Belief entropy is an effective uncertainty measurement in Dempster–Shafer evidence theory.
However, the weight ratio between discord and non-specificity in the belief entropy is static …

Abstract Complementary Label Learning (CLL) is a typical weakly supervised learning
protocol, where each instance is associated with one complementary label to specify a class …

Logical stochastic resonance (LSR) is a paradigm to realize reconfigurable robust Boolean
operations using specific nonlinearity in the presence of background noise. The stable-state …

Logical stochastic resonance (LSR) system is a physical system capable of performing
robust reconfigurable logical operations in the presence of background noise using specific …

Partial label learning aims to learn from training instances, each of which is associated with
a set of candidate labels but only one is a ground-truth label. Feature selection is an …

Abstract Gain-dissipative Ising machines (GIMs) are dedicated devices that can rapidly solve
combinatorial optimization problems. The noise intensity in traditional GIMs should be …

Physical reservoir computing (RC), a neuromorphic device, is becoming one of the main
types of physical artificial intelligence owing to its simple structure and efficient learning …

ABSTRACT Cognitive Reliability and Error Analysis Method (CREAM) is widely used in
human reliability analysis (HRA). It defines nine common performance conditions (CPCs) …

Semi-supervised Partial Label Learning (SPLL) aims to learn from a dataset comprised of
both partial label examples each of which is associated with a candidate label set and …

In real-world crowdsourcing annotation systems, due to differences in user knowledge and
cultural backgrounds, as well as the high cost of acquiring annotation information, the …