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— Human activity recognition is a key component for socially enabled robots to effectively and naturally interact with humans. In this paper we exploit the fact that many human activities produce characteristic sounds from which a robot can infer the corresponding actions. We propose a novel recognition approach called Non-Markovian Ensemble Voting (NEV)(More)
We present a new method for improving the efficiency of information extraction systems applied to biological literature, using the correlation between structural and functional classifications of gene products. The method evaluates extracted information by checking if gene products from a common family match a common set of biological properties. To(More)
— The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to model-based approaches typically taken in(More)
Human activity recognition is a key skill for socially enabled robots to effectively and naturally interact with humans. In this paper we exploit the fact that most human activities produce very characteristic sounds from which a robot can infer the corresponding actions. We propose a novel classification approach called Non-Markovian Ensemble Voting (NEV)(More)
Human activity recognition is a key component for socially enabled robots to effectively and naturally interact with humans. In this paper we exploit the fact that many human activities produce characteristic sounds from which a robot can infer the corresponding actions. We propose a novel recognition approach called Non-Markovian Ensemble Voting (NEV) able(More)
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