Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has
achieved great success in many software testing and analysis tasks. While effective and …

As large language models (LLMs) take on complex tasks, their inputs are supplemented with
longer contexts that incorporate domain knowledge. Yet using long contexts is challenging …

Graph Neural Networks (GNNs) have emerged as powerful tools to capture structural
information from graph-structured data, achieving state-of-the-art performance on …

The national economy's key pillar, agriculture has a significant influence on society. Plant
health monitoring and disease detection are essential for sustainable agriculture. To protect …

The advancement of open-source frameworks and user-friendly manipulation applications
has accelerated the spread of deep fakes. In this study, we proposed optimal features …

Deep learning recommendation models (DLRMs) are using increasingly larger embedding
tables to represent categorical sparse features such as video genres. Each embedding row …

Federated learning allows edge devices to collaboratively train a global model without
sharing their local private data. Yet, with limited network bandwidth at the edge …

Ripening is a very important process that contributes to cheese quality, as its characteristics
are determined by the biochemical changes that occur during this period. Therefore …

Applying deep learning models requires design and optimization when solving multifaceted
artificial intelligence tasks. Optimization relies on human expertise and is achieved only with …

Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices
to collaboratively train a shared model while preserving data privacy. However, intensive …