An increasingly popular way of experiencing remote places is by viewing 360° virtual tour videos, which show the surrounding view while traveling through an environment. However, finding particular locations in these videos can be difficult because current interfaces rely on distorted frame previews for navigation. To alleviate this usability issue, we propose Route Tapestries, continuous orthographic-perspective projection of scenes along camera routes. We first introduce an algorithm for automatically constructing Route Tapestries from a 360° video, inspired by the slit-scan photography technique. We then present a desktop video player interface using a Route Tapestry timeline for navigation. An online evaluation using a target-seeking task showed that Route Tapestries allowed users to locate targets 22% faster than with YouTube-style equirectangular previews and reduced the failure rate by 75% compared to a more conventional row-of-thumbnail strip preview. Our results highlight the value of reducing visual distortion and providing continuous visual contexts in previews for navigating 360°virtual tour videos.
{"title":"Route Tapestries: Navigating 360° Virtual Tour Videos Using Slit-Scan Visualizations","authors":"Jiannan Li, Jia-Ming Lyu, Maurício Sousa, Ravin Balakrishnan, Anthony Tang, Tovi Grossman","doi":"10.1145/3472749.3474746","DOIUrl":"https://doi.org/10.1145/3472749.3474746","url":null,"abstract":"An increasingly popular way of experiencing remote places is by viewing 360° virtual tour videos, which show the surrounding view while traveling through an environment. However, finding particular locations in these videos can be difficult because current interfaces rely on distorted frame previews for navigation. To alleviate this usability issue, we propose Route Tapestries, continuous orthographic-perspective projection of scenes along camera routes. We first introduce an algorithm for automatically constructing Route Tapestries from a 360° video, inspired by the slit-scan photography technique. We then present a desktop video player interface using a Route Tapestry timeline for navigation. An online evaluation using a target-seeking task showed that Route Tapestries allowed users to locate targets 22% faster than with YouTube-style equirectangular previews and reduced the failure rate by 75% compared to a more conventional row-of-thumbnail strip preview. Our results highlight the value of reducing visual distortion and providing continuous visual contexts in previews for navigating 360°virtual tour videos.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117274835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zain Tariq, M. Mannino, Mai Le Xuan Anh, Whitney Bagge, A. Abouzeid, D. Shasha
Model-driven policymaking for epidemic control is a challenging collaborative process. It begins when a team of public-health officials, epidemiologists, and economists construct a reasonably predictive disease model representative of the team’s region of interest as a function of its unique socio-economic and demographic characteristics. As the team considers possible interventions such as school closures, social distancing, vaccination drives, etc., they need to simultaneously model each intervention’s effect on disease spread and economic cost. The team then engages in an extensive what-if analysis process to determine a cost-effective policy: a schedule of when, where and how extensively each intervention should be applied. This policymaking process is often an iterative and laborious programming-intensive effort where parameters are introduced and refined, model and intervention behaviors are modified, and schedules changed. We have designed and developed EpiPolicy to support this effort. EpiPolicy is a policy aid and epidemic simulation tool that supports the mathematical specification and simulation of disease and population models, the programmatic specification of interventions and the declarative construction of schedules. EpiPolicy’s design supports a separation of concerns in the modeling process and enables capabilities such as the iterative and automatic exploration of intervention plans with Monte Carlo simulations to find a cost-effective one. We report expert feedback on EpiPolicy. In general, experts found EpiPolicy’s capabilities powerful and transformative, when compared with their current practice.
{"title":"Planning Epidemic Interventions with EpiPolicy","authors":"Zain Tariq, M. Mannino, Mai Le Xuan Anh, Whitney Bagge, A. Abouzeid, D. Shasha","doi":"10.1145/3472749.3474794","DOIUrl":"https://doi.org/10.1145/3472749.3474794","url":null,"abstract":"Model-driven policymaking for epidemic control is a challenging collaborative process. It begins when a team of public-health officials, epidemiologists, and economists construct a reasonably predictive disease model representative of the team’s region of interest as a function of its unique socio-economic and demographic characteristics. As the team considers possible interventions such as school closures, social distancing, vaccination drives, etc., they need to simultaneously model each intervention’s effect on disease spread and economic cost. The team then engages in an extensive what-if analysis process to determine a cost-effective policy: a schedule of when, where and how extensively each intervention should be applied. This policymaking process is often an iterative and laborious programming-intensive effort where parameters are introduced and refined, model and intervention behaviors are modified, and schedules changed. We have designed and developed EpiPolicy to support this effort. EpiPolicy is a policy aid and epidemic simulation tool that supports the mathematical specification and simulation of disease and population models, the programmatic specification of interventions and the declarative construction of schedules. EpiPolicy’s design supports a separation of concerns in the modeling process and enables capabilities such as the iterative and automatic exploration of intervention plans with Monte Carlo simulations to find a cost-effective one. We report expert feedback on EpiPolicy. In general, experts found EpiPolicy’s capabilities powerful and transformative, when compared with their current practice.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"387 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116007696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Text entry is an important and frequent task in interactive devices including augmented reality head-mounted displays (AR HMDs). In current AR HMDs, there are still two main open challenges to overcome for efficient and usable text entry: arm fatigue due to mid-air input and visual occlusion because of their small see-through displays. To address these challenges, we present iText, a technique for AR HMDs that is hands-free and is based on an imaginary (invisible) keyboard. We first show that it is feasible and practical to use an imaginary keyboard on AR HMDs. Then, we evaluated its performance and usability with three hands-free selection mechanisms: eye blinks (E-Type), dwell (D-Type), and swipe gestures (G-Type). Our results show that users could achieve an average text entry speed of 11.95, 9.03 and 9.84 words per minutes (WPM) with E-Type, D-Type, and G-Type, respectively. Given that iText with E-Type outperformed the other two selection mechanisms in text entry rate and subjective feedback, we ran a third, 5-day study. Our results show that iText with E-Type can achieve an average text entry rate of 13.76 WPM with a mean word error rate of 1.5%. In short, iText can enable efficient eyes-free text entry and can be useful for various application scenarios in AR HMDs.
{"title":"iText: Hands-free Text Entry on an Imaginary Keyboard for Augmented Reality Systems","authors":"Xueshi Lu, Difeng Yu, Hai-Ning Liang, Jorge Gonçalves","doi":"10.1145/3472749.3474788","DOIUrl":"https://doi.org/10.1145/3472749.3474788","url":null,"abstract":"Text entry is an important and frequent task in interactive devices including augmented reality head-mounted displays (AR HMDs). In current AR HMDs, there are still two main open challenges to overcome for efficient and usable text entry: arm fatigue due to mid-air input and visual occlusion because of their small see-through displays. To address these challenges, we present iText, a technique for AR HMDs that is hands-free and is based on an imaginary (invisible) keyboard. We first show that it is feasible and practical to use an imaginary keyboard on AR HMDs. Then, we evaluated its performance and usability with three hands-free selection mechanisms: eye blinks (E-Type), dwell (D-Type), and swipe gestures (G-Type). Our results show that users could achieve an average text entry speed of 11.95, 9.03 and 9.84 words per minutes (WPM) with E-Type, D-Type, and G-Type, respectively. Given that iText with E-Type outperformed the other two selection mechanisms in text entry rate and subjective feedback, we ran a third, 5-day study. Our results show that iText with E-Type can achieve an average text entry rate of 13.76 WPM with a mean word error rate of 1.5%. In short, iText can enable efficient eyes-free text entry and can be useful for various application scenarios in AR HMDs.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116558937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose a new class of haptic devices that provide haptic sensations by delivering liquid-stimulants to the user's skin; we call this chemical haptics. Upon absorbing these stimulants, which contain safe and small doses of key active ingredients, receptors in the user's skin are chemically triggered, rendering distinct haptic sensations. We identified five chemicals that can render lasting haptic sensations: tingling (sanshool), numbing (lidocaine), stinging (cinnamaldehyde), warming (capsaicin), and cooling (menthol). To enable the application of our novel approach in a variety of settings (such as VR), we engineered a self-contained wearable that can be worn anywhere on the user's skin (e.g., face, arms, legs). Implemented as a soft silicone patch, our device uses micropumps to push the liquid stimulants through channels that are open to the user's skin, enabling topical stimulants to be absorbed by the skin as they pass through. Our approach presents two unique benefits. First, it enables sensations, such as numbing, not possible with existing haptic devices. Second, our approach offers a new pathway, via the skin's chemical receptors, for achieving multiple haptic sensations using a single actuator, which would otherwise require combining multiple actuators (e.g., Peltier, vibration motors, electro-tactile stimulation). We evaluated our approach by means of two studies. In our first study, we characterized the temporal profiles of sensations elicited by each chemical. Using these insights, we designed five interactive VR experiences utilizing chemical haptics, and in our second user study, participants rated these VR experiences with chemical haptics as more immersive than without. Finally, as the first work exploring the use of chemical haptics on the skin, we offer recommendations to designers for how they may employ our approach for their interactive experiences.
{"title":"Chemical Haptics: Rendering Haptic Sensations via Topical Stimulants","authors":"Jasmine Lu, Ziwei Liu, Jas Brooks, Pedro Lopes","doi":"10.1145/3472749.3474747","DOIUrl":"https://doi.org/10.1145/3472749.3474747","url":null,"abstract":"We propose a new class of haptic devices that provide haptic sensations by delivering liquid-stimulants to the user's skin; we call this chemical haptics. Upon absorbing these stimulants, which contain safe and small doses of key active ingredients, receptors in the user's skin are chemically triggered, rendering distinct haptic sensations. We identified five chemicals that can render lasting haptic sensations: tingling (sanshool), numbing (lidocaine), stinging (cinnamaldehyde), warming (capsaicin), and cooling (menthol). To enable the application of our novel approach in a variety of settings (such as VR), we engineered a self-contained wearable that can be worn anywhere on the user's skin (e.g., face, arms, legs). Implemented as a soft silicone patch, our device uses micropumps to push the liquid stimulants through channels that are open to the user's skin, enabling topical stimulants to be absorbed by the skin as they pass through. Our approach presents two unique benefits. First, it enables sensations, such as numbing, not possible with existing haptic devices. Second, our approach offers a new pathway, via the skin's chemical receptors, for achieving multiple haptic sensations using a single actuator, which would otherwise require combining multiple actuators (e.g., Peltier, vibration motors, electro-tactile stimulation). We evaluated our approach by means of two studies. In our first study, we characterized the temporal profiles of sensations elicited by each chemical. Using these insights, we designed five interactive VR experiences utilizing chemical haptics, and in our second user study, participants rated these VR experiences with chemical haptics as more immersive than without. Finally, as the first work exploring the use of chemical haptics on the skin, we offer recommendations to designers for how they may employ our approach for their interactive experiences.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":" 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120828237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Will Brackenbury, A. Mcnutt, K. Chard, Aaron J. Elmore, Blase Ur
Users face many challenges in keeping their personal file collections organized. While current file-management interfaces help users retrieve files in disorganized repositories, they do not aid in organization. Pertinent files can be difficult to find, and files that should have been deleted may remain. To help, we designed KondoCloud, a file-browser interface for personal cloud storage. KondoCloud makes machine learning-based recommendations of files users may want to retrieve, move, or delete. These recommendations leverage the intuition that similar files should be managed similarly. We developed and evaluated KondoCloud through two complementary online user studies. In our Observation Study, we logged the actions of 69 participants who spent 30 minutes manually organizing their own Google Drive repositories. We identified high-level organizational strategies, including moving related files to newly created sub-folders and extensively deleting files. To train the classifiers that underpin KondoCloud’s recommendations, we had participants label whether pairs of files were similar and whether they should be managed similarly. In addition, we extracted ten metadata and content features from all files in participants’ repositories. Our logistic regression classifiers all achieved F1 scores of 0.72 or higher. In our Evaluation Study, 62 participants used KondoCloud either with or without recommendations. Roughly half of participants accepted a non-trivial fraction of recommendations, and some participants accepted nearly all of them. Participants who were shown the recommendations were more likely to delete related files located in different directories. They also generally felt the recommendations improved efficiency. Participants who were not shown recommendations nonetheless manually performed about a third of the actions that would have been recommended.
{"title":"KondoCloud: Improving Information Management in Cloud Storage via Recommendations Based on File Similarity","authors":"Will Brackenbury, A. Mcnutt, K. Chard, Aaron J. Elmore, Blase Ur","doi":"10.1145/3472749.3474736","DOIUrl":"https://doi.org/10.1145/3472749.3474736","url":null,"abstract":"Users face many challenges in keeping their personal file collections organized. While current file-management interfaces help users retrieve files in disorganized repositories, they do not aid in organization. Pertinent files can be difficult to find, and files that should have been deleted may remain. To help, we designed KondoCloud, a file-browser interface for personal cloud storage. KondoCloud makes machine learning-based recommendations of files users may want to retrieve, move, or delete. These recommendations leverage the intuition that similar files should be managed similarly. We developed and evaluated KondoCloud through two complementary online user studies. In our Observation Study, we logged the actions of 69 participants who spent 30 minutes manually organizing their own Google Drive repositories. We identified high-level organizational strategies, including moving related files to newly created sub-folders and extensively deleting files. To train the classifiers that underpin KondoCloud’s recommendations, we had participants label whether pairs of files were similar and whether they should be managed similarly. In addition, we extracted ten metadata and content features from all files in participants’ repositories. Our logistic regression classifiers all achieved F1 scores of 0.72 or higher. In our Evaluation Study, 62 participants used KondoCloud either with or without recommendations. Roughly half of participants accepted a non-trivial fraction of recommendations, and some participants accepted nearly all of them. Participants who were shown the recommendations were more likely to delete related files located in different directories. They also generally felt the recommendations improved efficiency. Participants who were not shown recommendations nonetheless manually performed about a third of the actions that would have been recommended.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114210390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuzhou Zhuang, Yuntao Wang, Yukang Yan, Xuhai Xu, Yuanchun Shi
3D position tracking on smartphones has the potential to unlock a variety of novel applications, but has not been made widely available due to limitations in smartphone sensors. In this paper, we propose ReflecTrack, a novel 3D acoustic position tracking method for commodity dual-microphone smartphones. A ubiquitous speaker (e.g., smartwatch or earbud) generates inaudible Frequency Modulated Continuous Wave (FMCW) acoustic signals that are picked up by both smartphone microphones. To enable 3D tracking with two microphones, we introduce a reflective surface that can be easily found in everyday objects near the smartphone. Thus, the microphones can receive sound from the speaker and echoes from the surface for FMCW-based acoustic ranging. To simultaneously estimate the distances from the direct and reflective paths, we propose the echo-aware FMCW technique with a new signal pattern and target detection process. Our user study shows that ReflecTrack achieves a median error of 28.4 mm in the 60cm × 60cm × 60cm space and 22.1 mm in the 30cm × 30cm × 30cm space for 3D positioning. We demonstrate the easy accessibility of ReflecTrack using everyday surfaces and objects with several typical applications of 3D position tracking, including 3D input for smartphones, fine-grained gesture recognition, and motion tracking in smartphone-based VR systems.
{"title":"ReflecTrack: Enabling 3D Acoustic Position Tracking Using Commodity Dual-Microphone Smartphones","authors":"Yuzhou Zhuang, Yuntao Wang, Yukang Yan, Xuhai Xu, Yuanchun Shi","doi":"10.1145/3472749.3474805","DOIUrl":"https://doi.org/10.1145/3472749.3474805","url":null,"abstract":"3D position tracking on smartphones has the potential to unlock a variety of novel applications, but has not been made widely available due to limitations in smartphone sensors. In this paper, we propose ReflecTrack, a novel 3D acoustic position tracking method for commodity dual-microphone smartphones. A ubiquitous speaker (e.g., smartwatch or earbud) generates inaudible Frequency Modulated Continuous Wave (FMCW) acoustic signals that are picked up by both smartphone microphones. To enable 3D tracking with two microphones, we introduce a reflective surface that can be easily found in everyday objects near the smartphone. Thus, the microphones can receive sound from the speaker and echoes from the surface for FMCW-based acoustic ranging. To simultaneously estimate the distances from the direct and reflective paths, we propose the echo-aware FMCW technique with a new signal pattern and target detection process. Our user study shows that ReflecTrack achieves a median error of 28.4 mm in the 60cm × 60cm × 60cm space and 22.1 mm in the 30cm × 30cm × 30cm space for 3D positioning. We demonstrate the easy accessibility of ReflecTrack using everyday surfaces and objects with several typical applications of 3D position tracking, including 3D input for smartphones, fine-grained gesture recognition, and motion tracking in smartphone-based VR systems.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121501993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the increasing complexity and scale of people’s online activities, browser interfaces have stayed largely the same since tabs were introduced in major browsers nearly 20 years ago. The gap between simple tab-based browser interfaces and the complexity of users’ tasks can lead to serious adverse effects – commonly referred to as “tab overload.” This paper introduces a Chrome extension called Tabs.do, which explores bringing a task-centric approach to the browser, helping users to group their tabs into tasks and then organize, prioritize, and switch between those tasks fluidly. To lower the cost of importing, Tabs.do uses machine learning to make intelligent suggestions for grouping users’ open tabs into task bundles by exploiting behavioral and semantic features. We conducted a field deployment study where participants used Tabs.do with their real-life tasks in the wild, and showed that Tabs.do can decrease tab clutter, enabled users to create rich task structures with lightweight interactions, and allowed participants to context-switch among tasks more efficiently.
{"title":"Tabs.do: Task-Centric Browser Tab Management","authors":"Joseph Chee Chang","doi":"10.1145/3472749.3474777","DOIUrl":"https://doi.org/10.1145/3472749.3474777","url":null,"abstract":"Despite the increasing complexity and scale of people’s online activities, browser interfaces have stayed largely the same since tabs were introduced in major browsers nearly 20 years ago. The gap between simple tab-based browser interfaces and the complexity of users’ tasks can lead to serious adverse effects – commonly referred to as “tab overload.” This paper introduces a Chrome extension called Tabs.do, which explores bringing a task-centric approach to the browser, helping users to group their tabs into tasks and then organize, prioritize, and switch between those tasks fluidly. To lower the cost of importing, Tabs.do uses machine learning to make intelligent suggestions for grouping users’ open tabs into task bundles by exploiting behavioral and semantic features. We conducted a field deployment study where participants used Tabs.do with their real-life tasks in the wild, and showed that Tabs.do can decrease tab clutter, enabled users to create rich task structures with lightweight interactions, and allowed participants to context-switch among tasks more efficiently.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124052455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolai Marquardt, Nathalie Henry Riche, Christian Holz, Hugo Romat, M. Pahud, Frederik Brudy, David Ledo, Chunjong Park, M. Nicholas, T. Seyed, E. Ofek, Bongshin Lee, W. Buxton, K. Hinckley
AirConstellations supports a unique semi-fixed style of cross-device interactions via multiple self-spatially-aware armatures to which users can easily attach (or detach) tablets and other devices. In particular, AirConstellations affords highly flexible and dynamic device formations where the users can bring multiple devices together in-air — with 2–5 armatures poseable in 7DoF within the same workspace — to suit the demands of their current task, social situation, app scenario, or mobility needs. This affords an interaction metaphor where relative orientation, proximity, attaching (or detaching) devices, and continuous movement into and out of ad-hoc ensembles can drive context-sensitive interactions. Yet all devices remain self-stable in useful configurations even when released in mid-air. We explore flexible physical arrangement, feedforward of transition options, and layering of devices in-air across a variety of multi-device app scenarios. These include video conferencing with flexible arrangement of the person-space of multiple remote participants around a shared task-space, layered and tiled device formations with overview+detail and shared-to-personal transitions, and flexible composition of UI panels and tool palettes across devices for productivity applications. A preliminary interview study highlights user reactions to AirConstellations, such as for minimally disruptive device formations, easier physical transitions, and balancing ”seeing and being seen” in remote work.
{"title":"AirConstellations: In-Air Device Formations for Cross-Device Interaction via Multiple Spatially-Aware Armatures","authors":"Nicolai Marquardt, Nathalie Henry Riche, Christian Holz, Hugo Romat, M. Pahud, Frederik Brudy, David Ledo, Chunjong Park, M. Nicholas, T. Seyed, E. Ofek, Bongshin Lee, W. Buxton, K. Hinckley","doi":"10.1145/3472749.3474820","DOIUrl":"https://doi.org/10.1145/3472749.3474820","url":null,"abstract":"AirConstellations supports a unique semi-fixed style of cross-device interactions via multiple self-spatially-aware armatures to which users can easily attach (or detach) tablets and other devices. In particular, AirConstellations affords highly flexible and dynamic device formations where the users can bring multiple devices together in-air — with 2–5 armatures poseable in 7DoF within the same workspace — to suit the demands of their current task, social situation, app scenario, or mobility needs. This affords an interaction metaphor where relative orientation, proximity, attaching (or detaching) devices, and continuous movement into and out of ad-hoc ensembles can drive context-sensitive interactions. Yet all devices remain self-stable in useful configurations even when released in mid-air. We explore flexible physical arrangement, feedforward of transition options, and layering of devices in-air across a variety of multi-device app scenarios. These include video conferencing with flexible arrangement of the person-space of multiple remote participants around a shared task-space, layered and tiled device formations with overview+detail and shared-to-personal transitions, and flexible composition of UI panels and tool palettes across devices for productivity applications. A preliminary interview study highlights user reactions to AirConstellations, such as for minimally disruptive device formations, easier physical transitions, and balancing ”seeing and being seen” in remote work.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127644791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Bethge, T. Kosch, L. Chuang, Albrecht Schmidt
Detecting emotions while driving remains a challenge in Human-Computer Interaction. Current methods to estimate the driver’s experienced emotions use physiological sensing (e.g., skin-conductance, electroencephalography), speech, or facial expressions. However, drivers need to use wearable devices, perform explicit voice interaction, or require robust facial expressiveness. We present VEmotion (Virtual Emotion Sensor), a novel method to predict driver emotions in an unobtrusive way using contextual smartphone data. VEmotion analyzes information including traffic dynamics, environmental factors, in-vehicle context, and road characteristics to implicitly classify driver emotions. We demonstrate the applicability in a real-world driving study (N = 12) to evaluate the emotion prediction performance. Our results show that VEmotion outperforms facial expressions by 29% in a person-dependent classification and by 8.5% in a person-independent classification. We discuss how VEmotion enables empathic car interfaces to sense the driver’s emotions and will provide in-situ interface adaptations on-the-go.
{"title":"VEmotion: Using Driving Context for Indirect Emotion Prediction in Real-Time","authors":"David Bethge, T. Kosch, L. Chuang, Albrecht Schmidt","doi":"10.1145/3472749.3474775","DOIUrl":"https://doi.org/10.1145/3472749.3474775","url":null,"abstract":"Detecting emotions while driving remains a challenge in Human-Computer Interaction. Current methods to estimate the driver’s experienced emotions use physiological sensing (e.g., skin-conductance, electroencephalography), speech, or facial expressions. However, drivers need to use wearable devices, perform explicit voice interaction, or require robust facial expressiveness. We present VEmotion (Virtual Emotion Sensor), a novel method to predict driver emotions in an unobtrusive way using contextual smartphone data. VEmotion analyzes information including traffic dynamics, environmental factors, in-vehicle context, and road characteristics to implicitly classify driver emotions. We demonstrate the applicability in a real-world driving study (N = 12) to evaluate the emotion prediction performance. Our results show that VEmotion outperforms facial expressions by 29% in a person-dependent classification and by 8.5% in a person-independent classification. We discuss how VEmotion enables empathic car interfaces to sense the driver’s emotions and will provide in-situ interface adaptations on-the-go.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"41 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128487501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge bases, such as Google knowledge graph, contain millions of entities (people, places, etc.) and billions of facts about them. While much is known about entities, little is known about the actions these entities relate to. On the other hand, the Web has lots of information about human tasks. A website for restaurant reservations, for example, implicitly knows about various restaurant-related actions (making reservations, delivering food, etc.), the inputs these actions require and their expected output; it can also be automated to execute those actions. To harvest action knowledge from websites, we propose Etna. Users demonstrate how to accomplish various tasks in a website, and Etna constructs an action-state model of the website visualized as an action graph. An action graph includes definitions of tasks and actions, knowledge about their start/end states, and execution scripts for their automation. We report on our experience in building action-state models of many commercial websites and use cases that leveraged them.
{"title":"Etna: Harvesting Action Graphs from Websites","authors":"Oriana Riva, Jason Kace","doi":"10.1145/3472749.3474752","DOIUrl":"https://doi.org/10.1145/3472749.3474752","url":null,"abstract":"Knowledge bases, such as Google knowledge graph, contain millions of entities (people, places, etc.) and billions of facts about them. While much is known about entities, little is known about the actions these entities relate to. On the other hand, the Web has lots of information about human tasks. A website for restaurant reservations, for example, implicitly knows about various restaurant-related actions (making reservations, delivering food, etc.), the inputs these actions require and their expected output; it can also be automated to execute those actions. To harvest action knowledge from websites, we propose Etna. Users demonstrate how to accomplish various tasks in a website, and Etna constructs an action-state model of the website visualized as an action graph. An action graph includes definitions of tasks and actions, knowledge about their start/end states, and execution scripts for their automation. We report on our experience in building action-state models of many commercial websites and use cases that leveraged them.","PeriodicalId":209178,"journal":{"name":"The 34th Annual ACM Symposium on User Interface Software and Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130974218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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