Exploring the effects of group size and table size on interactions with tabletop shared-display groupware

By K. Ryall, Clifton Forlines, Chia Shen, Meredith Ringel Morris. Conference on Computer Supported Cooperative Work, 2004

583 citations recorded by the source corpus.

Subfields: Information Systems and Management, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/01fbc50a7cdd19291d99a8f8f1c811d4d61120ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Yujin Zhang
2. Bhiksha Raj
3. Paris Smaragdis
4. Yu-Xiong Wang
5. Aarthi M. Reddy

In Human-Computer Interaction

1. Yujin Zhang
2. Bhiksha Raj
3. Paris Smaragdis
4. Yu-Xiong Wang
5. Aarthi M. Reddy

Citations in the corpus

• A General Flexible Framework for the Handling of Prior Information in Audio Source Separation (2012, background): “Finally, while it can model similar TF patterns to [33], it involves much fewer parameters, which typically leads to more robust parameter estimation.”
• Convolutive Speech Bases and Their Application to Supervised Speech Separation (2007, methodology): “Some examples of convolutive NMF analysis are presented in [13] and [15].”
• Soft Mask Methods for Single-Channel Speaker Separation (2007, methodology): “Smaragdis [24] and Virtanen [25] present algorithms based on nonnegative matrix factorization that attempt to decompose the power spectral vectors of mixed speech signals into linear combinations of nonnegative bases”
• Collaborative coupling over tabletop displays (2006, unknown): “Recent efforts in developing coordination mechanisms for tabletop interfaces have recognized the importance of existing work practices and social protocols [7,10,11,14,15].”
• Nonnegative Matrix Factorization: A Comprehensive Review (2013, methodology): “Smaragdis [111], [112] initiated the above Convolutive NMF model, and cast it as the solutions to the basis matrix sequence Ut and the coefficient primary matrix V .”
• K-SVD and its non-negative variant for dictionary design (2005, background): “Application for NMF are many and include dimensionality reduction [17] and analysis of data such as audio [18], text [19] and even data obtained from astronomical spectrometers [20].”
• The Impact of Missing Labels and Overlapping Sound Events on Multi-label Multi-instance Learning for Sound Event Classification (2019, background): “NMD is an extension of non-negative matrix factorization (NMF) and is capable of identifying components with a temporal structure [12, 13].”