Interpretable Categorization of Heterogeneous Time Series Data

Understanding heterogeneous multivariate time series data is important in many applications ranging from smart homes to aviation. Learning models of heterogeneous multivariate time series that are also human-interpretable is challenging and not adequately addressed by the existing literature. We propose grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs extend decision trees with a grammar framework. Logical expressions derived from a context-free grammar are used for branching in place of simple thresholds on attributes. The added expressivity enables support for a wide range of data types while retaining the interpretability of decision trees. In particular, when a grammar based on temporal logic is used, we show that GBDTs can be used for the interpretable classi cation of high-dimensional and heterogeneous time series data. Furthermore, we show how GBDTs can also be used for categorization, which is a combination of clustering and generating interpretable explanations for each cluster. We apply GBDTs to analyze the classic Australian Sign Language dataset as well as data on near mid-air collisions (NMACs). The NMAC data comes from aircraft simulations used in the development of the next-generation Airborne Collision Avoidance System (ACAS X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data Mining (SDM) 201

Responding to gratitude in elicited oral interaction. A taxonomy of communicative options

This study explores responses to gratitude as expressed in elicited oral interaction (mimetic-pretending open role-plays) produced by native speakers of American English. It first overviews the literature on this topic. It then presents a taxonomy of the head acts and supporting moves of the responses to gratitude instantiated in the corpus under examination, which considers their strategies and formulations. Finally, it reports on their frequency of occurrence and combinatorial options across communicative situations differing in terms of the social distance and power relationships between the interactants. The findings partly confirm what reported in the literature, but partly reveal the flexibility and adaptability of these reacting speech acts to the variable context in which they may be instantiated. On the one hand, the responses to gratitude identified tend to be encoded as simple utterances, and occasionally as complex combinations of head acts and/or supporting moves; also, their head acts show a preference for a small set of strategies and formulation types, while their supporting moves are much more varied in content and form, and thus situation-specific. On the other hand, the frequency of occurrence of the responses to gratitude, their dispersion across situations, and the range of their attested strategies and formulations are not in line with those reported in previous studies. I argue that these partly divergent findings are to be related to the different data collection and categorization procedures adopted, and the different communicative situations considered across studies. Overall, the study suggests that: responses to gratitude are a set of communicative events with fuzzy boundaries, which contains core (i.e. more prototypical) and peripheral (i.e. less prototypical) exemplars; although routinized in function, responses to gratitude are not completely conventionalized in their strategic or surface realizations; alternative research approaches may provide complementary insights into these reacting speech acts; and a higher degree of comparability across studies may be ensured if explicit pragmatic and semantic parameters are adopted in the classification of their shared object of study