Abstract. We introduce autonomous gossiping (A/G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A/G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish/subscribe, but uses ecological and economic principles in a selforganizing manner in order to achieve any arbitrary selectivity (flexible casting). The trade-off of using a stateless self-organizing mechanism like A/G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish/subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish/subscribe or other paradigms for selective dissemination in MANET environments.

Interactive question answering (QA), where a dialogue interface enables follow-up and clarification questions, is a recent although long-advocated field of research. We report on the design and implementation of YourQA, our open-domain, interactive QA system. YourQA relies on a Web search engine to obtain answers to both fact-based and complex questions, such as descriptions and definitions.We describe the dialogue moves and management model making YourQA interactive, and discuss the architecture, implementation and evaluation of its chat-based dialogue interface. Our Wizard-of-Oz study and final evaluation results show how the designed architecture can effectively achieve open-domain, interactive QA.

An accurate identification dialog acts (DAs), which represent the illocutionary aspect of communication, is essential to support the understanding of human conversations. This requires 1) the segmentation of human-human dialogs into turns, 2) the intra-turn segmentation into DA boundaries and 3) the classification of each segment according to a DA tag. This process is particularly challenging when both segmentation and tagging are automated and utterance hypotheses derive from the erroneous results of ASR. In this paper, we use Conditional Random Fields to learn models for simultaneous segmentation and labeling of DAs from whole human-human spoken dialogs. We identify the best performing lexical feature combinations on the LUNA and SWITCHBOARD human-human dialog corpora and compare performances to those of discriminative D classifiers based on manually segmented utterances. Additionally, we assess our models' robustness to recognition errors, showing that DA identification is robust in the presence of high word error rates.

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