical-feature-representation-and-fusion/","section":"publication","summary":"Traditional Chinese Medicine (TCM) constitution is a fundamental concept in TCM theory. It is determined by multimodal TCM clinical features which, in turn, are obtained from TCM clinical information of image (face, tongue, etc.), audio (pulse and voice), and text (inquiry) modality. The auto assessment of TCM constitution is faced with two major challenges: (1) learning discriminative TCM clinical feature representations; (2) jointly processing the features using multimodal fusion techniques. The TCM Constitution Assessment System (TCM-CAS) is proposed to provide an end-to-end solution to this task, along with auxiliary functions to aid TCM researchers. To improve the results of TCM constitution prediction, the system combines multiple machine learning algorithms such as facial landmark detection, image segmentation, graph neural networks and multimodal fusion. Extensive experiments are conducted on a four-category multimodal TCM constitution dataset, and the proposed method achieves state-of-the-art accuracy. Provided with datasets containing annotations of diseases, the system can also perform automatic disease diagnosis from a TCM perspective.","tags":[],"title":"An End-to-End Traditional Chinese Medicine Constitution Assessment System Based on Multimodal Clinical Feature Representation and Fusion","type":"publication"},{"authors":["Wenmeng Yu","Hua Xu"],"categories":[],"content":"","date":1635755044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1635755044,"objectID":"aa2e330cb394dfe7750182253390ced2","permalink":"https://thuiar.github.io/publication/co-attentive-multi-task-convolutional-neural-network-for-facial-expression-recognition/","publishdate":"2021-11-01T16:24:04+08:00","relpermalink":"/publication/co-attentive-multi-task-convolutional-neural-network-for-facial-expression-recognition/","section":"publication","summary":"Previous research on Facial Expression Recognition (FER) assisted by facial landmarks mainly focused on single-task learning or hard-parameter sharing based multi-task learning. However, soft-parameter shar- ing based methods have not been explored in this area. Therefore, this paper adopts Facial Landmark Detection (FLD) as the auxiliary task and explores new multi-task learning strategies for FER. First, three classical multi-task structures, including Hard-Parameter Sharing (HPS), Cross-Stitch Network (CSN), and Partially Shared Multi-task Convolutional Neural Network (PS-MCNN), are used to verify the advantages of multi-task learning for FER. Then, we propose a new end-to-end Co-attentive Multi-task Convolutional Neural Network (CMCNN), which is composed of the Channel Co-Attention Module (CCAM) and the Spa- tial Co-Attention Module (SCAM). Functionally, the CCAM generates the channel co-attention scores by capturing the inter-dependencies of different channels between FER and FLD tasks. The SCAM combines the max- and average-pooling operations to formulate the spatial co-attention scores. Finally, we con- duct extensive experiments on four widely used benchmark facial expression databases, including RAF, SFEW2, CK+, and Oulu-CASIA. Extensive experimental results show that our approach achieves better performance than single-task and multi-task baselines, fully validating multi-task learning s effectiveness and generalizability.","tags":[],"title":"Co-attentive multi-task convolutional neural network for facial expression recognition","type":"publication"},{"authors":["Ziqi Yuan","WeiLi","Hua Xu","Wenmeng Yu"],"categories":[],"content":"","date":1634718244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1634718244,"objectID":"dcb19b7e0d77ae162f602fb808c04194","permalink":"https://thuiar.github.io/publication/transformer-based-feature-reconstruction-network-for-robust-multimodal-sentiment-analysis/","publishdate":"2021-10-20T16:24:04+08:00","relpermalink":"/publication/transformer-based-feature-reconstruction-network-for-robust-multimodal-sentiment-analysis/","section":"publication","summary":"Improving robustness against data missing has become one of the core challenges in Multimodal Sentiment Analysis (MSA), which aims to judge speaker sentiments from the language, visual, and acoustic signals. In the current research, translation-based methods and tensor regularization methods are proposed for MSA with incomplete modality features. However, both of them fail to cope with random modality feature missing in non-aligned sequences. In this paper, a transformer-based feature reconstruction network (TFR-Net) is proposed to improve the robustness of models for the random missing in non-aligned modality sequences. First, intramodal and inter-modal attention-based extractors are adopted to learn robust representations for each element in modality sequences. Then, a reconstruction module is proposed to generate the missing modality features. With the supervision of SmoothL1Loss between generated and complete sequences, TFR-Net is expected to learn semantic-level features corresponding to missing features. Extensive experiments on two public benchmark datasets show that our model achieves good results against data missing across various missing modality combinations and various missing degrees.","tags":[],"title":"Transformer-based Feature Reconstruction Network for Robust Multimodal Sentiment Analysis","type":"publication"},{"authors":["Hanlei Zhang","Hua Xu","Ting-En Lin","Rui Lv"],"categories":[],"content":"","date":1621326244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1621326244,"objectID":"cd99071c4a1a70e6c6b3dd2f7236b7ab","permalink":"https://thuiar.github.io/publication/discovering-new-intents-with-deep-aligned-clustering/","publishdate":"2021-05-18T16:24:04+08:00","relpermalink":"/publication/discovering-new-intents-with-deep-aligned-clustering/","section":"publication","summary":"Discovering new intents is a crucial task in dialogue system. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These meth- ods also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping un- labeled intents. In this work, we propose an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster as- signments as pseudo-labels. Moreover, we propose an align- ment strategy to tackle the label inconsistency during cluster- ing assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods.","tags":["Conversational AI/Dialog Systems","(Deep) Neural Network Algorithms","Clustering","Unsupervised \u0026 Self-Supervised Learning"],"title":"Discovering New Intents with Deep Aligned Clustering","type":"publication"},{"authors":["Hanlei Zhang","Xiaoteng Li","Hua Xu","PanpanZhang","Kang Zhao","KaiGao"],"categories":[],"content":"","date":1620462244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1620462244,"objectID":"e506f9d300b6b691d19ea82504a0d692","permalink":"https://thuiar.github.io/publication/textoir/","publishdate":"2021-05-08T16:24:04+08:00","relpermalink":"/publication/textoir/","section":"publication","summary":"TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each module integrates most of the state-of-the-art algorithms and benchmark intent datasets. It also contains an overall framework connecting the two modules in a pipeline scheme. In addition, this platform has visualized tools for data and model management, training, evaluation and analysis of the performance from different aspects. TEXTOIR provides useful toolkits and convenient visualized interfaces for each sub-module , and designs a framework to implement a complete process to both identify known intents and discover open intents. Codes can be found at https://github.com/thuiar/TEXTOIR","tags":[],"title":"TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition","type":"publication"},{"authors":["Hanlei Zhang","Hua Xu","Tingen Lin","RuiLv"],"categories":[],"content":"","date":1613558326,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1613558326,"objectID":"ec89d89560bb97f9139ac0639d274dad","permalink":"https://thuiar.github.io/publication/deepaligned-clustering/","publishdate":"2021-02-17T18:38:46+08:00","relpermalink":"/publication/deepaligned-clustering/","section":"publication","summary":"Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These methods also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating lowconfidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-theart methods.","tags":[],"title":"Discovering New Intents with Deep Aligned Clustering","type":"publication"},{"authors":["Hanlei Zhang","Hua Xu","Tingen Lin"],"categories":[],"content":"","date":1613471926,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1613471926,"objectID":"e37b2d1ba3f5c8ec85d8e0c153e25fd7","permalink":"https://thuiar.github.io/publication/adaptive-decision-boundary/","publishdate":"2021-02-16T18:38:46+08:00","relpermalink":"/publication/adaptive-decision-boundary/","section":"publication","summary":"Open intent classification is a challenging task in dialogue systems. On the one hand, we should ensure the classification quality of known intents. On the other hand, we need to identify the open (unknown) intent during testing. Current models are limited in finding the appropriate decision boundary to balance the performances of both known and open intents. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we use the well-trained features to automatically learn the adaptive spherical decision boundaries for each known intent. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open samples and is free from modifying the model architecture. We find our approach is surprisingly insensitive with less labeled  data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods.","tags":[],"title":"Deep Open Intent Classification with Adaptive Decision Boundary","type":"publication"},{"authors":["Wenmeng Yu","Hua Xu","Ziqi Yuan","JieleWu"],"categories":[],"content":"","date":1613385526,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1613385526,"objectID":"54974a840628e4cbcc4a43055bc6d6c2","permalink":"https://thuiar.github.io/publication/self-mm/","publishdate":"2021-02-15T18:38:46+08:00","relpermalink":"/publication/self-mm/","section":"publication","summary":"Representation Learning is a significant and challenging task in multimodal learning. Effective modality representations should contain two parts of characteristics: the consistency and the difference. Due to the unified multimodal annotation, existing methods are restricted in capturing differentiated information. However, additional uni-modal annotations are high time- and labor-cost. In this paper, we design a label generation module based on the self-supervised learning strategy to acquire independent unimodal supervisions. Then, joint training the multi-modal and uni-modal tasks to learn the consistency and difference, respectively. Moreover, during the training stage, we design a weight-adjustment strategy to balance the learning progress among different subtasks. That is to guide the subtasks to focus on samples with a larger difference between modality supervisions. Last, we conduct extensive experiments on three public multimodal baseline datasets. The experimental results validate the reliability and stability of auto-generated unimodal supervisions. On MOSI and MOSEI datasets, our method surpasses the current state-of-the-art methods. On the SIMS dataset, our method achieves comparable performance than humanannotated unimodal labels.","tags":[],"title":"Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis","type":"publication"},{"authors":["Wenmeng Yu","Hua Xu","Ziqi Yuan","Jiele Wu"],"categories":[],"content":"","date":1612859044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1612859044,"objectID":"099cea93a11e02854a2116ce4d4bb094","permalink":"https://thuiar.github.io/publication/learning-modality-specific-representations-with-self-supervised-multi-task-learning-for-multimodal-sentiment-analysis/","publishdate":"2021-02-09T16:24:04+08:00","relpermalink":"/publication/learning-modality-specific-representations-with-self-supervised-multi-task-learning-for-multimodal-sentiment-analysis/","section":"publication","summary":"Representation Learning is a significant and challenging task in multimodal learning. Effective modality representations should contain two parts of characteristics: the consistency and the difference. Due to the unified multimodal annotation, existing methods are restricted in capturing differentiated information. However, additional uni-modal annotations are high time- and labor-cost. In this paper, we design a label generation module based on the self-supervised learning strategy to acquire independent unimodal supervisions. Then, joint training the multi-modal and uni-modal tasks to learn the consistency and difference, respectively. Moreover, during the training stage, we design a weight-adjustment strategy to balance the learning progress among different subtasks. That is to guide the subtasks to focus on samples with a larger difference between modality supervisions. Last, we conduct extensive experiments on three public multimodal baseline datasets. The experimental results validate the reliability and stability of auto-generated unimodal supervisions. On MOSI and MOSEI datasets, our method surpasses the current state-of-the-art methods. On the SIMS dataset, our method achieves comparable performance than human-annotated unimodal labels. The full codes are available at https://github.com/thuiar/Self-MM.","tags":["Multimodal Learning","Language Grounding \u0026 Multi-modal NLP","Text Classification \u0026 Sentiment Analysis"],"title":"Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis","type":"publication"},{"authors":["Kang Zhao","Hua Xu","Yue Cheng","Xiaoteng Li","Kai Gao"],"categories":[],"content":"","date":1612164244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1612164244,"objectID":"77a9e3c7a954af1d8eeaab76f429bd74","permalink":"https://thuiar.github.io/publication/rifre/","publishdate":"2021-03-04T15:24:04+08:00","relpermalink":"/publication/rifre/","section":"publication","summary":"Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text. However, few existing works consider possible relations information between entities before extracting them, which may lead to the fact that most of the extracted entities cannot constitute valid triples. In this paper, we propose a representation iterative fusion based on heterogeneous graph neural networks for relation extraction (RIFRE). We model relations and words as nodes on the graph and fuse the two types of semantic nodes by the message passing mechanism iteratively to obtain nodes representation that is more suitable for relation extraction tasks. The model performs relation extraction after nodes representation is updated. We evaluate RIFRE on two public relation extraction datasets: NYT and WebNLG. The results show that RIFRE can effectively extract triples and achieve state-of-the-art performance.1 Moreover, RIFRE is also suitable for the relation classification task, and significantly outperforms the previous methods on SemEval 2010 Task 8 datasets.","tags":[],"title":"Representation Iterative Fusion Based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction","type":"publication"},{"authors":["Hanlei Zhang","Hua Xu","Ting-En Lin"],"categories":[],"content":"","date":1608279844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1608279844,"objectID":"94684281133fb0686f37fc6537250929","permalink":"https://thuiar.github.io/publication/deep-open-intent-classification-with-adaptive-decision-boundary/","publishdate":"2020-12-18T16:24:04+08:00","relpermalink":"/publication/deep-open-intent-classification-with-adaptive-decision-boundary/","section":"publication","summary":"Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods. The codes are released at this https URL.","tags":["Conversational AI/Dialog Systems","(Deep) Neural Network Algorithms","Classification and Regression","Anomaly/Outlier Detection"],"title":"Deep Open Intent Classification with Adaptive Decision Boundary","type":"publication"},{"authors":["Kaicheng Yang","Hua Xu"],"categories":[],"content":"","date":1602491044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1602491044,"objectID":"b35f6778f6190ac4e449c3199d0422ce","permalink":"https://thuiar.github.io/publication/cmbert/","publishdate":"2020-09-01T16:24:04+08:00","relpermalink":"/publication/cmbert/","section":"publication","summary":"Multimodal sentiment analysis is an emerging research field that aims to enable machines to recognize, interpret, and express emotion. Through the cross-modal interaction, we can get more comprehensive emotional characteristics of the speaker. Bidirectional Encoder Representations from Transformers (BERT) is an efficient pre-trained language representation model. Fine-tuning it has obtained new state-of-the-art results on eleven natural language processing tasks like question answering and natural language inference. However, most previous works fine-tune BERT only base on text data, how to learn a better representation by introducing the multimodal information is still worth exploring. In this paper, we propose the Cross-Modal BERT (CM-BERT), which relies on the interaction of text and audio modality to fine-tune the pre-trained BERT model. As the core unit of the CM-BERT, masked multimodal attention is designed to dynamically adjust the weight of words by combining the information of text and audio modality. We evaluate our method on the public multimodal sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experiment results show that it has significantly improved the performance on all the metrics over previous baselines and text-only finetuning of BERT. Besides, we visualize the masked multimodal attention and proves that it can reasonably adjust the weight of words by introducing audio modality information.","tags":[],"title":"CM-BERT: Cross-Modal BERT for Text-Audio Sentiment Analysis","type":"publication"},{"authors":["Hua Xu","Jia Li"],"categories":[],"content":"","date":1598858644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598858644,"objectID":"ac11537788b44797f532b5c2b0333204","permalink":"https://thuiar.github.io/publication/a-joint-model-of-extended-lda-and-ibtm-over-streaming-chinese-short-texts/","publishdate":"2020-08-31T15:24:04+08:00","relpermalink":"/publication/a-joint-model-of-extended-lda-and-ibtm-over-streaming-chinese-short-texts/","section":"publication","summary":"With the prevalent of short texts, discovering the topics within them has become an important task. Biterm Topic Model (BTM) is more suitable to discover topics on short texts than traditional topic models. However, there are still some challenges that dealing short texts with BTM will always ignore the document-topic semantic information and lack the true intentions of users. In addition, it is a static method and can not manage streaming short texts when a new one arrives immediately. In order to keep document-topic information and get the topic distribution of a new short text at once, we propose a joint model based on online algorithms of Latent Dirichlet Allocation (LDA) and BTM, which combines the merits of both models. Not only does it alleviate the sparsity when addressing short texts with the online algorithm of BTM, namely Incremental Biterm Topic Model (IBTM), but also keeps document-topic information with extended LDA. And considering the differences between English and Chinese text in writing, we use combined words in short texts as key words to extend the length of short texts and keep the true intensions of users. As shown in the experiment results on two real world datasets, our method is better than other baseline methods. In the end, we explain an application of our method in the task of discovering user interest tags.","tags":[],"title":"A joint model of extended LDA and IBTM over streaming Chinese short texts","type":"publication"},{"authors":["Huadong Li","Hua Xu"],"categories":[],"content":"","date":1598858644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598858644,"objectID":"65a5fe0a5ee8edf5aab919f2eac8fdfa","permalink":"https://thuiar.github.io/publication/deep-reinforcement-learning-for-robust-emotional-classification-in-facial-expression-recognition/","publishdate":"2020-08-31T15:24:04+08:00","relpermalink":"/publication/deep-reinforcement-learning-for-robust-emotional-classification-in-facial-expression-recognition/","section":"publication","summary":"For emotion classification in facial expression recognition (FER), the performance of both traditional statistical methods and state-of-the-art deep learning methods are highly dependent on the quality of data. Traditional methods use image preprocessing (such as smoothing and segmentation) to improve image quality. However, the results still fail to meet the quality requirements of the emotion classifiers in FER. To address the above issues, this paper proposed a novel framework based on reinforcement learning for pre-selecting useful images(RLPS) for emotion classification in FER, which is made up of two modules: image selector and rough emotion classifier. Image selector is used to select useful images for emotion classification through reinforcement strategy and rough emotion classifier acts as a teacher to train image selector. Our framework improves classification performance by improving the quality of the dataset and can be applied to any classifier. Experiment results on RAF-DB, ExpW, and FER2013 datasets show that the proposed strategy achieves consistent improvements compared with the state-of-the-art emotion classification methods in FER","tags":[],"title":"Deep Reinforcement Learning for Robust Emotional Classification in Facial Expression Recognition","type":"publication"},{"authors":["Xingwei He","Hua Xu","Jia Li"],"categories":[],"content":"","date":1598858644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598858644,"objectID":"4fb03ebb5a5b433bffb675332469ded1","permalink":"https://thuiar.github.io/publication/fastbtm-reducing-the-sampling-time-for-biterm-topic-model/","publishdate":"2020-08-31T15:24:04+08:00","relpermalink":"/publication/fastbtm-reducing-the-sampling-time-for-biterm-topic-model/","section":"publication","summary":"Due to the popularity of social networks, such as microblogs and Twitter, a vast amount of short text data is created every day. Much recent research in short text becomes increasingly significant, such as topic inference for short text. Biterm topic model (BTM) benefits from the word co-occurrence patterns of the corpus, which makes it perform better than conventional topic models in uncovering latent semantic relevance for short text. However, BTM resorts to Gibbs sampling to infer topics, which is very time consuming, especially for large-scale datasets or when the number of topics is extremely large. It requires O(K) operations per sample for K topics, where K denotes the number of topics in the corpus. In this paper, we propose an acceleration algorithm of BTM, FastBTM, using an efficient sampling method for BTM, which converges much faster than BTM without degrading topic quality. FastBTM is based on Metropolis-Hastings and alias method, both of which have been widely adopted in Latent Dirichlet Allocation (LDA) model and achieved outstanding speedup. Our FastBTM can effectively reduce the sampling complexity of biterm topic model from O(K) to O(1) amortized time. We carry out a number of experiments on three datasets including two short text datasets, Tweets2011 Collection dataset and Yahoo! Answers dataset, and one long document dataset, Enron dataset. Our experimental results show that when the number of topics K increases, the gap in running time speed between FastBTM and BTM gets especially larger. In addition, our FastBTM is effective for both short text datasets and long document datasets.","tags":[],"title":"FastBTM: Reducing the Sampling Time for Biterm Topic Model","type":"publication"},{"authors":["Hua Xu","Jiyun Zou"],"categories":[],"content":"","date":1598858644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598858644,"objectID":"88c292635c82e75a0782bf542017692d","permalink":"https://thuiar.github.io/publication/hgfm/","publishdate":"2019-08-31T15:24:04+08:00","relpermalink":"/publication/hgfm/","section":"publication","summary":"To solve the problem of poor classification performance of multiple complex emotions in acoustic modalities, we propose a hierarchical grained and feature model (HGFM). The frame-level and utterance-level structures of acoustic samples are processed by the recurrent neural network. The model includes a frame-level representation module with before and after information, a utterance-level representation module with context information, and a different level acoustic feature fusion module. We take the output of frame-level structure as the input of utterance-level structure and extract the acoustic features of these two levels respectively for effective and complementary fusion. Experiments show that the proposed HGFM has better accuracy and robustness. By this method, we achieve the state-of-the-art performance on IEMOCAP and MELD datasets.","tags":[],"title":"HGFM : A Hierarchical Grained and Feature Model for Acoustic Emotion Recognition","type":"publication"},{"authors":["Zhijing Wu","Hua Xu"],"categories":[],"content":"","date":1598858644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598858644,"objectID":"3167acc98ad4edf09bf7f9a7a2fe5049","permalink":"https://thuiar.github.io/publication/improving-the-robustness-of-machine-reading-comprehension-model-with-hierarchical-knowledge-and-auxiliary-unanswerability-prediction/","publishdate":"2019-08-31T15:24:04+08:00","relpermalink":"/publication/improving-the-robustness-of-machine-reading-comprehension-model-with-hierarchical-knowledge-and-auxiliary-unanswerability-prediction/","section":"publication","summary":"Machine Reading Comprehension (MRC) aims to understand a passage and answer a series of related questions. With the development of deep learning and the release of large-scale MRC datasets, many end-to-end MRC neural networks have achieved remarkable success. However, these models are fragile and lack of robustness when there are some imperceptible adversarial perturbations in the input. In this paper, we propose an MRC model which has two main components to improve the robustness. On the one hand, we enhance the representation of the model by leveraging hierarchical knowledge from external knowledge bases. On the other hand, we introduce an auxiliary unanswerability prediction module and perform supervised multi-task learning along with a span prediction task. Experimental results on benchmark datasets show that our model can achieve consistent improvement compared with other strong baselines.","tags":[],"title":"Improving the Robustness of Machine Reading Comprehension Model with Hierarchical Knowledge and Auxiliary Unanswerability Prediction","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1598858644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598858644,"objectID":"ad2c2769a807e60939197f3659ff7519","permalink":"https://thuiar.github.io/publication/objective-reduction-in-many-objective-optimization-evolutionary-multiobjective-approaches-and-comprehensive-analysis/","publishdate":"2020-08-31T15:24:04+08:00","relpermalink":"/publication/objective-reduction-in-many-objective-optimization-evolutionary-multiobjective-approaches-and-comprehensive-analysis/","section":"publication","summary":"Many-objective optimization problems bring great difficulties to the existing multiobjective evolutionary algorithms, in terms of selection operators, computational cost, visualization of the high-dimensional tradeoff front, and so on. Objective reduction can alleviate such difficulties by removing the redundant objectives in the original objective set, which has become one of the most important techniques in many-objective optimization. In this paper, we suggest to view objective reduction as a multiobjective search problem and introduce three multiobjective formulations of the problem, where the first two formulations are both based on preservation of the dominance structure and the third one utilizes the correlation between objectives. For each multiobjective formulation, a multiobjective objective reduction algorithm is proposed by employing the nondominated sorting genetic algorithm II to generate a Pareto front of nondominated objective subsets that can offer decision support to the user. Moreover, we conduct a comprehensive analysis of two major categories of objective reduction approaches based on several theorems, with the aim of revealing their strengths and limitations. Lastly, the performance of the proposed multiobjective algorithms is studied extensively on various benchmark problems and two real-world problems. Numerical results and comparisons are then shown to highlight the effectiveness and superiority of the proposed multiobjective algorithms over existing state-of-the-art approaches in the related field.","tags":[],"title":"Objective Reduction in Many-Objective Optimization: Evolutionary Multiobjective Approaches and Comprehensive Analysis","type":"publication"},{"authors":["Wenmeng Yu","Hua Xu","FanyangMeng"],"categories":[],"content":"","date":1594538644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1594538644,"objectID":"c8c941131fbf6a3d92f09b21b261513a","permalink":"https://thuiar.github.io/publication/chsims/","publishdate":"2020-08-31T15:24:04+08:00","relpermalink":"/publication/chsims/","section":"publication","summary":"Previous studies in multimodal sentiment analysis have used limited datasets, which only contain unified multimodal annotations. However, the unified annotations do not always reflect the independent sentiment of single modalities and limit the model to capture the difference between modalities. In this paper, we introduce a Chinese single- and multi-modal sentiment analysis dataset, CH-SIMS, which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.Furthermore, we propose a multi-task learning framework based on late fusion as the baseline. Extensive experiments on the CH-SIMS show that our methods achieve state-of-the-art performance and learn more distinctive unimodal representations. The full dataset and codes are available for use at https://github.com/thuiar/MMSA.","tags":[],"title":"CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality","type":"publication"},{"authors":["Yan Zhang","Hua Xu","Yunfeng Xu","Junhui Deng","Juan Gu","Rui Ma","Jie Lai","Jiangtao Hu","Xiaoshuai Yu","Lei Hou","Lidong Gu","Yanling Wei","Yichao Xiao","Junhao Lua"],"categories":[],"content":"","date":1593561600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1593561600,"objectID":"ba01602f3afe9674f459bd1efa631bac","permalink":"https://thuiar.github.io/publication/finding-structural-hole-spanners-based-on-community-forest-model-and-diminishing-marginal-utility-in-large-scale-social-networks/","publishdate":"2020-07-01T16:24:04+08:00","relpermalink":"/publication/finding-structural-hole-spanners-based-on-community-forest-model-and-diminishing-marginal-utility-in-large-scale-social-networks/","section":"publication","summary":"Structural hole spanners play key role in information diffusion, community detection, epidemic diseases and rumors spreading, link prediction and viral marketing, the discovery for them is a key research work in the area of social networks. Some scholars have proposed inspired models and methods based on Mathematics, Sociology, and Economics. In this paper, we try to give a more visual and detailed definition of structural hole spanner based on the existing work, and propose a novel algorithm to identify structural hole spanner based on community forest model and diminishing marginal utility. Our work includes following four folds. Firstly we revealed the diminishing marginal utility phenomenon in the process of community reconstruction. Secondly we proved that metrics based on local or one-sided features cannot be used as a criterion for judging structural hole spanner. Thirdly we proved that the influence of SHS is not related with the distribution of SHS in the network. Fourthly we develop a novel algorithm to identify SHS. Our algorithm has slightly better performance than the state-of-the-art algorithms. It worked well on Zachary’s karate club, American College Football, ground-truth samples sampled from DBLP, ground-truth samples sampled from Youtube and large-scale collaboration network DBLP..","tags":["Community detection","Community forest model","Diminishing marginal utility","Structural hole spanner"],"title":"Finding structural hole spanners based on community forest model and diminishing marginal utility in large scale social networks","type":"publication"},{"authors":["XiliWang","Hua Xu","Xiaomin Sun","GuangcanTao"],"categories":[],"content":"","date":1588926244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1588926244,"objectID":"0e25854c74c1da03e587707a55038717","permalink":"https://thuiar.github.io/publication/combining-fine-tuning-with-a-feature-based-approach-for-aspect-extraction-on-reviews/","publishdate":"2020-05-08T16:24:04+08:00","relpermalink":"/publication/combining-fine-tuning-with-a-feature-based-approach-for-aspect-extraction-on-reviews/","section":"publication","summary":"One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. Generally, fine-tuning BERT with sophisticated task-specific layers can achieve better performance than only extend one extra task-specific layer (e.g., a fully-connected + softmax layer) since not all tasks can easily be represented by Transformer encoder architecture and special task-specific layer can capture task-specific features. However, BERT fine-tuning may be unstable on a small-scale dataset. Besides, in our experiments, directly fine-tuning BERT on extending sophisticated task-specific layers did not take advantage of the features of task-specific layers and even restrict the performance of BERT module. To address the above consideration, this paper combines Fine-tuning with a feature-based approach to extract aspect. To the best of our knowledge, this is the first paper to combine fine-tuning with a feature-based approach for aspect extraction.","tags":[],"title":"Combining Fine-Tuning with a Feature-Based Approach for Aspect Extraction on Reviews","type":"publication"},{"authors":["Ting-EnLin","Hua Xu","Hanlei Zhang"],"categories":[],"content":"","date":1588926244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1588926244,"objectID":"92e8b36b661fef4aaa79d8081c4f075e","permalink":"https://thuiar.github.io/publication/constrained-self-supervised-clustering-for-discovering-new-intents/","publishdate":"2020-05-08T16:24:04+08:00","relpermalink":"/publication/constrained-self-supervised-clustering-for-discovering-new-intents/","section":"publication","summary":"Discovering new user intents is an emerging task in the dialogue system. In this paper, we propose a self-supervised clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process and does not require intensive feature engineering. Extensive experiments on three benchmark datasets show that our method can yield significant improvements over strong baselines.","tags":[],"title":"Constrained Self-supervised Clustering for Discovering New Intents","type":"publication"},{"authors":["Li Chen","Hua Xu"],"categories":[],"content":"","date":1588926244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1588926244,"objectID":"c7826cc8687293824080e193f7873fd8","permalink":"https://thuiar.github.io/publication/coral-dmoea-correlation-alignment-based-information-transfer-for-dynamic-multi-objective-optimization/","publishdate":"2020-05-08T16:24:04+08:00","relpermalink":"/publication/coral-dmoea-correlation-alignment-based-information-transfer-for-dynamic-multi-objective-optimization/","section":"publication","summary":"One essential characteristic of dynamic multi-objective optimization problems is that Pareto-Optimal Front/Set (POF/POS) varies over time. Tracking the time-dependent POF/POS is a challenging problem. Since continuous environments are usually highly correlated, past information is critical for the next optimization process. In this paper, we integrate CORAL methodology into a dynamic multi-objective evolutionary algorithm, named CORAL-DMOEA. This approach employs CORAL to construct a transfer model which transfer past well-performed solutions to form an initial population for the next optimization process. Experimental results demonstrate that CORAL-DMOEA can effectively improve the quality of solutions and accelerate the evolution process.","tags":[],"title":"CORAL-DMOEA: Correlation Alignment-Based Information Transfer for Dynamic Multi-Objective Optimization","type":"publication"},{"authors":["YuCao","Hua Xu"],"categories":[],"content":"","date":1588926244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1588926244,"objectID":"cc355be145992c88c2355c1b09187af9","permalink":"https://thuiar.github.io/publication/satnet-symmetric-adversarial-transfer-network-based-on-two-level-alignment-strategy-towards-cross-domain-sentiment-classification/","publishdate":"2020-05-08T16:24:04+08:00","relpermalink":"/publication/satnet-symmetric-adversarial-transfer-network-based-on-two-level-alignment-strategy-towards-cross-domain-sentiment-classification/","section":"publication","summary":"In recent years, domain adaptation tasks have attracted much attention, especially, the task of cross-domain sentiment classification (CDSC). In this paper, we propose a novel domain adaptation method called Symmetric Adversarial Transfer Network (SATNet). Experiments on the Amazon reviews dataset demonstrate the effectiveness of SATNet.","tags":[],"title":"SATNet: Symmetric Adversarial Transfer Network Based on Two-Level Alignment Strategy towards Cross-Domain Sentiment Classification","type":"publication"},{"authors":["Zhijing Wu","Hua Xu"],"categories":[],"content":"","date":1581063844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1581063844,"objectID":"57424ce1b9ec098f52946a9847966e5e","permalink":"https://thuiar.github.io/publication/a-multi-task-learning-machine-reading-comprehension-model-for-noisy-document/","publishdate":"2020-02-07T16:24:04+08:00","relpermalink":"/publication/a-multi-task-learning-machine-reading-comprehension-model-for-noisy-document/","section":"publication","summary":"Current neural models for Machine Reading Comprehension (MRC) have achieved successful performance in recent years. However, the model is too fragile and lack robustness to tackle the imperceptible adversarial perturbations to the input. In this work, we propose a multi-task learning MRC model with a hierarchical knowledge enrichment to further improve the robustness for noisy document. Our model follows a typical encode-align-decode framework. Additionally, we apply a hierarchical method of adding background knowledge into the model from coarse-to-fine to enhance the language representations. Besides, we optimize our model by jointly training the answer span and unanswerability prediction, aiming to improve the robustness to noise. Experiment results on benchmark datasets confirm the superiority of our method, and our method can achieve competitive performance compared with other strong baselines.","tags":["Reading Comprehension","Neural Models","Successful Performance","Task Learning","Benchmark Datasets","Robustness To Noise","Learning Machine","Machine Reading","Coarse To Fine","Noise Experiment"],"title":"A Multi-Task Learning Machine Reading Comprehension Model for Noisy Document","type":"publication"},{"authors":["Yuxiang Xie","Hua Xu","JiaoeLi","Congcong Yang","KaiGao"],"categories":[],"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522715,"objectID":"d185324306c0bf4ed6fbfd5f3389cefe","permalink":"https://thuiar.github.io/publication/heterogeneous-graph-neural-networks-for-noisy-few-shot-relation-classification/","publishdate":"2020-09-19T13:38:34.616343Z","relpermalink":"/publication/heterogeneous-graph-neural-networks-for-noisy-few-shot-relation-classification/","section":"publication","summary":"Relation classification is an essential and fundamental task in natural language processing. Distant supervised methods have achieved great success on relation classification, which improve the performance of the task through automatically extending the dataset. However, the distant supervised methods also bring the problem of wrong labeling. Inspired by people learning new knowledge from only a few samples, we focus on predicting formerly unseen classes with a few labeled data. In this paper, we propose a heterogeneous graph neural network for few-shot relation classification, which contains sentence nodes and entity nodes. We build the heterogeneous graph based on the message passing between entity nodes and sentence nodes in the graph, which can capture rich neighborhood information of the graph. Besides, we introduce adversarial learning for training a robust model and evaluate our heterogeneous graph neural networks under the scene of introducing different rates of noise data. Experimental results have demonstrated that our model outperforms the state-of-the-art baseline models on the FewRel dataset.","tags":["\"Relation extraction\"","\"Heterogeneous graph neural networks\"","\"Few-shot learning\"","\"Adversarial learning\""],"title":"Heterogeneous graph neural networks for noisy few-shot relation classification","type":"publication"},{"authors":["Tingen Lin","Hua Xu","Hanlei Zhang"],"categories":[],"content":"","date":1567236244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567236244,"objectID":"6805e8b3972313b1e33d1ada36cd3ade","permalink":"https://thuiar.github.io/publication/discovering-new-intents-via-constrained-deep-adaptive-clustering-with-cluster-refinement/","publishdate":"2019-08-31T15:24:04+08:00","relpermalink":"/publication/discovering-new-intents-via-constrained-deep-adaptive-clustering-with-cluster-refinement/","section":"publication","summary":"Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines.","tags":[],"title":"Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement","type":"publication"},{"authors":["Yuxiang Xie","Hua Xu","Congcong Yang"],"categories":[],"content":"","date":1567236244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567236244,"objectID":"d9772fcbba83c5a10407a3b1f722685d","permalink":"https://thuiar.github.io/publication/multi-channel-convolutional-neural-networks-with-adversarial-training-for-few-shot-relation-classification/","publishdate":"2019-08-31T15:24:04+08:00","relpermalink":"/publication/multi-channel-convolutional-neural-networks-with-adversarial-training-for-few-shot-relation-classification/","section":"publication","summary":"The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.","tags":[],"title":"Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification","type":"publication"},{"authors":["Tingen Lin","Hua Xu"],"categories":[],"content":"","date":1561939200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600519031,"objectID":"1984795fca17733159611b9de6ee29b1","permalink":"https://thuiar.github.io/publication/deep-unknown-intent-detection-with-margin-loss/","publishdate":"2020-09-19T12:37:10.244831Z","relpermalink":"/publication/deep-unknown-intent-detection-with-margin-loss/","section":"publication","summary":"Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.","tags":[],"title":"Deep Unknown Intent Detection with Margin Loss","type":"publication"},{"authors":["HaoranLi","Hua Xu"],"categories":[],"content":"","date":1557303844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557303844,"objectID":"28944facaf2088b9637c61b23eecd882","permalink":"https://thuiar.github.io/publication/video-based-sentiment-analysis-with-hvnlbp-top-feature-and-bi-lstm/","publishdate":"2019-05-08T16:24:04+08:00","relpermalink":"/publication/video-based-sentiment-analysis-with-hvnlbp-top-feature-and-bi-lstm/","section":"publication","summary":"In this paper, we propose a new feature extraction method called hvnLBP-TOP for video-based sentiment analysis. Furthermore, we use principal component analysis (PCA) and bidirectional long short term memory (bi-LSTM) for dimensionality reduction and classification. We achieved an average recognition accuracy of 71.1% on the MOUD dataset and 63.9% on the CMU-MOSI dataset.","tags":[],"title":"Video-Based Sentiment Analysis with hvnLBP-TOP Feature and bi-LSTM","type":"publication"},{"authors":["Tingen Lin","Hua Xu"],"categories":[],"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522716,"objectID":"a8639de67fa0ab3e0c38111ee425a908","permalink":"https://thuiar.github.io/publication/a-post-processing-method-for-detecting-unknown-intent-of-dialogue-system-via-pre-trained-deep-neural-network-classifier/","publishdate":"2020-09-19T13:38:35.985341Z","relpermalink":"/publication/a-post-processing-method-for-detecting-unknown-intent-of-dialogue-system-via-pre-trained-deep-neural-network-classifier/","section":"publication","summary":"With the maturity and popularity of dialogue systems, detecting user’s unknown intent in dialogue systems has become an important task. It is also one of the most challenging tasks since we can hardly get examples, prior knowledge or the exact numbers of unknown intents. In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers. Our method can be flexibly applied on top of any classifiers trained in deep neural networks without changing the model architecture. We calibrate the confidence of the softmax outputs to compute the calibrated confidence score (i.e., SofterMax) and use it to calculate the decision boundary for unknown intent detection. Furthermore, we feed the feature representations learned by the deep neural networks into traditional novelty detection algorithm to detect unknown intents from different perspectives. Finally, we combine the methods above to perform the joint prediction. Our method classifies examples that differ from known intents as unknown and does not require any examples or prior knowledge of it. We have conducted extensive experiments on three benchmark dialogue datasets. The results show that our method can yield significant improvements compared with the state-of-the-art baselines1 1The code will be available at https://github.com/tnlin/SMDN..","tags":["\"Novelty detection\"","\"Open-world classification\"","\"Probability calibration\"","\"Platt scaling\"","\"Dialogue system\"","\"Deep neural network\""],"title":"A post-processing method for detecting unknown intent of dialogue system via pre-trained deep neural network classifier","type":"publication"},{"authors":["Hongyan Wang","Hua Xu","Yuan Yuan","Xiaomin Sun","Junhui Deng"],"categories":[],"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523728,"objectID":"7013dac9397a1bb754ec0da887b65a9d","permalink":"https://thuiar.github.io/publication/balancing-exploration-and-exploitation-in-multiobjective-batch-bayesian-optimization/","publishdate":"2020-09-19T13:55:27.195975Z","relpermalink":"/publication/balancing-exploration-and-exploitation-in-multiobjective-batch-bayesian-optimization/","section":"publication","summary":"Many applications such as hyper-parameter tunning in Machine Learning can be casted to multiobjective black-box problems and it is challenging to optimize them. Bayesian Optimization (BO) is an effective method to deal with black-box functions. This paper mainly focuses on balancing exploration and exploitation in multi-objective black-box optimization problems by multiple samplings in BBO. In each iteration, multiple recommendations are generated via two different trade-off strategies respectively the expected improvement (EI) and a multiobjective framework with the mean and variance function of the GP posterior forming two conflict objectives. We compare our algorithm with ParEGO by running on 12 test functions. Hypervolume (HV, also known as S-metric) results show that our algorithm works well in exploration-exploitation trade-off for multiobjective black-box optimization problems.","tags":["\"batch bayesian optimization\"","\"expensive multiobjective optimization\"","\"exploration and exploitation\"","\"gaussian process\"","\"ParEGO\""],"title":"Balancing Exploration and Exploitation in Multiobjective Batch Bayesian Optimization","type":"publication"},{"authors":["Hongyan Wang","Hua Xu","Yuan Yuan","Junhui Deng","Xiaomin Sun"],"categories":[],"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523729,"objectID":"ef91c0c1dd573e34d7cc5ccf176eb998","permalink":"https://thuiar.github.io/publication/noisy-multiobjective-black-box-optimization-using-bayesian-optimization/","publishdate":"2020-09-19T13:55:28.463251Z","relpermalink":"/publication/noisy-multiobjective-black-box-optimization-using-bayesian-optimization/","section":"publication","summary":"Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper surrogates. The most popular model used in Bayesian Optimization is the Gaussian process (GP) whose posterior is based on a joint GP prior built by initial observations, so the posterior is also a Gaussian process. Observations are often not noise-free, so in most of these cases, a noisy transformation of the objective space is observed. Many single objective optimization algorithms have succeeded in extending efficient global optimization (EGO) to noisy circumstances, while ParEGO fails to consider noise. In order to deal with noisy expensive black-box problems, we extending ParEGO to noisy optimization according to adding a Gaussian noisy error while approximating the surrogate. We call it noisy-ParEGO and results of S-metric indicate that the algorithm works well on optimizing noisy expensive multiobjective black-box problems.","tags":["\"gaussian noise\"","\"gaussian process\"","\"ParEGO\"","\"black-box optimization\"","\"expensive multiobjective optimization\""],"title":"Noisy Multiobjective Black-Box Optimization Using Bayesian Optimization","type":"publication"},{"authors":["Hua Xu"],"categories":null,"content":"Course Classification: Public Elective Courses of Tsinghua University\nLecturer: Hua Xu\nTarget Audience: All Undergraduate Students\nTeaching Time：2019 - Today\n","date":1546272000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546272000,"objectID":"407bfb7ec4580d302372641bb7e5039e","permalink":"https://thuiar.github.io/talk/internet-product-design/","publishdate":"2019-01-01T00:00:00+08:00","relpermalink":"/talk/internet-product-design/","section":"talk","summary":"Public Elective Courses of Tsinghua University","tags":[],"title":"Internet Product Design","type":"talk"},{"authors":["Kai Gao","Hua Xu; Chengliang Gao; Hanyong Hao; Junhui Deng; Xiaomin Sun"],"categories":[],"content":"","date":1531038244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1531038244,"objectID":"01e70b54832df996c6828fb6e94359c7","permalink":"https://thuiar.github.io/publication/attention-based-bilstm-network-with-lexical-feature-for-emotion-classification/","publishdate":"2018-07-08T16:24:04+08:00","relpermalink":"/publication/attention-based-bilstm-network-with-lexical-feature-for-emotion-classification/","section":"publication","summary":"Emotion classification is an important task for identifying users' emotional expressions in text. Though a variety of neural models have been proposed nowadays, these models mainly focus on modeling the content of words or characters without fully employing the emotional features in lexical features, especially the features of part-of -speech (POS). In this paper, we reveal that the information of POS as well as that of words is important for identifying the type of emotion in a given text. We propose two simple models to fully learn the emotional features of the POS of words. Every model consists of the long short-term memory (LSTM) network as input encoders and the component of attention mechanism. One model is to concatenate the POS tags of vectors into the hidden states of representations generated by LSTM as raw feature representations and put them into the component of attention mechanism to generate the text representation toward a special emotion. The other is to use both LSTM and attention mechanism to model the context representation of words and those of POS tags respectively and concatenate these context representations as the text representation toward a special emotion. We conduct some experiments on datasets for evaluation and demonstrate the effectiveness of our model, where the datasets consist of the open-source dataset from NLPCC\u0026 2014 and the dataset of manual annotation. Experimental results show that our models can achieve outstanding performance for emotion classification in Chinese Weibo texts and outperform classical baselines.","tags":["LSTM","Attention Mechanism","Part-of-Speech","Emotion Classification","Chinese Weibo Texts"],"title":"Attention-Based BiLSTM Network with Lexical Feature for Emotion Classification ","type":"publication"},{"authors":["Yunfeng Xu","Hua Xu","Longxia Zhu","Hanyong Hao","Junhui Deng","Xiaomin Sun","Xiaoli Bai"],"categories":[],"content":"","date":1530433444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1530433444,"objectID":"e10072f27b03d382c97270c6e162855a","permalink":"https://thuiar.github.io/publication/topic-discovery-for-streaming-short-texts-with-ctm/","publishdate":"2018-07-01T16:24:04+08:00","relpermalink":"/publication/topic-discovery-for-streaming-short-texts-with-ctm/","section":"publication","summary":"Short texts are prevalent on today’s Web, especially with the emergence of social media. However, how to discover the topics of streaming short texts has become an important task for many content analysis applications. Conventional topic models such as Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) will suffer from sparsity problem when we infer the latent topics from short texts with them. The reason is that they derive topics from document-level word co-occurrence by modeling each document as a mixture of topics. Different from the above idea, Biterm Topic Model (BTM) discovers topics in short texts by directly modeling the generation of word co-occurrence patterns in the whole corpus. But semantic information is lacking for short texts. In this paper, in order to alleviate the sparsity problem, keep the semantic information of documents and get the latent topic information of streaming short texts immediately, we propose a joint topic model for Chinese streaming short texts (CTM) based on the online algorithms of LDA and BTM. Experiments on short texts from Sina Weibo show that our joint topic model can discover more precise topics and carry out more applications. In addition, considering the preprocessing in Chinese text is different from English and errors in extracting key phrases, we use a combined word method to extend the length of short texts and reduce errors in extracting key phrases.","tags":["streaming chinese short texts","topic discovery","topic models","online algorithms"],"title":"Topic Discovery for Streaming Short Texts with CTM","type":"publication"},{"authors":["Chengliang Gao","Hua Xu","Xiaomin Sun","Junhui Deng","Xiaoli Bai","Xiaoming Zhang","Kai Gao"],"categories":[],"content":"","date":1526804644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1526804644,"objectID":"aad1fe6fc8ca687855b6a52d85bf0de4","permalink":"https://thuiar.github.io/publication/an-empirical-study-of-emotion-analysis-with-different-distributed-representation-methods-for-chinese-microblogs/","publishdate":"2018-05-20T16:24:04+08:00","relpermalink":"/publication/an-empirical-study-of-emotion-analysis-with-different-distributed-representation-methods-for-chinese-microblogs/","section":"publication","summary":"Distributed representation of text has the ability to effectively express the meaning of text. Up to now, most studies have adopted distributed representation methods for emotion analysis on Chinese microblogs without systematically comparing and analyzing the performance of them with different parameters. This paper conducts an empirical study for fine-grained emotion categorization on Chinese microblogs. Firstly, we collect a labeled corpus. It is processed into four types of experimental dataset based on different text granularity, i.e., the sentence-level dataset with characters (SLwC), the sentence-level dataset with words (SLwW), the paragraph-level dataset with characters (PLdC) and the paragraph-level dataset with words (PLdW). Secondly, five models of distributed representation (the average of word vectors, PV-DM, PV-CBOW, LSTM, and BiLSTM) are chosen. These methods are compared on the above four types of experimental datasets respectively. Finally, support vector machine (SVM) acts as an additional classifier to correctly classify users’ emotions in microblogs. The results indicate: 1) BiLSTM performs the best for generating emotional feature representation. Moreover, it performs the best on the paragraph-level dataset by combining with SVM; 2) Five models achieves better performance on paragraph-level dataset for emotion classification than sentence-level dataset whereas words as tokens of text are superior to those of characters 3) All models get better performance while the dimension of words between 300 with 500, especially for the LSTM-based with SVM methods.","tags":["Fine-Grained Emotion Classification","Chinese Microblogs","Distributed Representation of Text","LSTM","Paragraph Vector","Support Vector Machine"],"title":"An Empirical Study of Emotion Analysis with Different Distributed Representation Methods for Chinese Microblogs","type":"publication"},{"authors":["KaiGao","Hua Xu","ChengliangGao","Xiaomin Sun","Junhui Deng","XiaomingZhang"],"categories":[],"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523732,"objectID":"0ccef8b0b6a04d04383a4ffb12728a87","permalink":"https://thuiar.github.io/publication/two-stage-attention-network-for-aspect-level-sentiment-classification/","publishdate":"2020-09-19T13:55:31.487527Z","relpermalink":"/publication/two-stage-attention-network-for-aspect-level-sentiment-classification/","section":"publication","summary":"Currently, most of attention-based works adopt single-stage attention processes during generating context representations toward aspect, but their work lacks the deliberation process: A generated and aspect-related representation is directly used as final output without further polishing. In this work, we introduce the deliberation process to model context for further polishing of attention weights, and then propose a two-stage attention network for aspect-level sentiment classification. The network uses of a two-level attention model with LSTM, where the first-stage attention generates a raw aspect-related representation and the second-stage attention polishes and refines the raw representation by deliberation process. Since the deliberation component has global information what the representation to be generated might be, it has the potential to generate a better aspect-related representation by secondly looking into hidden state produced by LSTM. Experimental results on the dataset of SemEval-2016 task 5 about Laptop indicates that our model achieved the state-of-the-art accuracy of 76.56%.","tags":[],"title":"Two-Stage Attention Network for Aspect-Level Sentiment Classification","type":"publication"},{"authors":["Xingwei He","Hua Xu","Xiaomin Sun","Junhui Deng","Jia Li"],"categories":[],"content":"","date":1494750244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1494750244,"objectID":"ba86e1798c387fa08cbc931e22e73f54","permalink":"https://thuiar.github.io/publication/abircnn-with-neural-tensor-network-for-answer-selection/","publishdate":"2017-05-14T16:24:04+08:00","relpermalink":"/publication/abircnn-with-neural-tensor-network-for-answer-selection/","section":"publication","summary":"Answer selection is a very important task in domain question answering. However, because of the word variety between questions and answers, there exists the lexical gap between questions and answers, which is the major challenge in question answer matching. In this work, in order to overcome the lexical gap, we propose an attention based bidirectional gated convolution with neural tensor network (ABiRCNN+NTN), which can improve the representations for both questions and answers and model their interactions with a neural tensor network. We carry out large-scale experiments on answer selection dataset, InsuranceQA and achieve new state-of-the-art results on InsuranceQA dataset. The experimental results demonstrate that our model can effectively capture the complex semantic relations between questions and answers and encode them in a more effective way. The source code of our work can be obtained from https://github.com/paperstudy/AnswerSelection.","tags":["Neural networks","Tensile stress","Logic gates","Insurance","Convolution","Semantics","Knowledge discovery"],"title":"ABiRCNN with neural tensor network for answer selection ","type":"publication"},{"authors":["Yuxiang Bao","Hua Xu","Fei Jia","Xiaoli Bai"],"categories":[],"content":"","date":1493627044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1493627044,"objectID":"c80dce2cf9b0f941bc6affda89734676","permalink":"https://thuiar.github.io/publication/aspect-based-sentiment-analysis-using-abpcs-model-and-svmperf-in-chinese-reviews/","publishdate":"2017-05-01T16:24:04+08:00","relpermalink":"/publication/aspect-based-sentiment-analysis-using-abpcs-model-and-svmperf-in-chinese-reviews/","section":"publication","summary":"Aspect-based sentiment analysis has always been a difficult task since it consists of several core sub-tasks: feature detection, opinion extraction and polarity classification. Consequently, by now there is little work to summarize all of these works together. In this paper, we propose a brand new holistic system, which can deal with all the problems above simultaneously using aspect-based positive center similarity(ABPCS) model. We experiment our system on clothes and hotel domain, and the result shows considerable improvements over state-of-the-art baselines.","tags":["Feature extraction","Sentiment analysis","Support vector machines","Analytical models","Dictionaries","Training","Clustering algorithms"],"title":"Aspect-based sentiment analysis using ABPCS model and SVMPperf in Chinese reviews","type":"publication"},{"authors":["Xingwei He","Hua Xu","Xiaomin Sun","Junhui Deng","Xiaoli Bai","Jia Li"],"categories":[],"content":"","date":1493627044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1493627044,"objectID":"bb4d97f52607af7f0a49300a929a42e4","permalink":"https://thuiar.github.io/publication/optimize-collapsed-gibbs-sampling-for-biterm-topic-model-by-alias-method/","publishdate":"2017-05-01T16:24:04+08:00","relpermalink":"/publication/optimize-collapsed-gibbs-sampling-for-biterm-topic-model-by-alias-method/","section":"publication","summary":"With the popularity of social networks, such as mi-croblogs and Twitter, topic inference for short text is increasingly significant and essential for many content analysis tasks. Biterm topic model (BTM) is superior to conventional topic models in uncovering latent semantic relevance for short text. However, Gibbs sampling employed by BTM is very time consuming when inferring topics, especially for large-scale datasets. It requires O{K) operations per sample for K topics, where K denotes the number of topics in the corpus. In this paper, we propose an acceleration algorithm of BTM, FastBTM, using an efficient sampling method for BTM which only requires O(1) amortized time while the traditional ones scale linearly with the number of topics. FastBTM is based on Metropolis-Hastings and alias method, both of which have been widely adopted in latent Dirichlet allocation (LDA) model and achieved outstanding speedup. We carry out a number of experiments on Tweets2011 Collection dataset and Enron dataset, indicating that our method is robust enough for both short texts and normal documents. Our work can be approximately 9 times faster than traditional Gibbs sampling method per iteration, when setting K = 1000. The source code of FastBTM can be obtained from https://github.com/paperstudy/FastBTM.","tags":[],"title":"Optimize Collapsed Gibbs Sampling for Biterm Topic Model by Alias Method","type":"publication"},{"authors":["Yuan Yuan","Yew-Soon Ong","Abhishek Gupta","Hua Xu"],"categories":[],"content":"","date":1487751844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1487751844,"objectID":"ef1b20384c98f68211f1a9c081d9cca7","permalink":"https://thuiar.github.io/publication/objective-reduction-in-many-objective-optimization/","publishdate":"2017-02-22T16:24:04+08:00","relpermalink":"/publication/objective-reduction-in-many-objective-optimization/","section":"publication","summary":"Many-objective optimization problems bring great difficulties to the existing multiobjective evolutionary algorithms, in terms of selection operators, computational cost, visualization of the high-dimensional tradeoff front, and so on. Objective reduction can alleviate such difficulties by removing the redundant objectives in the original objective set, which has become one of the most important techniques in many-objective optimization. In this paper, we suggest to view objective reduction as a multiobjective search problem and introduce three multiobjective formulations of the problem, where the first two formulations are both based on preservation of the dominance structure and the third one utilizes the correlation between objectives. For each multiobjective formulation, a multiobjective objective reduction algorithm is proposed by employing the nondominated sorting genetic algorithm II to generate a Pareto front of nondominated objective subsets that can offer decision support to the user. Moreover, we conduct a comprehensive analysis of two major categories of objective reduction approaches based on several theorems, with the aim of revealing their strengths and limitations. Lastly, the performance of the proposed multiobjective algorithms is studied extensively on various benchmark problems and two real-world problems. Numerical results and comparisons are then shown to highlight the effectiveness and superiority of the proposed multiobjective algorithms over existing state-of-the-art approaches in the related field.","tags":["Many-objective optimization","multiobjective evolutionary algorithms (MOEAs)","multiobjective optimization","objective reduction"],"title":"Objective Reduction in Many-Objective Optimization","type":"publication"},{"authors":["Fan Zhang","Hua Xu","Xiaoli Bai"],"categories":[],"content":"","date":1483259044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1483259044,"objectID":"69729bbf409aca548c0b234bd462f072","permalink":"https://thuiar.github.io/publication/on-the-need-of-hierarchical-emotion-classification/","publishdate":"2017-01-01T16:24:04+08:00","relpermalink":"/publication/on-the-need-of-hierarchical-emotion-classification/","section":"publication","summary":"Nowadays in China, Sina Weibo has become the most popular microblog platform and researches about it are proposed increasingly. In this paper, the problem of emotion classification of Weibo’s posts is addressed in a hierarchical way using a constrained topic model and Support Vector Regression (SVR). Based on this topic model which is variation of Latent Dirichlet Allocation (LDA), an implicit emotion detection algorithm is proposed to identify the underlying emotions. Meanwhile, the constraints are generated based on prior knowledge extraction approaches to compact LDA in order to generate domain-specified topics. Furthermore, a hierarchical emotion structure is employed to classify emotions more precisely into 19 classes. This hierarchy can meet different research granularities. The whole architecture is proposed aimed at alleviating the pain of misclassification caused by feature imbalance and decreasing the labor cost. The experiment results validate that our model outperforms traditional methods with precision, recall and F-scores.","tags":["Text mining","emotion classification","microblog","topic model"],"title":"On the need of hierarchical emotion classification: Detecting the implicit feature using constrained topic model","type":"publication"},{"authors":["Jia Li","Hua Xu"],"categories":[],"content":"","date":1472628244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1472628244,"objectID":"bf0fc5a371becbc8bc558a236d65a68d","permalink":"https://thuiar.github.io/publication/suggest-what-to-tag-recommending-more-precise-hashtags-based-on-users-dynamic-interests-and-streaming-tweet-content/","publishdate":"2020-08-31T15:24:04+08:00","relpermalink":"/publication/suggest-what-to-tag-recommending-more-precise-hashtags-based-on-users-dynamic-interests-and-streaming-tweet-content/","section":"publication","summary":"Twitter is an online social networking microblogging service that allows registered users to broadcast 140-character messages called tweets. The service has gained worldwide popularity since it was created in March 2006, with more than 316 million monthly active users in June 2015 who posted 500 million tweets per day. As the number of available tweets grows, the problem of managing tweets becomes extremely difficult, which could lead to information overload. To avoid this problem, people use the hashtag symbol # before a relevant keyword or phrase in their tweets to categorize those tweets and help them show more easily in each Twitter search. Furthermore, hashtags can be used to collect public opinions on events and their ideas at the individual, community or even the world level. Incorporating hashtags to obtain better performance such as sentiment classification and breaking events detection also has attracted considerable research attention in recent years. However, there are very few tweets containing hashtags, which impedes the quality of search results and their further usage in various applications. Therefore, hashtag recommendation has become a particularly important research problem. In this paper, we first propose a novel model, namely online Twitter-User LDA to learn Twitter users’ dynamic interests. Then considering the shortness, sparsity, and high volume of tweets, we introduce an effective method to discover the latent topics of streaming tweet content, which uses recently proposed incremental biterm topic model (IBTM). We finally design an automatic hashtag recommendation method called User-IBTM by combining the online Twitter-User LDA and IBTM. As shown in the experimental results on real world data from Twitter, our design method based on dynamic user interests and streaming tweet content significantly outperforms several other baseline methods and can suggest more precise hashtags.","tags":[],"title":"Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content","type":"publication"},{"authors":["Fan Zhang","Hua Xu","Jiushuo Wang","Xiaomin Sun","Junhui Deng"],"categories":[],"content":"","date":1469348644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1469348644,"objectID":"a2477148058e9c0c89874ddbecbcf361","permalink":"https://thuiar.github.io/publication/grasp-the-implicit-features-hierarchical-emotion-classification-based-on-topic-model-and-svm/","publishdate":"2016-07-24T16:24:04+08:00","relpermalink":"/publication/grasp-the-implicit-features-hierarchical-emotion-classification-based-on-topic-model-and-svm/","section":"publication","summary":"Microblog post has been a hot research source for emotion classification in recent years. However, due to bloggers' free narrative style and topics' timeliness, the data from microblog post is usually implicit and imbalanced. In this paper, the problems of emotion classification in Chinese microblog posts are solved in a hierarchical way using a knowledge-based topic model and Support Vector Machine(SVM). Based on topic model, an implicit feature detection algorithm is proposed to identify the latent emotions underlying the microblog posts. Meanwhile, a hierarchical emotion structure is employed to classify emotions into 19 classes of four levels by SVM. This structure can meet different research requirements at three granularities. The experiment results validate that our model can achieve better performance in terms of precision, recall and F-scores.","tags":["Support vector machines","Mathematical model","Feature extraction","Knowledge based systems","Semantics","Blogs","Probabilistic logic"],"title":"Grasp the Implicit Features: Hierarchical Emotion Classification based on Topic Model and SVM","type":"publication"},{"authors":["Jia Li","Hua Xu","Junhui Deng","Xiaomin Sun"],"categories":[],"content":"","date":1469348644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1469348644,"objectID":"9ab3e779f4695c8f5c1e63f794deb774","permalink":"https://thuiar.github.io/publication/hyperbolic-linear-units-for-deep-convolutional-neural-networks/","publishdate":"2016-07-24T16:24:04+08:00","relpermalink":"/publication/hyperbolic-linear-units-for-deep-convolutional-neural-networks/","section":"publication","summary":"Recently, rectified linear units (ReLUs) have been used to solve the vanishing gradient problem. Their use has led to state-of-the-art results in various problems such as image classification. In this paper, we propose the hyperbolic linear units (HLUs) which not only speed up learning process in deep convolutional neural networks but also obtain better performance in image classification tasks. Unlike ReLUs, HLUs have inheriently negative values which could make mean unit outputs closer to zero. Mean unit outputs close to zero means we can speed up the learning process because they bring the normal gradient close to the natural gradient. Indeed, the difference called bias shift between natural gradient and the normal gradient is related to the mean activation of input units. Experiments with three popular CNN architectures, LeNet, Inception network and ResNet on various benchmarks including MNIST, CIFAR-10 and CIFAR-100 demonstrate that our proposed HLUs achieve significant improvement compared to other commonly used activation functions1.","tags":["Irrigation","Convolution","Sun"],"title":"Hyperbolic Linear Units for Deep Convolutional Neural Networks","type":"publication"},{"authors":["Jia Li","Hua Xu","Xingwei He","Junhui Deng","Xiaomin Sun"],"categories":[],"content":"","date":1469348644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1469348644,"objectID":"bcc7ca0dc025422065afd84b486885d7","permalink":"https://thuiar.github.io/publication/tweet-modeling-with-lstm-recurrent-neural-networks-for-hashtag-recommendation/","publishdate":"2016-07-24T16:24:04+08:00","relpermalink":"/publication/tweet-modeling-with-lstm-recurrent-neural-networks-for-hashtag-recommendation/","section":"publication","summary":"The hash symbol, called a hashtag, is used to mark the keyword or topic in a tweet. It was created organically by users as a way to categorize messages. Hashtags also provide valuable information for many research applications such as sentiment classification and topic analysis. However, only a small number of tweets are manually annotated. Therefore, an automatic hashtag recommendation method is needed to help users tag their new tweets. Previous methods mostly use conventional machine learning classifiers such as SVM or utilize collaborative filtering technique. A bottleneck of these approaches is that they all use the TF-IDF scheme to represent tweets and ignore the semantic information in tweets. In this paper, we also regard hashtag recommendation as a classification task but propose a novel recurrent neural network model to learn vector-based tweet representations to recommend hashtags. More precisely, we use a skip-gram model to generate distributed word representations and then apply a convolutional neural network to learn semantic sentence vectors. Afterwards, we make use of the sentence vectors to train a long short-term memory recurrent neural network (LSTM-RNN). We directly use the produced tweet vectors as features to classify hashtags without any feature engineering. Experiments on real world data from Twitter to recommend hashtags show that our proposed LSTM-RNN model outperforms state-of-the-art methods and LSTM unit also obtains the best performance compared to standard RNN and gated recurrent unit (GRU).","tags":["Twitter","Tagging","Recurrent neural networks","Semantics","Computational modeling","Logic gates"],"title":"Tweet modeling with LSTM recurrent neural networks for hashtag recommendation","type":"publication"},{"authors":["Yuan Yuan","Yew-Soo Ong","Abhishek Gupta","Puay Siew Tan","Hua Xu"],"categories":[],"content":"","date":1455524644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1455524644,"objectID":"3e7126b54461fa89e2fac4d49fb34464","permalink":"https://thuiar.github.io/publication/evolutionary-multitasking-in-permutation-based-combinatorial-optimization-problems-realization-with-tsp-qap-lop-and-jsp/","publishdate":"2022-02-15T16:24:04+08:00","relpermalink":"/publication/evolutionary-multitasking-in-permutation-based-combinatorial-optimization-problems-realization-with-tsp-qap-lop-and-jsp/","section":"publication","summary":"Evolutionary computation (EC) has gained increasing popularity in dealing with permutation-based combinatorial optimization problems (PCOPs). Traditionally, EC focuses on solving a single optimization task at a time. However, in complex multi-echelon supply chain networks (SCNs), there usually exist various kinds of PCOPs at the same time, e.g., travel salesman problem (TSP), job-shop scheduling problem (JSP), etc. So, it is desirable to solve several PCOPs at once with both effectiveness and efficiency. Very recently, a new paradigm in EC, namely, multifactorial optimization (MFO) has been introduced to explore the potential of evolutionary multitasking, which can serve the purpose of simultaneously optimizing multiple PCOPs in SCNs. In this paper, the evolutionary multitasking of PCOPs is studied. In particular, based on a recently proposed multitasking engine known as the multifactorial evolutionary algorithm (MFEA), two novel mechanisms, namely, a new unified representation and a new survivor selection procedure, are introduced to better adapt to PCOPs. Experimental results obtained on well-known benchmark problems not only show the benefits of the two new mechanisms but also verify the promise of evolutionary multitasking for PCOPs. In addition, the results on a test case involving four optimization tasks demonstrate the potential scalability of evolutionary multitasking to many-task environments.","tags":[],"title":"Evolutionary multitasking in permutation-based combinatorial optimization problems: Realization with TSP, QAP, LOP, and JSP","type":"publication"},{"authors":["WenhaoZhang","JianqiuJi","JunZhu","JianminLi","Hua Xu","BoZhang"],"categories":[],"content":"","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522718,"objectID":"ca2321ef330a70a051b1971c748ae7ae","permalink":"https://thuiar.github.io/publication/bithash/","publishdate":"2020-09-19T13:38:37.383344Z","relpermalink":"/publication/bithash/","section":"publication","summary":"Locality Sensitive Hashing has been applied to detecting near-duplicate images, videos and web documents. In this paper we present a Bitwise Locality Sensitive method by using only one bit per hash value (BitHash), the storage space for storing hash values is significantly reduced, and the estimator can be computed much faster. The method provides an unbiased estimate of pairwise Jaccard similarity, and the estimator is a linear function of Hamming distance, which is very simple. We rigorously analyze the variance of One-Bit Min-Hash (BitHash), showing that for high Jaccard similarity. BitHash may provide accurate estimation, and as the pairwise Jaccard similarity increases, the variance ratio of BitHash over the original min-hash decreases. Furthermore, BitHash compresses each data sample into a compact binary hash code while preserving the pairwise similarity of the original data. The binary code can be used as a compressed and informative representation in replacement of the original data for subsequent processing. For example, it can be naturally integrated with a classifier like SVM. We apply BitHash to two typical applications, near-duplicate image detection and sentiment analysis. Experiments on real user’s photo collection and a popular sentiment analysis data set show that, the classification accuracy of our proposed method for two applications could approach the state-of-the-art method, while BitHash only requires a significantly smaller storage space.","tags":["\"Locality Sensitive Hashing\"","\"BitHash\"","\"Near-duplicate detection\"","\"Machine learning\"","\"Sentiment analysis\"","\"Storage efficiency\""],"title":"BitHash: An efficient bitwise Locality Sensitive Hashing method with applications","type":"publication"},{"authors":["YunfengXu","Hua Xu","DongwenZhang","YanZhang"],"categories":[],"content":"","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522717,"objectID":"495d62bb5e5488950d5cefcd567c9840","permalink":"https://thuiar.github.io/publication/finding-overlapping-community-from-social-networks-based-on-community-forest-model/","publishdate":"2020-09-19T13:38:36.677344Z","relpermalink":"/publication/finding-overlapping-community-from-social-networks-based-on-community-forest-model/","section":"publication","summary":"Overlapping community detection is the key research work to discover and explore the social networks. A great deal of work has been devoted to detect overlapping communities, but no one can give a clear formula definition of community from the internal structure to the external boundary. More in depth, there are four challenges to existing research works. In this paper, firstly we propose overlapping community forest model and disjoint community forest model based on the community forest model, secondly give a clear formula definition of overlapping community and disjoint community based on the backbone degree and expansion, thirdly propose a novel algorithm to find overlapping communities based on the backbone degree and expansion to resolve the four challenges. This algorithm has better performance than four related algorithms mentioned by this paper in large scale social networks. It works well on American college football, Zachary’s Karate Club, Netscience-coauthor, Condensed matter collaborations, LFR etc. data sets.","tags":["\"Community detection\"","\"Social network\"","\"Expansion\"","\"Community forest model\""],"title":"Finding overlapping community from social networks based on community forest model","type":"publication"},{"authors":["Jia Li","Hua Xu"],"categories":[],"content":"","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523739,"objectID":"6e98a902ace34eeb8d2c7de9cf6425e5","permalink":"https://thuiar.github.io/publication/user-ibtm/","publishdate":"2020-09-19T13:55:38.430568Z","relpermalink":"/publication/user-ibtm/","section":"publication","summary":"Twitter, the global social networking microblogging service, allows registered users to post 140-character messages known as tweets. People use the hashtag symbol `#' before a relevant keyword or phrase in their tweets to categorize the tweets and help them show more easily in a Twitter search. However, there are very few tweets contain hashtags, which impedes the quality of the search results and their applications. Therefore, how to automatically generate or recommend hashtags has become a particularly important academic research problem. Although many attempts have been made for solving this problem, previous methods mostly do not take the dynamic nature of hashtags into consideration. Furthermore, most previous work focuses on exploiting the similarity between tweets and ignores semantics in tweets.","tags":[],"title":"User-IBTM: An Online Framework for Hashtag Suggestion in Twitter","type":"publication"},{"authors":["Hua Xu"],"categories":null,"content":"Course Classification: Public Elective Courses of Tsinghua University\nLecturer: Hua Xu\nTarget Audience: All Undergraduate Students\nTeaching Time：2016 - Today\n","date":1451577600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451577600,"objectID":"050eb509d73d451227a478cbd25c314e","permalink":"https://thuiar.github.io/talk/intelligent-mobile-robot/","publishdate":"2016-01-01T00:00:00+08:00","relpermalink":"/talk/intelligent-mobile-robot/","section":"talk","summary":"Public Elective Courses of Tsinghua University","tags":[],"title":"Intelligent Mobile Robot: Design, Programming and Practice","type":"talk"},{"authors":["Bo Wang","Hua Xu","Yuan Yuan"],"categories":[],"content":"","date":1432542244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1432542244,"objectID":"32f57cf1e74fd81818497efc0bf82489","permalink":"https://thuiar.github.io/publication/scale-adaptive-reproduction-operator-for-decomposition-based-estimation-of-distribution-algorithm/","publishdate":"2015-05-25T16:24:04+08:00","relpermalink":"/publication/scale-adaptive-reproduction-operator-for-decomposition-based-estimation-of-distribution-algorithm/","section":"publication","summary":"Multi-objective evolutionary algorithm based on decomposition (MOEA/D) uses crossover operator which often either breaks the building blocks or mix them ineffectively. Multi-objective estimation of distribution algorithm based on decomposition (MEDA/D) evolves a probability vector for each sub-problem to guide the search instead of using crossover operator.However, since the number of the weight vectors in the neighborhood of each weight vector is relatively small and MEDA/D does not provide a way to maintain diversity, the performance of MEDA/D is limited. To overcome the drawbacks of MEDA/D, we proposed a new reproduction operator. This operator could promote diversity. We introduced it into MOEA/D framework and the new algorithm is called s-MEDA/D. We also prove that the parameter newly introduced has physical significance and the reproduction operator is not susceptible to the scale of the problem. The s-MEDA/D was tested on nine instances of the 0/1 multi-objective knapsack problem. Empirical evaluation suggests that the proposed algorithm is effective and efficient. ","tags":["Optimization","Algorithm design and analysis","Evolutionary computation","Sociology","Statistics","Measurement"],"title":"Scale Adaptive Reproduction Operator for Decomposition based Estimation of Distribution Algorithm ","type":"publication"},{"authors":["YunfengXu","Hua Xu","DongwenZhang"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522715,"objectID":"ca45fcc14ebd1b3801a6280c4cba52bc","permalink":"https://thuiar.github.io/publication/a-novel-disjoint-community-detection-algorithm-for-social-networks-based-on-backbone-degree-and-expansion/","publishdate":"2020-09-19T13:38:35.312341Z","relpermalink":"/publication/a-novel-disjoint-community-detection-algorithm-for-social-networks-based-on-backbone-degree-and-expansion/","section":"publication","summary":"Community detection in social networks is a key point to discover the functions and structure of social networks. A great deal of work has been done for overlapping community detection and disjoint community detection, and numerous techniques such as spectral clustering, modularity maximization, random walks, differential equation, and statistical mechanics are used to identify a community in networks, but most of these work adopts pure mathematic and physical methods to discover communities from social networks, on the contrary ignoring the social and biological properties of communities and social networks. In this paper, firstly we propose the community forest model based on these social and biological properties to characterize the structure of real-world large-scale networks, secondly we mainly define a new metric named backbone degree to measure the strength of the edge and the similarity of vertices and give a new sense definition to community based on expansion, thirdly we develop a novel algorithm that based on backbone degree and expansion to discover disjoint communities from real social networks. This algorithm has better performance and effects compared with CNM and GN algorithms in computational cost and visibility. It has worked well on Email-Enron, American College Football, karate club etc. data sets.","tags":["\"Community detection\"","\"Social network\"","\"Expansion\"","\"Conductance\""],"title":"A novel disjoint community detection algorithm for social networks based on backbone degree and expansion","type":"publication"},{"authors":["KaiGao","Hua Xu","JiushuoWang"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522719,"objectID":"fb00db4f249eb0a0bb6dd341b1afe2e4","permalink":"https://thuiar.github.io/publication/a-rule-based-approach-to-emotion-cause-detection-for-chinese-micro-blogs/","publishdate":"2020-09-19T13:38:38.726346Z","relpermalink":"/publication/a-rule-based-approach-to-emotion-cause-detection-for-chinese-micro-blogs/","section":"publication","summary":"Emotion analysis and emotion cause extraction are key research tasks in natural language processing and public opinion mining. This paper presents a rule-based approach to emotion cause component detection for Chinese micro-blogs. Our research has important scientific values on social network knowledge discovery and data mining. It also has a great potential in analyzing the psychological processes of consumers. Firstly, this paper proposes a rule-based system underlying the conditions that trigger emotions based on an emotional model. Secondly, this paper extracts the corresponding cause events in fine-grained emotions from the results of events, actions of agents and aspects of objects. Meanwhile, it is reasonable to get the proportions of different cause components under different emotions by constructing the emotional lexicon and identifying different linguistic features, and the proposed approach is based on Bayesian probability. Finally, this paper presents the experiments on an emotion corpus of Chinese micro-blogs. The experimental results validate the feasibility of the approach. The existing problems and the further works are also present at the end.","tags":["\"Text mining\"","\"Emotion causes\"","\"Micro-blog\"","\"Cause component proportion\""],"title":"A rule-based approach to emotion cause detection for Chinese micro-blogs","type":"publication"},{"authors":["Yuan Yuan","Hua Xu","Bo Wang"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523740,"objectID":"3959cdd0b5a4c70d47851688c5787682","permalink":"https://thuiar.github.io/publication/an-experimental-investigation-of-variation-operators-in-reference-point-based-many-objective-optimization/","publishdate":"2020-09-19T13:55:39.432978Z","relpermalink":"/publication/an-experimental-investigation-of-variation-operators-in-reference-point-based-many-objective-optimization/","section":"publication","summary":"Reference-point based multi-objective evolutionary algorithms (MOEAs) have shown promising performance in many-objective optimization. However, most of existing research within this area focused on improving the environmental selection procedure, and little work has been done on the effect of variation operators. In this paper, we conduct an experimental investigation of variation operators in a typical reference-point based MOEA, i.e., NSGA-III. First, we provide a new NSGA-III variant, i.e., NSGA-III-DE, which introduces differential evolution (DE) operator into NSGA-III, and we further examine the effect of two main control parameters in NSGA-III-DE. Second, we have an experimental analysis of the search behavior of NSGA-III-DE and NSGA-III. We observe that NSGA-III-DE is generally better at exploration whereas NSGA-III normally has advantages in exploitation. Third, based on this observation, we present two other NSGA-III variants, where DE operator and genetic operators are simply combined to reproduce solutions. Experimental results on several benchmark problems show that very encouraging performance can be achieved by three suggested new NSGA-III variants. Our work also indicates that the performance of NSGA-III is significantly bottlenecked by its variation operators, providing opportunities for the study of the other alternative ones.","tags":["\"many-objective optimization\"","\"differential evolution\"","\"NSGA-III\"","\"variation operators\"","\"reference-point\""],"title":"An Experimental Investigation of Variation Operators in Reference-Point Based Many-Objective Optimization","type":"publication"},{"authors":["DongwenZhang","Hua Xu","ZengcaiSu","YunfengXu"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522720,"objectID":"40e454c53b8e10ba6e38793897e964c6","permalink":"https://thuiar.github.io/publication/chinese-comments-sentiment-classification-based-on-word2vec-and-svmperf/","publishdate":"2020-09-19T13:38:40.089341Z","relpermalink":"/publication/chinese-comments-sentiment-classification-based-on-word2vec-and-svmperf/","section":"publication","summary":"Since the booming development of e-commerce in the last decade, the researchers have begun to pay more attention to extract the valuable information from consumers comments. Sentiment classification, which focuses on classify the comments into positive class and negative class according to the polarity of sentiment, is one of the studies. Machine learning-based method for sentiment classification becomes mainstream due to its outstanding performance. Most of the existing researches are centered on the extraction of lexical features and syntactic features, while the semantic relationships between words are ignored. In this paper, in order to get the semantic features, we propose a method for sentiment classification based on word2vec and SVMperf. Our research consists of two parts of work. First of all, we use word2vec to cluster the similar features for purpose of showing the capability of word2vec to capture the semantic features in selected domain and Chinese language. And then, we train and classify the comment texts using word2vec again and SVMperf. In the process, the lexicon-based and part-of-speech-based feature selection methods are respectively adopted to generate the training file. We conduct the experiments on the data set of Chinese comments on clothing products. The experimental results show the superior performance of our method in sentiment classification.","tags":["\"Sentiment classification\"","\"Word2vec\"","\"SVM\"","\"Semantic features\""],"title":"Chinese comments sentiment classification based on word2vec and SVMperf","type":"publication"},{"authors":["KaiGao","Hua Xu","JiushuoWang"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523741,"objectID":"eb8e5efc3b3554edc09e1c944ff88526","permalink":"https://thuiar.github.io/publication/emotion-cause-detection-for-chinese-micro-blogs-based-on-ecocc-model/","publishdate":"2020-09-19T13:55:40.414417Z","relpermalink":"/publication/emotion-cause-detection-for-chinese-micro-blogs-based-on-ecocc-model/","section":"publication","summary":"Micro-blog emotion mining and emotion cause extraction are essential in social network data mining. This paper presents a novel approach on Chinese micro-blog emotion cause detection based on the ECOCC model, focusing on mining factors for eliciting some kinds of emotions. In order to do so, the corresponding emotion causes are extracted. Moreover, the proportions of different cause components under different emotions are also calculated by means of combining the emotional lexicon with multiple characteristics (e.g., emoticon, punctuation, etc.). Experimental results show the feasibility of the approach. The proposed approaches have important scientific values on social network knowledge discovery and data mining.","tags":[],"title":"Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model","type":"publication"},{"authors":["Hua Xu","WeiweiYang","JiushuoWang"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522718,"objectID":"aa88fc0a8b87d130c3a2b9ef4c196586","permalink":"https://thuiar.github.io/publication/hierarchical-emotion-classification-and-emotion-component-analysis-on-chinese-micro-blog-posts/","publishdate":"2020-09-19T13:38:38.052343Z","relpermalink":"/publication/hierarchical-emotion-classification-and-emotion-component-analysis-on-chinese-micro-blog-posts/","section":"publication","summary":"Text emotion analysis has long been a hot topic. With the development of social network, text emotion analysis on micro-blog posts becomes a new trend in recent years. However, most researchers classify posts into coarse-grained emotion classes, which cannot depict the emotions accurately. Besides, flat classification is mostly adopted, which brings difficulty for classifiers when given a large dataset. In this paper, by data preprocessing, feature extraction and feature selection, we classify Chinese micro-blog posts into fine-grained emotion classes, employing hierarchical classification to improve the performance of classifiers. Moreover, based on the regression values in classification procedure, we propose an algorithm to detect the principal emotions in posts and calculate their ratios.","tags":["\"Micro-blog\"","\"Text mining\"","\"Emotion classification\"","\"Emotion component analysis\""],"title":"Hierarchical emotion classification and emotion component analysis on chinese micro-blog posts","type":"publication"},{"authors":["Hua Xu","FanZhang","WeiWang"],"categories":[],"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522720,"objectID":"f94f2e5520658436dee499d326958f94","permalink":"https://thuiar.github.io/publication/implicit-feature-identification-in-chinese-reviews-using-explicit-topic-mining-model/","publishdate":"2020-09-19T13:38:39.422363Z","relpermalink":"/publication/implicit-feature-identification-in-chinese-reviews-using-explicit-topic-mining-model/","section":"publication","summary":"The essential work of feature-specific opinion mining is centered on the product features. Previous related research work has often taken into account explicit features but ignored implicit features, However, implicit feature identification, which can help us better understand the reviews, is an essential aspect of feature-specific opinion mining. This paper is mainly centered on implicit feature identification in Chinese product reviews. We think that based on the explicit synonymous feature group and the sentences which contain explicit features, several Support Vector Machine (SVM) classifiers can be established to classify the non-explicit sentences. Nevertheless, instead of simply using traditional feature selection methods, we believe an explicit topic model in which each topic is pre-defined could perform better. In this paper, we first extend a popular topic modeling method, called Latent Dirichlet Allocation (LDA), to construct an explicit topic model. Then some types of prior knowledge, such as: must-links, cannot-links and relevance-based prior knowledge, are extracted and incorporated into the explicit topic model automatically. Experiments show that the explicit topic model, which incorporates pre-existing knowledge, outperforms traditional feature selection methods and other existing methods by a large margin and the identification task can be completed better.","tags":["\"Opinion mining\"","\"Implicit feature\"","\"Topic model\"","\"Support vector machine\"","\"Product review\""],"title":"Implicit feature identification in Chinese reviews using explicit topic mining model","type":"publication"},{"authors":["Kai Gao","Hua Xu","Jiushuo Wang"],"categories":[],"content":"","date":1419841444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1419841444,"objectID":"4bef14cb20d42bd38d71c7039819b51b","permalink":"https://thuiar.github.io/publication/emotion-classification-based-on-structured-information/","publishdate":"2014-12-29T16:24:04+08:00","relpermalink":"/publication/emotion-classification-based-on-structured-information/","section":"publication","summary":"In the era of information explosion, more social network applications present a platform for people to share various news and information sources, which brings people into the era of big data. And the processing of structured information attracts more researchers' attention. In this paper, we propose a method of feature extraction based on the syntactic and grammar structure to discover the emotion of a sentence. Firstly, an emotional lexicon is constructed by the combination of Chi-square test, PMI with word2vec which is based on different types of neural networks. Secondly, we improve the quality of selected features by exploring Part-Of-Speech features, capturing various types of relationships through syntactic analysis, and focusing on the emotional words features in context. Then we experiment with diverse linguistically motivated features. The experimental results validate the feasibility of our approach in selecting informative features, and the existing problems and the future works are also present in the end. ","tags":["Feature extraction","Syntactics","Support vector machines","Context","Grammar","Semantics","Hidden Markov models"],"title":"Emotion Classification Based on Structured Information","type":"publication"},{"authors":["Zengcai Su","Hua Xu","Dongwen Zhang","Yunfeng Xu"],"categories":[],"content":"","date":1409559844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1409559844,"objectID":"a0fbf7094260b6f435421377d44db6cc","permalink":"https://thuiar.github.io/publication/chinese-sentiment-classification-using-a-neural-network-tool-word2vec/","publishdate":"2014-09-01T16:24:04+08:00","relpermalink":"/publication/chinese-sentiment-classification-using-a-neural-network-tool-word2vec/","section":"publication","summary":"Sentiment classification is the main and popular task in the field of sentiment analysis. Most of the existing researches focus on how to extract the effective features, such as lexical features and syntactic features, while limited work has been done on the extraction of semantic features, which can make more contributions to sentiment classification. This paper presents a method for sentiment classification based on word2vec. Word2vec is a tool, which establishes the neural network models to learn the vector representations of words in the high dimensional vector space. So it can extract the deep semantic relationships between words. In this paper, firstly, we cluster the similar features together using word2vec. And then we use word2vec again to learn the word representations as candidate feature vectors. After feature selection, the SVMperf package is adopted to train and classify the comment texts. To conduct the experiments, we collect a large number of Chinese comments on clothing products as data set. The experimental results show that the accuracy of sentiment classification is over 90 percent, which proves the effectiveness of proposed method for Chinese sentiment classification.","tags":["Feature extraction","Training","Accuracy","Vectors","Semantics","Support vector machine classification"],"title":"Chinese Sentiment Classification Using A Neural Network Tool-Word2vec","type":"publication"},{"authors":["Bo Wang","Hua Xu","Yuan Yuan"],"categories":[],"content":"","date":1404635044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1404635044,"objectID":"1cc256db2d09d0581ec4464da1669e67","permalink":"https://thuiar.github.io/publication/quantum-inspired-evolutionary-algorithm-with-linkage-learning_/","publishdate":"2014-07-06T16:24:04+08:00","relpermalink":"/publication/quantum-inspired-evolutionary-algorithm-with-linkage-learning_/","section":"publication","summary":"The quantum-inspired evolutionary algorithm (QEA) uses several quantum computing principles to optimize problems on a classical computer. QEA possesses a number of quantum individuals, which are all probability vectors. They work well for linear problems but fail on problems with strong interactions among variables. Moreover, many optimization problems have multiple global optima. And because of the genetic drift, these problems are difficult for evolutionary algorithms to find all global optima. Local and global migration that QEA uses to synchronize different individuals prevent QEA from finding multiple optima. To overcome these difficulties, we proposed a quantum-inspired evolutionary algorithm with linkage learning (QEALL). QEALL uses a modified concept-guide operator based on low order statistics to learn linkage. We also replaced the migration procedure by a niching technology to prevent genetic drift, accordingly to find all global optima and to expedite convergence speed. The performance of QEALL was tested on a number of benchmarks including both unimodal and multimodal problems. Empirical evaluation suggests that the proposed algorithm is effective and efficient. ","tags":["Evolutionary computation","Sociology","Statistics","Couplings","Probabilistic logic","Quantum computing","Vectors"],"title":"Quantum-Inspired Evolutionary Algorithm with Linkage Learning","type":"publication"},{"authors":["Bo Wang","Hua Xu","Yuan Yuan"],"categories":[],"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523744,"objectID":"d7bba3e9273dc823226987afedbe2bb4","permalink":"https://thuiar.github.io/publication/a-two-level-hierarchical-eda-using-conjugate-priori/","publishdate":"2020-09-19T13:55:43.415724Z","relpermalink":"/publication/a-two-level-hierarchical-eda-using-conjugate-priori/","section":"publication","summary":"Estimation of distribution algorithms (EDAs) are stochastic optimization methods that guide the search by building and sampling probabilistic models. Inspired by Bayesian inference, we proposed a two-level hierarchical model based on beta distribution. Beta distribution is the conjugate priori for binomial distribution. Besides, we introduced a learning rate function into the framework to control the model update. To demonstrate the effectiveness and applicability of our proposed algorithm, experiments are carried out on the 01-knapsack problems. Experimental results show that the proposed algorithm outperforms cGA, PBIL and QEA.","tags":["\"empirical study\"","\"combinatorial optimization\"","\"artificial intelligence\""],"title":"A Two-Level Hierarchical EDA Using Conjugate Priori","type":"publication"},{"authors":["Yuan Yuan","Hua Xu","Bo Wang"],"categories":[],"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523742,"objectID":"07fdb59a6a221f58189e33ee29a38145","permalink":"https://thuiar.github.io/publication/an-improved-nsga-iii-procedure-for-evolutionary-many-objective-optimization/","publishdate":"2020-09-19T13:55:41.38986Z","relpermalink":"/publication/an-improved-nsga-iii-procedure-for-evolutionary-many-objective-optimization/","section":"publication","summary":"Many-objective (four or more objectives) optimization problems pose a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and SPEA2. This is mainly due to the fact that the selection pressure based on Pareto-dominance degrades severely with the number of objectives increasing. Very recently, a reference-point based NSGA-II, referred as NSGA-III, is suggested to deal with many-objective problems, where the maintenance of diversity among population members is aided by supplying and adaptively updating a number of well-spread reference points. However, NSGA-III still relies on Pareto-dominance to push the population towards Pareto front (PF), leaving room for the improvement of its convergence ability. In this paper, an improved NSGA-III procedure, called 牟-NSGA-III, is proposed, aiming to better tradeoff the convergence and diversity in many-objective optimization. In 牟-NSGA-III, the non-dominated sorting scheme based on the proposed 牟-dominance is employed to rank solutions in the environmental selection phase, which ensures both convergence and diversity. Computational experiments have shown that 牟-NSGA-III is significantly better than the original NSGA-III and MOEA/D on most instances no matter in convergence and overall performance.","tags":["\"non-dominated sorting\"","\"NSGA-III\"","\"many-objective optimization\"","\"牟-dominance\""],"title":"An Improved NSGA-III Procedure for Evolutionary Many-Objective Optimization","type":"publication"},{"authors":["JingfeiDu","Hua Xu","XiaoqiuHuang"],"categories":[],"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522721,"objectID":"ac2e290c927f9283c96ae5146e882efc","permalink":"https://thuiar.github.io/publication/box-office-prediction-based-on-microblog/","publishdate":"2020-09-19T13:38:40.789342Z","relpermalink":"/publication/box-office-prediction-based-on-microblog/","section":"publication","summary":"As the importance and popularity of online social media has become more obvious, there are more researches aiming at making use of information from them. One important topic of this is predicting the future with social media. This paper focuses on predicting box offices using microblog. Compared with previous work which makes use of the count of related microblogs simply, the information from social media has been utilized more deeply in this paper. Two sets of features have been extracted: count based features and content based features. For the former, the information in the aspect of users, which decrease the influence of garbage microblogs, has been exploited. For content based features, a new box office oriented semantic classification method has been provided to make the features more relative with box offices. Meanwhile, more complex machine learning models such as SVM and neutral network have been applied to the prediction method. Our prediction model is more accurate and reliable. With our prediction method, the data in Tencent microblog has been utilized to predict box offices of certain movies in China. With the results, the strength of our method and predictive power of online social media can be completely demonstrated.","tags":["\"Box office\"","\"Microblog\"","\"Social media\"","\"Prediction model\""],"title":"Box office prediction based on microblog","type":"publication"},{"authors":["Yuan Yuan","Hua Xu","Bo Wang"],"categories":[],"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523743,"objectID":"01ba67e540e1e3d02a7bad897af1edad","permalink":"https://thuiar.github.io/publication/evolutionary-many-objective-optimization-using-ensemble-fitness-ranking/","publishdate":"2020-09-19T13:55:42.386312Z","relpermalink":"/publication/evolutionary-many-objective-optimization-using-ensemble-fitness-ranking/","section":"publication","summary":"In this paper, a new framework, called ensemble fitness ranking (EFR), is proposed for evolutionary many-objective optimization that allows to work with different types of fitness functions and ensemble ranking schemes. The framework aims to rank the solutions in the population more appropriately by combing the ranking results from many simple individual rankers. As to the form of EFR, it can be regarded as an extension of average and maximum ranking methods which have been shown promising for many-objective problems. The significant change is that EFR adopts more general fitness functions instead of objective functions, which would make it easier for EFR to balance the convergence and diversity in many-objective optimization. In the experimental studies, the influence of several fitness functions and ensemble ranking schemes on the performance of EFR is fist investigated. Afterwards, EFR is compared with two state-of-the-art methods (MOEA/D and NSGA-III) on well-known test problems. The computational results show that EFR significantly outperforms MOEA/D and NSGA-III on most instances, especially for those having a high number of objectives.","tags":["\"average ranking\"","\"MOEA/D\"","\"maximum ranking\"","\"ensemble fitness ranking\"","\"fitness function\"","\"NSGA-III\"","\"many-objective optimization\""],"title":"Evolutionary Many-Objective Optimization Using Ensemble Fitness Ranking","type":"publication"},{"authors":["WeiyuanLi","Hua Xu"],"categories":[],"content":"","date":1388534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522722,"objectID":"eb4f236771b0188dbca7d3092873ab5e","permalink":"https://thuiar.github.io/publication/text-based-emotion-classification-using-emotion-cause-extraction/","publishdate":"2020-09-19T13:38:41.466347Z","relpermalink":"/publication/text-based-emotion-classification-using-emotion-cause-extraction/","section":"publication","summary":"In recent years, increasing impact of social networks on people’s opinions and decision making has attracted lots of attention. Microblogging, one of the most popular social network applications that allows people to share ideas and discuss over various topics, is taken as a rich resource of opinion and emotion data. In this paper, we propose and implement a novel method for identifying emotions in microblog posts. Unlike traditional approaches which are mostly based on statistical methods, we try to infer and extract the reasons of emotions by importing knowledge and theories from other fields such as Sociology. Based on the theory that a triggering cause event is an integral part of emotion, the technique of emotion cause extraction is used as a crucial step to improve the quality of selected features. First, after thorough analysis on sample data we constructed an automatic rule-based system to detect and extract the cause event of each emotional post. We build an emotion corpus with Chinese microblog posts labeled by human annotators. Then a classifier is trained to classify emotions in microblog posts based on extracted cause events. The overall performance of our system is very promising. The experiment results show that our approach is effective in selecting informative features. Our system outperformed the baseline noticeably in most cases, suggesting its great potential. This exploration should provide a new way to look at the emotion classification task and lay the ground for future research on textual emotion processing.","tags":["\"Emotion classification\"","\"Emotion cause extraction\"","\"Microblogging\"","\"Weibo\""],"title":"Text-based emotion classification using emotion cause extraction","type":"publication"},{"authors":["WeiWang","Hua Xu","Xiaoqiu Huang"],"categories":[],"content":"","date":1380585600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523746,"objectID":"19ba18be40238c84b2af736b7ef526bc","permalink":"https://thuiar.github.io/publication/implicit-feature-detection-via-a-constrained-topic-model-and-svm/","publishdate":"2020-09-19T13:55:45.407589Z","relpermalink":"/publication/implicit-feature-detection-via-a-constrained-topic-model-and-svm/","section":"publication","summary":"","tags":[],"title":"Implicit Feature Detection via a Constrained Topic Model and SVM","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1376900644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1376900644,"objectID":"8159000fc76211255dda4e709f1e5185","permalink":"https://thuiar.github.io/publication/multiobjective-flexible-job-shop-scheduling-using-memetic-algorithms/","publishdate":"2013-08-19T16:24:04+08:00","relpermalink":"/publication/multiobjective-flexible-job-shop-scheduling-using-memetic-algorithms/","section":"publication","summary":"In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms.","tags":["Flexible job shop scheduling","local search","memetic algorithm (MA)","mutliobjective","nondominated sorting genetic algorithm II (NSGA-II)"],"title":"Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1372667044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1372667044,"objectID":"d8a802910d52b8f5ea00e330f38fa18a","permalink":"https://thuiar.github.io/publication/a-memetic-algorithm-for-the-multi-objective-job-shop-flexible-scheduling-problem/","publishdate":"2013-07-01T16:24:04+08:00","relpermalink":"/publication/a-memetic-algorithm-for-the-multi-objective-job-shop-flexible-scheduling-problem/","section":"publication","summary":"In this paper, a new memetic algorithm (MA) is proposed for the muti-objective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload and critical workload. By using well-designed chromosome encoding/decoding scheme and genetic operators, the non-dominated sorting genetic algorithm II (NSGA-II) is first adapted for the MO-FJSP. Then the MA is developed by incorporating a novel local search algorithm into the adapted NSGA-II, where several mechanisms to balance the genetic search and local search are employed. In the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. Experimental results on well-known benchmark instances show that the proposed MA outperforms significantly two off-the-shelf multi-objective evolutionary algorithms and four state-of-the-art algorithms specially proposed for the MO-FJSP.","tags":["Computing methodologies","Artificial intelligence","Planning and scheduling","Search methodologies","Heuristic function construction"],"title":"A Memetic Algorithm for the Multi-objective Job Shop Flexible Scheduling Problem ","type":"publication"},{"authors":["Hua Xu","Jiangong Yang","PeifaJia","YiDing"],"categories":[],"content":"","date":1358234644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1358234644,"objectID":"752a8294c6f5c801df998aee2be9386e","permalink":"https://thuiar.github.io/publication/effective-structure-learning-for-estimation-of-distribution-algorithms-via-l1-regularized-bayesian-networks/","publishdate":"2022-03-15T15:24:04+08:00","relpermalink":"/publication/effective-structure-learning-for-estimation-of-distribution-algorithms-via-l1-regularized-bayesian-networks/","section":"publication","summary":"Estimation of distribution algorithms (EDAs), as an extension of genetic algorithms, samples new solutions from the probabilistic model, which characterizes the distribution of promising solutions in the search space at each generation. This paper introduces and evaluates a novel estimation of a distribution algorithm, called L1-regularized Bayesian optimization algorithm, L1BOA. In L1BOA, Bayesian networks as probabilistic models are learned in two steps. First, candidate parents of each variable in Bayesian networks are detected by means of L1-regularized logistic regression, with the aim of leading a sparse but nearly optimized network structure. Second, the greedy search, which is restricted to the candidate parent-child pairs, is deployed to identify the final structure. Compared with the Bayesian optimization algorithm (BOA), L1BOA improves the efficiency of structure learning due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems show that L1BOA not only outperforms BOA when no prior knowledge about problem structure is available, but also achieves and even exceeds the best performance of BOA that applies explicit controls on network complexity. Furthermore, Bayesian networks built by L1BOA and BOA during evolution are analysed and compared, which demonstrates that L1BOA is able to build simpler, yet more accurate probabilistic models.","tags":[],"title":"Effective Structure Learning for Estimation of Distribution Algorithms via L1-Regularized Bayesian Networks","type":"publication"},{"authors":["Yuan Yuan","Hua Xu","Jiadong Yang"],"categories":[],"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522723,"objectID":"46c5262473bbe0d43c6d815a83c10361","permalink":"https://thuiar.github.io/publication/a-hybrid-harmony-search-algorithm-for-the-flexible-job-shop-scheduling-problem/","publishdate":"2020-09-19T13:38:42.870343Z","relpermalink":"/publication/a-hybrid-harmony-search-algorithm-for-the-flexible-job-shop-scheduling-problem/","section":"publication","summary":"In this paper, a novel hybrid harmony search (HHS) algorithm based on the integrated approach, is proposed for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize makespan. First of all, to make the harmony search (HS) algorithm adaptive to the FJSP, the converting techniques are developed to convert the continuous harmony vector to a kind of discrete two-vector code for the FJSP. Secondly, the harmony vector is mapped into a feasible active schedule through effectively decoding the transformed two-vector code, which could largely reduce the search space. Thirdly, a resultful initialization scheme combining heuristic and random strategies is introduced to make the initial harmony memory (HM) occur with certain quality and diversity. Furthermore, a local search procedure is embedded in the HS algorithm to enhance the local exploitation ability, whereas HS is employed to perform exploration by evolving harmony vectors in the HM. To speed up the local search process, the improved neighborhood structure based on common critical operations is presented in detail. Empirical results on various benchmark instances validate the effectiveness and efficiency of our proposed algorithm. Our work also indicates that a well designed HS-based method is a competitive alternative for addressing the FJSP.","tags":["\"Scheduling\"","\"Flexible job shop\"","\"Harmony search\"","\"Local search\"","\"Neighborhood structure\"","\"Makespan\""],"title":"A hybrid harmony search algorithm for the flexible job shop scheduling problem","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523747,"objectID":"33634febc225010f12f0381325109774","permalink":"https://thuiar.github.io/publication/a-memetic-algorithm-for-the-multi-objective-flexible-job-shop-scheduling-problem/","publishdate":"2020-09-19T13:55:46.409Z","relpermalink":"/publication/a-memetic-algorithm-for-the-multi-objective-flexible-job-shop-scheduling-problem/","section":"publication","summary":"In this paper, a new memetic algorithm (MA) is proposed for the muti-objective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload and critical workload. By using well-designed chromosome encoding/decoding scheme and genetic operators, the non-dominated sorting genetic algorithm II (NSGA-II) is first adapted for the MO-FJSP. Then the MA is developed by incorporating a novel local search algorithm into the adapted NSGA-II, where several mechanisms to balance the genetic search and local search are employed. In the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. Experimental results on well-known benchmark instances show that the proposed MA outperforms significantly two off-the-shelf multi-objective evolutionary algorithms and four state-of-the-art algorithms specially proposed for the MO-FJSP.","tags":["\"local search\"","\"non-dominated sorting genetic algorithm ii (nsga-ii)\"","\"muti-objective\"","\"flexible job shop scheduling\"","\"memetic algorithm\""],"title":"A Memetic Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problem","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522722,"objectID":"f70bae8e147e1bf4c12326da9d9623ba","permalink":"https://thuiar.github.io/publication/an-integrated-search-heuristic-for-large-scale-flexible-job-shop-scheduling-problems/","publishdate":"2020-09-19T13:38:42.161342Z","relpermalink":"/publication/an-integrated-search-heuristic-for-large-scale-flexible-job-shop-scheduling-problems/","section":"publication","summary":"The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem (JSP), where each operation is allowed to be processed by any machine from a given set, rather than one specified machine. In this paper, two algorithm modules, namely hybrid harmony search (HHS) and large neighborhood search (LNS), are developed for the FJSP with makespan criterion. The HHS is an evolutionary-based algorithm with the memetic paradigm, while the LNS is typical of constraint-based approaches. To form a stronger search mechanism, an integrated search heuristic, denoted as HHS/LNS, is proposed for the FJSP based on the two algorithms, which starts with the HHS, and then the solution is further improved by the LNS. Computational simulations and comparisons demonstrate that the proposed HHS/LNS shows competitive performance with state-of-the-art algorithms on large-scale FJSP problems, and some new upper bounds among the unsolved benchmark instances have even been found.","tags":["\"Scheduling\"","\"Flexible job shop\"","\"Harmony search\"","\"Large neighborhood search\"","\"Makespan\""],"title":"An integrated search heuristic for large-scale flexible job shop scheduling problems","type":"publication"},{"authors":["Jiadong Yang","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522724,"objectID":"e0c67343da5590e9a45a78068d487e4e","permalink":"https://thuiar.github.io/publication/effective-search-for-genetic-based-machine-learning-systems-via-estimation-of-distribution-algorithms-and-embedded-feature-reduction-techniques/","publishdate":"2020-09-19T13:38:43.598345Z","relpermalink":"/publication/effective-search-for-genetic-based-machine-learning-systems-via-estimation-of-distribution-algorithms-and-embedded-feature-reduction-techniques/","section":"publication","summary":"Genetic-based machine learning (GBML) systems, which employ evolutionary algorithms (EAs) as search mechanisms, evolve rule-based classification models to represent target concepts. Compared to Michigan-style GBML, Pittsburgh-style GBML is expected to achieve more compact solutions. It has been shown that standard recombination operators in EAs do not assure an effective evolutionary search to solve sophisticated problems that contain strong interactions between features. On the other hand, when dealing with real-world classification tasks, irrelevant features not only complicate the problem but also incur unnecessary matchings in GBML systems, which increase the computational cost a lot. To handle the two problems mentioned above in an integrated manner, a new Pittsburgh-style GBML system is proposed. In the proposed method, classifiers are generated and recombined at two levels. At the high level, classifiers are recombined by rule-wise uniform crossover operators since each classifier consists of a variable-size rule set. At the low level, single rules contained in classifiers are reproduced via sampling Bayesian networks that characterize the global statistical information extracted from promising rules found so far. Furthermore, according to the statistical information in the rule population, an embedded approach is presented to detect and remove redundant features incrementally following the evolution of rule population. Results of empirical evaluation show that the proposed method outperforms the original Pittsburgh-style GBML system in terms of classification accuracy while reducing the computational cost. Furthermore, the proposed method is also competitive to other non-evolutionary, highly used machine learning methods. With respect to the performance of feature reduction, the proposed embedded approach is able to deliver solutions with higher classification accuracy when removing the same number of features as other feature reduction techniques do.","tags":["\"Genetic-based machine learning systems\"","\"Estimation of distribution algorithms\"","\"Features reduction\"","\"Evolutionary computation\""],"title":"Effective search for genetic-based machine learning systems via estimation of distribution algorithms and embedded feature reduction techniques","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522724,"objectID":"0d4a7810a06e4b6916c5f71d8b49e6ed","permalink":"https://thuiar.github.io/publication/flexible-job-shop-scheduling-using-hybrid-differential-evolution-algorithms/","publishdate":"2020-09-19T13:38:44.301342Z","relpermalink":"/publication/flexible-job-shop-scheduling-using-hybrid-differential-evolution-algorithms/","section":"publication","summary":"This paper proposes hybrid differential evolution (HDE) algorithms for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize the makespan. Firstly, a novel conversion mechanism is developed to make the differential evolution (DE) algorithm that works on the continuous domain adaptive to explore the problem space of the discrete FJSP. Secondly, a local search algorithm based on the critical path is embedded in the DE framework to balance the exploration and exploitation by enhancing the local searching ability. In addition, in the local search phase, the speed-up method to find an acceptable schedule within the neighborhood structure is presented to improve the efficiency of whole algorithms. Extensive computational results and comparisons show that the proposed algorithms are very competitive with the state of the art, some new best known solutions for well known benchmark instances have even been found.","tags":["\"Scheduling\"","\"Flexible job shop\"","\"Differential evolution\"","\"Local search\"","\"Neighborhood structure\"","\"Makespan\""],"title":"Flexible job shop scheduling using hybrid differential evolution algorithms","type":"publication"},{"authors":["WeiWang","Hua Xu","Wei Wan"],"categories":[],"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522725,"objectID":"ff12f93c98434c71db85027a1a96d805","permalink":"https://thuiar.github.io/publication/implicit-feature-identification-via-hybrid-association-rule-mining/","publishdate":"2020-09-19T13:38:44.974341Z","relpermalink":"/publication/implicit-feature-identification-via-hybrid-association-rule-mining/","section":"publication","summary":"In sentiment analysis, a finer-grained opinion mining method not only focuses on the view of the product itself, but also focuses on product features, which can be a component or attribute of the product. Previous related research mainly relied on explicit features but ignored implicit features. However, the implicit features, which are implied by some words or phrases, are so significant that they can express the users’ opinion and help us to better understand the users’ comments. It is a big challenge to detect these implicit features in Chinese product reviews, due to the complexity of Chinese. This paper is mainly centered on implicit features identification in Chinese product reviews. A novel hybrid association rule mining method is proposed for this task. The core idea of this approach is mining as many association rules as possible via several complementary algorithms. Firstly, we extract candidate feature indicators based word segmentation, part-of-speech (POS) tagging and feature clustering, then compute the co-occurrence degree between the candidate feature indicators and the feature words using five collocation extraction algorithms. Each indicator and the corresponding feature word constitute a rule (feature indicator → feature word). The best rules in five different rule sets are chosen as the basic rules. Next, three methods are proposed to mine some possible reasonable rules from the lower co-occurrence feature indicators and non indicator words. Finally, the latest rules are used to identify implicit features and the results are compared with the previous. Experiment results demonstrate that our proposed approach is competent at the task, especially via using several expanding methods. The recall is effectively improved, suggesting that the shortcomings of the basic rules have been overcome to certain extent. Besides those high co-occurrence degree indicators, the final rules also contain uncommon rules.","tags":["\"Opinion mining\"","\"Implicit features\"","\"Hybrid association rule mining\"","\"Collocation extraction\""],"title":"Implicit feature identification via hybrid association rule mining","type":"publication"},{"authors":["Hua Xu"],"categories":null,"content":"Course Classification: Public Elective Courses of Tsinghua University\nLecturer: Hua Xu\nTarget Audience: All Graduate and Undergraduate Students\nTeaching Time：2013 - Today\n","date":1356969600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1356969600,"objectID":"e7d4ecfaf4b1f29cd65dd9ec01f1f6ad","permalink":"https://thuiar.github.io/talk/industrial-data-mining/","publishdate":"2013-01-01T00:00:00+08:00","relpermalink":"/talk/industrial-data-mining/","section":"talk","summary":"Public Elective Courses of Tsinghua University","tags":[],"title":"Industrial Data Mining","type":"talk"},{"authors":["Yuan Wang","Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1350894244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1350894244,"objectID":"154f7ec83ba61ae026343d969f5abdfa","permalink":"https://thuiar.github.io/publication/a-hop-by-hop-network-coding-algorithm-for-aviation-communications-networks/","publishdate":"2012-10-22T16:24:04+08:00","relpermalink":"/publication/a-hop-by-hop-network-coding-algorithm-for-aviation-communications-networks/","section":"publication","summary":"Considering the feature of intensive node mobility in aviation communication networks, a hop-by-hop network coding algorithm based on Ad Hoc networks is proposed in this paper. A typical network was built in a network simulator, and receiving accuracy rate and receiving delay were collected, to analyze the performance of the proposed algorithm in scalable networks with different traffic modeling. The simulation results prove that the presented algorithm has better performance in enhancing receiving accuracy rate and shortening receiving delay, compared with traditional networks without network coding. It also applies to both bidirectional and directional traffic flows, and achieves better performance in large-scale networks. Therefore, this algorithm has great potentials in large-scale multi-hop aviation communication networks.","tags":[],"title":"A hop-by-hop network coding algorithm for aviation communications networks","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1339316644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1339316644,"objectID":"94bc61f131b0d85c1d4eebb83b732151","permalink":"https://thuiar.github.io/publication/hhs_lns/","publishdate":"2012-06-10T16:24:04+08:00","relpermalink":"/publication/hhs_lns/","section":"publication","summary":"The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem (JSP), where each operation is allowed to be processed by any machine from a given set, rather than one specified machine. In this paper, two algorithm modules, namely, hybrid harmony search (HHS) and large neighborhood search (LNS) are developed for the FJSP with makespan criterion. The HHS is an evolutionary-based algorithm with the memetic paradigm, while the LNS is typical of constraint-based approaches. To form a stronger search mechanism, an integrated search method is proposed for the FJSP based on the two algorithms, which starts with the HHS, and then the solution is further improved by the LNS. Computational simulations and comparisons demonstrate that, the proposed HHS alone can effectively solve some medium to large FJSP instances, when integrated with the LNS, it shows competitive performance with state-of-the-art algorithms on very hard and large-scale problems, some new upper bounds among the unsolved benchmark instances have even been found. ","tags":["Vectors","Schedules","Search problems","Support vector machines","Job shop scheduling","Memetics"],"title":"HHS/LNS: An integrated search method for flexible job shop scheduling","type":"publication"},{"authors":["Jiadong Yang","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1325376000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522727,"objectID":"f75a908859cf123bb5152d3a7c4fe155","permalink":"https://thuiar.github.io/publication/effective-search-for-pittsburgh-learning-classifier-systems-via-estimation-of-distribution-algorithms/","publishdate":"2020-09-19T13:38:46.367342Z","relpermalink":"/publication/effective-search-for-pittsburgh-learning-classifier-systems-via-estimation-of-distribution-algorithms/","section":"publication","summary":"Pittsburgh-style learning classifier systems (LCSs), in which an entire candidate solution is represented as a set of variable number of rules, combine supervised learning with genetic algorithms (GAs) to evolve rule-based classification models. It has been shown that standard crossover operators in GAs do not guarantee an effective evolutionary search in many sophisticated problems that contain strong interactions between features. In this paper, we propose a Pittsburgh-style learning classifier system based on the Bayesian optimization algorithm with the aim of improving the effectiveness and efficiency of the rule structure exploration. In the proposed method, classifiers are generated and recombined at two levels. At the lower level, single rules contained in classifiers are produced by sampling Bayesian networks which characterize the global statistical information extracted from the current promising rules in the search space. At the higher level, classifiers are recombined by rule-wise uniform crossover operators to keep the semantics of rules in each classifier. Experimental studies on both artificial and real world binary classification problems show that the proposed method converges faster while achieving solutions with the same or even higher accuracy compared with the original Pittsburgh-style LCSs.","tags":["\"Learning classifier system\"","\"Genetics-based machine learning\"","\"Estimation of distribution algorithm\"","\"Bayesian optimization algorithm\"","\"Evolutionary computation\""],"title":"Effective search for Pittsburgh learning classifier systems via estimation of distribution algorithms","type":"publication"},{"authors":["WenhaoZhang","Hua Xu","Wei Wan"],"categories":[],"content":"","date":1325376000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522726,"objectID":"3491f2d19347a554bb739ade0035aded","permalink":"https://thuiar.github.io/publication/weakness-finder/","publishdate":"2020-09-19T13:38:45.661348Z","relpermalink":"/publication/weakness-finder/","section":"publication","summary":"Finding the weakness of the products from the customers’ feedback can help manufacturers improve their product quality and competitive strength. In recent years, more and more people express their opinions about products online, and both the feedback of manufacturers’ products or their competitors’ products could be easily collected. However, it’s impossible for manufacturers to read every review to analyze the weakness of their products. Therefore, finding product weakness from online reviews becomes a meaningful work. In this paper, we introduce such an expert system, Weakness Finder, which can help manufacturers find their product weakness from Chinese reviews by using aspects based sentiment analysis. An aspect is an attribute or component of a product, such as price, degerm, moisturizing are the aspects of the body wash products. Weakness Finder extracts the features and groups explicit features by using morpheme based method and Hownet based similarity measure, and identify and group the implicit features with collocation selection method for each aspect. Then utilize sentence based sentiment analysis method to determine the polarity of each aspect in sentences. The weakness of product could be found because the weakness is probably the most unsatisfied aspect in customers’ reviews, or the aspect which is more unsatisfied when compared with their competitor’s product reviews. Weakness Finder has been used to help a body wash manufacturer find their product weakness, and our experimental results demonstrate the good performance of the Weakness Finder.","tags":["\"Product weakness\"","\"Business intelligence\"","\"Sentiment analysis\"","\"Feature grouping\""],"title":"Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis","type":"publication"},{"authors":["Yun Wen","Hua Xu"],"categories":[],"content":"","date":1311063844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1311063844,"objectID":"342943427232eecfdba63e0c3ab9e8ce","permalink":"https://thuiar.github.io/publication/a-cooperative-coevolution-based-pittsburgh-learning-classifier-system-embedded-with-memetic-feature-selection/","publishdate":"2011-07-19T16:24:04+08:00","relpermalink":"/publication/a-cooperative-coevolution-based-pittsburgh-learning-classifier-system-embedded-with-memetic-feature-selection/","section":"publication","summary":"Given that real-world classification tasks always have irrelevant or noisy features which degrade both prediction accuracy and computational efficiency, feature selection is an effective data reduction technique showing promising perfor- mance. This paper presents a cooperative coevolution framework to make the feature selection process embedded into the clas- sification model construction within the genetic-based machine learning paradigm. The proposed approach utilizes the divide- and-conquer strategy to manage two populations in parallel, corresponding to the selected feature subsets and the rule sets of classifier respectively, in which a memetic feature selection algorithm is adopted to evolve the feature subset population while a Pittsburgh-style learning classifier system is used to carry out the classifier evolution. These two coevolving populations cooperate with each other regarding the fitness evaluation and the final solution is obtained via collaborations between the best individuals from each population. Empirical results on several benchmark data sets chosen from the UCI repository, together with a non-parametric statistical test, validate that the proposed approach is able to deliver classifiers of better prediction accuracy and higher stability with fewer selected features, compared with the original learning classifier system. In addition, the incorporated feature selection process is shown to help improve the computational efficiency as well.","tags":["Accuracy","Memetics","Biological cells","Genetic algorithms","Genetics","Machine learning","Optimization"],"title":"A cooperative coevolution-based Pittsburgh learning classifier system embedded with memetic feature selection","type":"publication"},{"authors":["Wei Wang","Longqing Zou","Hua Xu","Youwei An","Peifa Jia","Bo Li","Yuan Luo"],"categories":[],"content":"","date":1309508644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1309508644,"objectID":"698fa785e3c42ebd1a09519689e7088d","permalink":"https://thuiar.github.io/publication/a-design-method-of-multiple-protocols-communication-module-in-semiconductor-equipment-simulation-platform/","publishdate":"2011-07-01T16:24:04+08:00","relpermalink":"/publication/a-design-method-of-multiple-protocols-communication-module-in-semiconductor-equipment-simulation-platform/","section":"publication","summary":"This paper presents a design method for communication module design in semiconductor equipment simulation platform. There are several kinds of standardized protocols in semiconductor equipments intra communication, the method with which these standardized protocols can be configured and integrated in single simulation platform. These protocols can be configured statically and dynamically respectively. In static state, protocol configuration provides various protocols for chosen and edit. In the dynamic state, the virtual devices can be communicated with control system with communication module. As a case study, a process chamber system simulator with designed the communication module has been illustrated. The designed communication module accomplishes the data transmission between control system and the chamber simulation system. The devices in chamber system receive the instruction form control system and execute the required actions. The experiment results show that the simulation platform is feasible and effective.","tags":["Communication Module","Semiconductor Equipment","Simulation Platform"],"title":"A design method of multiple protocols communication module in semiconductor equipment simulation platform ","type":"publication"},{"authors":["Wei Wang","Bo Li","Hua Xu","Lei Li","Peifa Jia","Longqing Zou"],"categories":[],"content":"","date":1298535844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1298535844,"objectID":"b32bb032242176973043dcdbd02a5bed","permalink":"https://thuiar.github.io/publication/design-of-component-simulation-platform-for-integrated-circuit-manufacturing-equipments/","publishdate":"2011-02-24T16:24:04+08:00","relpermalink":"/publication/design-of-component-simulation-platform-for-integrated-circuit-manufacturing-equipments/","section":"publication","summary":"Simulation plays an important role in researching, developing and testing Integrated Circuit (IC) manufacturing equipments. It is possible to test most of the functions and performance on simulation platform, and simulation platform can be used to verify the consistency of the process and the reliability of IC manufacturing equipment at the same time. In order to achieve a class of general and configurable simulation platform, which is based on SEMI standard. This paper describes component simulation platform framework and design methodology. Equipment maintenance time could be reduced and equipment utilization could be maximized with this simulation system. As a case study, the gas deliver subsystem test simulation is investigated on the designed component simulation platform, the simulation results show that the component simulation platform is helpful for rapid installation of IC equipment and verify its functions due to the automotic configuration of the component simulation middleware.","tags":["IC manufacturing equipment","SEMI standard","ressure subsystem;"],"title":"Design of component simulation platform for integrated circuit manufacturing equipments","type":"publication"},{"authors":["Yun Wen","Hua Xu","Jiadong Yang"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522729,"objectID":"99cf60281c3f77112b52eefd34837b20","permalink":"https://thuiar.github.io/publication/a-heuristic-based-hybrid-genetic-variable-neighborhood-search-algorithm-for-task-scheduling-in-heterogeneous-multiprocessor-system/","publishdate":"2020-09-19T13:38:49.188341Z","relpermalink":"/publication/a-heuristic-based-hybrid-genetic-variable-neighborhood-search-algorithm-for-task-scheduling-in-heterogeneous-multiprocessor-system/","section":"publication","summary":"Effective task scheduling, which is essential for achieving high performance in a heterogeneous multiprocessor system, remains a challenging problem despite extensive studies. In this article, a heuristic-based hybrid genetic-variable neighborhood search algorithm is proposed for the minimization of makespan in the heterogeneous multiprocessor scheduling problem. The proposed algorithm distinguishes itself from many existing genetic algorithm (GA) approaches in three aspects. First, it incorporates GA with the variable neighborhood search (VNS) algorithm, a local search metaheuristic, to exploit the intrinsic structure of the solutions for guiding the exploration process of GA. Second, two novel neighborhood structures are proposed, in which problem-specific knowledge concerned with load balancing and communication reduction is utilized respectively, to improve both the search quality and efficiency of VNS. Third, the proposed algorithm restricts the use of GA to evolve the task-processor mapping solutions, while taking advantage of an upward-ranking heuristic mostly used by traditional list scheduling approaches to determine the task sequence assignment in each processor. Empirical results on benchmark task graphs of several well-known parallel applications, which have been validated by the use of non-parametric statistical tests, show that the proposed algorithm significantly outperforms several related algorithms in terms of the schedule quality. Further experiments are carried out to reveal that the proposed algorithm is able to maintain high performance within a wide range of parameter settings.","tags":["\"Variable neighborhood search\"","\"Genetic algorithm\"","\"Hybrid metaheuristic\"","\"Memetic algorithm\"","\"Heterogeneous multiprocessor scheduling\""],"title":"A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system","type":"publication"},{"authors":["ZhongwuZhai","BingLiu","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523750,"objectID":"0b6bbf66b6b5dc4e868add1d7a4780d6","permalink":"https://thuiar.github.io/publication/clustering-product-features-for-opinion-mining/","publishdate":"2020-09-19T13:55:49.375311Z","relpermalink":"/publication/clustering-product-features-for-opinion-mining/","section":"publication","summary":"In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin.","tags":["\"product feature grouping\"","\"opinion mining\""],"title":"Clustering Product Features for Opinion Mining","type":"publication"},{"authors":["ZhongwuZhai","BingLiu","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523749,"objectID":"8045747747311ac63a85201fbab02533","permalink":"https://thuiar.github.io/publication/constrained-lda-for-grouping-product-features-in-opinion-mining/","publishdate":"2020-09-19T13:55:48.426867Z","relpermalink":"/publication/constrained-lda-for-grouping-product-features-in-opinion-mining/","section":"publication","summary":"In opinion mining of product reviews, one often wants to produce a summary of opinions based on product features. However, for the same feature, people can express it with different words and phrases. To produce an effective summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature. Topic modeling is a suitable method for the task. However, instead of simply letting topic modeling find groupings freely, we believe it is possible to do better by giving it some pre-existing knowledge in the form of automatically extracted constraints. In this paper, we first extend a popular topic modeling method, called Latent Dirichlet Allocation (LDA), with the ability to process large scale constraints. Then, two novel methods are proposed to extract two types of constraints automatically. Finally, the resulting constrained-LDA and the extracted constraints are applied to group product features. Experiments show that constrained-LDA outperforms the original LDA and the latest mLSA by a large margin.","tags":[],"title":"Constrained LDA for Grouping Product Features in Opinion Mining","type":"publication"},{"authors":["ZhongwuZhai","Hua Xu","BadaKang","PeifaJia"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522730,"objectID":"d36c5e5a1ea50571022a176b086059bf","permalink":"https://thuiar.github.io/publication/exploiting-effective-features-for-chinese-sentiment-classification/","publishdate":"2020-09-19T13:38:49.919342Z","relpermalink":"/publication/exploiting-effective-features-for-chinese-sentiment-classification/","section":"publication","summary":"Features play a fundamental role in sentiment classification. How to effectively select different types of features to improve sentiment classification performance is the primary topic of this paper. Ngram features are commonly employed in text classification tasks; in this paper, sentiment-words, substrings, substring-groups, and key-substring-groups, which have never been considered in sentiment classification area before, are also extracted as features. The extracted features are then compared and analyzed. To demonstrate generality, we use two authoritative Chinese data sets in different domains to conduct our experiments. Our statistical analysis of the experimental results indicate the following: (1) different types of features possess different discriminative capabilities in Chinese sentiment classification; (2) character bigram features perform the best among the Ngram features; (3) substring-group features have greater potential to improve the performance of sentiment classification by combining substrings of different lengths; (4) sentiment words or phrases extracted from existing sentiment lexicons are not effective for sentiment classification; (5) effective features are usually at varying lengths rather than fixed lengths.","tags":["\"Sentiment classification\"","\"Substring features\"","\"Substring-group\"","\"Suffix tree\""],"title":"Exploiting effective features for Chinese sentiment classification","type":"publication"},{"authors":["ZhongwuZhai","BingLiu","LeiZhang","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523748,"objectID":"dc94ebc0f2c13a4d42abbfe0640ccf11","permalink":"https://thuiar.github.io/publication/identifying-evaluative-sentences-in-online-discussions/","publishdate":"2020-09-19T13:55:47.457403Z","relpermalink":"/publication/identifying-evaluative-sentences-in-online-discussions/","section":"publication","summary":"","tags":[],"title":"Identifying Evaluative Sentences in Online Discussions","type":"publication"},{"authors":["Wei Wan","Hua Xu","WenhaoZhang","XinchengHu","GangDeng"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522727,"objectID":"6a0bd186488f3ae758f71e2d65b97a67","permalink":"https://thuiar.github.io/publication/questionnaires-based-skin-attribute-prediction-using-elman-neural-network/","publishdate":"2020-09-19T13:38:47.128344Z","relpermalink":"/publication/questionnaires-based-skin-attribute-prediction-using-elman-neural-network/","section":"publication","summary":"Skin attribute tests, especially for women, have become critical in the development of daily cosmetics in recent years. However, clinical skin attribute testing is often costly and time consuming. In this paper, a novel prediction approach based on questionnaires using recurrent neural network models is proposed for participants’ skin attribute prediction. The prediction engine, which is the most important part of this novel approach, is composed of three prediction models. Each of these models is a neural network allocated to predict different skin attributes: Tone, Spots, and Hydration. We also provide a detailed analysis and solution about the preprocessing of data, the selection of key features, and the evaluation of results. Our prediction system is much faster and more cost effective than traditional clinical skin attribute tests. The system performs very well, and the prediction results show good precision, especially for Tone.","tags":["\"Skin attribute prediction\"","\"Key features\"","\"Neural network\""],"title":"Questionnaires-based skin attribute prediction using Elman neural network","type":"publication"},{"authors":["Jiadong Yang","Hua Xu","LiPan","PeifaJia","FeiLong","MingJie"],"categories":[],"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522729,"objectID":"ef9916f0b605d05c5e40c758b51a0b99","permalink":"https://thuiar.github.io/publication/task-scheduling-using-bayesian-optimization-algorithm-for-heterogeneous-computing-environments/","publishdate":"2020-09-19T13:38:48.497342Z","relpermalink":"/publication/task-scheduling-using-bayesian-optimization-algorithm-for-heterogeneous-computing-environments/","section":"publication","summary":"Abstract Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platforms, remains one of the challenge problems despite of numerous studies. This paper presents a novel scheduling algorithm based on the Bayesian optimization algorithm (BOA) for heterogeneous computing environments. In the proposed algorithm, scheduling is divided into two phases. First, according to the task graph of multiprocessor scheduling problems, Bayesian networks are initialized and learned to capture the dependencies between different tasks. And the promising solutions assigning tasks to different processors are generated by sampling the Bayesian network. Second, the execution sequence of tasks on the same processor is set by the heuristic-based priority used in the list scheduling approach. The proposed algorithm is evaluated and compared with the related approaches by means of the empirical studies on random task graphs and benchmark applications. The experimental results show that the proposed algorithm is able to deliver more efficient schedules. Further experiments indicate that the proposed algorithm maintains almost the same performance with different parameter settings.","tags":["\"Multiprocessor scheduling\"","\"Heterogeneous\"","\"Parallel computing\"","\"Bayesian optimization algorithm\""],"title":"Task scheduling using Bayesian optimization algorithm for heterogeneous computing environments","type":"publication"},{"authors":["Hua Xu"],"categories":null,"content":"Course Classification: Public Elective Courses of Tsinghua University\nLecturer: Hua Xu\nTarget Audience: All Undergraduate Students\nTeaching Time：2011 - Today\n","date":1293811200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1293811200,"objectID":"3e7631952ecff1e05471581a1af8e97d","permalink":"https://thuiar.github.io/talk/data-mining-method-and-application/","publishdate":"2011-01-01T00:00:00+08:00","relpermalink":"/talk/data-mining-method-and-application/","section":"talk","summary":"Public Elective Courses of Tsinghua University","tags":[],"title":"Data Mining: Methods and Applications","type":"talk"},{"authors":["Anqi Cui","Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1292747044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1292747044,"objectID":"4889a4457c74624c756d2a8d8d4bb58a","permalink":"https://thuiar.github.io/publication/an-elman-neural-network-based-model-for-predicting-anti-germ-performances-and-ingredient-levels-with-limited-experimental-data/","publishdate":"2010-12-19T16:24:04+08:00","relpermalink":"/publication/an-elman-neural-network-based-model-for-predicting-anti-germ-performances-and-ingredient-levels-with-limited-experimental-data/","section":"publication","summary":"Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time-consuming. In this paper, we present an Elman neural network-based model to predict detergents’ anti-germ performance and ingredient levels, respectively. The model made it much faster and cost effective than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on limited experimental data. The model can find out the relationship between ingredient levels and the corresponding anti-germ performance, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high accuracy and fitting with the monotonicity rule mostly.","tags":["Anti-germ performance prediction","Ingredient level prediction","Artificial neural networks","Monotonicity rule","Preprocessing methods"],"title":"An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data","type":"publication"},{"authors":["ZhongwuZhai","BingLiu","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1280620800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523754,"objectID":"83f57721b887d915f9a92e65625776df","permalink":"https://thuiar.github.io/publication/grouping-product-features-using-semi-supervised-learning-with-soft-constraints/","publishdate":"2020-09-19T13:55:53.405011Z","relpermalink":"/publication/grouping-product-features-using-semi-supervised-learning-with-soft-constraints/","section":"publication","summary":"","tags":[],"title":"Grouping Product Features Using Semi-Supervised Learning with Soft-Constraints","type":"publication"},{"authors":["Yun Wen","Hua Xu","Jiadong Yang"],"categories":[],"content":"","date":1262304000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523752,"objectID":"9df8a63e1b2dc10a24c56922df802103","permalink":"https://thuiar.github.io/publication/a-heuristic-based-hybrid-genetic-algorithm-for-heterogeneous-multiprocessor-scheduling/","publishdate":"2020-09-19T13:55:51.366191Z","relpermalink":"/publication/a-heuristic-based-hybrid-genetic-algorithm-for-heterogeneous-multiprocessor-scheduling/","section":"publication","summary":"Effective task scheduling, which is essential for achieving high performance of parallel processing, remains challenging despite of extensive studies. In this paper, a heuristic-based hybrid Genetic Algorithm (GA) is proposed for solving the heterogeneous multiprocessor scheduling problem. The pro- posed algorithm extends traditional GA-based approaches in three aspects. First, it incorporates GA with Variable Neighborhood Search (VNS), a local search metaheuristic, to enhance the balance between global exploration and local exploitation of search space. Second, two novel neighbor- hood structures, in which problem-specific knowledge con- cerned with load balancing and communication reduction is utilized, are proposed to improve both the search quality and efficiency of VNS. Third, the use of GA is restricted to map tasks to processors while an upward-ranking heuris- tic is introduced to determine the task sequence assignment in each processor. Simulation results indicate that our pro- posed algorithm consistently outperforms several state-of- art scheduling algorithms in terms of the schedule quality while maintaining high performance within a wide range of parameter settings. Further experiments are carried out to validate the effectiveness of the hybridized VNS.","tags":["\"genetic algorithm\"","\"heterogeneous multiprocessor scheduling\"","\"memetic algorithm\"","\"variable neighborhood search\""],"title":"A Heuristic-Based Hybrid Genetic Algorithm for Heterogeneous Multiprocessor Scheduling","type":"publication"},{"authors":["ZhongwuZhai","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1262304000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600522731,"objectID":"080d38026ab842828db02f1173288682","permalink":"https://thuiar.github.io/publication/an-empirical-study-of-unsupervised-sentiment-classification-of-chinese-reviews/","publishdate":"2020-09-19T13:38:50.591349Z","relpermalink":"/publication/an-empirical-study-of-unsupervised-sentiment-classification-of-chinese-reviews/","section":"publication","summary":"This paper is an empirical study of unsupervised sentiment classification of Chinese reviews. The focus is on exploring the ways to improve the performance of the unsupervised sentiment classification based on limited existing sentiment resources in Chinese. On the one hand, all available Chinese sentiment lexicons — individual and combined — are evaluated under our proposed framework. On the other hand, the domain dependent sentiment noise words are identified and removed using unlabeled data, to improve the classification performance. To the best of our knowledge, this is the first such attempt. Experiments have been conducted on three open datasets in two domains, and the results show that the proposed algorithm for sentiment noise words removal can improve the classification performance significantly.","tags":["\"sentiment noise words\"","\"unsupervised sentiment classification\"","\"domain dependent\""],"title":"An Empirical Study of Unsupervised Sentiment Classification of Chinese Reviews","type":"publication"},{"authors":["Jiadong Yang","Hua Xu","YunpengCai","PeifaJia"],"categories":[],"content":"","date":1262304000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523751,"objectID":"ae598d59064019a767f90eda93e1b207","permalink":"https://thuiar.github.io/publication/effective-structure-learning-for-eda-via-l1-regularizedbayesian-networks/","publishdate":"2020-09-19T13:55:50.396743Z","relpermalink":"/publication/effective-structure-learning-for-eda-via-l1-regularizedbayesian-networks/","section":"publication","summary":"The Bayesian optimization algorithm (BOA) uses Bayesian networks to explore the dependencies between decision variables of an optimization problem in pursuit of both faster speed of convergence and better solution quality. In this paper, a novel method that learns the structure of Bayesian networks for BOA is proposed. The proposed method, called L1BOA, uses L1-regularized regression to find the candidate parents of each variable, which leads to a sparse but nearly optimized network structure. The proposed method improves the efficiency of the structure learning in BOA due to the reduction and automated control of network complexity introduced with L1-regularized learning. Experimental studies on different types of benchmark problems are carried out, which show that L1BOA outperforms the standard BOA when no a-priori knowledge about the problem structure is available, and nearly achieves the best performance of BOA that applies explicit complexity controls.","tags":["\"estimation of distribution algorithms\"","\"bayesian optimization algorithm\"","\"regularization paths\"","\"l1-penalized regression\"","\"bayesian network\""],"title":"Effective Structure Learning for EDA via L1-Regularizedbayesian Networks","type":"publication"},{"authors":["ZhongwuZhai","Hua Xu","JunLi","PeifaJia"],"categories":[],"content":"","date":1262304000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523753,"objectID":"72d1f77f9618e15c8656404e85739c91","permalink":"https://thuiar.github.io/publication/feature-subsumption-for-sentiment-classification-in-multiple-languages/","publishdate":"2020-09-19T13:55:52.371598Z","relpermalink":"/publication/feature-subsumption-for-sentiment-classification-in-multiple-languages/","section":"publication","summary":"An open problem in machine learning-based sentiment classification is how to extract complex features that outperform simple features; figuring out which types of features are most valuable is another. Most of the studies focus primarily on character or word Ngrams features, but substring-group features have never been considered in sentiment classification area before. In this study, the substring-group features are extracted and selected for sentiment classification by means of transductive learning-based algorithm. To demonstrate generality, experiments have been conducted on three open datasets in three different languages: Chinese, English and Spanish. The experimental results show that the proposed algorithm's performance is usually superior to the best performance in related work, and the proposed feature subsumption algorithm for sentiment classification is multilingual. Compared to the inductive learning-based algorithm, the experimental results also illustrate that the transductive learning-based algorithm can significantly improve the performance of sentiment classification. As for term weighting, the experiments show that the ``tfidf-c'' outperforms all other term weighting approaches in the proposed algorithm.","tags":[],"title":"Feature Subsumption for Sentiment Classification in Multiple Languages","type":"publication"},{"authors":["Xingli HUANG","Hua XU","Peifa JIA"],"categories":[],"content":"","date":1261383844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1261383844,"objectID":"5b9a4e169f3943586e5ac88e6a201999","permalink":"https://thuiar.github.io/publication/fuzzy-timed-agent-based-petri-nets-for-modeling-cooperative-multi-robot-systems/","publishdate":"2009-12-21T16:24:04+08:00","relpermalink":"/publication/fuzzy-timed-agent-based-petri-nets-for-modeling-cooperative-multi-robot-systems/","section":"publication","summary":"A cooperative multi-robot system (CMRS) modeling method called fuzzy timed agent based Petri nets (FTAPN) is proposed in this paper, which has been extended from fuzzy timed object-oriented Petri net (FTOPN). The proposed FTAPN can be used to model and illustrate both the structural and dynamic aspects of CMRS, which is a typical multi-agent system (MAS). At the same time, supervised learning is supported in FTAPN. As a special type of high-level object, agent is introduced into FTAPN, which is used as a common modeling object in its model. The proposed FTAPN can not only be used to model CMRS and represent system aging effect, but also be refined into the object-oriented implementation easily. At the same time, it can also be regarded as a conceptual and practical artificial intelligence (AI) tool for multi-agent systems (MAS) into the mainstream practice of the software development.","tags":["Petri nets","Multi Cooperative Robot Systems","Multi-Agent Systems"],"title":"Fuzzy Timed Agent Based Petri Nets for Modeling Cooperative Multi-Robot Systems","type":"publication"},{"authors":["Jiadong Yang","Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1260519844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1260519844,"objectID":"357b3d6982ea6d97871a09190bb8f8aa","permalink":"https://thuiar.github.io/publication/task-scheduling-for-heterogeneous-computing-based-on-bayesian-optimization-algorithm/","publishdate":"2009-12-11T16:24:04+08:00","relpermalink":"/publication/task-scheduling-for-heterogeneous-computing-based-on-bayesian-optimization-algorithm/","section":"publication","summary":"Efficient task scheduling, as a crucial step to achieve high performance for multiprocessor platform, remains one of the challenge problems despite of numrous studies. This paper presents a novel scheduling algorithm based on Bayesian optimization algorithm (BOA) for heterogeneous computing environment. In the proposed algorithm, BOA constructs and updates Bayesian network according to the task graph of scheduling problems to find the optimal solution assigning tasks to different processors, and the execution sequence of tasks on the same processor is set by the heuristic used in the list scheduling approach. The proposed algorithm is sufficiently evaluated and compared with the related approaches by means of the empirical studies on benchmark applications. The experimental results confirm that the proposed algorithm is able to deliver more efficient schedules. Further experiments also indicate that the proposed algorithm maintains almost the same performance with different parameter settings.","tags":["task scheduling","parallel computing","Bayesian optimization algorithm"],"title":"Task scheduling for heterogeneous computing based on Bayesian optimization algorithm","type":"publication"},{"authors":["Zhongwu Zhai","Hua Xu","Jun Li","Peifa Jia"],"categories":[],"content":"","date":1257495844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1257495844,"objectID":"32a335df549710ed75547b9f7c596ccf","permalink":"https://thuiar.github.io/publication/sentiment-classification-for-chinese-reviews-based-on-key-substring-features/","publishdate":"2009-11-06T16:24:04+08:00","relpermalink":"/publication/sentiment-classification-for-chinese-reviews-based-on-key-substring-features/","section":"publication","summary":"One of the most widely-studied sub-problems of opinion mining is sentiment classification, which classifies evaluative texts as positive or negative to help people automatically identify the viewpoints underlying the online user-generated information. Most of the existing methods for sentiment classification ignore word sequence and unlabeled test documents' structural information. This paper proposes a transductive learning based algorithm considering both of these two types of information. The proposed algorithm is implemented by firstly selecting key substrings in the suffix tree constructed from the strings in training and unlabeled test documents and then converting each original text document to a bag of numbers of the key substrings. Finally, SVM is employed to classify the converted documents. Experiments on the open dataset (16,000 Chinese reviews) demonstrate promising performance of the proposed algorithm, the accuracy being over 93.15%, which is much better than the performance of the existing sentiment classification methods, such as n-gram features based classification algorithms. Experimental results also show that “tfidf-c” performs much better than other term weighting approaches in sentiment classification for large text corpus. In particular, the reasons behind the proposed algorithm's outstanding performance are further studied and analyzed in this paper. Moreover, the proposed algorithm can avoid the messy and rather artificial problem of defining word boundaries in Chinese language.","tags":["Sentiment Classification","Substring","Transductive Learning","Opinion Mining"],"title":"Sentiment classification for Chinese reviews based on key substring features","type":"publication"},{"authors":["Yuan Wang","Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1254212644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1254212644,"objectID":"a94930b862b4549c8027a9441917af4f","permalink":"https://thuiar.github.io/publication/a-hierarchical-routing-architecture-for-the-aviation-communication-network/","publishdate":"2009-09-29T16:24:04+08:00","relpermalink":"/publication/a-hierarchical-routing-architecture-for-the-aviation-communication-network/","section":"publication","summary":"Aviation communication network is composed of various kinds of IP-enabled aerial mobile Ad Hoc network, IP-enabled ground network and aerial data link system that don't support IP protocol. Satellite data links would also be available in the future. Different types of networks and systems construct one hierarchical network structure, so single routing protocol or incompatible protocols couldn't be applied to such a kind of network at the same time. This paper, for the first time, proposes a hierarchical routing architecture, which is able to accommodate the unique characteristics of military aviation communication network, which has been verified by computer simulations. The proposed architecture provides multiple routing protocols that can be used for diverse networks and scenarios, and also solutions of inter-operation between different network routing protocols, enabling the architecture suitable for hierarchical heterogeneous networks, which present valuable guidelines for the designs of the routing method in aviation communication network.","tags":["mobile ad hoc network","proactive routing protocol","reactive routing protocol","hierarchical routing architecture"],"title":"A hierarchical routing architecture for the aviation communication network","type":"publication"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":1251703444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1251703444,"objectID":"ad0ea045d30af138b1150739b7fa3258","permalink":"https://thuiar.github.io/publication/balancing-convergence-and-diversity-in-decomposition-based-many-objective-optimizers/","publishdate":"2009-08-31T15:24:04+08:00","relpermalink":"/publication/balancing-convergence-and-diversity-in-decomposition-based-many-objective-optimizers/","section":"publication","summary":"The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make use of aggregation functions to decompose a multiobjective optimization problem into multiple single-objective optimization problems. However, due to the nature of contour lines for the adopted aggregation functions, they usually fail to preserve the diversity in high-dimensional objective space even by using diverse weight vectors. To address this problem, we propose to maintain the desired diversity of solutions in their evolutionary process explicitly by exploiting the perpendicular distance from the solution to the weight vector in the objective space, which achieves better balance between convergence and diversity in many-objective optimization. The idea is implemented to enhance two well-performing decomposition-based algorithms, i.e., MOEA, based on decomposition and ensemble fitness ranking. The two enhanced algorithms are compared to several state-of-the-art algorithms and a series of comparative experiments are conducted on a number of test problems from two well-known test suites. The experimental results show that the two proposed algorithms are generally more effective than their predecessors in balancing convergence and diversity, and they are also very competitive against other existing algorithms for solving many-objective optimization problems.","tags":[],"title":"Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers","type":"publication"},{"authors":["Anqi Cui","Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1249115044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1249115044,"objectID":"e4149f43e6fdbf1e9d6294d930777696","permalink":"https://thuiar.github.io/publication/anti-germ-performance-prediction-for-detergents-based-on-elman-network-on-small-data-sets/","publishdate":"2009-08-01T16:24:04+08:00","relpermalink":"/publication/anti-germ-performance-prediction-for-detergents-based-on-elman-network-on-small-data-sets/","section":"publication","summary":"Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time consuming. In this paper, we present a neural network based model to predict the performance. The model made it much faster and cost less than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on small data sets. This model performs well though the training data sets are small. Its input is the actual value of key ingredients, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high precision and fitting with the monotonicity rule mostly. Experts in biochemical area also give good evaluations to the proposed model.","tags":["Anti-germ performance prediction","Artificial neural networks","Monotonicity rule","Pre-processing methods"],"title":"Anti-germ performance prediction for detergents based on Elman network on small data sets","type":"publication"},{"authors":["Zhongwu Zhai","Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1228811044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1228811044,"objectID":"5f6b50d3eb3ac87be7218b597e0159ec","permalink":"https://thuiar.github.io/publication/identifying-opinion-leaders-in-bbs/","publishdate":"2008-12-09T16:24:04+08:00","relpermalink":"/publication/identifying-opinion-leaders-in-bbs/","section":"publication","summary":"Opinion leaders play a very important role in information diffusion; they are found in all fields of society and influence the opinions of the masses in their fields. Most proposed algorithms on identifying opinion leaders in internet social network are global measure algorithms and usually omit the fact that opinion leaders are field-limited. We propose and test several algorithms, including interest-field based algorithms and global measure algorithms, to identify opinion leaders in BBS. Our experiments show that different algorithms are sensitive to different indicators; the interest-field based algorithms which not only take into account of the social networks’ structure but also the users’ interest space are more reasonable and effective in identifying opinion leaders in BBS. The interest-field based algorithms are sensitive to the high status nodes in the social network, and their performance relies on the quality of field discovery.","tags":["Opinion Leader","Social Network","Interest Field"],"title":"Identifying opinion leaders in BBS","type":"publication"},{"authors":["Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1222244644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1222244644,"objectID":"f2b5bf0fa7c3bc0d96e110d3ae385e6d","permalink":"https://thuiar.github.io/publication/a-fuzzy-timed-object-oriented-petri-net-for-multi-agent-systems/","publishdate":"2008-09-24T16:24:04+08:00","relpermalink":"/publication/a-fuzzy-timed-object-oriented-petri-net-for-multi-agent-systems/","section":"publication","summary":"In this paper, a multi-agent system (MAS) modeling method called fuzzy timed object-oriented Petri nets (FTOPN) is proposed. FTO-PN has extended Petri nets (PN) supporting object-oriented modeling and temporal fuzzy learning based on timed hierarchical object-oriented Petri net (TOPN) and fuzzy timed Petri net (FTPN). Our focus is the adaptation according to TOPN concepts of cooperation objects for supporting synchronous and asynchronous communications and the temporal fuzzy learning proposed in FTPN. These two diagrams have been chosen because they are the most commonly used in modeling MAS and describing agent learning and reasoning ability. That is to say, they can be used to model and illustrate both the structural and dynamic aspects of MAS. Not only the proposed FTOPN can be used to model complex MAS, but also FTOPN model can be refined into the object-oriented implementation easily. It has bridged the gap between the formal modeling and the system refinement, which can overcome the development problems in agent-oriented software engineering. At the same time, it also can be regarded as a conceptual and practical artificial intelligence (AI) tool for the integration of MAS into the mainstream practice of software development.","tags":["Support Vector Machine","algorithms","data analysis","data mining","machine learning","modeling","pattern recognition","unsupervised learning"],"title":"A Fuzzy Timed Object-Oriented Petri Net for Multi-Agent Systems ","type":"publication"},{"authors":["Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1222244644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1222244644,"objectID":"cd0a3efa4e7b8950aeab66026fd46554","permalink":"https://thuiar.github.io/publication/topn-based-temporal-performance-evaluation-method-of-neural-network-based-robot-controller/","publishdate":"2008-09-24T16:24:04+08:00","relpermalink":"/publication/topn-based-temporal-performance-evaluation-method-of-neural-network-based-robot-controller/","section":"publication","summary":"These years, for some neural network (NN) controller based time critical systems, temporal performance is always required to be evaluated. In order to model complex time critical systems, timed hierarchical object-oriented Petri net (TOPN) has been proposed. On the base of TOPN method, this paper has proposed worst case execution time (WCET) calculation method called time accumulation effect (TAE) calculation, whose goal is to evaluate the performance of TOPN models such as NN based robot controller systems etc al. TAE method can be used to calculate the worst case execution time interval directly on the base of integral linear programming (ILP) method. The use and benefits of TAE calculation for TOPN models have also been illustrated by analyzing one robot controller system model.","tags":["Neural network","Petri nets","Robot controller"],"title":"TOPN based temporal performance evaluation method of neural network based robot controller","type":"publication"},{"authors":["Hua Xu","YunPeng Cai","Peifa Jia","Yonglin Chi","Hui Zhang"],"categories":[],"content":"","date":1192609444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1192609444,"objectID":"a0fc14370b947d64fe48ee7ab44ce931","permalink":"https://thuiar.github.io/publication/control-middleware-for-open-robot-controllers/","publishdate":"2007-10-17T16:24:04+08:00","relpermalink":"/publication/control-middleware-for-open-robot-controllers/","section":"publication","summary":"To organize the robot controllers in an open manner and manage heterogeneous and concurrent accesses to hardware devices is a tough problem. This paper proposes the control middleware architecture to tackle this problem. In this architecture, the control middleware encapsulates all hardware operations and interacts with logical controllers in a client-server manner. All devices are mapped into data channels, and the middleware system acts as a data server providing read and write access function to client controllers. A wrapper method is adopted to organize heterogeneous hardware devices into few types of abstract devices, so that users can extend the system to support new types of physical devices easily. A communication interface is set up to enable command exchange between the middleware and the logical controllers either locally or remotely. The middleware defines a standard protocol that the logical controllers can perform manipulations to desired device regardless of their driving mechanism. On one hand, the control middleware can manage various types of peripheral devices, including the functions of extension, substitution, maintenance, scheduling and channel mapping. On the other hand, the control middleware can also provide uniform access software channels for high-level logical control layer, to enable access control of low-level device drivers.","tags":["robots","control engineering computing","middleware","International uniform access software channels","control middleware","open robot controllers","logical controllers","client-server manner","data channels","data server","wrapper method","heterogeneous hardware devices","communication interface","peripheral devices","channel mapping","robot controller","open architecture"],"title":"Control middleware for open robot controllers","type":"publication"},{"authors":["Hua Xu","Yuan Wang","Peifa Jia"],"categories":[],"content":"","date":1183451044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1183451044,"objectID":"cdaf79fb934c19f6d55c31e1c7f42bdd","permalink":"https://thuiar.github.io/publication/a-rough-set-and-fuzzy-neural-petri-net-based-method-for-dynamic-knowledge-extraction-representation-and-inference-in-cooperative-multiple-robot-system/","publishdate":"2007-07-03T16:24:04+08:00","relpermalink":"/publication/a-rough-set-and-fuzzy-neural-petri-net-based-method-for-dynamic-knowledge-extraction-representation-and-inference-in-cooperative-multiple-robot-system/","section":"publication","summary":"In cooperative multiple robot systems (CMRS), dynamic knowledge representation and inference is the key in scheduling robots to fulfill the cooperation requirements. The first goal of this work is to use rough set based rules generation method to extract dynamic knowledge of our CMRS. Kang’s rough set based rules generation method is used to get fuzzy dynamic knowledge from practical decision data. The second goal of this work is to use Fuzzy Neural Petri nets (FNPN) to represent and infer the dynamic knowledge on the base of dynamic knowledge extraction with self-learning ability. In particular, we investigate a new way to extract, represent and infer dynamic knowledge with self-learning ability in CMRS. Finally, the effectiveness of the dynamic knowledge extraction, representation and inference procedure are demonstrated.","tags":["Decision Table","Direction Angle","Horn Clause","Fuzzy Reasoning","Fuzzy Interval"],"title":"A rough set and fuzzy neural Petri net based method for dynamic knowledge extraction, representation and inference in cooperative multiple robot system","type":"publication"},{"authors":["Hua Xu","Yuan Wang","Peifa Jia"],"categories":[],"content":"","date":1180859044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1180859044,"objectID":"5b45dd78c8613c3f9071955f1b4aba2b","permalink":"https://thuiar.github.io/publication/fuzzy-neural-petri-nets/","publishdate":"2007-06-03T16:24:04+08:00","relpermalink":"/publication/fuzzy-neural-petri-nets/","section":"publication","summary":"Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. But it is lack of learning mechanism, which is the main weakness while modeling uncertain knowledge systems. Fuzzy neural Petri net (FNPN) is proposed in this paper, in which fuzzy neuron components are introduced into FPN as a sub-net model of FNPN. For neuron components in FNPN, back propagation (BP) learning algorithm of neural network is introduced. And the parameters of fuzzy production rules in FNPN neurons can be learnt and trained by this means. At the same time, different neurons on different layers can be learnt and trained independently. The FNPN proposed in this paper is meaningful for Petri net models and fuzzy systems.","tags":["fuzzy","Petri Nets","artificial neural networks","expert system","back propagation","learning"],"title":"Fuzzy neural Petri nets","type":"publication"},{"authors":["Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1180254244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1180254244,"objectID":"4ea8ac929b3941060c956f23d958720f","permalink":"https://thuiar.github.io/publication/a-novel-modeling-method-for-cooperative-multi-robot-systems-using-fuzzy-timed-agent-based-petri-nets/","publishdate":"2007-05-27T16:24:04+08:00","relpermalink":"/publication/a-novel-modeling-method-for-cooperative-multi-robot-systems-using-fuzzy-timed-agent-based-petri-nets/","section":"publication","summary":"This paper proposes a cooperative multi-robot system (CMRS) modeling method called fuzzy timed agent based Petri nets (FTAPN), which has been extended from fuzzy timed object-oriented Petri net (FTOPN). The proposed FTAPN can be used to model and illustrate both the structural and dynamic aspects of CMRS. Supervised learning is supported in FTAPN. As a special type of high-level object, agent is introduced, which is used as a common modeling object in FTAPN models. The proposed FTAPN can not only be used to model CMRS and represent system aging effect, but also be refined into the object-oriented implementation easily. At the same time, it can also be regarded as a conceptual and practical artificial intelligence (AI) tool for multi-agent system (MAS) into the mainstream practice of software development.","tags":["Fuzzy","Agent","Petri nets","Object-oriented","Multi-robot system"],"title":"A novel modeling method for cooperative multi-robot systems using fuzzy timed agent based Petri nets","type":"publication"},{"authors":["YunpengCai","Xiaomin Sun","Hua Xu","PeifaJia"],"categories":[],"content":"","date":1167609600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1600523755,"objectID":"0286b1471b9d65e2909dba8dd02240b8","permalink":"https://thuiar.github.io/publication/cross-entropy-and-adaptive-variance-scaling-in-continuous-eda/","publishdate":"2020-09-19T13:55:54.412453Z","relpermalink":"/publication/cross-entropy-and-adaptive-variance-scaling-in-continuous-eda/","section":"publication","summary":"This paper deals with the adaptive variance scaling issue incontinuous Estimation of Distribution Algorithms. A phenomenon is discovered that current adaptive variance scaling method in EDA suffers from imprecise structure learning. A new type of adaptation method is proposed to overcome this defect. The method tries to measure the difference between the obtained population and the prediction of the probabilistic model, then calculate the scaling factor by minimizing the cross entropy between these two distributions. This approach calculates the scaling factor immediately rather than adapts it incrementally. Experiments show that this approach extended the class of problems that can be solved, and improve the search efficiency in some cases. Moreover, the proposed approach features in that each decomposed subspace can be assigned an individual scaling factor, which helps to solve problems with special dimension property.","tags":["\"cross entropy\"","\"adaptive variance scaling\"","\"estimation of distirbution algorithms\"","\"evolutionary computation\""],"title":"Cross Entropy and Adaptive Variance Scaling in Continuous EDA","type":"publication"},{"authors":["Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1159691044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1159691044,"objectID":"bee064c58ba515f2fd47b30c18fb6465","permalink":"https://thuiar.github.io/publication/rtoc/","publishdate":"2006-10-01T16:24:04+08:00","relpermalink":"/publication/rtoc/","section":"publication","summary":"An open robot control system pursues easy extension, flexible reconfiguration, facile portability and jointless interoperation. Therefore, the system elements from multi-disciplinary areas can be integrated and reconfigured easily in such a system. Also the system modules can be ported flexibly. In this paper, a Rt-Linux based open robot controller (RTOC) is investigated. A reference model for robot controlling is proposed, in which hardware platform, operating system module and application modules are included. Then for the implementation of RTOC, two critical implementation problems - layered architecture and the intra-layer interfaces are discussed on the base of its reference model. The RTOC openness is also analyzed. Consequently, the proposed RTOC is applied to an industrial arc welding robot.","tags":["Open systems","Real time systems","Robot programming","Robotics"],"title":"RTOC: A Rt-Linux Based Open Robot Controller","type":"publication"},{"authors":["Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1126081444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1126081444,"objectID":"9860a6c8e6de5859c1bc9871061768ab","permalink":"https://thuiar.github.io/publication/a-novel-dynamic-knowledge-extraction-method-in-cooperative-multiple-robot-system-using-rough-set/","publishdate":"2005-09-07T16:24:04+08:00","relpermalink":"/publication/a-novel-dynamic-knowledge-extraction-method-in-cooperative-multiple-robot-system-using-rough-set/","section":"publication","summary":"Dynamic knowledge extraction is one of the critical problems in dynamic systems especially in cooperative multiple robot systems (CMRS). The knowledge may be fuzzy, because the information from dynamic environments is incomplete and uncertain. So it is difficult for traditional methods to extract dynamic knowledge effectively. According to the dynamic knowledge extraction requirements in CMRS, this paper proposes a novel dynamic knowledge extraction method in CMRS on the base of KANG’s rough set based rules generation method. It has been demonstrated effective in our CMRS.","tags":["Rough sets","Fuzzy sets","Robotics","Knowledge extraction"],"title":"A novel dynamic knowledge extraction method in cooperative multiple robot system using rough set","type":"publication"},{"authors":["Hua Xu","Peifa Jia"],"categories":[],"content":"","date":1126081444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1126081444,"objectID":"c6cf525f49821804508ff8403f7d34f7","permalink":"https://thuiar.github.io/publication/fuzzy-timed-object-oriented-petri-net/","publishdate":"2005-09-07T16:24:04+08:00","relpermalink":"/publication/fuzzy-timed-object-oriented-petri-net/","section":"publication","summary":"The goal of this work is to extend a model of timed object-oriented Petri nets (TOPN) to allow modeling and analyzing dynamic systems with timing effect on system information. In the proposed Fuzzy timed object-oriented Petri net (FTOPN), we attach temporal fuzzy sets to each transition objects accounting for the aging of information. In particular, we investigate a new way to represent and deal with timing effect in dynamic systems. FTOPN also supports learning similar to that in fuzzy timed Petri net (FTPN). Finally, we use FTOPN to model a real decision making model of our cooperative multiple robot system (CMRS) to demonstrate its following benefits: independent training for its supporting object abstraction and size reconfiguration for its object granularity control function.","tags":["Petri nets","Fuzzy sets","Object-oriented method","Timing effect","Learning"],"title":"Fuzzy timed object-oriented Petri net","type":"publication"},{"authors":["Hua Xu","Peifa Jia","Xuegong Zhang"],"categories":[],"content":"","date":1117441444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1117441444,"objectID":"373c0cc57e38a3bb5e0b84c03d1a8cca","permalink":"https://thuiar.github.io/publication/a-novel-chamber-scheduling-method-in-etching-tools-using-adaptive-neural-networks/","publishdate":"2005-05-30T16:24:04+08:00","relpermalink":"/publication/a-novel-chamber-scheduling-method-in-etching-tools-using-adaptive-neural-networks/","section":"publication","summary":"Chamber scheduling in etching tools is an important but difficult task in integrated circuit manufacturing. In order to effectively solve such combinatorial optimization problems in etching tools, this paper presents a novel chamber scheduling approach on the base of Adaptive Artificial Neural Networks (ANNs). Feed forward, multi-layered neural network meta-models were trained through the back-error-propagation (BEP) learning algorithm to provide a versatile job-shop scheduling analysis framework. At the same time, an adaptive selection mechanism has been extended into ANN. By testing the practical data set, the method is able to provide near-optimal solutions for practical chamber scheduling problems, and the results are superior to those generated by what have been reported in the neural network scheduling literature.","tags":["Computing methodologies","Machine learning","Machine learning approaches","Neural networks"],"title":"A Novel Chamber Scheduling Method in Etching Tools Using Adaptive Neural Networks","type":"publication"},{"authors":["Hua Xu","Yiming Cao","Zehong Yang","Peifa Jia","Xuegong Zhang"],"categories":[],"content":"","date":1093163044,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1093163044,"objectID":"ea6adfa4bf20a5a1cf5460bdefa0d2f4","permalink":"https://thuiar.github.io/publication/the-rtoc-open-system-for-controlling-fictitious-roaming-platform/","publishdate":"2004-08-22T16:24:04+08:00","relpermalink":"/publication/the-rtoc-open-system-for-controlling-fictitious-roaming-platform/","section":"publication","summary":"Openness is one of the aims pursued by modern controller systems. The typical features of one controller system include easy extension, flexible reconfiguration, facile portability and jointless interoperation. Therefore, in one open controller, the system elements from multi-disciplinary areas can be integrated and reconfigured easily. Also the system modules can be ported flexibly. In this paper, a real time operating system “Rt-Linux” based open controller system (RTOC) is investigated. A reference model for the open controller is proposed, in which hardware platform, operating system module and application software modules are included. Then the RTOC is realized on Rt-Linux. In RTOC, not only the modules of application software but also those of the Rt-Linux can be extended or reconfigured because of the openness of the “Linux” operating system platform. Two different kinds of reconfiguration — system reconfiguration and module reconfiguration, can be conducted. At the same time, not only the application modules completed in Standard C can be ported to other control systems, but also the software part of RTOC can be ported to other hardware platforms because of the universality of Linux. That is to say, two different levels of portability — system portability and module portability have been realized at the same time. To preserve jointless interoperation, file system based communication methods and hardware independent interface (HII) have been completed in RTOC. Moreover, as modules are allocated, the critical RTOC modules are inserted into the Linux kernel mode, so the real-time performance can be preserved. Consequently, the proposed RTOC is applied to control a fictitious roaming platform (FRP). Its efficiency and performance has been demonstrated.","tags":["Modular computer Systems","Open systems","Real time systems","Robot programming","Robotics"],"title":"The RTOC Open System for Controlling Fictitious Roaming Platform","type":"publication"},{"authors":["Hua Xu","Peifa Jia","Ming Yang","Haozhi Liu"],"categories":[],"content":"","date":1035793444,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1035793444,"objectID":"adcafe0293503001fd13641b3a1020b9","permalink":"https://thuiar.github.io/publication/portability-in-general-open-controllers/","publishdate":"2002-10-28T16:24:04+08:00","relpermalink":"/publication/portability-in-general-open-controllers/","section":"publication","summary":"Openness is one of the features of modern open controllers. The porting ability of open control software is one of the most important aspects related to openness. In order to realize portability in future open control software, a thorough discussion about its conception is presented for a typical hierarchical control software architecture. A relative comparison of the three main corresponding programming language standards is presented because the realization of portability mostly depends on developing tools. The discussion and comparative results may be helpful to improve or preserve portability of future open control software.","tags":["Openness","Portability","Control Software","Programming Language"],"title":"Portability in General Open Controllers","type":"publication"},{"authors":["Hua Xu","Zehong Yang","Peifa Jia","Yannan Zhao"],"categories":[],"content":"","date":1025511844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1025511844,"objectID":"bbc14600ece4d26ec358baf4464203e5","permalink":"https://thuiar.github.io/publication/a-conceptual-communication-model-based-on-hoonet-in-general-open-controllers/","publishdate":"2002-07-01T16:24:04+08:00","relpermalink":"/publication/a-conceptual-communication-model-based-on-hoonet-in-general-open-controllers/","section":"publication","summary":"Communication is one of the important functions in the middle layer of the 3-layered typical architecture of open controllers. Most of the realized communication is based on the OSI Reference Model which may affect system performance if the communication behaves in local ranges. In order to improve system performance and to keep the reliability of communication, the hierarchical object-oriented Petri net (HOONet) is modified and time information is attached to it. Then the simplified synchronous and asynchronous communication model is completed based on the modified HOONet.","tags":["Communication system control","Object oriented modeling","Control systems","Computer architecture","System performance","Robot control","Control system synthesis","Automatic control","Laboratories","Intelligent systems"],"title":"A Conceptual Communication Model Based On HOONet in General Open Controllers ","type":"publication"},{"authors":["Hua Xu","JingLiang Fang","Yanan Zhao"],"categories":[],"content":"","date":1004343844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1004343844,"objectID":"1ffd6942ffb3db39595e1724baac2bd2","permalink":"https://thuiar.github.io/publication/a-generic-conceptual-model-of-interface-components-in-open-robot-controller/","publishdate":"2001-10-29T16:24:04+08:00","relpermalink":"/publication/a-generic-conceptual-model-of-interface-components-in-open-robot-controller/","section":"publication","summary":"Openness is one of the features of modern open robot controllers. The extending ability of control software is one of the important aspects related to openness. In order to overcome the limit of two existing extending mechanisms, a composite extending mechanism which has mixed their merits is presented. On the foundations of this mechanism, a conceptual model of interface component in the extending mechanism is completed by the means of a hierarchical object-oriented Petri net (HOONet) according to the function analysis results of interface components, because HOONet can support a dynamic extending model and encompass the strong ability of abstraction and verification. This kind of interface component can support not only a communicating function but also a reliability function (information feedback function).","tags":[],"title":"A Generic Conceptual Model of Interface Components in Open Robot Controller","type":"publication"},{"authors":["Hua Xu","Peifa Jia","Yannan Zhao"],"categories":[],"content":"","date":1004343844,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1004343844,"objectID":"7a5aa812ea3d7b8c60e8ca08d4915716","permalink":"https://thuiar.github.io/publication/conceiving-analyzing-modeling-verifying-and-developing-the-clews-of-modeling-open-software-architectures-of-robot-controllers/","publishdate":"2001-10-29T16:24:04+08:00","relpermalink":"/publication/conceiving-analyzing-modeling-verifying-and-developing-the-clews-of-modeling-open-software-architectures-of-robot-controllers/","section":"publication","summary":"Openness is one of the features of modem robot controllers. Although many modeling technologies about how to model and develop open robot controllers have been discussed, the focus is always on some detail problems in some respects. While the relative complete modeling clews have never been discussed. In this paper, an initial modeling clew is presented. The corresponding contents including basic conceptions, modeling methods, requirement analysis, and testing strategies are discussed in detail.","tags":["Control Systems","Robotics","Robot Controller","Software Engineering"],"title":"Conceiving, analyzing, modeling, verifying and developing-the clews of modeling open software architectures of robot controllers","type":"publication"},{"authors":["Junhui Deng"],"categories":null,"content":"Course Classification: Tsinghua University Computer Department Undergraduate Professional Basic Course\nLecturer：Junhui Deng\nTarget Audience: Computer Science Undergraduate\nTeaching Time：2001 - 2006\n","date":978278400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":978278400,"objectID":"b7caabb0e4bde45ecc0519114b6a051c","permalink":"https://thuiar.github.io/talk/data-structure-cs/","publishdate":"2001-01-01T00:00:00+08:00","relpermalink":"/talk/data-structure-cs/","section":"talk","summary":"Tsinghua University Computer Department Undergraduate Professional Basic Course","tags":[],"title":"Data Strcture","type":"talk"},{"authors":["Junhui Deng"],"categories":null,"content":"Course Classification: Public Elective Courses of Tsinghua University\nLecturer：Junhui Deng\nTarget Audience: All Undergraduate Students\nTeaching Time：2002 - Today\n","date":978278400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":978278400,"objectID":"970c56d891d3e1abdca0aaf1b5140c8d","permalink":"https://thuiar.github.io/talk/data-structure/","publishdate":"2001-01-01T00:00:00+08:00","relpermalink":"/talk/data-structure/","section":"talk","summary":"National Excellent Course, Public Elective Course of Tsinghua University","tags":[],"title":"Data Strcture","type":"talk"},{"authors":["Hua Xu","Yannan Zhao","Peifa Jia"],"categories":[],"content":"","date":966846244,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":966846244,"objectID":"5fc0f7eeae3406ffd034e44ed99164ed","permalink":"https://thuiar.github.io/publication/a-modified-genetic-algorithm-algorithm-description/","publishdate":"2000-08-21T16:24:04+08:00","relpermalink":"/publication/a-modified-genetic-algorithm-algorithm-description/","section":"publication","summary":"Based on the analyses of the limitation of a common genetic algorithm and the model of the structure and development of human society, the paper discusses a modified genetic algorithm. The algorithm applies unidirectional inheritance and hierarchical structure to solve the optimizing problems. This method can not only reserve the useful genetic information, but also make the succeeding inheritance more goal-oriented. It utilizes the intrinsic genetic knowledge to calculate. ","tags":["Genetic algorithms","Humans","Modems","Algorithm design and analysis","Genetic mutations","Laboratories","Intelligent systems","Intelligent structures","Computer science","Electronic mail"],"title":"A Modified Genetic Algorithm-Algorithm Description","type":"publication"},{"authors":["Hua Xu","Yannan Zhao","Zehong Yang","Xiaomin Sun","Peifa Jia"],"categories":[],"content":"","date":962180644,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":962180644,"objectID":"e7e1fd9e2852d5fe31c9cf10ed071272","permalink":"https://thuiar.github.io/publication/an-adaptive-method-of-signal-selecting-based-on-the-strategy-of-fuzzy-clustering-and-expert-reasoning/","publishdate":"2000-06-28T16:24:04+08:00","relpermalink":"/publication/an-adaptive-method-of-signal-selecting-based-on-the-strategy-of-fuzzy-clustering-and-expert-reasoning/","section":"publication","summary":"In order to pick up effective signals a real control system, the paper discusses an adaptive method of signal selection based on the strategy of fuzzy clustering and expert reasoning. Compared with known methods, it can not only pick up system characteristic frequency efficiently in the allowable error range, but also reduce the calculation and enhance the real time property. In order to demonstrate the merits of this method, an application example is then given.","tags":["Fuzzy systems","Fuzzy control","Fuzzy reasoning","Sun","Computer science","Laboratories","Intelligent systems","Control systems","Programmable control","Adaptive control"],"title":"An Adaptive Method of Signal Selecting Based on the Strategy of Fuzzy Clustering and Expert Reasoning ","type":"publication"},{"authors":["Junhui Deng","Hua Xu"],"categories":null,"content":"Course Classification: Tsinghua University Computer Department Graduate Basic Theory Course\nLecturer：Junhui Deng, Hua Xu\nTarget Audience: All Graduate Students\nTeaching Time：1997 - Today\n","date":852048000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":852048000,"objectID":"f93308c8be1670e9b2dc795f64282a4a","permalink":"https://thuiar.github.io/talk/computational-geometry/","publishdate":"1997-01-01T00:00:00+08:00","relpermalink":"/talk/computational-geometry/","section":"talk","summary":"Tsinghua University Computer Department Graduate Basic Theory Course","tags":[],"title":"Computational Geometry","type":"talk"},{"authors":["Yuan Yuan","Hua Xu"],"categories":[],"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6b7b52354494c1b60185acf84b1a8696","permalink":"https://thuiar.github.io/publication/a-new-dominance-relation-based-evolutionary-algorithm-for-many-objective-optimization/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/publication/a-new-dominance-relation-based-evolutionary-algorithm-for-many-objective-optimization/","section":"publication","summary":"Many-objective optimization has posed a great challenge to the classical Pareto dominance-based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary algorithm based on a new dominance relation is proposed for many-objective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in the MOEA based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the nondominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is evaluated on a number of well-known benchmark problems having 3-15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test functions considered in this paper, and it is compared favorably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.","tags":[],"title":"A New Dominance Relation Based Evolutionary Algorithm for Many-Objective Optimization","type":"publication"}]