This paper presents the basic theory and methodology underlying the building of multilingual terminological e-dictionary in the domain of cultural heritage. A multilingual terminological e-dictionary is broadly defined as a reference resource that gathers, structures and describes in a systematic way linguistic data in a specific domain, by means of concepts (extralinguistic information) denoted by terms (linguistic information). The aim is to define cultural heritage terminology based on ontology. The results are: 1) a multilingual terminology e-dictionary in cultural heritage; 2) a new tool-assisted methodology for humanities scholars to build a multilingual terminology e-dictionary. In the context of digital humanities, it is believed that such an approach also contributes to the drafting of well- structured and more logically consistent definitions in natural language for specialized communication purposes.
Estimating the All-Terminal Network Reliability (ATNR) by using Artificial Neural Networks (ANNs) has emerged as a promissory alternative to classical exact NP- hard algorithms. Approaches based on traditional ANNs have usually considered the network reliability upper bound as part of the inputs, which implies additional time-consuming calculations during both training and testing phases. This paper briefly reviews and compares the results of our recent work on advanced neural networks for ATNR, which dispense with upper bound input need and offer improved performance. The results are compared with traditional ANNs in terms of features such as the error (RMSE), execution time, or the ability to relax the perfects nodes assumption, among others. A quick discussion highlights the fact that modern neural networks outperform traditional ANN; however, there are trade-offs in the performance of advanced neural networks. Such trade-offs provide an opportunity for future research efforts as, suggested in this paper as well.