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Found 7 papers, 0 papers with code
A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection
Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks.
Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection
Due to the different training objectives and the performance of the models, it is interesting to consider whether the models have learned the semantics of code relevant to vulnerability detection, namely bug semantics, and if so, how the alignment to bug semantics relates to model performance.
Towards Causal Deep Learning for Vulnerability Detection
Our results show that CausalVul consistently improved the model accuracy, robustness and OOD performance for all the state-of-the-art models and datasets we experimented.
Emotion Detection From Social Media Posts
We have deployed different traditional machine learning techniques such as Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forest, as well as deep neural network models such as LSTM, CNN, GRU, BiLSTM, BiGRU to classify these tweets into four emotion categories (Fear, Anger, Joy, and Sadness).
Predicting Participants' Performance in Programming Contests using Deep Learning Techniques
In recent days, the number of technology enthusiasts is increasing day by day with the prevalence of technological products and easy access to the internet.
An Empirical Study of Deep Learning Models for Vulnerability Detection
Deep learning (DL) models of code have recently reported great progress for vulnerability detection.
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection
Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.
