We describe a general Multi-Party Computation (MPC) protocol for arithmetic circuitsthat is secure against a static malicious adversary corrupting up to a 1/7 fraction of theparties. The protocol requires each party to send an average of O(mn log 3 n)bits, and computeO(mn log 4 n)operations in a network of size n, where m is the size of ci...

The outlook to apply the highly energetic biogas from anaerobic digestion into fuel cells will result in a significantly higher electrical efficiency and can contribute to an increase of renewable energy production. The practical bottleneck is the fuel cell poisoning caused by several gaseous trace compounds like hydrogen sulfide and ammonia. Hence artificial neural networks were developed to p...

The opportunistic networks are the variants of Delay Tolerant Networks (DTNs). These networks can be useful for routing in places where there are few base stations and connected routes for long distances. In an opportunistic network, when nodes move away or turn off their power to conserve energy, links may be disrupted or shut down periodically. These events result in intermittent connectivity...

Falsification is drawing attention in quality assurance of heterogeneous systems whose complexities are beyond most verification techniques’ scalability. In this paper we introduce the idea of causality aid in falsification: by providing a falsification solver—that relies on stochastic optimization of a certain cost function—with suitable causal information expressed by a Bayesian network, sear...

In order to find out the key input parameters, which aroused the output quality out of control during the manufacturing process, an integrated quality diagnosis algorithm for input parameters was proposed. The diagnosis method extends the traditional quality control and diagnosis method that only for the output quality of manufacturing process. It can detect the input parameters of the manufact...

The work presented in this paper aims at developing a novel meshless parameter estimation framework for system of partial differential equations (PDEs) using artificial neural network (ANN) approximations. PDE models to be treated consist linear and nonlinear PDEs, with Dirichlet Neumann boundary conditions, considering both regular irregular boundaries. This focuses on testing the applicabilit...