Human interlocutors may use emotions as an important signaling device for coordinating interaction. In this context, predicting a significant change in speaker’s emotion be regulating the Given nonlinear and noisy nature of human conversations relatively short time series they produce, such predictive model is open challenge, both modeling behavior engineering artificial intelligence systems ch...

The distribution of ordinal patterns in time series has been found to reflect important qualitative features of the underlying system dynamics. Abrupt changes in the dynamics typically result in clearly visible differences between the distributions before and after the break. Recurring dynamical regimes can be discovered by classifying the distributions in different parts of the time series. Th...

We present a novel class of mixed membership models for joint distributions of groups of observations that co-occur with ordinal response variables for each group for learning statistical associations between the ordinal response variables and the observation groups. The class of proposed models addresses a requirement for predictive and diagnostic methods in a wide range of practical contempor...

Ordinal symbolic dynamics is a promising new approach to the investigation of time series and the systems behind them, with the important properties of being conceptually simple and relatively robust with respect to noise. The idea behind it is to consider the order relation between the values of a time series instead of the values themselves. Roughly speaking, a given time series is transforme...

Purpose of this article is to introduce different approaches in reviewing the mutual relationship between the built environment and citizen’s behavioral patterns in public spaces with applying analytical and comparative methods by using a questionnaire, a sample of 200 members of the audience in two streets of Mashhad, Kouhsangi and Qaranei, by using two tests of the ordinal logistic regression...