In this paper, we propose a framework which allows remote users to form conversation groups based on spatial relationship in a shared virtual space. Our proposed framework can tra...
The ability to efficiently discover information using partial knowledge (for example keywords, attributes or ranges) is important in large, decentralized, resource sharing distri...
Drawing on ethnographic studies of (landscape) architects at work, and interdisciplinary cooperation with them, this paper presents a human-centered approach to information visual...
Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional mani...
We present a way of analyzing sensed context information formulated to help in the generation, documentation and assessment of the designs of context-aware applications. Starting w...