More Like this

1. RPamide neuropeptides NLP-22 and NLP-2 act through GnRH-like receptors to promote sleep and wakefulness in C. elegans

   https://doi.org/10.1038/s41598-020-66536-2  

   Van der Auwera, Petrus ; Frooninckx, Lotte ; Buscemi, Kristen ; Vance, Ryan T. ; Watteyne, Jan ; Mirabeau, Olivier ; Temmerman, Liesbet ; De Haes, Wouter ; Fancsalszky, Luca ; Gottschalk, Alexander ; et al ( December 2020 , Scientific Reports)
2. CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-art NLP Deep Learning Architectures on Commonsense Reasoning Task

   Saeedi, S. ; Panahi, A. ; Saeedi, S. ; and Fong A. ( December 2020 , Proceedings of the 14th International Workshop on Semantic Evaluation)
3. Invariant Signature, Logic Reasoning, and Semantic Natural Language Processing (NLP)-Based Automated Building Code Compliance Checking (I-SNACC) Framework

   https://doi.org/10.36680/j.itcon.2023.001  

   Wu, Jin ; Xue, Xiaorui ; Zhang, Jiansong ( January 2023 , Journal of Information Technology in Construction)

   Traditional manual building code compliance checking is costly, time-consuming, and human error-prone. With the adoption of Building Information Modeling (BIM), automation in such a checking process becomes more feasible. However, existing methods still face limited automation when applied to different building codes. To address that, in this paper, the authors proposed a new framework that requires minimal input from users and strives for full automation, namely, the Invariant signature, logic reasoning, and Semantic Natural language processing (NLP)-based Automated building Code compliance Checking (I-SNACC) framework. The authors developed an automated building code compliance checking (ACC) prototype system under this framework and tested it on Chapter 10 of the International Building Codes 2015 (IBC 2015). The system was tested on two real projects and achieved 95.2% precision and 100% recall in non-compliance detection. The experiment showed that the framework is promising in building code compliance checking. Compared to the state-of-the-art methods, the new framework increases the degree of automation and saves manual efforts for finding non-compliance cases. 

   more » « less
4. Adversarial Attack on Hyperdimensional Computing-based NLP Applications

   https://doi.org/10.23919/DATE56975.2023.10137289  

   Zhang, Sizhe ; Wang, Zhao ; Jiao, Xun ( April 2023 , 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE))
5. Phishing Email Detection Using Robust NLP Techniques

   https://doi.org/10.1109/ICDMW.2018.00009  

   Egozi, Gal ; Verma, Rakesh ( November 2018 , IEEE International Conference on Data Mining Workshops)

   Even with many successful phishing email detectors, phishing emails still cost businesses and individuals millions of dollars per year. Most of these models seem to ignore features like word count, stopword count, and punctuations; they use features like n-grams and part of speech tagging. Previous phishing email research ignores or removes the stopwords, and features relating to punctuation only count as a minor part of the detector. Even with a strong unconventional focus on features like word counts, stopwords, punctuation, and uniqueness factors, an ensemble learning model based on a linear kernel SVM gave a true positive rate of 83% and a true negative rate of 96%. Moreover, these features are robustly detected even in noisy email data. It is much easier to detect our features than correct part-of-speech tags or named entities in emails. 

   more » « less