Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Abstract Industry 4.0 (I4. 0) is the fourth industrial revolution and a synonym for intelligent
manufacturing. It drives the convergence of several cutting-edge technologies to provoke …

The crux of semi-supervised semantic segmentation is to assign pseudo-labels to the pixels
of unlabeled images. A common practice is to select the highly confident predictions as the …

On-device session-based recommendation systems have been achieving increasing
attention on account of the low energy/resource consumption and privacy protection while …

While modern 5-axis computer numerical control (CNC) systems offer enhanced design
flexibility and reduced production time, the dimensional accuracy of the workpiece is …

This paper aims to present a comprehensive review of advanced techniques and models
with a specific focus on deep neural network (DNN) for resource-constrained environments …

Sparsification-based pruning has been an important category in model compression.
Existing methods commonly set sparsity-inducing penalty terms to suppress the importance …

Recently, deep learning models have been widely studied and applied in fault diagnosis.
However, two common drawbacks are: 1) they usually require a large amount of storage …

Recent object re-identification (Re-ID) methods gain high efficiency via lightweight student
models trained by knowledge distillation (KD). However, the huge architectural difference …

The rise of smart cities, smart homes, and smart health powered by the Internet of Things
(IoT) presents significant challenges in design, deployment, and security. The seamless data …