Belief propagation is widely used in inference of graphical models. It yields exact solutions when the underlying graph is singly connected. When the graph contains loops, double-counting of evidence may degrade the accuracy of converged beliefs or even prohibit the convergence of messages. In this paper, we propose an exact belief propagation algorithm on Euler graphs. An Euler graph contains ...

Real life networks are generally modelled as scale-free networks. Information diffusion in such networks in decentralised environment is a difficult and resource consuming affair. Gossip algorithms have come up as a good solution to this problem. In this paper, we have proposed Adaptive First-Push-Then-Pull gossip algorithm. We show that algorithm works with minimum cost when the transition rou...

Prediction models are at the heart of modern acoustic engineering and are used in a diverse range of applications from refining the acoustic design of classrooms and concert halls to predicting how noise exposure varies through an urban environment. They also allow Auralisation to be performed for buildings and spaces before they are built or long after they are lost. Current commercial room ac...

In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...

In this paper, a gradient-based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro-fuzzy system. Premise and consequent parameters of the neuro-fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output wi...

Most research on influence maximization focuses the network structure features of diffusion process but lacks consideration multi-dimensional characteristics. This paper proposes attributed based crowd emotion, aiming to apply user’s emotion and group study characteristics information propagation. To measure interaction effects individual emotions, we define user power cluster credibility, prop...

Loopy belief propagation performs approximate inference on graphical models with loops. One might hope to compensate for the approximation by adjusting model parameters. Learning algorithms for this purpose have been explored previously, and the claim has been made that every set of locally consistent marginals can arise from belief propagation run on a graphical model. On the contrary, here we...

Loopy belief propagation performs approximate inference on graphical models with loops. One might hope to compensate for the approximation by adjusting model parameters. Learning algorithms for this purpose have been explored previously, and the claim has been made that every set of locally consistent marginals can arise from belief propagation run on a graphical model. On the contrary, here we...

This paper deals with the implementation of an efficient Arbitrary Lagrangian Eulerian (ALE) formulation for the three dimensional finite element modeling of mode I self-similar dynamic fracture process. Contrary to the remeshing technique, the presented algorithm can continuously advance the crack with the one mesh topology. The uncoupled approach is employed to treat the equations. So, each t...