2 citations · 5 across the 12 of their papers we have counts for
13 papers
Evidential Information Fusion on Possibilistic Structure
Qianli Zhou, Ye Cui, Zhen Li +2
Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restric…
Evidential Quantum Vertical Federated Learning
Hao Luo, Zhiyuan Zhai, Qianli Zhou +3
Quantum federated learning (QFL) has recently emerged as a promising paradigm for privacy-preserving collaborative learning, yet most existing studies focus on horizontal federated…
Quantum Information Fusion and Correction under the Transferable Belief Model
Qianli Zhou, Hao Luo, Lipeng Pan +2
The transferable belief model (TBM), developed within Dempster-Shafer theory, represents ambiguity and partial ignorance through set-valued belief masses. Its classical operations,…
Isopignistic Canonical Decomposition via Belief Evolution Network
Qianli Zhou, Tianxiang Zhan, Yong Deng
Developing a general information processing model in uncertain environments is fundamental for the advancement of explainable artificial intelligence. Dempster-Shafer theory of evi…
Attribute Fusion-based Evidential Classifier on Quantum Circuits
Hao Luo, Qianli Zhou, Lipeng Pan +2
Dempster-Shafer Theory (DST) as an effective and robust framework for handling uncertain information is applied in decision-making and pattern classification. Unfortunately, its re…
Repeatable Random Permutation Set
Wenran Yang, Yong Deng
Random permutation set (RPS), as a recently proposed theory, enables powerful information representation by traversing all possible permutations. However, the repetition of items i…
Belief Evolution Network-based Probability Transformation and Fusion
Qianli Zhou, Yusheng Huang, Yong Deng
Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have…
BF-QC: Belief Functions on Quantum Circuits
Qianli Zhou, Guojing Tian, Yong Deng
Dempster-Shafer Theory (DST) of belief function is a basic theory of artificial intelligence, which can represent the underlying knowledge more reasonably than Probability Theory (…
A Negation Quantum Decision Model to Predict the Interference Effect in Categorization
Qinyuan Wu, Yong Deng
Categorization is a significant task in decision-making, which is a key part of human behavior. An interference effect is caused by categorization in some cases, which breaks the t…
Higher order information volume of mass function
Qianli Zhou, Yong Deng
For a certain moment, the information volume represented in a probability space can be accurately measured by Shannon entropy. But in real life, the results of things usually chang…
Fractal-based Belief Entropy
Qianli Zhou, Yong Deng
The total uncertainty measurement of basic probability assignment (BPA) in Dempster-Shafer evidence theory (DSET) has always been an open issue. Although some scholars put forward…
Exponential Negation of a Probability Distribution
Qinyuan Wu, Yong Deng, Neal Xiong
Negation operation is important in intelligent information processing. Different with existing arithmetic negation, an exponential negation is presented in this paper. The new nega…
Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty
Yongchang Deng, Yuze Zou, Shimin Gong +3
In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflect…