Over the recent years, computational trust and reputation models have become an invaluable method to improve computer-computer and human-computer interaction. As a result, a considerable amount of research has been published trying to solve open problems and improving existing models. This survey will bring additional structure into the already conducted research on both topics. After recapitulating the major underlying concepts, a new integrated review and analysis scheme for reputation and trust models is put forward. Using highly recognized review papers in this domain as a basis, this article will also introduce additional evaluation metrics to account for characteristics so far unstudied. A subsequent application of the new review schema on 40 top recent publications in this scientific field revealed interesting insights. While the area of computational trust and reputation models is still a very active research branch, the analysis carried out here was able to show that some aspects have already started to converge, whereas others are still subject to vivid discussions.

Alzheimer's disease is a degenerative disorder of the brain that is still without cure and affects millions of people around the world. Understanding the disease mechanisms is important for therapeutics. A first step would be to use an explanatory model of the disease's symptoms. For that one would need an adaptive computational approach that resembles the biological system, upon which the Alzheimer's lesions like are to be simulated. Artificial Neural Networks may function as the needed test bed; Venn network is an artificial neural network (ANN) that has capability of simulating the behavior of a functioning brain under physiological and pathological scenarios. Hopfield network is another ANN that can recover previously stored patterns. This paper aims at presenting a computational approach that combines Venn and Hopfield networks in order to model of Alzheimer's disease. During the modeling phase, we have developed an artificial neural network structure based on Venn networks and the training algorithm of standard Hopfield model. The neural network was trained to recognize certain patterns of training, in this case, binary images. On top of that the Alzheimer's disease was modeled computationally taking into consideration some of its neuropathological aspects. Throughout various simulations, we have found that the Alzheimer's disease model disturbed the performance of a regular trained neural network, thus mimicking the pathological effects in the human brain.

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