Background Brain cancer is a destructive and life-threatening disease that imposes
immense negative effects on patients' lives. Therefore, the detection of brain tumors at an …

A natural way to characterize the cluster structure of a dataset is by finding regions
containing a high density of data. This can be done in a nonparametric way with a kernel …

We address the problem of generalized category discovery (GCD) that aims to partition a
partially labeled collection of images; only a small part of the collection is labeled and the …

Clustering is an important technique to deal with large scale data which are explosively
created in internet. Most data are high-dimensional with a lot of noise, which brings great …

Previous feature selection methods equivalently consider the samples to select important
features. However, the samples are often diverse. For example, the outliers should have …

Salient object detection aims to locate objects that capture human attention within images.
Previous approaches often pose this as a problem of image contrast analysis. In this work …

It is still a challenging problem to characterize uncertainty and imprecision between specific
(singleton) clusters with arbitrary shapes and sizes. In order to solve such a problem, we …

Federated Learning (FL), allowing data owners to conduct model training without sending
their raw data to third-party servers, can enhance data privacy in Mobile Edge Computing …

The COVID-19 pandemic has led to an incredible amount of fake news and conspiracy
theories around the world. Calls for the integration of COVID-19 and fake news-related …

A challenging issue of clustering in real-word application is to detect clusters with arbitrary
shapes and densities in complex data. Many conventional clustering algorithms are capable …