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Barking is perhaps the most characteristic form of vocalization in dogs; however, very little is known about its role in the intraspecific communication of this species. Besides the obvious need for ethological research, both in the field and in the laboratory, the possible information content of barks can also be explored by computerized acoustic analyses. This study compares four different supervised learning methods (naive Bayes, classification trees, k-nearest neighbors and logistic regression) combined with three strategies for selecting variables (all variables, filter and wrapper feature subset selections) to classify Mudi dogs by sex, age, context and individual from their barks. The classification accuracy of the models obtained was estimated by means of K-fold cross-validation. Percentages of correct classifications were 85.13 % for determining sex, 80.25 % for predicting age (recodified as young, adult and old), 55.50 % for classifying contexts (seven situations) and 67.63 % for recognizing individuals (8 dogs), so the results are encouraging. The best-performing method was k-nearest neighbors following a A. Larranaga Student at the Universidad Alfonso X El Sabio, Av. Universidad, 1, 28691 Villanueva de la Canada, Madrid, Spain wrapper feature selection approach. The results for classifying contexts and recognizing individual dogs were better with this method than they were for other approaches reported in the specialized literature. This is the first time that the sex and age of domestic dogs have been predicted with the help of sound analysis. This study shows that dog barks carry ample information regarding the caller's indexical features. Our computerized analysis provides indirect proof that barks may serve as an important source of information for dogs as well.

Data on household travel patterns represent key information to the development of travel demand models. The technology of Global Positioning Systems (GPS) may substitute or be used in association with traditional data collection approaches. However, it is important to know how the quality of this information influences the results for planning purposes, such as in travel demand analysis. The objective of this study is to evaluate the influence of different sources of travel information -GPS-recorded compared to self-reported -in travel demand models. Several structures of discrete choice models were tested to represent choice behavior: multinomial logit, mixed logit with random coefficients and nested logit, trying to include possible correlations between alternatives and heterogeneity of individuals. Subjects were recruited from a list of contacts of the Transport Laboratory at the Federal University of Rio Grande do Sul, Brazil. The results showed that GPS technology collects the travel patterns more precisely reducing the bias by collecting data from short trips not reported in traditional surveys. The models estimated with GPS data showed greater significance due to less measurement error. The cost of processing GPS information must be considered. An adequate modeling with self-reported data, by more complex models incorporating heterogeneity and correlation among alternatives, allowed an equivalent adjustment to those estimated with GPS data. The self-reported data is less precise due to respondents under / overestimation of travel times.More complex models allow capturing measurement errors inherent to self-reported travel surveys.