Inline Co-Evolution between Users and Information Presentation for Data Exploration

Rajaonarivo, Landy; Courgeon, Matthieu; Maisel, Éric; Loor, Pierre De

doi:10.1145/3025171.3025226

Cited by 5 publications

(7 citation statements)

References 16 publications

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“…Word Clouds. Word or tag clouds [65], [98] are a prominent method of visualization and verbalization to represent metadata aspects of a collection. Tags or keywords can be derived either from existing object classification, mined from object titles and related textual descriptions, or generated through crowdsourcing or computer-vision methods.…”

Section: Non-temporal Visualization Methodsmentioning

confidence: 99%

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Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges

Windhager

Federico

Schreder

et al. 2019

IEEE Trans. Visual. Comput. Graphics

140

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After decades of digitization, large cultural heritage collections have emerged on the web, which contain massive stocks of content from galleries, libraries, archives, and museums. This increase in digital cultural heritage data promises new modes of analysis and increased levels of access for academic scholars and casual users alike. Going beyond the standard representations of search-centric and grid-based interfaces, a multitude of approaches has recently started to enable visual access to cultural collections, and to explore them as complex and comprehensive information spaces by the means of interactive visualizations. In contrast to conventional web interfaces, we witness a widening spectrum of innovative visualization types specially designed for rich collections from the cultural heritage sector. This new class of information visualizations gives rise to a notable diversity of interaction and representation techniques while lending currency and urgency to a discussion about principles such as serendipity, generosity, and criticality in connection with visualization design. With this survey, we review information visualization approaches to digital cultural heritage collections and reflect on the state of the art in techniques and design choices. We contextualize our survey with humanist perspectives on the field and point out opportunities for future research.

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Section: Non-temporal Visualization Methodsmentioning

confidence: 99%

“…Going beyond the two dimensions of flat InfoVis design, some interfaces also use a third dimension to encode CH collection data [30], [99]. This includes hybrid systems that merge the visualization of abstract data aspects as (or within) virtual spatial environments [32], [54], [92], [98].…”

Section: Non-temporal Visualization Methodsmentioning

confidence: 99%

Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges

Windhager

Federico

Schreder

et al. 2019

IEEE Trans. Visual. Comput. Graphics

140

View full text Add to dashboard Cite

show abstract

“…Interactive reinforcement learning has been recently applied in HCI [84], with promising applications in exploratory search [10,44] and adaptive environments [40,80]. Integrating user feedback in reinforcement learning algorithms is computationally feasible [94], helps agents learn better [57], can make data-driven design more accessible [68], and holds potential for rich human-computer collaboration [95].…”

Section: Interactive Reinforcement Learningmentioning

confidence: 99%

Designing Deep Reinforcement Learning for Human Parameter Exploration

Scurto

Kerrebroeck

Caramiaux

et al. 2021

ACM Trans. Comput.-Hum. Interact.

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Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this article, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users’ exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human–machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviours throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications.

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“…Interactive reinforcement learning has been recently applied in HCI [70], with promising applications in exploratory search [40,9] and adaptive environments [36,66]. Integrating user feedback in reinforcement learning algorithms is computationally feasible [79], helps agents learn better [50], can make data-driven design more accessible [56], and holds potential for rich human-computer collaboration [80].…”

Section: Interactive Reinforcement Learningmentioning

confidence: 99%

Designing Deep Reinforcement Learning for Human Parameter Exploration

Scurto,

Van Kerrebroeck,

Caramiaux

et al. 2019

Preprint

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Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this paper, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users for sound design. We describe a series of user-centred studies to probe the creative benefits of these agents and adapting their design to exploration. Preliminary studies observing users' exploration strategies with parametric interfaces and testing different agent exploration behaviours led to the design of a fully-functioning prototype, called Co-Explorer, that we evaluated in a workshop with professional sound designers. We found that the Co-Explorer enables a novel creative workflow centred on human-machine partnership, which has been positively received by practitioners. We also highlight varied user exploration behaviors throughout partnering with our system. Finally, we frame design guidelines for enabling such co-exploration workflow in creative digital applications. KeywordsInteraction Design • Machine Learning • Audio/Video Preprint. Under review for publication in ACM Transactions on Computer-Human Interaction (TOCHI).

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Inline Co-Evolution between Users and Information Presentation for Data Exploration

Cited by 5 publications

References 16 publications

Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges

Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges

Designing Deep Reinforcement Learning for Human Parameter Exploration

Designing Deep Reinforcement Learning for Human Parameter Exploration

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