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Found 7 papers, 2 papers with code
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish
Pre-trained Language Models such as BERT have become ubiquitous in NLP where they have achieved state-of-the-art performance in most NLP tasks.
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
This efficiency, coupled with its state-of-the-art performance, highlights LaFiCMIL's potential as a groundbreaking approach in the field of large file classification.
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Central to applying ML to software artifacts (like source or executable code) is converting them into forms suitable for learning.
A two-steps approach to improve the performance of Android malware detectors
For the subset of "difficult" samples, we rely on GUIDED RETRAINING, which leverages the correct predictions and the errors made by the base malware detector to guide the retraining process.
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain.
Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition
In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF).
A First Look at Android Applications in Google Play related to Covid-19
Due to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world.
Software Engineering Computers and Society
