The rise of large language model-based code generation (LLCG) has enabled various
commercial services and APIs. Training LLCG models is often expensive and time …

Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary
could tamper with a small number of model parameter bits to break the integrity of DNNs. To …

Crowdsourcing Federated learning (CFL) is a new crowdsourcing development paradigm
for the Deep Neural Network (DNN) models, also called “software 2.0”. In practice, the …

Over the past decade, Artificial Intelligence (AI) has had great success recently and is being
used in a wide range of academic and industrial fields. More recently, Large Language …

Multi-label Image Classification Systems (MICSs) developed based on Deep Neural
Networks (DNNs) are extensively used in people's daily life. Currently, although there are a …

While Large Language Models (LLMs) have seen widespread applications across
numerous fields, their limited interpretability poses concerns regarding their safe operations …

Deep neural networks (DNNs) have been widely deployed in software to address various
tasks (eg, autonomous driving, medical diagnosis). However, they can also produce …

Deep Neural Networks (DNNs) are increasingly deployed across applications. However,
ensuring their reliability remains a challenge, and in many situations, alternative models with …

Deep neural networks (DNN) are being used in a wide range of applications including safety-
critical systems. Several DNN test generation approaches have been proposed to generate …

Model compression can significantly reduce the sizes of deep neural network (DNN) models
and thus facilitate the dissemination of sophisticated, sizable DNN models, especially for …