People use imitation to encourage each other during conversation. We have conducted an experiment to investigate how imitation by a robot affect people's perceptions of their conversation with it. The robot operated in one of three ways: full head gesture mimicking, partial head gesture mimicking (nodding), and non-mimicking (blinking). Participants rated how satisfied they were with the interaction. We hypothesized that participants in the full head gesture condition will rate their interaction the most positively, followed by the partial and non-mimicking conditions. We also performed gesture analysis to see if any differences existed between groups, and did find that men made significantly more gestures than women while interacting with the robot. Finally, we interviewed participants to try to ascertain additional insight into their feelings of rapport with the robot, which revealed a number of valuable insights.
People use imitation to encourage each other during conversation. We have conducted an experiment to investigate how imitation by a robot affect people's perceptions of their conversation with it. The robot operated in one of three ways: full head gesture mimicking, partial head gesture mimicking (nodding), and non-mimicking (blinking). Participants rated how satisfied they were with the interaction. We hypothesized that participants in the full head gesture condition will rate their interaction the most positively, followed by the partial and non-mimicking conditions. We also performed gesture analysis to see if any differences existed between groups, and did find that men made significantly more gestures than women while interacting with the robot. Finally, we interviewed participants to try to ascertain additional insight into their feelings of rapport with the robot, which revealed a number of valuable insights.
“…Existing visualization idioms provide plenty of potential for rapid deployment and effective application to epidemiology. This holds in the case of considerable complexity [ 135 , 139 , 147 ], dynamism [ 151 , 155 ] and uncertainty [ 138 ]. Much of what was achieved was novel in terms of the designs developed, algorithms produced and the epidemiological context to which the ideas were applied—such as the use of interactive linked views to represent uncertainty and error: ‘a first under the epidemiological setting’ as captured in [ 138 ]: 427 .…”
Section: Findings—results and Claimsmentioning
confidence: 99%
“…Equally, design and process insights—knowledge relevant to visualization —that may be reusable were achieved, such as the population weighted fading of glyphs used to make more populated areas more salient and effectively ‘focus the eye on areas where population numbers are higher for relative changes’ [ 155 ]: 788 (as explained in figure 3 ); a new semi-automated approach to mapping datasets with particular characteristics to plausible visualization designs for dissemination [ 150 ]: 674 (see figure 4 ); a method for validating such recommendations [ 149 ]: 699 ; confirmation of the effective use of narrative patterns [ 152 ]; and an automated process for daily data updates to visualization dashboards for dynamic data dissemination [ 151 ]. We say more about the visualization design process in §3(e) (with a particular focus in 3(e)(iii)), but can claim with some confidence that: Visualization resulted in insights about epidemiology and the design and use of data graphics Figure 4 We developed an effective means of generating thousands of viable online interactive visualizations and hundreds of dashboards by developing a means of Propagating Visual Designs from the SCRC data streams [ 149 ].…”
Section: Findings—results and Claimsmentioning
confidence: 99%
“…an automated process for daily data updates to visualization dashboards for dynamic data dissemination [ 151 ].…”
Section: Findings—results and Claimsmentioning
confidence: 99%
“…Interactive notebooks describing each are available as electronic supplementary materials [104]. D3 Prototyping [133]an expressive JavaScript library for delivering VIS on the Web New Data Spaces [156]exploring data in transformed data spaces VIS Design Study [155]modifying design study methodology for emergency response Gridded GlyphMap [154]interactive animated maps show model outputs by age group and disease state Pictograms [152]pictorial symbols for informing the public about modelling outputs GMap [159]understanding models by visualizing network clusters as regions NAPA Cards [151]narrative design patterns for data driven VIS storytelling Linked PPLots [134]explore relationships between parameters with parallel coordinates Alternative Views [132]using complimentary visualizations to explore dynamic networks Visual Faceting [146]interactively cut, sort and align time series for comparison VA for Modelling [145]integrate algorithmic tools for VA into modelling workflows…”
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs—a series of ideas, approaches and methods taken from existing visualization research and practice—deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See
https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/
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This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
In this paper we present Hyvve as a flexible, distributed, modular lighting system. Our experiences with designing various intelligent lighting environments taught us that the available equipment is often cumbersome and puzzling to install. Moreover software applications to control lighting equipment often do not provide the freedom the designer requires. Hyvve provides an infrastructure that combines both hard-and software. It consists of hexagonal-shaped ceiling tiles that comprise of a wireless node and three warm white, three cool white LEDs and two LED drivers. Sensors can be connected to the tile by means of various sensor boxes. JAVA based software packages facilitate easy set up and control of the Hyvve system. These packages allow for programming new applications and behaviors into the system. To exemplify the versatility of the system we present three different applications. The showcases corroborate the flexible, modular, and aesthetic qualities of Hyvve as a versatile tool for designing lighting environments.
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