45,216 research outputs found
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-based Acoustic Big Data
This paper presents a novel BigEAR big data framework that employs
psychological audio processing chain (PAPC) to process smartphone-based
acoustic big data collected when the user performs social conversations in
naturalistic scenarios. The overarching goal of BigEAR is to identify moods of
the wearer from various activities such as laughing, singing, crying, arguing,
and sighing. These annotations are based on ground truth relevant for
psychologists who intend to monitor/infer the social context of individuals
coping with breast cancer. We pursued a case study on couples coping with
breast cancer to know how the conversations affect emotional and social well
being. In the state-of-the-art methods, psychologists and their team have to
hear the audio recordings for making these inferences by subjective evaluations
that not only are time-consuming and costly, but also demand manual data coding
for thousands of audio files. The BigEAR framework automates the audio
analysis. We computed the accuracy of BigEAR with respect to the ground truth
obtained from a human rater. Our approach yielded overall average accuracy of
88.76% on real-world data from couples coping with breast cancer.Comment: 6 pages, 10 equations, 1 Table, 5 Figures, IEEE International
Workshop on Big Data Analytics for Smart and Connected Health 2016, June 27,
2016, Washington DC, US
Multi-party Interaction in a Virtual Meeting Room
This paper presents an overview of the work carried out at the HMI group of the University of Twente in the domain of multi-party interaction. The process from automatic observations of behavioral aspects through interpretations resulting in recognized behavior is discussed for various modalities and levels. We show how a virtual meeting room can be used for visualization and evaluation of behavioral models as well as a research tool for studying the effect of modified stimuli on the perception of behavior
Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction
A dialogue act (DA) represents the meaning of an utterance at the
illocutionary force level (Austin 1962) such as a question, a request, and a
greeting. Since DAs take charge of the most fundamental part of communication,
we believe that the elucidation of DA learning mechanism is important for
cognitive science and artificial intelligence. The purpose of this study is to
verify that scaffolding takes place when a human teaches a robot, and to let a
robot learn to estimate DAs and to make a response based on them step by step
utilizing scaffolding provided by a human. To realize that, it is necessary for
the robot to detect changes in utterance and rewards given by the partner and
continue learning accordingly. Experimental results demonstrated that
participants who continued interaction for a sufficiently long time often gave
scaffolding for the robot. Although the number of experiments is still
insufficient to obtain a definite conclusion, we observed that 1) the robot
quickly learned to respond to DAs in most cases if the participants only spoke
utterances that match the situation, 2) in the case of participants who builds
scaffolding differently from what we assumed, learning did not proceed quickly,
and 3) the robot could learn to estimate DAs almost exactly if the participants
kept interaction for a sufficiently long time even if the scaffolding was
unexpected.Comment: Published as a conference paper at IJCNN 201
An automatic technique for visual quality classification for MPEG-1 video
The Centre for Digital Video Processing at Dublin City University developed Fischlar [1], a web-based system for recording, analysis, browsing and playback of digitally captured television programs. One major issue for Fischlar is the automatic evaluation of video quality in order to avoid processing and storage of corrupted data. In this paper we propose an automatic classification technique that detects the video content quality in order to provide a decision criterion for the processing and storage stages
Analyzing Use of Thanks to You: Insights for Language Teaching and Assessment in Second and Foreign Language Contexts
This investigation of thanks to you in British and American usage was precipitated by a situation at an American university, in which a native Arabic speaker said thanks to you in isolation, making his intended meaning unclear. The study analyzes use of thanks to you in the Corpus of Contemporary American English and the British National Corpus to gain insights for English language instruction /assessment in the American context, as well as English-as-a-lingua-franca contexts where the majority of speakers are not native speakers of English or are speakers of different varieties of English but where American or British English are for educational purposes the standard varieties. Analysis of the two corpora revealed three functions for thanks to you common to British and American usage: expressing gratitude, communicating "because of you" positively, and communicating "because of you" negatively (as in sarcasm). A fourth use of thanks to you, thanking journalists/guests for being on news programs/talk shows, occurred in the American corpus only. Analysis indicates that felicitous use of thanks to you for each of these meanings depends on the presence of a range of factors, both linguistic and material, in the context of utterance
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