publications

2026

1. Usenix (under review)

   TBA

   Qingjie Lu, and  TBA

   Submitted to Usenix Security: USENIX Security Symposium, 2026

   Under Review

   Bib

   @article{lu2025horloge,
     title = {TBA},
     author = {Lu, Qingjie and TBA},
     journal = {Submitted to Usenix Security: USENIX Security Symposium},
     year = {2026},
     note = {Under Review}
   }

2025

1. HotNets ’25

   Modeling Metastability

   Ali Farahbakhsh, Andreas Haeberlen, Qingjie Lu, and 3 more authors

   In Proceedings of the 24th ACM Workshop on Hot Topics in Networks (HotNets ’25), 2025

   Bib

   @inproceedings{lu2025modeling,
     title = {Modeling Metastability},
     author = {Farahbakhsh, Ali and Haeberlen, Andreas and Lu, Qingjie and Alvisi, Lorenzo and van Renesse, Robbert and Gahtan, Shir Cohen},
     booktitle = {Proceedings of the 24th ACM Workshop on Hot Topics in Networks (HotNets '25)},
     year = {2025},
   }

2022

1. ISET

   Neural Network-Based Approaches for Aspect-Based Sentiment Analysis

   Qingjie Lu

   Highlights in Science, Engineering and Technology, 2022

   Proceedings of the 4th International Conference on Information Science and Electronic Technology

   Abs DOI Bib PDF

   The research of Aspect-based Sentiment Analysis which is a process that has a more specific focus than general sentiment analysis is trending upwards in numbers. Stemming from Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), novel approaches introduced new components like Graph Convolutional Networks (GCNs) and Transformers that improved the overall accuracy dramatically. Along with summarizing the models, the focus of this survey will be on comparing the several novel methods. Although this paper found that Dependency graph enhanced dual-transformer network (DGEDT) coupled with Bidirectional Encoder Representations from Transformers (BERT) is the best performing model thus far, this paper also identified challenges that needed to be addressed in order to better evaluate current and future models.

   @article{lu2022aspectbased,
     title = {Neural Network-Based Approaches for Aspect-Based Sentiment Analysis},
     author = {Lu, Qingjie},
     journal = {Highlights in Science, Engineering and Technology},
     volume = {12},
     pages = {222--229},
     year = {2022},
     doi = {10.54097/hset.v12i.1457},
     url = {https://drpress.org/ojs/index.php/HSET/article/view/1457},
     note = {Proceedings of the 4th International Conference on Information Science and Electronic Technology}
   }