Search Results for author:
Found 8 papers, 3 papers with code
Generalized Attacks on Face Verification Systems
This paper provides an in-depth study of attacks on FV systems.
An Empirical Study on Log-based Anomaly Detection Using Machine Learning
Further, the evaluation of different ML techniques is mostly based on the assessment of their detection accuracy.
Measuring Improvement of F$_1$-Scores in Detection of Self-Admitted Technical Debt
We find that our trained BERT model improves over the best performance of all previous methods in 19 of the 20 projects in cross-project scenarios.
Automated Imbalanced Classification via Layered Learning
The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations.
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns.
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms
The high volume of increasingly sophisticated cyber threats is drawing growing attention to cybersecurity, where many challenges remain unresolved.
UBL: an R package for Utility-based Learning
The main goal of UBL package is to facilitate the utility-based predictive analytic task by providing a set of methods to deal with this type of problems in the R environment.
A Survey of Predictive Modelling under Imbalanced Distributions
Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable.
