Number of co-authors:10
Number of publications with 3 favourite co-authors:Maneesh Agrawala:3Jeffrey Heer:2Manolis Savva:1
Nicholas Kong's 3 most productive colleagues in number of publications:Ravin Balakrishnan:108Tovi Grossman:42Maneesh Agrawala:36
Knowledge is commonly socially constructed, through collaborative efforts towards shared objectives or by dialogues and challenges brought about by different persons' perspectives.
-- G. Salomon (in "Distributed Cognitions: Psychological and Educational Considerations")
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Publications by Nicholas Kong (bibliography)
Kong, Nicholas, Convertino, Gregorio, Hanrahan, Benjamin and Chi, Ed (2011): VisualWikiCurator: a corporate Wiki plugin. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1549-1554.
Knowledge workers who maintain corporate wikis face high costs for organizing and updating content on wikis. This problem leads to low adoption rates and compromises the utility of such tools in organizations. We describe a system that seeks to reduce the interactions costs of updating and organizing wiki pages by combining human and machine intelligence. We then present preliminary results of an ongoing evaluation of the tool.
© All rights reserved Kong et al. and/or their publisher
Savva, Manolis, Kong, Nicholas, Chhajta, Arti, Fei-Fei, Li, Agrawala, Maneesh and Heer, Jeffrey (2011): ReVision: automated classification, analysis and redesign of chart images. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 393-402.
Poorly designed charts are prevalent in reports, magazines, books and on the Web. Most of these charts are only available as bitmap images; without access to the underlying data it is prohibitively difficult for viewers to create more effective visual representations. In response we present ReVision, a system that automatically redesigns visualizations to improve graphical perception. Given a bitmap image of a chart as input, ReVision applies computer vision and machine learning techniques to identify the chart type (e.g., pie chart, bar chart, scatterplot, etc.). It then extracts the graphical marks and infers the underlying data. Using a corpus of images drawn from the web, ReVision achieves image classification accuracy of 96% across ten chart categories. It also accurately extracts marks from 79% of bar charts and 62% of pie charts, and from these charts it successfully extracts data from 71% of bar charts and 64% of pie charts. ReVision then applies perceptually-based design principles to populate an interactive gallery of redesigned charts. With this interface, users can view alternative chart designs and retarget content to different visual styles.
© All rights reserved Savva et al. and/or ACM Press
Heer, Jeffrey, Kong, Nicholas and Agrawala, Maneesh (2009): Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1303-1312.
We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs -- a space-efficient time series visualization technique -- across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We identify transition points at which reducing the chart height results in significantly differing drops in estimation accuracy across the compared chart types, and we find optimal positions in the speed-accuracy tradeoff curve at which viewers performed quickly without attendant drops in accuracy. Based on these results, we propose approaches for increasing data density that optimize graphical perception.
© All rights reserved Heer et al. and/or ACM Press
Kong, Nicholas and Agrawala, Maneesh (2009): Perceptual interpretation of ink annotations on line charts. In: Proceedings of the ACM Symposium on User Interface Software and Technology 2009. pp. 233-236.
Asynchronous collaborators often use freeform ink annotations to point to visually salient perceptual features of line charts such as peaks or humps, valleys, rising slopes and declining slopes. We present a set of techniques for interpreting such annotations to algorithmically identify the corresponding perceptual parts. Our approach is to first apply a parts-based segmentation algorithm that identifies the visually salient perceptual parts in the chart. Our system then analyzes the freeform annotations to infer the corresponding peaks, valleys or sloping segments. Once the system has identified the perceptual parts it can highlight them to draw further attention and reduce ambiguity of interpretation in asynchronous collaborative discussions.
© All rights reserved Kong and Agrawala and/or their publisher
Grossman, Tovi, Kong, Nicholas and Balakrishnan, Ravin (2007): Modeling pointing at targets of arbitrary shapes. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 463-472.
We investigate pointing at graphical targets of arbitrary shapes. We first describe a previously proposed probabilistic Fitts' law model  which, unlike previous models that only account for rectangular targets, has the potential to handle arbitrary shapes. Three methods of defining the centers of arbitrarily shaped targets for use within the model are developed. We compare these methods of defining target centers, and validate the model using a pointing experiment in which the targets take on various shapes. Results show that the model can accurately account for the varying target shapes. We discuss the implications of our results to interface design.
© All rights reserved Grossman et al. and/or ACM Press
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