Titlebook: Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data; Third International Andreas Holzinger,Gabriella Pas

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978-3-642-39145-3Springer-Verlag Berlin Heidelberg 2013

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Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data978-3-642-39146-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

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厚颜

Challenges from Cross-Disciplinary Learning Relevant for KDD Methods in Intercultural HCI Designogy and processes necessary in intercultural HCI design to be able to use them in the intercultural context. This also has implications for the use of KDD methods in the cultural context. Hence, the challenges evolved during the intercultural HCI design process are subject to analysis and some aspects useful in reaching the goal are suggested.

注视

Intent Recognition Using Neural Networks and Kalman Filters achieved more than 63% accuracy of target prediction in a standard multiple distractor task while our model can recognize the correct target before the user spent 70% of total pointing time, indicating a 30% reduction of pointing time in 63% pointing tasks.

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0302-9743 kshop on Human-Computer Interaction and Knowledge Discovery, HCI-KDD 2013, held in Maribor, Slovenia, in July 2013, at SouthCHI 2013. The 20 revised papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on human-computer interaction a