Artificial Intelligence

In order to improve the efficiency and quality of salt-and-pepper denoising and realize the strategy of ‘denoising after judging’, the noise image classification network (CNN-J) is needed to judge whether the input image is a noisy image. For noisy images, the noise marking network (CNN-M) and noise denoising network (CNN-D) are combined for denoising processing, and the clean image will be directly output. In order to train the above three networks, three datasets are provided here, which are dataset_J, dataset_M and dataset_D, respectively.

eterogeneous graph representation learning is crit-

ical for analyzing complex data structures. Metapaths within this

field are vital as they elucidate high-order relationships across the

graph, significantly enhancing the model’s accuracy and depth of

understanding. However, metapaths tend to prioritize long-range

dependencies of the target node, which can lead to the oversight of

potentially crucial 1st-order heterogeneous neighbors or short-

range dependencies. To address this challenge and circumvent

The small rectal cancer dataset only consists of 190 MR images (including ADC, DCE, DWI and T2WI images), which can be categorized into 2 classes, i.e., T2 and T3. Fig. 2 illustrates some of the original MR images about T2 and T3 in the rectal cancer dataset. The ground-truth labels about tumor and rectum for all MR images are defined by one gastrointestinal radiologist.

Abstract—In massive Internet of Things (IoT) deployments,

the efficient allocation of computing resources to IoT devices

while preserving devices’ data poses a significant challenge.

This paper proposes a new online probabilistic model to address

uncertainties in demand and resource allocation for IoT

networks, where the task computing of requesting devices is

addressed by serving devices. The model incorporates uncertainty

and formulates an optimization problem, concerning available