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*'''<span style="font-size:large;">· Exploiting Global Semantic Similarity Bitermsfor Short-text Topic Discovery</span>''' <span style="font-size:large;">'''Lu&nbsp;H''', Ge G, Li Y, Wang C, Xie J.&nbsp;Exploiting Global Semantic Similarity Bitermsfor Short</span> <span style="font-text Topic Discovery[C].&nbspsize:large;">In&nbsp;''Proceedings of'''&nbsp;''' '''ICTAI-18'''''. Volos. Greece, 2018. [Accepted]</span> *'''<span style="font-size:large;">· Online Single Homogeneous Source Transfer Learning Based on AdaBoost</span>''' <span style="font-size:large;">Du Y, Chen Q,&nbsp;'''Lu H''', Wang C.</span>&nbsp; <span style="font-size:large;">Online Single Homogeneous Source Transfer Learning Based on AdaBoost.In&nbsp;''Proceedings of&nbsp; '''ICTAI-18'''''.&nbsp;Volos. Greece, 2018.[Accepted]</span> *'''<span style="font-size:large;">· Constructing Pseudo Documents with Semantic Similarity for Short Text Topic Discovery</span>''' <span style="font-size:large;">'''Lu H''', Li Y, Tang C, Wang C, Xie J.&nbsp</span> <span style="font-size:large;Constructing Pseudo Documents with Semantic Similarity for Short Text Topic Discovery[C].">In&nbsp;''Proceedings of '''ICONIP-18'''''.'''&nbsp;'''Siem Reap. Cambodia, 2018.[Accepted]</span>''' *'''<span style="font-size:large;">· Topics may Evolve: Using Complaint Date for Analysis&nbsp;</span>'''<span style="font-size:large;">[[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8372098 PDF]]</span> <span style="font-size:large;">Xie L, Wang L, '''Lu H''', Li N, Wang C.</span>&nbsp; <span style="font-size:large;">Topics may Evolve: Using Complaint Date for Analysis[C]. In&nbsp;''Proceedings of '''ICTAI-17'''''. Boston, MA. USA, 2017: 1296-1303. </span> '''<span style="font-size:large;">· Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery&nbsp;</span>'''<span style="font-size:large;">[[https://ieeexplore.ieeeaaai.org/stampocs/stampindex.jsp?tp=&arnumber=8372098 php/AAAI/AAAI17/paper/view/14172/13900 PDF]]</span> *<span style="font-size:large;">'''Lu H''', Xie L, Kang N, Wang C and Xie J.</span>&nbsp; <span style="font-size:large;">Don't Forget the Quantifiable Relationship between Words: Using Recurrent Neural Network for Short Text Topic Discovery[C]. In&nbsp;''Proceedings of '''AAAI-17'''''. San Francisco, USA, 2017:1192-1198. </span> <span style="font-size:large;">'''· ''''''Biterm''''''Pseudo Document Topic Model for Short Text'''&nbsp;[[https://aaaiieeexplore.ieee.org/ocsstamp/indexstamp.php/AAAI/AAAI17/paper/view/14172/13900 jsp?tp=&arnumber=7814694 PDF]]</span> *<span style="font-size:large;">Jiang L, '''Lu H''', Xu M and Wang C.</span> <span style="font-size:large;">&nbsp;Biterm Pseudo Document Topic Model for Short Text[C]. In&nbsp;''Proceedings of '''ICTAI-16'''''. San Jose, USA, 2016: 865-872. </span> <span style="font-size:large;">'''· Never Ignore The Significance Of Different Anomalies: A Cost-sensitive Algorithm Based On Loss Function For Anomaly Detection&nbsp;'''[[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7814694 7372253 PDF]]</span> *<span style="font-size:large;">'''Lu H''', Chen F, Xu M, Wang C and Xie J.</span>&nbsp; <span style="font-size:large;">Never Ignore The Significance Of Different Anomalies: A Cost-sensitive Algorithm Based On Loss Function For Anomaly Detection[C]. In&nbsp; ''Proceedings of''&nbsp;'''''ICTAI-15'''''. Vietri sul Mare,&nbsp;Italy,&nbsp;2015: 1099-1105. [[https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7372253 PDF]]</span>

'''<span style="font-size:large;">· Utilizing Recurrent Neural Network for Topic Discovery in Short Text&nbsp;Scenarios[J]</span>'''

'''<span style="font-size:large;">· A Region of Interest Detection Method for Mammography based on Multi-cue Feature Integration[J]</span>'''