A closer look at gaze

In this paper, we describe the formatting guidelines for ACM SI

Algebraic Aspects of Families of Fuzzy Languages

We study operations on fuzzy languages such as union, concatenation, Kleene $\star$, intersection with regular fuzzy languages, and several kinds of (iterated) fuzzy substitution. Then we consider families of fuzzy languages, closed under a fixed collection of these operations, which results in the concept of full Abstract Family of Fuzzy Languages or full AFFL. This algebraic structure is the fuzzy counterpart of the notion of full Abstract Family of Languages that has been encountered frequently in investigating families of crisp (i.e., non-fuzzy) languages. Some simpler and more complicated algebraic structures (such as full substitution-closed AFFL, full super-AFFL, full hyper-AFFL) will be considered as well.\ud In the second part of the paper we focus our attention to full AFFL's closed under iterated parallel fuzzy substitution, where the iterating process is prescribed by given crisp control languages. Proceeding inductively over the family of these control languages, yields an infinite sequence of full AFFL-structures with increasingly stronger closure properties

Differences in Listener Responses between Procedural and Narrative Tasks

In the long tradition of corpus based research on listener behavior, whether it entails linguistic analysis or social signal processing, many different tasks have been used during the recording of the corpus. So far in no study the task which has been given to the participants has been an independent variable and no studies have looked into the effect of this variable on listener responses. In this paper we present the results of our comparison between listening behavior elicited by procedural and narrative tasks which were used during the recording of our MultiLis corpus. We will show that listeners in the procedural tasks show more agreement in their responses than listeners in the narrative tasks. Furthermore we will show that the long procedural task elicits more responses per minute than the short procedural task. We will reflect on these results in light of cognitive load and grounding theory

Selecting appropriate agent responses based on non-content features

This paper describes work-in-progress on a study to create models of responses of virtual agents that are selected only based on non-content features, such as prosody and facial expressions. From a corpus of human-human interactions, in which one person was playing the part of an agent and the second person a user, we extracted the turns of the user and gave these to annotators. The annotators had to select utterances from a list of phrases in the repertoire of our agent that would be a good response to the user utterance. The corpus is used to train response selection models based on automatically extracted features and on human annotations of the user-turns