Interactive Intelligent Systems (TIIS)


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Volume 7, Issue 4

Special Issue on Highlights of IUI 2016

  • Exploring Audience Response in Performing Arts with a Brain-Adaptive Digital Performance System

    By Shuo Yan, Gangyi Ding, Hongsong Li, Ningxiao Sun, Zheng Guan, Yufeng Wu, Longfei Zhang, Tianyu Huang

    Audience response is an important indicator of the quality of performing arts. Psychophysiological measurements enable researchers to perceive and understand audience response by collecting their bio-signals during a live performance. However, how the audience respond and how the performance is affected by these responses are the key elements but are hard to implement. To address this issue, we designed a brain-computer interactive system called Brain-Adaptive Digital Performance (BADP) for the measurement and analysis of audience engagement level through an interactive three-dimensional virtual theater. The BADP system monitors audience engagement in real time using electroencephalography (EEG) measurement and tries to improve it by applying content-related performing cues when the engagement level decreased.

    In this article, we generate EEG-based engagement level and build thresholds to determine the decrease and re-engage moments. In the experiment, we simulated two types of theatre performance to provide participants a high-fidelity virtual environment using the BADP system. We also create content-related performing cues for each performance under three different conditions. The results of these evaluations show that our algorithm could accurately detect the engagement status and the performing cues have a positive impact on regaining audience engagement across different performance types. Our findings open new perspectives in audience-based theatre performance design.

  • Adaptive Contextualization Methods for Combating Selection Bias during High-Dimensional Visualization

    By David Gotz, Shun Sun, Nan Cao, Rita Kundu, Anne-Marie Meyer

    Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. This article describes Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the invisible introduction of selection bias. The AC approach (1) monitors and models a user’s visual data selection activity, (2) computes metrics over that model to quantify the amount of selection bias after each step, (3) visualizes the metric results, and (4) provides interactive tools that help users assess and avoid bias-related problems. This article expands on an earlier article presented at ACM IUI 2016 [16] by providing a more detailed review of the AC methodology and additional evaluation results.

  • Quantifying Collaboration with a Co-Creative Drawing Agent

    By N. Davis, C. Hsiao, K. Y. Singh, B. Lin, B. Magerko

    This article describes a new technique for quantifying creative collaboration and applies it to the user study evaluation of a co-creative drawing agent. We present a cognitive framework called creative sense-making that provides a new method to visualize and quantify the interaction dynamics of creative collaboration, for example, the rhythm of interaction, style of turn taking, and the manner in which participants are mutually making sense of a situation. The creative sense-making framework includes a qualitative coding technique, interaction coding software, an analysis method, and the cognitive theory behind these applications. This framework and analysis method are applied to empirical studies of the Drawing Apprentice collaborative sketching system to compare human collaboration with a co-creative AI agent vs. a Wizard of Oz setup. The analysis demonstrates how the proposed technique can be used to analyze interaction data using continuous functions (e.g., integrations and moving averages) to measure and evaluate how collaborations unfold through time.

  • Issue-in-Progress more articles to come

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