Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication
- Publication Type
Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Fuzzy Reasoning Procedure For Ontologies Based On Rough Membership Approximation, Armand Florentin Donfack Kana, Babatunde Opeoluwa Akinkunmi
Fuzzy Reasoning Procedure For Ontologies Based On Rough Membership Approximation, Armand Florentin Donfack Kana, Babatunde Opeoluwa Akinkunmi
Future Computing and Informatics Journal
One of the major challenges in modeling a real-world domain is how to effectively represent uncertain and incomplete knowledge of that domain. Several techniques for representing uncertainty in ontologies have been proposed with some of the techniques lacking provision for vague inference. The classical tableaux-based algorithm does not provide the flexibility for reasoning over such vague ontologies. However, several extensions of the tableaux-based algorithm have been proposed to cope with fuzzy reasoning. Similarly, several alternative reasoning methods for incomplete, inconsistent, and uncertain ontologies have been proposed. One of the major limitations of most of those techniques is that they require …
Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to learn scenario representation for a class of SIP instances. Specifically, we design a graph convolutional network based encoder to embed each scenario with the deterministic part of its instance (i.e. context) into a low-dimensional latent space, from which a decoder reconstructs the scenario from its latent representation conditioned on the context. Such a design effectively captures the dependencies of …
Knowledge Graph Embedding By Normalizing Flows, Changyi Xiao, Xiangnan He, Yixin Cao
Knowledge Graph Embedding By Normalizing Flows, Changyi Xiao, Xiangnan He, Yixin Cao
Research Collection School Of Computing and Information Systems
A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified perspective of embedding and introduce uncertainty into KGE from the view of group theory. Our model can incorporate existing models (i.e., generality), ensure the computation is tractable (i.e., efficiency) and enjoy the expressive power of complex random variables (i.e., expressiveness). The core idea is that we embed entities/relations as elements of a symmetric group, i.e., permutations of a set. Permutations of different sets can reflect different properties of embedding. And the …
Uncertainty Simulation Method Based On Deep Bayesian Networks Learning, Nie Kai, Kejun Zeng, Qinghai Meng
Uncertainty Simulation Method Based On Deep Bayesian Networks Learning, Nie Kai, Kejun Zeng, Qinghai Meng
Journal of System Simulation
Abstract: There are lots of uncertain elements in battlefields situation assessment and the uncertainty simulation would enhance the ability of situation assessment. A deep variational autoencoder bayesian networks (BN) model with memory module is proposed aiming at the problem of being unable to represent the uncertainties exactly caused by the various combat objects and more uncertain elements. Based on the deep BN learning, the situation assessment model is designed from the deep generative model. The principle of deep generative model mixing with the memory module is discussed and the leaning and reasoning process of the model is explained. The proposed …
Jointly-Learnt Networks For Future Action Anticipation Via Self-Knowledge Distillation And Cycle Consistency, Md Moniruzzaman, Zhaozheng Yin, Zhihai He, Ming-Chuan Leu, Ruwen Qin
Jointly-Learnt Networks For Future Action Anticipation Via Self-Knowledge Distillation And Cycle Consistency, Md Moniruzzaman, Zhaozheng Yin, Zhihai He, Ming-Chuan Leu, Ruwen Qin
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Future action anticipation aims to infer future actions from the observation of a small set of past video frames. In this paper, we propose a novel Jointly learnt Action Anticipation Network (J-AAN) via Self-Knowledge Distillation (Self-KD) and cycle consistency for future action anticipation. In contrast to the current state-of-the-art methods which anticipate the future actions either directly or recursively, our proposed J-AAN anticipates the future actions jointly in both direct and recursive ways. However, when dealing with future action anticipation, one important challenge to address is the future's uncertainty since multiple action sequences may come from or be followed by …