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81.
Hartholt, Arno; Mozgai, Sharon
Creating Virtual Worlds with the Virtual Human Toolkit and the Rapid Integration & Development Environment Proceedings Article
In: Intelligent Human Systems Integration (IHSI 2023): Integrating People and Intelligent Systems, AHFE Open Acces, 2023, ISBN: 978-1-958651-45-2, (ISSN: 27710718 Issue: 69).
@inproceedings{hartholt_creating_2023,
title = {Creating Virtual Worlds with the Virtual Human Toolkit and the Rapid Integration & Development Environment},
author = {Arno Hartholt and Sharon Mozgai},
url = {https://openaccess.cms-conferences.org/publications/book/978-1-958651-45-2/article/978-1-958651-45-2_41},
doi = {10.54941/ahfe1002856},
isbn = {978-1-958651-45-2},
year = {2023},
date = {2023-01-01},
urldate = {2023-03-31},
booktitle = {Intelligent Human Systems Integration (IHSI 2023): Integrating People and Intelligent Systems},
volume = {69},
publisher = {AHFE Open Acces},
abstract = {The research and development of virtual humans, and the virtual worlds they inhabit, is inherently complex, requiring interdisciplinary approaches that combine social sciences, computer science, design, art, production, and domain expertise. Our previous work in managing this complexity has resulted in the release of the Virtual Human Toolkit (VHToolkit), aimed at lowering the burden of creating embodied conversational agents. In our current efforts, we are integrating the VHToolkit with the Rapid Integration & Development Environment (RIDE), a rapid prototyping modeling and simulation middleware platform that leverages real-time game engines. This integration results in the ability to mix and match commercial AI services from AWS, Azure, and Google, as well as leverage novel 3D geospatial terrain creation pipelines. Combined with dedicated authoring tools that have been developed through human-centered design processes, the platform enables researchers, developers, and domain experts to rapidly create digital worlds with virtual humans for both military and civilian contexts. Our approach is highly interdisciplinary, including academia, government, and industry collaborators. The demonstration shows a user interacting with an embodied conversational agent embedded within real-world captured and virtualized terrain. Further research and development features of the platform are shown, including scripted agent behaviors, networked team play, and machine learning interfaces.},
note = {ISSN: 27710718
Issue: 69},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The research and development of virtual humans, and the virtual worlds they inhabit, is inherently complex, requiring interdisciplinary approaches that combine social sciences, computer science, design, art, production, and domain expertise. Our previous work in managing this complexity has resulted in the release of the Virtual Human Toolkit (VHToolkit), aimed at lowering the burden of creating embodied conversational agents. In our current efforts, we are integrating the VHToolkit with the Rapid Integration & Development Environment (RIDE), a rapid prototyping modeling and simulation middleware platform that leverages real-time game engines. This integration results in the ability to mix and match commercial AI services from AWS, Azure, and Google, as well as leverage novel 3D geospatial terrain creation pipelines. Combined with dedicated authoring tools that have been developed through human-centered design processes, the platform enables researchers, developers, and domain experts to rapidly create digital worlds with virtual humans for both military and civilian contexts. Our approach is highly interdisciplinary, including academia, government, and industry collaborators. The demonstration shows a user interacting with an embodied conversational agent embedded within real-world captured and virtualized terrain. Further research and development features of the platform are shown, including scripted agent behaviors, networked team play, and machine learning interfaces.
82.
Vlake, Johan H.; Bommel, Jasper; Riva, Giuseppe; Wiederhold, Brenda K.; Cipresso, Pietro; Rizzo, Albert Skip; Botella, Cristina; Hooft, Lotty; Bienvenu, O. Joseph; Geerts, Bart; Wils, Evert-Jan; Gommers, Diederik; Genderen, Michel E.
Reporting the early stage clinical evaluation of virtual-reality-based intervention trials: RATE-VR Journal Article
In: Nat Med, vol. 29, no. 1, pp. 12–13, 2023, ISSN: 1546-170X, (Number: 1 Publisher: Nature Publishing Group).
@article{vlake_reporting_2023,
title = {Reporting the early stage clinical evaluation of virtual-reality-based intervention trials: RATE-VR},
author = {Johan H. Vlake and Jasper Bommel and Giuseppe Riva and Brenda K. Wiederhold and Pietro Cipresso and Albert Skip Rizzo and Cristina Botella and Lotty Hooft and O. Joseph Bienvenu and Bart Geerts and Evert-Jan Wils and Diederik Gommers and Michel E. Genderen},
url = {https://www.nature.com/articles/s41591-022-02085-7},
doi = {10.1038/s41591-022-02085-7},
issn = {1546-170X},
year = {2023},
date = {2023-01-01},
urldate = {2023-03-31},
journal = {Nat Med},
volume = {29},
number = {1},
pages = {12–13},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
83.
Chawla, Kushal; Clever, Rene; Ramirez, Jaysa; Lucas, Gale M.; Gratch, Jonathan
Towards Emotion-Aware Agents for Improved User Satisfaction and Partner Perception in Negotiation Dialogues Journal Article
In: IEEE Transactions on Affective Computing, pp. 1–12, 2023, ISSN: 1949-3045, (Conference Name: IEEE Transactions on Affective Computing).
@article{chawla_towards_2023,
title = {Towards Emotion-Aware Agents for Improved User Satisfaction and Partner Perception in Negotiation Dialogues},
author = {Kushal Chawla and Rene Clever and Jaysa Ramirez and Gale M. Lucas and Jonathan Gratch},
url = {https://ieeexplore.ieee.org/abstract/document/10021626},
doi = {10.1109/TAFFC.2023.3238007},
issn = {1949-3045},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Affective Computing},
pages = {1–12},
abstract = {Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans by the means of language are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the role of emotion in the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. We devise ways to measure and compare different degrees of emotion expression in negotiation dialogues, consisting of emoticon, lexical, and contextual variables. Through an extensive analysis of a large-scale dataset in chat-based negotiations, we find that incorporating emotion expression explains significantly more variance, above and beyond the demographics and personality traits of the participants. Further, our temporal analysis reveals that emotive information from both early and later stages of the negotiation contributes to this prediction, indicating the need for a continual learning model of capturing emotion for automated agents. Finally, we extend our analysis to another dataset, showing promise that our findings generalize to more complex scenarios. We conclude by discussing our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces.},
note = {Conference Name: IEEE Transactions on Affective Computing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans by the means of language are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the role of emotion in the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. We devise ways to measure and compare different degrees of emotion expression in negotiation dialogues, consisting of emoticon, lexical, and contextual variables. Through an extensive analysis of a large-scale dataset in chat-based negotiations, we find that incorporating emotion expression explains significantly more variance, above and beyond the demographics and personality traits of the participants. Further, our temporal analysis reveals that emotive information from both early and later stages of the negotiation contributes to this prediction, indicating the need for a continual learning model of capturing emotion for automated agents. Finally, we extend our analysis to another dataset, showing promise that our findings generalize to more complex scenarios. We conclude by discussing our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces.
84.
Lei, Su; Gratch, Jonathan
Emotional Expressivity is a Reliable Signal of Surprise Journal Article
In: IEEE Transactions on Affective Computing, pp. 1–12, 2023, ISSN: 1949-3045, (Conference Name: IEEE Transactions on Affective Computing).
@article{lei_emotional_2023,
title = {Emotional Expressivity is a Reliable Signal of Surprise},
author = {Su Lei and Jonathan Gratch},
doi = {10.1109/TAFFC.2023.3234015},
issn = {1949-3045},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Affective Computing},
pages = {1–12},
abstract = {We consider the problem of inferring what happened to a person in a social task from momentary facial reactions. To approach this, we introduce several innovations. First, rather than predicting what (observers think) someone feels, we predict objective features of the event that immediately preceded the facial reactions. Second, we draw on appraisal theory, a key psychological theory of emotion, to characterize features of this immediately-preceded event. Specifically, we explore if facial expressions reveal if the event is expected, goal-congruent, and norm-compatible. Finally, we argue that emotional expressivity serves as a better feature for characterizing momentary expressions than traditional facial features. Specifically, we use supervised machine learning to predict third-party judgments of emotional expressivity with high accuracy, and show this model improves inferences about the nature of the event that preceded an emotional reaction. Contrary to common sense, “genuine smiles” failed to predict if an event advanced a person's goals. Rather, expressions best revealed if an event violated expectations. We discussed the implications of these findings for the interpretation of facial displays and potential limitations that could impact the generality of these findings.},
note = {Conference Name: IEEE Transactions on Affective Computing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of inferring what happened to a person in a social task from momentary facial reactions. To approach this, we introduce several innovations. First, rather than predicting what (observers think) someone feels, we predict objective features of the event that immediately preceded the facial reactions. Second, we draw on appraisal theory, a key psychological theory of emotion, to characterize features of this immediately-preceded event. Specifically, we explore if facial expressions reveal if the event is expected, goal-congruent, and norm-compatible. Finally, we argue that emotional expressivity serves as a better feature for characterizing momentary expressions than traditional facial features. Specifically, we use supervised machine learning to predict third-party judgments of emotional expressivity with high accuracy, and show this model improves inferences about the nature of the event that preceded an emotional reaction. Contrary to common sense, “genuine smiles” failed to predict if an event advanced a person's goals. Rather, expressions best revealed if an event violated expectations. We discussed the implications of these findings for the interpretation of facial displays and potential limitations that could impact the generality of these findings.
85.
Lucas, Gale M.; Mell, Johnathan; Boberg, Jill; Zenone, Forrest; Visser, Ewart J.; Tossell, Chad; Seech, Todd
Customizing virtual interpersonal skills training applications may not improve trainee performance Journal Article
In: Sci Rep, vol. 13, no. 1, pp. 78, 2023, ISSN: 2045-2322, (Number: 1 Publisher: Nature Publishing Group).
@article{lucas_customizing_2023,
title = {Customizing virtual interpersonal skills training applications may not improve trainee performance},
author = {Gale M. Lucas and Johnathan Mell and Jill Boberg and Forrest Zenone and Ewart J. Visser and Chad Tossell and Todd Seech},
url = {https://www.nature.com/articles/s41598-022-27154-2},
doi = {10.1038/s41598-022-27154-2},
issn = {2045-2322},
year = {2023},
date = {2023-01-01},
urldate = {2023-03-31},
journal = {Sci Rep},
volume = {13},
number = {1},
pages = {78},
abstract = {While some theoretical perspectives imply that the context of a virtual training should be customized to match the intended context where those skills would ultimately be applied, others suggest this might not be necessary for learning. It is important to determine whether manipulating context matters for performance in training applications because customized virtual training systems made for specific use cases are more costly than generic “off-the-shelf” ones designed for a broader set of users. Accordingly, we report a study where military cadets use a virtual platform to practice their negotiation skills, and are randomly assigned to one of two virtual context conditions: military versus civilian. Out of 28 measures capturing performance in the negotiation, there was only one significant result: cadets in the civilian condition politely ask the agent to make an offer significantly more than those in the military condition. These results imply that—for this interpersonal skills application, and perhaps ones like it—virtual context may matter very little for performance during social skills training, and that commercial systems may yield real benefits to military scenarios with little-to-no modification.},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
While some theoretical perspectives imply that the context of a virtual training should be customized to match the intended context where those skills would ultimately be applied, others suggest this might not be necessary for learning. It is important to determine whether manipulating context matters for performance in training applications because customized virtual training systems made for specific use cases are more costly than generic “off-the-shelf” ones designed for a broader set of users. Accordingly, we report a study where military cadets use a virtual platform to practice their negotiation skills, and are randomly assigned to one of two virtual context conditions: military versus civilian. Out of 28 measures capturing performance in the negotiation, there was only one significant result: cadets in the civilian condition politely ask the agent to make an offer significantly more than those in the military condition. These results imply that—for this interpersonal skills application, and perhaps ones like it—virtual context may matter very little for performance during social skills training, and that commercial systems may yield real benefits to military scenarios with little-to-no modification.
86.
Adami, Pooya; Singh, Rashmi; Rodrigues, Patrick Borges; Becerik-Gerber, Burcin; Soibelman, Lucio; Copur-Gencturk, Yasemin; Lucas, Gale
In: Advanced Engineering Informatics, vol. 55, pp. 101837, 2023, ISSN: 1474-0346.
@article{adami_participants_2023,
title = {Participants matter: Effectiveness of VR-based training on the knowledge, trust in the robot, and self-efficacy of construction workers and university students},
author = {Pooya Adami and Rashmi Singh and Patrick Borges Rodrigues and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale Lucas},
url = {https://www.sciencedirect.com/science/article/pii/S1474034622002956},
doi = {10.1016/j.aei.2022.101837},
issn = {1474-0346},
year = {2023},
date = {2023-01-01},
urldate = {2023-03-31},
journal = {Advanced Engineering Informatics},
volume = {55},
pages = {101837},
abstract = {Virtual Reality (VR)-based training has gained attention from the scientific community in the Architecture, Engineering, and Construction (AEC) industry as a cost-effective and safe method that eliminates the safety risks that may impose on workers during the training compared to traditional training methods (e.g., in-person hands-on training, apprenticeship). Although researchers have developed VR-based training for construction workers, some have recruited students rather than workers to understand the effect of their VR-based training. However, students are different from construction workers in many ways, which can threaten the validity of such studies. Hence, research is needed to investigate the extent to which the findings of a VR-based training study are contingent on whether students or construction workers were used as the study sample. This paper strives to compare the effectiveness of VR-based training on university students’ and construction workers’ knowledge acquisition, trust in the robot, and robot operation self-efficacy in remote operation of a construction robot. Twenty-five construction workers and twenty-five graduate construction engineering students were recruited to complete a VR-based training for remote operating a demolition robot. We used quantitative analyses to answer our research questions. Our study shows that the results are dependent on the target sample in that students gained more knowledge, whereas construction workers gained more trust in the robot and more self-efficacy in robot operation. These findings suggest that the effectiveness of VR-based training on students may not necessarily associate with its effectiveness on construction workers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Virtual Reality (VR)-based training has gained attention from the scientific community in the Architecture, Engineering, and Construction (AEC) industry as a cost-effective and safe method that eliminates the safety risks that may impose on workers during the training compared to traditional training methods (e.g., in-person hands-on training, apprenticeship). Although researchers have developed VR-based training for construction workers, some have recruited students rather than workers to understand the effect of their VR-based training. However, students are different from construction workers in many ways, which can threaten the validity of such studies. Hence, research is needed to investigate the extent to which the findings of a VR-based training study are contingent on whether students or construction workers were used as the study sample. This paper strives to compare the effectiveness of VR-based training on university students’ and construction workers’ knowledge acquisition, trust in the robot, and robot operation self-efficacy in remote operation of a construction robot. Twenty-five construction workers and twenty-five graduate construction engineering students were recruited to complete a VR-based training for remote operating a demolition robot. We used quantitative analyses to answer our research questions. Our study shows that the results are dependent on the target sample in that students gained more knowledge, whereas construction workers gained more trust in the robot and more self-efficacy in robot operation. These findings suggest that the effectiveness of VR-based training on students may not necessarily associate with its effectiveness on construction workers.
87.
Harvey, Philip D.; Depp, Colin A.; Rizzo, Albert A.; Strauss, Gregory P.; Spelber, David; Carpenter, Linda L.; Kalin, Ned H.; Krystal, John H.; McDonald, William M.; Nemeroff, Charles B.; Rodriguez, Carolyn I.; Widge, Alik S.; Torous, John
Technology and Mental Health: State of the Art for Assessment and Treatment Journal Article
In: AJP, vol. 179, no. 12, pp. 897–914, 2022, ISSN: 0002-953X, 1535-7228.
@article{harvey_technology_2022,
title = {Technology and Mental Health: State of the Art for Assessment and Treatment},
author = {Philip D. Harvey and Colin A. Depp and Albert A. Rizzo and Gregory P. Strauss and David Spelber and Linda L. Carpenter and Ned H. Kalin and John H. Krystal and William M. McDonald and Charles B. Nemeroff and Carolyn I. Rodriguez and Alik S. Widge and John Torous},
url = {http://ajp.psychiatryonline.org/doi/10.1176/appi.ajp.21121254},
doi = {10.1176/appi.ajp.21121254},
issn = {0002-953X, 1535-7228},
year = {2022},
date = {2022-12-01},
urldate = {2023-08-22},
journal = {AJP},
volume = {179},
number = {12},
pages = {897–914},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
88.
Maihofer, Adam X.; Engchuan, Worrawat; Huguet, Guillaume; Klein, Marieke; MacDonald, Jeffrey R.; Shanta, Omar; Thiruvahindrapuram, Bhooma; Jean-louis, Martineau; Saci, Zohra; Jacquemont, Sebastien; Scherer, Stephen W.; Ketema, Elizabeth; Aiello, Allison E.; Amstadter, Ananda B.; Avdibegović, Esmina; Babic, Dragan; Baker, Dewleen G.; Bisson, Jonathan I.; Boks, Marco P.; Bolger, Elizabeth A.; Bryant, Richard A.; Bustamante, Angela C.; Caldas-de-Almeida, Jose Miguel; Cardoso, Graça; Deckert, Jurgen; Delahanty, Douglas L.; Domschke, Katharina; Dunlop, Boadie W.; Dzubur-Kulenovic, Alma; Evans, Alexandra; Feeny, Norah C.; Franz, Carol E.; Gautam, Aarti; Geuze, Elbert; Goci, Aferdita; Hammamieh, Rasha; Jakovljevic, Miro; Jett, Marti; Jones, Ian; Kaufman, Milissa L.; Kessler, Ronald C.; King, Anthony P.; Kremen, William S.; Lawford, Bruce R.; Lebois, Lauren A. M.; Lewis, Catrin; Liberzon, Israel; Linnstaedt, Sarah D.; Lugonja, Bozo; Luykx, Jurjen J.; Lyons, Michael J.; Mavissakalian, Matig R.; McLaughlin, Katie A.; McLean, Samuel A.; Mehta, Divya; Mellor, Rebecca; Morris, Charles Phillip; Muhie, Seid; Orcutt, Holly K.; Peverill, Matthew; Ratanatharathorn, Andrew; Risbrough, Victoria B.; Rizzo, Albert; Roberts, Andrea L.; Rothbaum, Alex O.; Rothbaum, Barbara O.; Roy-Byrne, Peter; Ruggiero, Kenneth J.; Rutten, Bart P. F.; Schijven, Dick; Seng, Julia S.; Sheerin, Christina M.; Sorenson, Michael A.; Teicher, Martin H.; Uddin, Monica; Ursano, Robert J.; Vinkers, Christiaan H.; Voisey, Joanne; Weber, Heike; Winternitz, Sherry; Xavier, Miguel; Yang, Ruoting; Young, Ross McD; Zoellner, Lori A.; Salem, Rany M.; Shaffer, Richard A.; Wu, Tianying; Ressler, Kerry J.; Stein, Murray B.; Koenen, Karestan C.; Sebat, Jonathan; Nievergelt, Caroline M.
Rare copy number variation in posttraumatic stress disorder Journal Article
In: Mol Psychiatry, vol. 27, no. 12, pp. 5062–5069, 2022, ISSN: 1476-5578, (Number: 12 Publisher: Nature Publishing Group).
@article{maihofer_rare_2022,
title = {Rare copy number variation in posttraumatic stress disorder},
author = {Adam X. Maihofer and Worrawat Engchuan and Guillaume Huguet and Marieke Klein and Jeffrey R. MacDonald and Omar Shanta and Bhooma Thiruvahindrapuram and Martineau Jean-louis and Zohra Saci and Sebastien Jacquemont and Stephen W. Scherer and Elizabeth Ketema and Allison E. Aiello and Ananda B. Amstadter and Esmina Avdibegović and Dragan Babic and Dewleen G. Baker and Jonathan I. Bisson and Marco P. Boks and Elizabeth A. Bolger and Richard A. Bryant and Angela C. Bustamante and Jose Miguel Caldas-de-Almeida and Graça Cardoso and Jurgen Deckert and Douglas L. Delahanty and Katharina Domschke and Boadie W. Dunlop and Alma Dzubur-Kulenovic and Alexandra Evans and Norah C. Feeny and Carol E. Franz and Aarti Gautam and Elbert Geuze and Aferdita Goci and Rasha Hammamieh and Miro Jakovljevic and Marti Jett and Ian Jones and Milissa L. Kaufman and Ronald C. Kessler and Anthony P. King and William S. Kremen and Bruce R. Lawford and Lauren A. M. Lebois and Catrin Lewis and Israel Liberzon and Sarah D. Linnstaedt and Bozo Lugonja and Jurjen J. Luykx and Michael J. Lyons and Matig R. Mavissakalian and Katie A. McLaughlin and Samuel A. McLean and Divya Mehta and Rebecca Mellor and Charles Phillip Morris and Seid Muhie and Holly K. Orcutt and Matthew Peverill and Andrew Ratanatharathorn and Victoria B. Risbrough and Albert Rizzo and Andrea L. Roberts and Alex O. Rothbaum and Barbara O. Rothbaum and Peter Roy-Byrne and Kenneth J. Ruggiero and Bart P. F. Rutten and Dick Schijven and Julia S. Seng and Christina M. Sheerin and Michael A. Sorenson and Martin H. Teicher and Monica Uddin and Robert J. Ursano and Christiaan H. Vinkers and Joanne Voisey and Heike Weber and Sherry Winternitz and Miguel Xavier and Ruoting Yang and Ross McD Young and Lori A. Zoellner and Rany M. Salem and Richard A. Shaffer and Tianying Wu and Kerry J. Ressler and Murray B. Stein and Karestan C. Koenen and Jonathan Sebat and Caroline M. Nievergelt},
url = {https://www.nature.com/articles/s41380-022-01776-4},
doi = {10.1038/s41380-022-01776-4},
issn = {1476-5578},
year = {2022},
date = {2022-12-01},
urldate = {2023-03-31},
journal = {Mol Psychiatry},
volume = {27},
number = {12},
pages = {5062–5069},
abstract = {Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24–71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029},
note = {Number: 12
Publisher: Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24–71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029
89.
Becerik-Gerber, Burcin; Lucas, Gale; Aryal, Ashrant; Awada, Mohamad; Bergés, Mario; Billington, Sarah; Boric-Lubecke, Olga; Ghahramani, Ali; Heydarian, Arsalan; Höelscher, Christoph; Jazizadeh, Farrokh; Khan, Azam; Langevin, Jared; Liu, Ruying; Marks, Frederick; Mauriello, Matthew Louis; Murnane, Elizabeth; Noh, Haeyoung; Pritoni, Marco; Roll, Shawn; Schaumann, Davide; Seyedrezaei, Mirmahdi; Taylor, John E.; Zhao, Jie; Zhu, Runhe
The field of human building interaction for convergent research and innovation for intelligent built environments Journal Article
In: Sci Rep, vol. 12, no. 1, pp. 22092, 2022, ISSN: 2045-2322, (Number: 1 Publisher: Nature Publishing Group).
@article{becerik-gerber_field_2022,
title = {The field of human building interaction for convergent research and innovation for intelligent built environments},
author = {Burcin Becerik-Gerber and Gale Lucas and Ashrant Aryal and Mohamad Awada and Mario Bergés and Sarah Billington and Olga Boric-Lubecke and Ali Ghahramani and Arsalan Heydarian and Christoph Höelscher and Farrokh Jazizadeh and Azam Khan and Jared Langevin and Ruying Liu and Frederick Marks and Matthew Louis Mauriello and Elizabeth Murnane and Haeyoung Noh and Marco Pritoni and Shawn Roll and Davide Schaumann and Mirmahdi Seyedrezaei and John E. Taylor and Jie Zhao and Runhe Zhu},
url = {https://www.nature.com/articles/s41598-022-25047-y},
doi = {10.1038/s41598-022-25047-y},
issn = {2045-2322},
year = {2022},
date = {2022-12-01},
urldate = {2023-03-31},
journal = {Sci Rep},
volume = {12},
number = {1},
pages = {22092},
abstract = {Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.
90.
Becerik-Gerber, Burçin; Lucas, Gale; Aryal, Ashrant; Awada, Mohamad; Bergés, Mario; Billington, Sarah L; Boric-Lubecke, Olga; Ghahramani, Ali; Heydarian, Arsalan; Jazizadeh, Farrokh; Liu, Ruying; Zhu, Runhe; Marks, Frederick; Roll, Shawn; Seyedrezaei, Mirmahdi; Taylor, John E.; Höelscher, Christoph; Khan, Azam; Langevin, Jared; Mauriello, Matthew Louis; Murnane, Elizabeth; Noh, Haeyoung; Pritoni, Marco; Schaumann, Davide; Zhao, Jie
Ten questions concerning human-building interaction research for improving the quality of life Journal Article
In: Building and Environment, vol. 226, pp. 109681, 2022, ISSN: 0360-1323.
@article{becerik-gerber_ten_2022,
title = {Ten questions concerning human-building interaction research for improving the quality of life},
author = {Burçin Becerik-Gerber and Gale Lucas and Ashrant Aryal and Mohamad Awada and Mario Bergés and Sarah L Billington and Olga Boric-Lubecke and Ali Ghahramani and Arsalan Heydarian and Farrokh Jazizadeh and Ruying Liu and Runhe Zhu and Frederick Marks and Shawn Roll and Mirmahdi Seyedrezaei and John E. Taylor and Christoph Höelscher and Azam Khan and Jared Langevin and Matthew Louis Mauriello and Elizabeth Murnane and Haeyoung Noh and Marco Pritoni and Davide Schaumann and Jie Zhao},
url = {https://www.sciencedirect.com/science/article/pii/S0360132322009118},
doi = {10.1016/j.buildenv.2022.109681},
issn = {0360-1323},
year = {2022},
date = {2022-12-01},
urldate = {2023-03-31},
journal = {Building and Environment},
volume = {226},
pages = {109681},
abstract = {This paper seeks to address ten questions that explore the burgeoning field of Human-Building Interaction (HBI), an interdisciplinary field that represents the next frontier in convergent research and innovation to enable the dynamic interplay of human and building interactional intelligence. The field of HBI builds on several existing efforts in historically separate research fields/communities and aims to understand how buildings affect human outcomes and experiences, as well as how humans interact with, adapt to, and affect the built environment and its systems, to support buildings that can learn, enable adaptation, and evolve at different scales to improve the quality-of-life of its users while optimizing resource usage and service availability. Questions were developed by a diverse group of researchers with backgrounds in design, engineering, computer science, social science, and health science. Answers to these questions draw conclusions from what has been achieved to date as reported in the available literature and establish a foundation for future HBI research. This paper aims to encourage interdisciplinary collaborations in HBI research to change the way people interact with and perceive technology within the context of buildings and inform the design, construction, and operation of next-generation, intelligent built environments. In doing so, HBI research can realize a myriad of benefits for human users, including improved productivity, health, cognition, convenience, and comfort, all of which are essential to societal well-being.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper seeks to address ten questions that explore the burgeoning field of Human-Building Interaction (HBI), an interdisciplinary field that represents the next frontier in convergent research and innovation to enable the dynamic interplay of human and building interactional intelligence. The field of HBI builds on several existing efforts in historically separate research fields/communities and aims to understand how buildings affect human outcomes and experiences, as well as how humans interact with, adapt to, and affect the built environment and its systems, to support buildings that can learn, enable adaptation, and evolve at different scales to improve the quality-of-life of its users while optimizing resource usage and service availability. Questions were developed by a diverse group of researchers with backgrounds in design, engineering, computer science, social science, and health science. Answers to these questions draw conclusions from what has been achieved to date as reported in the available literature and establish a foundation for future HBI research. This paper aims to encourage interdisciplinary collaborations in HBI research to change the way people interact with and perceive technology within the context of buildings and inform the design, construction, and operation of next-generation, intelligent built environments. In doing so, HBI research can realize a myriad of benefits for human users, including improved productivity, health, cognition, convenience, and comfort, all of which are essential to societal well-being.
91.
Zhu, Runhe; Lucas, Gale M.; Becerik-Gerber, Burcin; Southers, Erroll G.; Landicho, Earl
The impact of security countermeasures on human behavior during active shooter incidents Journal Article
In: Sci Rep, vol. 12, no. 1, pp. 929, 2022, ISSN: 2045-2322.
@article{zhu_impact_2022,
title = {The impact of security countermeasures on human behavior during active shooter incidents},
author = {Runhe Zhu and Gale M. Lucas and Burcin Becerik-Gerber and Erroll G. Southers and Earl Landicho},
url = {https://www.nature.com/articles/s41598-022-04922-8},
doi = {10.1038/s41598-022-04922-8},
issn = {2045-2322},
year = {2022},
date = {2022-12-01},
urldate = {2022-09-26},
journal = {Sci Rep},
volume = {12},
number = {1},
pages = {929},
abstract = {Abstract Active shooter incidents represent an increasing threat to American society, especially in commercial and educational buildings. In recent years, a wide variety of security countermeasures have been recommended by public and governmental agencies. Many of these countermeasures are aimed to increase building security, yet their impact on human behavior when an active shooter incident occurs remains underexplored. To fill this research gap, we conducted virtual experiments to evaluate the impact of countermeasures on human behavior during active shooter incidents. A total of 162 office workers and middle/high school teachers were recruited to respond to an active shooter incident in virtual office and school buildings with or without the implementation of multiple countermeasures. The experiment results showed countermeasures significantly influenced participants’ response time and decisions (e.g., run, hide, fight). Participants’ responses and perceptions of the active shooter incident were also contingent on their daily roles, as well as building and social contexts. Teachers had more concerns for occupants’ safety than office workers. Moreover, teachers had more positive perceptions of occupants in the school, whereas office workers had more positive perceptions of occupants in the office.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Active shooter incidents represent an increasing threat to American society, especially in commercial and educational buildings. In recent years, a wide variety of security countermeasures have been recommended by public and governmental agencies. Many of these countermeasures are aimed to increase building security, yet their impact on human behavior when an active shooter incident occurs remains underexplored. To fill this research gap, we conducted virtual experiments to evaluate the impact of countermeasures on human behavior during active shooter incidents. A total of 162 office workers and middle/high school teachers were recruited to respond to an active shooter incident in virtual office and school buildings with or without the implementation of multiple countermeasures. The experiment results showed countermeasures significantly influenced participants’ response time and decisions (e.g., run, hide, fight). Participants’ responses and perceptions of the active shooter incident were also contingent on their daily roles, as well as building and social contexts. Teachers had more concerns for occupants’ safety than office workers. Moreover, teachers had more positive perceptions of occupants in the school, whereas office workers had more positive perceptions of occupants in the office.
92.
Difede, JoAnn; Rothbaum, Barbara O.; Rizzo, Albert A.; Wyka, Katarzyna; Spielman, Lisa; Reist, Christopher; Roy, Michael J.; Jovanovic, Tanja; Norrholm, Seth D.; Cukor, Judith; Olden, Megan; Glatt, Charles E.; Lee, Francis S.
In: Transl Psychiatry, vol. 12, no. 1, pp. 299, 2022, ISSN: 2158-3188.
@article{difede_enhancing_2022,
title = {Enhancing exposure therapy for posttraumatic stress disorder (PTSD): a randomized clinical trial of virtual reality and imaginal exposure with a cognitive enhancer},
author = {JoAnn Difede and Barbara O. Rothbaum and Albert A. Rizzo and Katarzyna Wyka and Lisa Spielman and Christopher Reist and Michael J. Roy and Tanja Jovanovic and Seth D. Norrholm and Judith Cukor and Megan Olden and Charles E. Glatt and Francis S. Lee},
url = {https://www.nature.com/articles/s41398-022-02066-x},
doi = {10.1038/s41398-022-02066-x},
issn = {2158-3188},
year = {2022},
date = {2022-12-01},
urldate = {2022-09-13},
journal = {Transl Psychiatry},
volume = {12},
number = {1},
pages = {299},
abstract = {Abstract Posttraumatic stress disorder (PTSD) is a significant public health issue. Yet, there are limited treatment options and no data to suggest which treatment will work for whom. We tested the efficacy of virtual reality exposure (VRE) or prolonged imaginal exposure (PE), augmented with D-cycloserine (DCS) for combat-related PTSD. As an exploratory aim, we examined whether brain-derived neurotrophic factor (BDNF) and fatty acid amide hydrolase (FAAH) moderated treatment response. Military personnel with PTSD ( n = 192) were recruited into a multisite double-blind randomized controlled trial to receive nine weeks of VRE or PE, with DCS or placebo. Primary outcome was the improvement in symptom severity. Randomization was stratified by comorbid depression (MDD) and site. Participants in both VRE and PE showed similar meaningful clinical improvement with no difference between the treatment groups. A significant interaction ( p = 0.45) suggested VRE was more effective for depressed participants (CAPS difference M = 3.51 [95% CI 1.17–5.86]},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Posttraumatic stress disorder (PTSD) is a significant public health issue. Yet, there are limited treatment options and no data to suggest which treatment will work for whom. We tested the efficacy of virtual reality exposure (VRE) or prolonged imaginal exposure (PE), augmented with D-cycloserine (DCS) for combat-related PTSD. As an exploratory aim, we examined whether brain-derived neurotrophic factor (BDNF) and fatty acid amide hydrolase (FAAH) moderated treatment response. Military personnel with PTSD ( n = 192) were recruited into a multisite double-blind randomized controlled trial to receive nine weeks of VRE or PE, with DCS or placebo. Primary outcome was the improvement in symptom severity. Randomization was stratified by comorbid depression (MDD) and site. Participants in both VRE and PE showed similar meaningful clinical improvement with no difference between the treatment groups. A significant interaction ( p = 0.45) suggested VRE was more effective for depressed participants (CAPS difference M = 3.51 [95% CI 1.17–5.86]
93.
Feng, Andrew; Shin, Samuel; Yoon, Youngwoo
A Tool for Extracting 3D Avatar-Ready Gesture Animations from Monocular Videos Proceedings Article
In: Proceedings of the 15th ACM SIGGRAPH Conference on Motion, Interaction and Games, pp. 1–7, ACM, Guanajuato Mexico, 2022, ISBN: 978-1-4503-9888-6.
@inproceedings{feng_tool_2022,
title = {A Tool for Extracting 3D Avatar-Ready Gesture Animations from Monocular Videos},
author = {Andrew Feng and Samuel Shin and Youngwoo Yoon},
url = {https://dl.acm.org/doi/10.1145/3561975.3562953},
doi = {10.1145/3561975.3562953},
isbn = {978-1-4503-9888-6},
year = {2022},
date = {2022-11-01},
urldate = {2023-08-04},
booktitle = {Proceedings of the 15th ACM SIGGRAPH Conference on Motion, Interaction and Games},
pages = {1–7},
publisher = {ACM},
address = {Guanajuato Mexico},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
94.
Lu, Shuhong; Feng, Andrew
The DeepMotion entry to the GENEA Challenge 2022 Proceedings Article
In: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, pp. 790–796, ACM, Bengaluru India, 2022, ISBN: 978-1-4503-9390-4.
@inproceedings{lu_deepmotion_2022,
title = {The DeepMotion entry to the GENEA Challenge 2022},
author = {Shuhong Lu and Andrew Feng},
url = {https://dl.acm.org/doi/10.1145/3536221.3558059},
doi = {10.1145/3536221.3558059},
isbn = {978-1-4503-9390-4},
year = {2022},
date = {2022-11-01},
urldate = {2023-08-24},
booktitle = {INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION},
pages = {790–796},
publisher = {ACM},
address = {Bengaluru India},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
95.
Yin, Yufeng; Xu, Jiashu; Zu, Tianxin; Soleymani, Mohammad
X-Norm: Exchanging Normalization Parameters for Bimodal Fusion Proceedings Article
In: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, pp. 605–614, ACM, Bengaluru India, 2022, ISBN: 978-1-4503-9390-4.
@inproceedings{yin_x-norm_2022,
title = {X-Norm: Exchanging Normalization Parameters for Bimodal Fusion},
author = {Yufeng Yin and Jiashu Xu and Tianxin Zu and Mohammad Soleymani},
url = {https://dl.acm.org/doi/10.1145/3536221.3556581},
doi = {10.1145/3536221.3556581},
isbn = {978-1-4503-9390-4},
year = {2022},
date = {2022-11-01},
urldate = {2023-08-24},
booktitle = {INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION},
pages = {605–614},
publisher = {ACM},
address = {Bengaluru India},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
96.
Hartholt, Arno; Mozgai, Sharon
Platforms and Tools for SIA Research and Development Book Section
In: The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, Application, vol. 48, pp. 261–304, Association for Computing Machinery, New York, NY, USA, 2022, ISBN: 978-1-4503-9896-1.
@incollection{hartholt_platforms_2022,
title = {Platforms and Tools for SIA Research and Development},
author = {Arno Hartholt and Sharon Mozgai},
url = {https://doi.org/10.1145/3563659.3563668},
isbn = {978-1-4503-9896-1},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
booktitle = {The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, Application},
volume = {48},
pages = {261–304},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
edition = {1},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
97.
Traum, David
Socially Interactive Agent Dialogue Book Section
In: The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, Application, vol. 48, pp. 45–76, Association for Computing Machinery, New York, NY, USA, 2022, ISBN: 978-1-4503-9896-1.
@incollection{traum_socially_2022,
title = {Socially Interactive Agent Dialogue},
author = {David Traum},
url = {https://doi.org/10.1145/3563659.3563663},
isbn = {978-1-4503-9896-1},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
booktitle = {The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, Application},
volume = {48},
pages = {45–76},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
edition = {1},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
98.
Lugrin, Birgit; Pelachaud, Catherine; André, Elisabeth; Aylett, Ruth; Bickmore, Timothy; Breazeal, Cynthia; Broekens, Joost; Dautenhahn, Kerstin; Gratch, Jonathan; Kopp, Stefan; Nadel, Jacqueline; Paiva, Ana; Wykowska, Agnieszka
In: The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, Application, vol. 48, pp. 561–626, Association for Computing Machinery, New York, NY, USA, 2022, ISBN: 978-1-4503-9896-1.
@incollection{lugrin_challenge_2022,
title = {Challenge Discussion on Socially Interactive Agents: Considerations on Social Interaction, Computational Architectures, Evaluation, and Ethics},
author = {Birgit Lugrin and Catherine Pelachaud and Elisabeth André and Ruth Aylett and Timothy Bickmore and Cynthia Breazeal and Joost Broekens and Kerstin Dautenhahn and Jonathan Gratch and Stefan Kopp and Jacqueline Nadel and Ana Paiva and Agnieszka Wykowska},
url = {https://doi.org/10.1145/3563659.3563677},
isbn = {978-1-4503-9896-1},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
booktitle = {The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, Application},
volume = {48},
pages = {561–626},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
edition = {1},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
99.
Liu, Shichen; Cai, Yunxuan; Chen, Haiwei; Zhou, Yichao; Zhao, Yajie
Rapid Face Asset Acquisition with Recurrent Feature Alignment Journal Article
In: ACM Trans. Graph., vol. 41, no. 6, pp. 214:1–214:17, 2022, ISSN: 0730-0301.
@article{liu_rapid_2022,
title = {Rapid Face Asset Acquisition with Recurrent Feature Alignment},
author = {Shichen Liu and Yunxuan Cai and Haiwei Chen and Yichao Zhou and Yajie Zhao},
url = {https://dl.acm.org/doi/10.1145/3550454.3555509},
doi = {10.1145/3550454.3555509},
issn = {0730-0301},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {41},
number = {6},
pages = {214:1–214:17},
abstract = {We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.
100.
Pauw, Lisanne S.; Sauter, Disa A.; Kleef, Gerben A.; Lucas, Gale M.; Gratch, Jonathan; Fischer, Agneta H.
The avatar will see you now: Support from a virtual human provides socio-emotional benefits Journal Article
In: Computers in Human Behavior, vol. 136, pp. 107368, 2022, ISSN: 07475632.
@article{pauw_avatar_2022,
title = {The avatar will see you now: Support from a virtual human provides socio-emotional benefits},
author = {Lisanne S. Pauw and Disa A. Sauter and Gerben A. Kleef and Gale M. Lucas and Jonathan Gratch and Agneta H. Fischer},
url = {https://linkinghub.elsevier.com/retrieve/pii/S074756322200190X},
doi = {10.1016/j.chb.2022.107368},
issn = {07475632},
year = {2022},
date = {2022-11-01},
urldate = {2022-09-28},
journal = {Computers in Human Behavior},
volume = {136},
pages = {107368},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Filter
2021
Chaffey, Patricia; Traum, David
Identity models for role-play dialogue characters Proceedings Article
In: 2021.
Links | BibTeX | Tags: Dialogue, DTIC, UARC
@inproceedings{chaffey_identity_2021,
title = {Identity models for role-play dialogue characters},
author = {Patricia Chaffey and David Traum},
url = {http://semdial.org/anthology/papers/Z/Z21/Z21-4022/},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
keywords = {Dialogue, DTIC, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Lugrin, Birgit; Pelachaud, Catherine; Traum, David (Ed.)
1, ACM, New York, NY, USA, 2021, ISBN: 978-1-4503-8720-0.
Links | BibTeX | Tags: Dialogue, Virtual Humans
@book{lugrin_handbook_2021,
title = {The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, Cognition},
editor = {Birgit Lugrin and Catherine Pelachaud and David Traum},
url = {https://dl.acm.org/doi/book/10.1145/3477322},
doi = {10.1145/3477322},
isbn = {978-1-4503-8720-0},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
publisher = {ACM},
address = {New York, NY, USA},
edition = {1},
keywords = {Dialogue, Virtual Humans},
pubstate = {published},
tppubtype = {book}
}
Yin, Yufeng; Lu, Liupei; Xiao, Yao; Xu, Zhi; Cai, Kaijie; Jiang, Haonan; Gratch, Jonathan; Soleymani, Mohammad
Contrastive Learning for Domain Transfer in Cross-Corpus Emotion Recognition Proceedings Article
In: 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 1–8, IEEE, Nara, Japan, 2021, ISBN: 978-1-66540-019-0.
Links | BibTeX | Tags: DTIC, Emotions, Virtual Humans
@inproceedings{yin_contrastive_2021,
title = {Contrastive Learning for Domain Transfer in Cross-Corpus Emotion Recognition},
author = {Yufeng Yin and Liupei Lu and Yao Xiao and Zhi Xu and Kaijie Cai and Haonan Jiang and Jonathan Gratch and Mohammad Soleymani},
url = {https://ieeexplore.ieee.org/document/9597453/},
doi = {10.1109/ACII52823.2021.9597453},
isbn = {978-1-66540-019-0},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-23},
booktitle = {2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)},
pages = {1–8},
publisher = {IEEE},
address = {Nara, Japan},
keywords = {DTIC, Emotions, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Awada, Mohamad; Lucas, Gale; Becerik-Gerber, Burcin; Roll, Shawn
Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience Journal Article
In: WOR, vol. 69, no. 4, pp. 1171–1189, 2021, ISSN: 10519815, 18759270.
Abstract | Links | BibTeX | Tags: DTIC, UARC
@article{awada_working_2021,
title = {Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience},
author = {Mohamad Awada and Gale Lucas and Burcin Becerik-Gerber and Shawn Roll},
url = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/WOR-210301},
doi = {10.3233/WOR-210301},
issn = {10519815, 18759270},
year = {2021},
date = {2021-08-01},
urldate = {2022-09-26},
journal = {WOR},
volume = {69},
number = {4},
pages = {1171–1189},
abstract = {BACKGROUND: With the COVID-19 pandemic, organizations embraced Work From Home (WFH). An important component of transitioning to WFH is the effect on workers, particularly related to their productivity and work experience. OBJECTIVES: The objective of this study is to examine how worker-, workspace-, and work-related factors affected productivity and time spent at a workstation on a typical WFH day during the pandemic. METHODS: An online questionnaire was designed and administered to collect the necessary information. Data from 988 respondents were included in the analyses. RESULTS: Overall perception of productivity level among workers did not change relative to their in-office productivity before the pandemic. Female, older, and high-income workers were likely to report increased productivity. Productivity was positively influenced by better mental and physical health statuses, having a teenager, increased communication with coworkers and having a dedicated room for work. Number of hours spent at a workstation increased by approximately 1.5 hours during a typical WFH day. Longer hours were reported by individuals who had school age children, owned an office desk or an adjustable chair, and had adjusted their work hours. CONCLUSION: The findings highlight key factors for employers and employees to consider for improving the WFH experience.},
keywords = {DTIC, UARC},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND: With the COVID-19 pandemic, organizations embraced Work From Home (WFH). An important component of transitioning to WFH is the effect on workers, particularly related to their productivity and work experience. OBJECTIVES: The objective of this study is to examine how worker-, workspace-, and work-related factors affected productivity and time spent at a workstation on a typical WFH day during the pandemic. METHODS: An online questionnaire was designed and administered to collect the necessary information. Data from 988 respondents were included in the analyses. RESULTS: Overall perception of productivity level among workers did not change relative to their in-office productivity before the pandemic. Female, older, and high-income workers were likely to report increased productivity. Productivity was positively influenced by better mental and physical health statuses, having a teenager, increased communication with coworkers and having a dedicated room for work. Number of hours spent at a workstation increased by approximately 1.5 hours during a typical WFH day. Longer hours were reported by individuals who had school age children, owned an office desk or an adjustable chair, and had adjusted their work hours. CONCLUSION: The findings highlight key factors for employers and employees to consider for improving the WFH experience.
Kennedy, Alana A. U.; Thacker, Ian; Nye, Benjamin D.; Sinatra, Gale M.; Swartout, William; Lindsey, Emily
Promoting interest, positive emotions, and knowledge using augmented reality in a museum setting Journal Article
In: International Journal of Science Education, Part B, vol. 11, no. 3, pp. 242–258, 2021, ISSN: 2154-8455, (Publisher: Routledge _eprint: https://doi.org/10.1080/21548455.2021.1946619).
Abstract | Links | BibTeX | Tags: AR, Learning Sciences, UARC
@article{kennedy_promoting_2021,
title = {Promoting interest, positive emotions, and knowledge using augmented reality in a museum setting},
author = {Alana A. U. Kennedy and Ian Thacker and Benjamin D. Nye and Gale M. Sinatra and William Swartout and Emily Lindsey},
url = {https://doi.org/10.1080/21548455.2021.1946619},
doi = {10.1080/21548455.2021.1946619},
issn = {2154-8455},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {International Journal of Science Education, Part B},
volume = {11},
number = {3},
pages = {242–258},
abstract = {Informal learning environments, such as museums, provide unique opportunities for science learning. They are deliberately designed to impact public understanding of science and shape visitors’ attitudes and behaviors. As a developing technology, augmented reality (AR) offers the transformative potential to support museums’ educational missions by enhancing visitors’ experience, thereby creating effective conditions for learning and personalized interactions with science. We implemented an AR-enhanced exhibit at the La Brea Tar Pits (LBTP) to reduce scientific misconceptions and explore the role of interest and emotions around science and AR technology as it related to learning and knowledge revision. Using a pretest-posttest design, 62 adults completed an AR experience that addressed two scientific misconceptions related to the consistency of tar and frequency of large animal entrapment. We found that participants had significantly fewer misconceptions at posttest than at pretest. Participants also reported higher levels of interest in science content than AR technology and discriminated between emotions they experienced with regard to science content and AR technology. Feelings of curiosity predicted knowledge revision and interest in both science content and AR technology. These findings may be useful for museums and other science communicators seeking to create AR interventions that support learning and conceptual change.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/21548455.2021.1946619},
keywords = {AR, Learning Sciences, UARC},
pubstate = {published},
tppubtype = {article}
}
Informal learning environments, such as museums, provide unique opportunities for science learning. They are deliberately designed to impact public understanding of science and shape visitors’ attitudes and behaviors. As a developing technology, augmented reality (AR) offers the transformative potential to support museums’ educational missions by enhancing visitors’ experience, thereby creating effective conditions for learning and personalized interactions with science. We implemented an AR-enhanced exhibit at the La Brea Tar Pits (LBTP) to reduce scientific misconceptions and explore the role of interest and emotions around science and AR technology as it related to learning and knowledge revision. Using a pretest-posttest design, 62 adults completed an AR experience that addressed two scientific misconceptions related to the consistency of tar and frequency of large animal entrapment. We found that participants had significantly fewer misconceptions at posttest than at pretest. Participants also reported higher levels of interest in science content than AR technology and discriminated between emotions they experienced with regard to science content and AR technology. Feelings of curiosity predicted knowledge revision and interest in both science content and AR technology. These findings may be useful for museums and other science communicators seeking to create AR interventions that support learning and conceptual change.
Rizzo, Albert “Skip”; Hartholt, Arno; Mozgai, Sharon
From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality Journal Article
In: Journal of Technology in Human Services, vol. 39, no. 3, pp. 314–347, 2021, ISSN: 1522-8835, (Publisher: Routledge _eprint: https://doi.org/10.1080/15228835.2021.1915931).
Abstract | Links | BibTeX | Tags: MedVR, UARC, VHTL, Virtual Humans
@article{rizzo_combat_2021,
title = {From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality},
author = {Albert “Skip” Rizzo and Arno Hartholt and Sharon Mozgai},
url = {https://doi.org/10.1080/15228835.2021.1915931},
doi = {10.1080/15228835.2021.1915931},
issn = {1522-8835},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {Journal of Technology in Human Services},
volume = {39},
number = {3},
pages = {314–347},
abstract = {Research has documented the efficacy of clinical applications that leverage Virtual Reality (VR) for assessment and treatment purposes across a wide range of domains, including pain, phobias, and posttraumatic stress disorder (PTSD). As the field of Clinical VR matures, it is important to review its origins and examine how these initial explorations have progressed, what gaps remain, and what opportunities the community can pursue. We do this by reflecting on our personal scientific journey against the backdrop of the field in general. In particular, this paper discusses how a clinical research program that was initially designed to deliver trauma-focused VR exposure therapy (VRET) for combat-related PTSD has been evolved to expand its impact and address a wider range of trauma sources. Such trauma sources include sexual trauma and the needs of first responders and healthcare professionals serving on the frontlines of the COVID-19 pandemic. We provide an overview of the field and its general trends, discuss the genesis of our research agenda and its current status, and summarize upcoming opportunities, together with common challenges and lessons learned.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/15228835.2021.1915931},
keywords = {MedVR, UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Research has documented the efficacy of clinical applications that leverage Virtual Reality (VR) for assessment and treatment purposes across a wide range of domains, including pain, phobias, and posttraumatic stress disorder (PTSD). As the field of Clinical VR matures, it is important to review its origins and examine how these initial explorations have progressed, what gaps remain, and what opportunities the community can pursue. We do this by reflecting on our personal scientific journey against the backdrop of the field in general. In particular, this paper discusses how a clinical research program that was initially designed to deliver trauma-focused VR exposure therapy (VRET) for combat-related PTSD has been evolved to expand its impact and address a wider range of trauma sources. Such trauma sources include sexual trauma and the needs of first responders and healthcare professionals serving on the frontlines of the COVID-19 pandemic. We provide an overview of the field and its general trends, discuss the genesis of our research agenda and its current status, and summarize upcoming opportunities, together with common challenges and lessons learned.
Stocco, Andrea; Sibert, Catherine; Steine-Hanson, Zoe; Koh, Natalie; Laird, John E.; Lebiere, Christian J.; Rosenbloom, Paul
Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains Journal Article
In: NeuroImage, vol. 235, pp. 118035, 2021, ISSN: 10538119.
@article{stocco_analysis_2021-1,
title = {Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains},
author = {Andrea Stocco and Catherine Sibert and Zoe Steine-Hanson and Natalie Koh and John E. Laird and Christian J. Lebiere and Paul Rosenbloom},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921003128},
doi = {10.1016/j.neuroimage.2021.118035},
issn = {10538119},
year = {2021},
date = {2021-07-01},
urldate = {2021-04-30},
journal = {NeuroImage},
volume = {235},
pages = {118035},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dukes, Daniel; Abrams, Kathryn; Adolphs, Ralph; Ahmed, Mohammed E.; Beatty, Andrew; Berridge, Kent C.; Broomhall, Susan; Brosch, Tobias; Campos, Joseph J.; Clay, Zanna; Clément, Fabrice; Cunningham, William A.; Damasio, Antonio; Damasio, Hanna; D’Arms, Justin; Davidson, Jane W.; Gelder, Beatrice; Deonna, Julien; Sousa, Ronnie; Ekman, Paul; Ellsworth, Phoebe C.; Fehr, Ernst; Fischer, Agneta; Foolen, Ad; Frevert, Ute; Grandjean, Didier; Gratch, Jonathan; Greenberg, Leslie; Greenspan, Patricia; Gross, James J.; Halperin, Eran; Kappas, Arvid; Keltner, Dacher; Knutson, Brian; Konstan, David; Kret, Mariska E.; LeDoux, Joseph E.; Lerner, Jennifer S.; Levenson, Robert W.; Loewenstein, George; Manstead, Antony S. R.; Maroney, Terry A.; Moors, Agnes; Niedenthal, Paula; Parkinson, Brian; Pavlidis, Ioannis; Pelachaud, Catherine; Pollak, Seth D.; Pourtois, Gilles; Roettger-Roessler, Birgitt; Russell, James A.; Sauter, Disa; Scarantino, Andrea; Scherer, Klaus R.; Stearns, Peter; Stets, Jan E.; Tappolet, Christine; Teroni, Fabrice; Tsai, Jeanne; Turner, Jonathan; Reekum, Carien Van; Vuilleumier, Patrik; Wharton, Tim; Sander, David
The rise of affectivism Journal Article
In: Nat Hum Behav, vol. 5, no. 7, pp. 816–820, 2021, ISSN: 2397-3374.
Links | BibTeX | Tags: Emotions
@article{dukes_rise_2021,
title = {The rise of affectivism},
author = {Daniel Dukes and Kathryn Abrams and Ralph Adolphs and Mohammed E. Ahmed and Andrew Beatty and Kent C. Berridge and Susan Broomhall and Tobias Brosch and Joseph J. Campos and Zanna Clay and Fabrice Clément and William A. Cunningham and Antonio Damasio and Hanna Damasio and Justin D’Arms and Jane W. Davidson and Beatrice Gelder and Julien Deonna and Ronnie Sousa and Paul Ekman and Phoebe C. Ellsworth and Ernst Fehr and Agneta Fischer and Ad Foolen and Ute Frevert and Didier Grandjean and Jonathan Gratch and Leslie Greenberg and Patricia Greenspan and James J. Gross and Eran Halperin and Arvid Kappas and Dacher Keltner and Brian Knutson and David Konstan and Mariska E. Kret and Joseph E. LeDoux and Jennifer S. Lerner and Robert W. Levenson and George Loewenstein and Antony S. R. Manstead and Terry A. Maroney and Agnes Moors and Paula Niedenthal and Brian Parkinson and Ioannis Pavlidis and Catherine Pelachaud and Seth D. Pollak and Gilles Pourtois and Birgitt Roettger-Roessler and James A. Russell and Disa Sauter and Andrea Scarantino and Klaus R. Scherer and Peter Stearns and Jan E. Stets and Christine Tappolet and Fabrice Teroni and Jeanne Tsai and Jonathan Turner and Carien Van Reekum and Patrik Vuilleumier and Tim Wharton and David Sander},
url = {http://www.nature.com/articles/s41562-021-01130-8},
doi = {10.1038/s41562-021-01130-8},
issn = {2397-3374},
year = {2021},
date = {2021-07-01},
urldate = {2022-09-28},
journal = {Nat Hum Behav},
volume = {5},
number = {7},
pages = {816–820},
keywords = {Emotions},
pubstate = {published},
tppubtype = {article}
}
Stocco, Andrea; Sibert, Catherine; Steine-Hanson, Zoe; Koh, Natalie; Laird, John E.; Lebiere, Christian J.; Rosenbloom, Paul
Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains Journal Article
In: NeuroImage, vol. 235, pp. 118035, 2021, ISSN: 10538119.
@article{stocco_analysis_2021,
title = {Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains},
author = {Andrea Stocco and Catherine Sibert and Zoe Steine-Hanson and Natalie Koh and John E. Laird and Christian J. Lebiere and Paul Rosenbloom},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921003128},
doi = {10.1016/j.neuroimage.2021.118035},
issn = {10538119},
year = {2021},
date = {2021-07-01},
urldate = {2021-05-06},
journal = {NeuroImage},
volume = {235},
pages = {118035},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Xiang, Sitao
Eliminating topological errors in neural network rotation estimation using self-selecting ensembles Journal Article
In: ACM Trans. Graph., vol. 40, no. 4, pp. 167:1–167:21, 2021, ISSN: 0730-0301.
Abstract | Links | BibTeX | Tags: VGL
@article{xiang_eliminating_2021,
title = {Eliminating topological errors in neural network rotation estimation using self-selecting ensembles},
author = {Sitao Xiang},
url = {https://dl.acm.org/doi/10.1145/3450626.3459882},
doi = {10.1145/3450626.3459882},
issn = {0730-0301},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {40},
number = {4},
pages = {167:1–167:21},
abstract = {Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
The Human-Interpreter Problem in Youth Encounters with AI Journal Article
In: Proceedings of the 15th International Conference of the Learning Sciences, pp. 1107–1108, 2021, (Publisher: International Society of the Learning Sciences).
Abstract | Links | BibTeX | Tags: AI, UARC
@article{greenwald_human-interpreter_2021,
title = {The Human-Interpreter Problem in Youth Encounters with AI},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://repository.isls.org//handle/1/7421},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Proceedings of the 15th International Conference of the Learning Sciences},
pages = {1107–1108},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.},
note = {Publisher: International Society of the Learning Sciences},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.
Holder, Eric; Wang, Ning
Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst Journal Article
In: Hum.-Intell. Syst. Integr., vol. 3, no. 2, pp. 139–153, 2021, ISSN: 2524-4884.
Abstract | Links | BibTeX | Tags: AI, UARC
@article{holder_explainable_2021,
title = {Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst},
author = {Eric Holder and Ning Wang},
url = {https://doi.org/10.1007/s42454-020-00021-z},
doi = {10.1007/s42454-020-00021-z},
issn = {2524-4884},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Hum.-Intell. Syst. Integr.},
volume = {3},
number = {2},
pages = {139–153},
abstract = {There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.
Horstmann, Aike C.; Gratch, Jonathan; Krämer, Nicole C.
I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person Journal Article
In: International Journal of Human-Computer Studies, pp. 102683, 2021, ISSN: 10715819.
Abstract | Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@article{horstmann_i_2021,
title = {I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person},
author = {Aike C. Horstmann and Jonathan Gratch and Nicole C. Krämer},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1071581921001014},
doi = {10.1016/j.ijhcs.2021.102683},
issn = {10715819},
year = {2021},
date = {2021-06-01},
urldate = {2021-06-18},
journal = {International Journal of Human-Computer Studies},
pages = {102683},
abstract = {Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.
Bonial, Claire; Abrams, Mitchell; Traum, David; Voss, Clare
Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains Proceedings Article
In: Proceedings of the 14th International Conference on Computational Semantics (IWCS), pp. 173–183, Association for Computational Linguistics, Groningen, The Netherlands (online), 2021.
Abstract | Links | BibTeX | Tags: Dialogue, DTIC
@inproceedings{bonial_builder_2021,
title = {Builder, we have done it: Evaluating & Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains},
author = {Claire Bonial and Mitchell Abrams and David Traum and Clare Voss},
url = {https://aclanthology.org/2021.iwcs-1.17},
year = {2021},
date = {2021-06-01},
urldate = {2022-09-23},
booktitle = {Proceedings of the 14th International Conference on Computational Semantics (IWCS)},
pages = {173–183},
publisher = {Association for Computational Linguistics},
address = {Groningen, The Netherlands (online)},
abstract = {We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.},
keywords = {Dialogue, DTIC},
pubstate = {published},
tppubtype = {inproceedings}
}
We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into “Dialogue-AMR,” which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.
Doran, Bethany; Mei, Chaoqun; Varosy, Paul D.; Kao, David P.; Saxon, Leslie A.; Feldman, Arthur M.; DeMets, David; Bristow, Michael R.
The Addition of a Defibrillator to Resynchronization Therapy Decreases Mortality in Patients With Nonischemic Cardiomyopathy Journal Article
In: JACC: Heart Failure, vol. 9, no. 6, pp. 439–449, 2021, (Publisher: American College of Cardiology Foundation).
Links | BibTeX | Tags: MedVR, UARC
@article{doran_addition_2021,
title = {The Addition of a Defibrillator to Resynchronization Therapy Decreases Mortality in Patients With Nonischemic Cardiomyopathy},
author = {Bethany Doran and Chaoqun Mei and Paul D. Varosy and David P. Kao and Leslie A. Saxon and Arthur M. Feldman and David DeMets and Michael R. Bristow},
url = {https://www.jacc.org/doi/abs/10.1016/j.jchf.2021.02.013},
doi = {10.1016/j.jchf.2021.02.013},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {JACC: Heart Failure},
volume = {9},
number = {6},
pages = {439–449},
note = {Publisher: American College of Cardiology Foundation},
keywords = {MedVR, UARC},
pubstate = {published},
tppubtype = {article}
}
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 17, pp. 15526–15533, 2021, ISSN: 2374-3468, (Number: 17).
Abstract | Links | BibTeX | Tags: AI, UARC
@article{greenwald_learning_2021,
title = {Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17828},
doi = {10.1609/aaai.v35i17.17828},
issn = {2374-3468},
year = {2021},
date = {2021-05-01},
urldate = {2023-03-31},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {35},
number = {17},
pages = {15526–15533},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.},
note = {Number: 17},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.
Nikolovski, Janeta; Koldijk, Martin; Weverling, Gerrit Jan; Spertus, John; Turakhia, Mintu; Saxon, Leslie; Gibson, Mike; Whang, John; Sarich, Troy; Zambon, Robert; Ezeanochie, Nnamdi; Turgiss, Jennifer; Jones, Robyn; Stoddard, Jeff; Burton, Paul; Navar, Ann Marie
Factors indicating intention to vaccinate with a COVID-19 vaccine among older UṠ. adults Journal Article
In: PLOS ONE, vol. 16, no. 5, pp. e0251963, 2021, ISSN: 1932-6203, (Publisher: Public Library of Science).
Abstract | Links | BibTeX | Tags: CBC, UARC
@article{nikolovski_factors_2021,
title = {Factors indicating intention to vaccinate with a COVID-19 vaccine among older UṠ. adults},
author = {Janeta Nikolovski and Martin Koldijk and Gerrit Jan Weverling and John Spertus and Mintu Turakhia and Leslie Saxon and Mike Gibson and John Whang and Troy Sarich and Robert Zambon and Nnamdi Ezeanochie and Jennifer Turgiss and Robyn Jones and Jeff Stoddard and Paul Burton and Ann Marie Navar},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251963},
doi = {10.1371/journal.pone.0251963},
issn = {1932-6203},
year = {2021},
date = {2021-05-01},
urldate = {2023-03-31},
journal = {PLOS ONE},
volume = {16},
number = {5},
pages = {e0251963},
abstract = {Background The success of vaccination efforts to curb the COVID-19 pandemic will require broad public uptake of immunization and highlights the importance of understanding factors associated with willingness to receive a vaccine. Methods U.S. adults aged 65 and older enrolled in the HeartlineTM clinical study were invited to complete a COVID-19 vaccine assessment through the HeartlineTM mobile application between November 6–20, 2020. Factors associated with willingness to receive a COVID-19 vaccine were evaluated using an ordered logistic regression as well as a Random Forest classification algorithm. Results Among 9,106 study participants, 81.3% (n = 7402) responded and had available demographic data. The majority (91.3%) reported a willingness to be vaccinated. Factors most strongly associated with vaccine willingness were beliefs about the safety and efficacy of COVID-19 vaccines and vaccines in general. Women and Black or African American respondents reported lower willingness to vaccinate. Among those less willing to get vaccinated, 66.2% said that they would talk with their health provider before making a decision. During the study, positive results from the first COVID-19 vaccine outcome study were released; vaccine willingness increased after this report. Conclusions Even among older adults at high-risk for COVID-19 complications who are participating in a longitudinal clinical study, 1 in 11 reported lack of willingness to receive COVID-19 vaccine in November 2020. Variability in vaccine willingness by gender, race, education, and income suggests the potential for uneven vaccine uptake. Education by health providers directed toward assuaging concerns about vaccine safety and efficacy can help improve vaccine acceptance among those less willing. Trial registration Clinicaltrials.gov NCT04276441.},
note = {Publisher: Public Library of Science},
keywords = {CBC, UARC},
pubstate = {published},
tppubtype = {article}
}
Background The success of vaccination efforts to curb the COVID-19 pandemic will require broad public uptake of immunization and highlights the importance of understanding factors associated with willingness to receive a vaccine. Methods U.S. adults aged 65 and older enrolled in the HeartlineTM clinical study were invited to complete a COVID-19 vaccine assessment through the HeartlineTM mobile application between November 6–20, 2020. Factors associated with willingness to receive a COVID-19 vaccine were evaluated using an ordered logistic regression as well as a Random Forest classification algorithm. Results Among 9,106 study participants, 81.3% (n = 7402) responded and had available demographic data. The majority (91.3%) reported a willingness to be vaccinated. Factors most strongly associated with vaccine willingness were beliefs about the safety and efficacy of COVID-19 vaccines and vaccines in general. Women and Black or African American respondents reported lower willingness to vaccinate. Among those less willing to get vaccinated, 66.2% said that they would talk with their health provider before making a decision. During the study, positive results from the first COVID-19 vaccine outcome study were released; vaccine willingness increased after this report. Conclusions Even among older adults at high-risk for COVID-19 complications who are participating in a longitudinal clinical study, 1 in 11 reported lack of willingness to receive COVID-19 vaccine in November 2020. Variability in vaccine willingness by gender, race, education, and income suggests the potential for uneven vaccine uptake. Education by health providers directed toward assuaging concerns about vaccine safety and efficacy can help improve vaccine acceptance among those less willing. Trial registration Clinicaltrials.gov NCT04276441.
Nye, Benjamin D.; Sanghrajka, Rushit; Bodhwani, Vinit; Acob, Martin; Budziwojski, Daniel; Carr, Kayla; Kirshner, Larry; Swartout, William R.
OpenTutor: Designing a Rapid-Authored Tutor that Learns as you Grade Journal Article
In: The International FLAIRS Conference Proceedings, vol. 34, 2021, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Learning Sciences, UARC
@article{nye_opentutor_2021,
title = {OpenTutor: Designing a Rapid-Authored Tutor that Learns as you Grade},
author = {Benjamin D. Nye and Rushit Sanghrajka and Vinit Bodhwani and Martin Acob and Daniel Budziwojski and Kayla Carr and Larry Kirshner and William R. Swartout},
url = {https://journals.flvc.org/FLAIRS/article/view/128576},
doi = {10.32473/flairs.v34i1.128576},
issn = {2334-0762},
year = {2021},
date = {2021-04-01},
urldate = {2023-03-31},
journal = {The International FLAIRS Conference Proceedings},
volume = {34},
abstract = {Despite strong evidence that dialog-based intelligent tutoring systems (ITS) can increase learning gains, few courses include these tutors. In this research, we posit that existing dialog-based tutoring systems are not widely used because they are too complex and unfamiliar for a typical teacher to adapt or augment. OpenTutor is an open-source research project intended to scale up dialog-based tutoring by enabling ordinary teachers to rapidly author and improve dialog-based ITS, where authoring is presented through familiar tasks such as assessment item creation and grading. Formative usability results from a set of five non-CS educators are presented, which indicate that the OpenTutor system was relatively easy to use but that teachers would closely consider the cost benefit for time vs. student outcomes. Specifically, while OpenTutor grading was faster than expected, teachers reported that they would only spend any additional time (compared to a multiple choice) if the content required deeper learning. To decrease time to train answer classifiers, OpenTutor is investigating ways to reduce cold-start problems for tutoring dialogs.},
keywords = {Learning Sciences, UARC},
pubstate = {published},
tppubtype = {article}
}
Despite strong evidence that dialog-based intelligent tutoring systems (ITS) can increase learning gains, few courses include these tutors. In this research, we posit that existing dialog-based tutoring systems are not widely used because they are too complex and unfamiliar for a typical teacher to adapt or augment. OpenTutor is an open-source research project intended to scale up dialog-based tutoring by enabling ordinary teachers to rapidly author and improve dialog-based ITS, where authoring is presented through familiar tasks such as assessment item creation and grading. Formative usability results from a set of five non-CS educators are presented, which indicate that the OpenTutor system was relatively easy to use but that teachers would closely consider the cost benefit for time vs. student outcomes. Specifically, while OpenTutor grading was faster than expected, teachers reported that they would only spend any additional time (compared to a multiple choice) if the content required deeper learning. To decrease time to train answer classifiers, OpenTutor is investigating ways to reduce cold-start problems for tutoring dialogs.
Nye, Benjamin Daniel; Shiel, Aaron; Olmez, Ibrahim Burak; Mittal, Anirudh; Latta, Jason; Auerbach, Daniel; Copur-Gencturk, Yasemin
Virtual Agents for Real Teachers: Applying AI to Support Professional Development of Proportional Reasoning Journal Article
In: The International FLAIRS Conference Proceedings, vol. 34, 2021, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Learning Sciences, UARC
@article{nye_virtual_2021,
title = {Virtual Agents for Real Teachers: Applying AI to Support Professional Development of Proportional Reasoning},
author = {Benjamin Daniel Nye and Aaron Shiel and Ibrahim Burak Olmez and Anirudh Mittal and Jason Latta and Daniel Auerbach and Yasemin Copur-Gencturk},
url = {https://journals.flvc.org/FLAIRS/article/view/128574},
doi = {10.32473/flairs.v34i1.128574},
issn = {2334-0762},
year = {2021},
date = {2021-04-01},
urldate = {2023-03-31},
journal = {The International FLAIRS Conference Proceedings},
volume = {34},
abstract = {Despite the critical role of teachers in the educational process, few advanced learning technologies have been developed to support teacher-instruction or professional development. This lack of support is particularly acute for middle school math teachers, where only 37% felt well prepared to scaffold instruction to address the needs of diverse students in a national sample. To address this gap, the Advancing Middle School Teachers’ Understanding of Proportional Reasoning project is researching techniques to apply pedagogical virtual agents and dialog-based tutoring to enhance teachers' content knowledge and pedagogical content knowledge. This paper describes the design of a conversational, agent-based intelligent tutoring system to support teachers' professional development. Pedagogical strategies are presented that leverage a virtual human facilitator to tutor pedagogical content knowledge (how to teach proportions to students), as opposed to content knowledge (understanding proportions). The roles for different virtual facilitator capabilities are presented, including embedding actions into virtual agent dialog, open-response versus choice-based tutoring, ungraded pop-up sub-activities (e.g. whiteboard, calculator, note-taking). Usability feedback for a small cohort of instructors pursuing graduate studies was collected. In this feedback, teachers rated the system ease of use and perceived usefulness moderately well, but also reported confusion about what to expect from the system in terms of flow between lessons and support by the facilitator.},
keywords = {Learning Sciences, UARC},
pubstate = {published},
tppubtype = {article}
}
Despite the critical role of teachers in the educational process, few advanced learning technologies have been developed to support teacher-instruction or professional development. This lack of support is particularly acute for middle school math teachers, where only 37% felt well prepared to scaffold instruction to address the needs of diverse students in a national sample. To address this gap, the Advancing Middle School Teachers’ Understanding of Proportional Reasoning project is researching techniques to apply pedagogical virtual agents and dialog-based tutoring to enhance teachers' content knowledge and pedagogical content knowledge. This paper describes the design of a conversational, agent-based intelligent tutoring system to support teachers' professional development. Pedagogical strategies are presented that leverage a virtual human facilitator to tutor pedagogical content knowledge (how to teach proportions to students), as opposed to content knowledge (understanding proportions). The roles for different virtual facilitator capabilities are presented, including embedding actions into virtual agent dialog, open-response versus choice-based tutoring, ungraded pop-up sub-activities (e.g. whiteboard, calculator, note-taking). Usability feedback for a small cohort of instructors pursuing graduate studies was collected. In this feedback, teachers rated the system ease of use and perceived usefulness moderately well, but also reported confusion about what to expect from the system in terms of flow between lessons and support by the facilitator.
Aryal, Ashrant; Becerik-Gerber, Burcin; Lucas, Gale M.; Roll, Shawn C.
Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation Journal Article
In: IEEE Internet Things J., vol. 8, no. 8, pp. 7089–7100, 2021, ISSN: 2327-4662, 2372-2541.
@article{aryal_intelligent_2021,
title = {Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation},
author = {Ashrant Aryal and Burcin Becerik-Gerber and Gale M. Lucas and Shawn C. Roll},
url = {https://ieeexplore.ieee.org/document/9260148/},
doi = {10.1109/JIOT.2020.3038378},
issn = {2327-4662, 2372-2541},
year = {2021},
date = {2021-04-01},
urldate = {2022-10-24},
journal = {IEEE Internet Things J.},
volume = {8},
number = {8},
pages = {7089–7100},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Journal Article
In: pp. 13, 2021.
Abstract | Links | BibTeX | Tags: ARL, Dialogue, UARC, Virtual Humans
@article{gervits_classication-based_2021,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
url = {https://link.springer.com/chapter/10.1007/978-981-15-9323-9_10},
doi = {https://doi.org/10.1007/978-981-15-9323-9_10},
year = {2021},
date = {2021-03-01},
pages = {13},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {ARL, Dialogue, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multifloor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
Melo, Celso M.; Gratch, Jonathan; Krueger, Frank
Heuristic thinking and altruism toward machines in people impacted by COVID-19 Journal Article
In: iScience, vol. 24, no. 3, pp. 102228, 2021, ISSN: 25890042.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{de_melo_heuristic_2021,
title = {Heuristic thinking and altruism toward machines in people impacted by COVID-19},
author = {Celso M. Melo and Jonathan Gratch and Frank Krueger},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004221001966},
doi = {10.1016/j.isci.2021.102228},
issn = {25890042},
year = {2021},
date = {2021-03-01},
urldate = {2021-04-14},
journal = {iScience},
volume = {24},
number = {3},
pages = {102228},
abstract = {Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.
Gramlich, Michael A.; Smolenski, Derek J.; Norr, Aaron M.; Rothbaum, Barbara O.; Rizzo, Albert A.; Andrasik, Frank; Fantelli, Emily; Reger, Greg M.
In: Depression and Anxiety, pp. da.23141, 2021, ISSN: 1091-4269, 1520-6394.
Abstract | Links | BibTeX | Tags: MedVR
@article{gramlich_psychophysiology_2021,
title = {Psychophysiology during exposure to trauma memories: Comparative effects of virtual reality and imaginal exposure for posttraumatic stress disorder},
author = {Michael A. Gramlich and Derek J. Smolenski and Aaron M. Norr and Barbara O. Rothbaum and Albert A. Rizzo and Frank Andrasik and Emily Fantelli and Greg M. Reger},
url = {https://onlinelibrary.wiley.com/doi/10.1002/da.23141},
doi = {10.1002/da.23141},
issn = {1091-4269, 1520-6394},
year = {2021},
date = {2021-03-01},
urldate = {2021-04-14},
journal = {Depression and Anxiety},
pages = {da.23141},
abstract = {Background: This investigation involved an in‐depth examination of psychophysiological responses during exposure to the trauma memory across 10 sessions among active duty soldiers with combat‐related posttraumatic stress disorder (PTSD) treated by Prolonged Exposure (PE) or Virtual Reality Exposure (VRE). We compared psychophysiological changes, session‐by‐session, between VRE and traditional imaginal exposure. Methods: Heart rate (HR), galvanic skin response (GSR), and peripheral skin temperature were collected every 5 min during exposure sessions with 61 combat veterans of Iraq/Afghanistan and compared to the PTSD Checklist (PCL‐C) and Clinician‐Administered PTSD Scale (CAPS) outcomes using multilevel modeling. Results: Over the course of treatment, participants in the PE group had higher HR arousal compared to participants in the VRE group. With reference to GSR, in earlier sessions, participants demonstrated a within‐session increase, whereas, in later sessions, participants showed a within‐session habituation response. A significant interaction was found for GSR and treatment assignment for within‐session change, withinperson effect, predicting CAPS (d = 0.70) and PCL‐C (d = 0.66) outcomes. Conclusion: Overall, these findings suggest that exposure to traumatic memories activates arousal across sessions, with GSR being most associated with reductions in PTSD symptoms for participants in the PE group.},
keywords = {MedVR},
pubstate = {published},
tppubtype = {article}
}
Background: This investigation involved an in‐depth examination of psychophysiological responses during exposure to the trauma memory across 10 sessions among active duty soldiers with combat‐related posttraumatic stress disorder (PTSD) treated by Prolonged Exposure (PE) or Virtual Reality Exposure (VRE). We compared psychophysiological changes, session‐by‐session, between VRE and traditional imaginal exposure. Methods: Heart rate (HR), galvanic skin response (GSR), and peripheral skin temperature were collected every 5 min during exposure sessions with 61 combat veterans of Iraq/Afghanistan and compared to the PTSD Checklist (PCL‐C) and Clinician‐Administered PTSD Scale (CAPS) outcomes using multilevel modeling. Results: Over the course of treatment, participants in the PE group had higher HR arousal compared to participants in the VRE group. With reference to GSR, in earlier sessions, participants demonstrated a within‐session increase, whereas, in later sessions, participants showed a within‐session habituation response. A significant interaction was found for GSR and treatment assignment for within‐session change, withinperson effect, predicting CAPS (d = 0.70) and PCL‐C (d = 0.66) outcomes. Conclusion: Overall, these findings suggest that exposure to traumatic memories activates arousal across sessions, with GSR being most associated with reductions in PTSD symptoms for participants in the PE group.
Mell, Johnathan; Beissinger, Markus; Gratch, Jonathan
An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied data Journal Article
In: J Multimodal User Interfaces, 2021, ISSN: 1783-7677, 1783-8738.
Abstract | Links | BibTeX | Tags: DTIC, Machine Learning, UARC, Virtual Humans
@article{mell_expert-model_2021,
title = {An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied data},
author = {Johnathan Mell and Markus Beissinger and Jonathan Gratch},
url = {http://link.springer.com/10.1007/s12193-021-00368-w},
doi = {10.1007/s12193-021-00368-w},
issn = {1783-7677, 1783-8738},
year = {2021},
date = {2021-03-01},
urldate = {2021-04-15},
journal = {J Multimodal User Interfaces},
abstract = {We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other techniques (such as linear regression models or boosted decision trees). In a follow-up study, we show that the most successful models change as the dataset size increases and the prediction targets change, and show that boosted decision trees may not be suitable for the negotiation domain. We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation. Further, we show that this approach can be a stepping stone from purely exploratory research to targeted human-behavioral experimentation. Through our approach, areas of social artificial intelligence that have historically benefited from expert knowledge and traditional AI approaches can be combined with more recent proven-effective machine learning algorithms.},
keywords = {DTIC, Machine Learning, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other techniques (such as linear regression models or boosted decision trees). In a follow-up study, we show that the most successful models change as the dataset size increases and the prediction targets change, and show that boosted decision trees may not be suitable for the negotiation domain. We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation. Further, we show that this approach can be a stepping stone from purely exploratory research to targeted human-behavioral experimentation. Through our approach, areas of social artificial intelligence that have historically benefited from expert knowledge and traditional AI approaches can be combined with more recent proven-effective machine learning algorithms.
Melo, Celso M.; Gratch, Jonathan; Krueger, Frank
Heuristic thinking and altruism toward machines in people impacted by COVID-19 Journal Article
In: iScience, vol. 24, no. 3, pp. 102228, 2021, ISSN: 25890042.
Abstract | Links | BibTeX | Tags:
@article{de_melo_heuristic_2021-1,
title = {Heuristic thinking and altruism toward machines in people impacted by COVID-19},
author = {Celso M. Melo and Jonathan Gratch and Frank Krueger},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004221001966},
doi = {10.1016/j.isci.2021.102228},
issn = {25890042},
year = {2021},
date = {2021-03-01},
urldate = {2021-04-14},
journal = {iScience},
volume = {24},
number = {3},
pages = {102228},
abstract = {Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.
Wang, Ning; Jajodia, Aditya; Karpurapu, Abhilash; Merchant, Chirag
Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors Proceedings Article
In: Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose; Dimitrova, Vania (Ed.): Artificial Intelligence in Education, pp. 372–377, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-78270-2.
Abstract | Links | BibTeX | Tags: AI, Social Simulation, UARC
@inproceedings{wang_charisma_2021,
title = {Charisma and Learning: Designing Charismatic Behaviors for Virtual Human Tutors},
author = {Ning Wang and Aditya Jajodia and Abhilash Karpurapu and Chirag Merchant},
editor = {Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova},
url = {https://link.springer.com/chapter/10.1007/978-3-030-78270-2_66},
doi = {10.1007/978-3-030-78270-2_66},
isbn = {978-3-030-78270-2},
year = {2021},
date = {2021-01-01},
booktitle = {Artificial Intelligence in Education},
pages = {372–377},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.},
keywords = {AI, Social Simulation, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Charisma is a powerful device of communication. Research on charisma on a specific type of leader in a specific type of organization – teachers in the classroom - has indicated the positive influence of a teacher’s charismatic behaviors, often referred to as immediacy behaviors, on student learning. How do we realize such behaviors in a virtual tutor? How do such behaviors impact student learning? In this paper, we discuss the design of a charismatic virtual human tutor. We developed verbal and nonverbal (with the focus on voice) charismatic strategies and realized such strategies through scripted tutorial dialogues and pre-recorded voices. A study with the virtual human tutor has shown an intriguing impact of charismatic behaviors on student learning.
Chen, Haiwei; Liu, Shichen; Chen, Weikai; Li, Hao; Hill, Randall
Equivariant Point Network for 3D Point Cloud Analysis Proceedings Article
In: pp. 14514–14523, 2021.
Links | BibTeX | Tags: UARC, VGL
@inproceedings{chen_equivariant_2021,
title = {Equivariant Point Network for 3D Point Cloud Analysis},
author = {Haiwei Chen and Shichen Liu and Weikai Chen and Hao Li and Randall Hill},
url = {https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Equivariant_Point_Network_for_3D_Point_Cloud_Analysis_CVPR_2021_paper.html},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-31},
pages = {14514–14523},
keywords = {UARC, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Pallavicini, Federica; Giglioli, Irene Alice Chicchi; Kim, Gerard Jounghyun; Alcañiz, Mariano; Rizzo, Albert
Editorial: Virtual Reality, Augmented Reality and Video Games for Addressing the Impact of COVID-19 on Mental Health Journal Article
In: Frontiers in Virtual Reality, vol. 2, 2021, ISSN: 2673-4192.
Links | BibTeX | Tags: MedVR, UARC
@article{pallavicini_editorial_2021,
title = {Editorial: Virtual Reality, Augmented Reality and Video Games for Addressing the Impact of COVID-19 on Mental Health},
author = {Federica Pallavicini and Irene Alice Chicchi Giglioli and Gerard Jounghyun Kim and Mariano Alcañiz and Albert Rizzo},
url = {https://www.frontiersin.org/articles/10.3389/frvir.2021.719358},
issn = {2673-4192},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-31},
journal = {Frontiers in Virtual Reality},
volume = {2},
keywords = {MedVR, UARC},
pubstate = {published},
tppubtype = {article}
}
Nye, Benjamin D.; Core, Mark G.; Jaiswa, Shikhar; Ghosal, Aviroop; Auerbach, Daniel
Acting Engaged: Leveraging Play Persona Archetypes for Semi-Supervised Classification of Engagement Technical Report
International Educational Data Mining Society 2021, (Publication Title: International Educational Data Mining Society ERIC Number: ED615498).
Abstract | Links | BibTeX | Tags: Learning Sciences, UARC
@techreport{nye_acting_2021,
title = {Acting Engaged: Leveraging Play Persona Archetypes for Semi-Supervised Classification of Engagement},
author = {Benjamin D. Nye and Mark G. Core and Shikhar Jaiswa and Aviroop Ghosal and Daniel Auerbach},
url = {https://eric.ed.gov/?id=ED615498},
year = {2021},
date = {2021-01-01},
urldate = {2023-03-31},
institution = {International Educational Data Mining Society},
abstract = {Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify engagement patterns without expert input, by using a play persona methodology where labeled archetype data is generated by novice testers acting out different engagement patterns in a system. Domain-agnostic task features (e.g., response time to an activity, scores/correctness, task difficulty) are extracted from standardized data logs for both archetype and authentic user sessions. A semi-supervised methodology was used to label engagement; bottom-up clusters were combined with archetype data to build a classifier. This approach was analyzed with a focus on cold-start performance on small samples, using two metrics: consistency with larger full-sample cluster assignments and stability of points staying in the same cluster once assigned. These were compared against a baseline of clustering without an incrementally trained classifier. Findings on a data set from a branching multiple-choice scenario-based tutoring system indicated that approximately 52 unlabeled samples and 51 play-test labeled samples were sufficient to classify holdout sessions at 85% consistency with a full set of 145 unsupervised samples. Additionally, alignment to play persona samples for the full set matched expert labels for clusters. Use-cases and limitations of this approach are discussed. [For the full proceedings, see ED615472.]},
note = {Publication Title: International Educational Data Mining Society
ERIC Number: ED615498},
keywords = {Learning Sciences, UARC},
pubstate = {published},
tppubtype = {techreport}
}
Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify engagement patterns without expert input, by using a play persona methodology where labeled archetype data is generated by novice testers acting out different engagement patterns in a system. Domain-agnostic task features (e.g., response time to an activity, scores/correctness, task difficulty) are extracted from standardized data logs for both archetype and authentic user sessions. A semi-supervised methodology was used to label engagement; bottom-up clusters were combined with archetype data to build a classifier. This approach was analyzed with a focus on cold-start performance on small samples, using two metrics: consistency with larger full-sample cluster assignments and stability of points staying in the same cluster once assigned. These were compared against a baseline of clustering without an incrementally trained classifier. Findings on a data set from a branching multiple-choice scenario-based tutoring system indicated that approximately 52 unlabeled samples and 51 play-test labeled samples were sufficient to classify holdout sessions at 85% consistency with a full set of 145 unsupervised samples. Additionally, alignment to play persona samples for the full set matched expert labels for clusters. Use-cases and limitations of this approach are discussed. [For the full proceedings, see ED615472.]
Nye, Benjamin D.; Core, Mark G.; Ghosal, Aviroop; Walker, Peter B.
Metrics for Engagement in Games and Simulations for Learning Book Section
In: Using Cognitive and Affective Metrics in Educational Simulations and Games, Routledge, 2021, (Num Pages: 24).
Abstract | Links | BibTeX | Tags: Learning Sciences, UARC
@incollection{nye_metrics_2021,
title = {Metrics for Engagement in Games and Simulations for Learning},
author = {Benjamin D. Nye and Mark G. Core and Aviroop Ghosal and Peter B. Walker},
url = {https://www.taylorfrancis.com/chapters/edit/10.4324/9780429282201-5/metrics-engagement-games-simulations-learning-benjamin-nye-mark-core-aviroop-ghosal-peter-walker},
year = {2021},
date = {2021-01-01},
booktitle = {Using Cognitive and Affective Metrics in Educational Simulations and Games},
publisher = {Routledge},
abstract = {Games and simulations can be more engaging than other educational tools (e.g., textbooks, videos, problem sets), and this engagement can lead to improved short- and long-term learning. However, engagement in game-based learning is not automatic, and instead requires iterative design. In this work, we explore and compare metrics from research on learning sciences and from game design, considering different time scales of human action, ranging from biological engagement (e.g., eye gaze) up to lasting social ties (e.g., community building). Certain game-design approaches used for commercial games may be useful for game-based learning, such as establishing bottom-line metrics aligned to why the game was built or analyzing engagement in terms of facets or archetypes rather than on a unidirectional scale. Further research is required to study the interaction between engagement at different time scales, particularly for cases where higher long-term engagement is indicated by lower short-term engagement (e.g., skipping easy content).},
note = {Num Pages: 24},
keywords = {Learning Sciences, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Games and simulations can be more engaging than other educational tools (e.g., textbooks, videos, problem sets), and this engagement can lead to improved short- and long-term learning. However, engagement in game-based learning is not automatic, and instead requires iterative design. In this work, we explore and compare metrics from research on learning sciences and from game design, considering different time scales of human action, ranging from biological engagement (e.g., eye gaze) up to lasting social ties (e.g., community building). Certain game-design approaches used for commercial games may be useful for game-based learning, such as establishing bottom-line metrics aligned to why the game was built or analyzing engagement in terms of facets or archetypes rather than on a unidirectional scale. Further research is required to study the interaction between engagement at different time scales, particularly for cases where higher long-term engagement is indicated by lower short-term engagement (e.g., skipping easy content).
Bell, Benjamin; Bennett, Winston Wink; Nye, Benjamin; Kelsey, Elaine
Helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations Proceedings Article
In: Sottilare, Robert A.; Schwarz, Jessica (Ed.): Adaptive Instructional Systems. Adaptation Strategies and Methods, pp. 3–14, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-77873-6.
Abstract | Links | BibTeX | Tags: Machine Learning, Virtual Humans
@inproceedings{bell_helping_2021,
title = {Helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations},
author = {Benjamin Bell and Winston Wink Bennett and Benjamin Nye and Elaine Kelsey},
editor = {Robert A. Sottilare and Jessica Schwarz},
url = {https://link.springer.com/chapter/10.1007/978-3-030-77873-6_1},
doi = {10.1007/978-3-030-77873-6_1},
isbn = {978-3-030-77873-6},
year = {2021},
date = {2021-01-01},
booktitle = {Adaptive Instructional Systems. Adaptation Strategies and Methods},
pages = {3–14},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Computer Science},
abstract = {Adapting training in real time can be challenging for instructors. Real-time simulation can present rapid sequences of events, making it difficult for an instructor to attribute errors or omissions to specific underling gaps in skills and knowledge. Monitoring multiple students simultaneously imposes additional attentional workload on an instructor. This challenge can be further exacerbated when an instructor’s view of the student is obscured by virtual reality (VR) equipment. To support instructors’ ability to adapt training, Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during training simulations and offer recommendations for restoring lapses in engagement. We have created a system, called the Observational Motivation and Engagement Generalized Appliance (OMEGA), which we tested in the context of a new U.S. Air Force approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN). PTN integrates traditional flying sorties with VR-enabled ground-based training devices to achieve training efficiencies, improve readiness, and increase throughput. The virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We created a testbed for data capture to construct the ML models, based on theoretical foundations we developed previously. Our research explores OMEGA’s potential to help alert an instructor pilot (IP) to student distraction by flagging attention and engagement lapses. Our hypothesis is that OMEGA could help an IP adapt learning, and potentially manage multiple students at the same time, with alerts of lapsed attention and recommendations for restoring engagement. To test this hypothesis, we ran pilots through multiple PTN scenarios to create data for training the model. In this paper, we report on work to create machine learning models using three different techniques, and present model performance data using standard machine learning metrics. We discuss the modeling approach used to generate instructor recommendations. Future work will present results from a formative evaluation using instructor pilots. These early findings provide preliminary validation for the use of ML models for learning to detect engagement from the rich data sources characteristic of virtual environments. These findings will be applicable across a broad range of conventional and VR training applications.},
keywords = {Machine Learning, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Adapting training in real time can be challenging for instructors. Real-time simulation can present rapid sequences of events, making it difficult for an instructor to attribute errors or omissions to specific underling gaps in skills and knowledge. Monitoring multiple students simultaneously imposes additional attentional workload on an instructor. This challenge can be further exacerbated when an instructor’s view of the student is obscured by virtual reality (VR) equipment. To support instructors’ ability to adapt training, Eduworks and USC’s Institute for Creative Technologies are developing machine learning (ML) models that can measure user engagement during training simulations and offer recommendations for restoring lapses in engagement. We have created a system, called the Observational Motivation and Engagement Generalized Appliance (OMEGA), which we tested in the context of a new U.S. Air Force approach to Specialized Undergraduate Pilot Training (SUPT) called Pilot Training Next (PTN). PTN integrates traditional flying sorties with VR-enabled ground-based training devices to achieve training efficiencies, improve readiness, and increase throughput. The virtual environment provides a rich source of raw data that machine learning models can use to associate user activity with user engagement. We created a testbed for data capture to construct the ML models, based on theoretical foundations we developed previously. Our research explores OMEGA’s potential to help alert an instructor pilot (IP) to student distraction by flagging attention and engagement lapses. Our hypothesis is that OMEGA could help an IP adapt learning, and potentially manage multiple students at the same time, with alerts of lapsed attention and recommendations for restoring engagement. To test this hypothesis, we ran pilots through multiple PTN scenarios to create data for training the model. In this paper, we report on work to create machine learning models using three different techniques, and present model performance data using standard machine learning metrics. We discuss the modeling approach used to generate instructor recommendations. Future work will present results from a formative evaluation using instructor pilots. These early findings provide preliminary validation for the use of ML models for learning to detect engagement from the rich data sources characteristic of virtual environments. These findings will be applicable across a broad range of conventional and VR training applications.
Gordon, Andrew S.; Wang, Timothy S.
Narrative Text Generation from Abductive Interpretations Using Axiom-Specific Templates Book Section
In: Mitchell, Alex; Vosmeer, Mirjam (Ed.): Interactive Storytelling, vol. 13138, pp. 71–79, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-92299-3 978-3-030-92300-6.
Links | BibTeX | Tags: DTIC, Narrative, UARC
@incollection{gordon_narrative_2021,
title = {Narrative Text Generation from Abductive Interpretations Using Axiom-Specific Templates},
author = {Andrew S. Gordon and Timothy S. Wang},
editor = {Alex Mitchell and Mirjam Vosmeer},
url = {https://link.springer.com/10.1007/978-3-030-92300-6_7},
doi = {10.1007/978-3-030-92300-6_7},
isbn = {978-3-030-92299-3 978-3-030-92300-6},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-22},
booktitle = {Interactive Storytelling},
volume = {13138},
pages = {71–79},
publisher = {Springer International Publishing},
address = {Cham},
keywords = {DTIC, Narrative, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Nye, Benjamin; Nelson, David; Herrick, Imogen; Sinatra, Gale; Swartout, Bill; Porter, Molly; Davis, Matt; Lindsey, Emily
SCIENCE BIG and SMALL: Visiting the Ice Age through Miniature and Life-Sized Augmented Reality Experiences Proceedings Article
In: TMS Proceedings 2021, American Psychological Association, 2021.
Links | BibTeX | Tags: AR, MxR
@inproceedings{nye_science_2021,
title = {SCIENCE BIG and SMALL: Visiting the Ice Age through Miniature and Life-Sized Augmented Reality Experiences},
author = {Benjamin Nye and David Nelson and Imogen Herrick and Gale Sinatra and Bill Swartout and Molly Porter and Matt Davis and Emily Lindsey},
url = {https://tmb.apaopen.org/pub/djue4kjf},
doi = {10.1037/tms0000106},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-21},
booktitle = {TMS Proceedings 2021},
publisher = {American Psychological Association},
keywords = {AR, MxR},
pubstate = {published},
tppubtype = {inproceedings}
}
Döveling, Katrin; Konijn, Elly A. (Ed.)
Routledge international handbook of emotions and media Book
Routledge, New York, 2021, ISBN: 978-1-138-61049-1 978-1-03-211461-3.
@book{doveling_routledge_2021,
title = {Routledge international handbook of emotions and media},
editor = {Katrin Döveling and Elly A. Konijn},
isbn = {978-1-138-61049-1 978-1-03-211461-3},
year = {2021},
date = {2021-01-01},
publisher = {Routledge},
address = {New York},
series = {Routledge international handbooks},
abstract = {"In times of a worldwide pandemic, the election of a new US president, "MeToo," and "Fridays for Future," to name but a few examples, one thing becomes palpable: the emotional impact of media on individuals and society cannot be underestimated. The relations between media, people, and society are to a great extent based on human emotions. Emotions are essential in understanding how media messages are processed and how media affect individual and social behavior as well as public social life. Adopting a thoroughly interdisciplinary approach to the study of emotions in the context of media, the second, entirely revised and updated, edition of Routledge International Handbook of Emotions and Media comprises areas such as evolutionary psychology, media psychology, media sociology, cultural studies, media entertainment, and political and digital communication. Leading experts from across the globe explore cutting-edge research on the role of emotion in selecting and processing media contents, the emotional consequences of media use, politics and public emotion, emotions in political communication and persuasion, as well as emotions in digital, interactive, and virtual encounters. This compelling and authoritative Handbook is an essential reference tool for scholars and students of media, communication science, media psychology, emotion, cognitive and social psychology, cultural studies, media sociology, and related fields"–},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
"In times of a worldwide pandemic, the election of a new US president, "MeToo," and "Fridays for Future," to name but a few examples, one thing becomes palpable: the emotional impact of media on individuals and society cannot be underestimated. The relations between media, people, and society are to a great extent based on human emotions. Emotions are essential in understanding how media messages are processed and how media affect individual and social behavior as well as public social life. Adopting a thoroughly interdisciplinary approach to the study of emotions in the context of media, the second, entirely revised and updated, edition of Routledge International Handbook of Emotions and Media comprises areas such as evolutionary psychology, media psychology, media sociology, cultural studies, media entertainment, and political and digital communication. Leading experts from across the globe explore cutting-edge research on the role of emotion in selecting and processing media contents, the emotional consequences of media use, politics and public emotion, emotions in political communication and persuasion, as well as emotions in digital, interactive, and virtual encounters. This compelling and authoritative Handbook is an essential reference tool for scholars and students of media, communication science, media psychology, emotion, cognitive and social psychology, cultural studies, media sociology, and related fields"–
Chen, Meida; Feng, Andrew; Hou, Yu; McCullough, Kyle; Prasad, Pratusha Bhuvana; Soibelman, Lucio
Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach Journal Article
In: 2021.
Abstract | Links | BibTeX | Tags: DTIC, Simulation, UARC
@article{chen_ground_2021,
title = {Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach},
author = {Meida Chen and Andrew Feng and Yu Hou and Kyle McCullough and Pratusha Bhuvana Prasad and Lucio Soibelman},
url = {https://arxiv.org/abs/2109.12221},
doi = {10.48550/ARXIV.2109.12221},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-27},
abstract = {In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided additional high-resolution imaging capabilities with low cost for mapping a relatively large area of interest. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations. Our previous works have demonstrated the importance of information extraction from the derived photogrammetric data to create semantic-rich virtual environments (Chen et al., 2019). For example, an increase of simulation realism and fidelity was achieved by segmenting and replacing photogrammetric trees with game-ready tree models. In this work, we further investigated the semantic information extraction problem and focused on the ground material segmentation and object detection tasks. The main innovation of this work was that we leveraged both the original 2D images and the derived 3D photogrammetric data to overcome the challenges faced when using each individual data source. For ground material segmentation, we utilized an existing convolutional neural network architecture (i.e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data. We improved its performance for outdoor photogrammetric data by introducing a depth pooling layer in the architecture to take into consideration the distance between the source images and the reconstructed terrain model. To test the performance of our improved 3DMV, a ground truth ground material database was created using data from the One World Terrain (OWT) data repository. Finally, a workflow for importing the segmented ground materials into a virtual simulation scene was introduced, and visual results are reported in this paper.},
keywords = {DTIC, Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided additional high-resolution imaging capabilities with low cost for mapping a relatively large area of interest. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations. Our previous works have demonstrated the importance of information extraction from the derived photogrammetric data to create semantic-rich virtual environments (Chen et al., 2019). For example, an increase of simulation realism and fidelity was achieved by segmenting and replacing photogrammetric trees with game-ready tree models. In this work, we further investigated the semantic information extraction problem and focused on the ground material segmentation and object detection tasks. The main innovation of this work was that we leveraged both the original 2D images and the derived 3D photogrammetric data to overcome the challenges faced when using each individual data source. For ground material segmentation, we utilized an existing convolutional neural network architecture (i.e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data. We improved its performance for outdoor photogrammetric data by introducing a depth pooling layer in the architecture to take into consideration the distance between the source images and the reconstructed terrain model. To test the performance of our improved 3DMV, a ground truth ground material database was created using data from the One World Terrain (OWT) data repository. Finally, a workflow for importing the segmented ground materials into a virtual simulation scene was introduced, and visual results are reported in this paper.
Gratch, Jonathan
The field of Affective Computing: An interdisciplinary perspective Journal Article
In: Transactions of the Japanese Society for Artificial Intelligence, vol. 36, no. 1, pp. 13, 2021.
Links | BibTeX | Tags: Virtual Humans
@article{gratch_field_2021,
title = {The field of Affective Computing: An interdisciplinary perspective},
author = {Jonathan Gratch},
url = {https://people.ict.usc.edu/~gratch/CSCI534/Readings/Gratch%20-%20The%20field%20of%20affective%20computing.pdf},
year = {2021},
date = {2021-01-01},
journal = {Transactions of the Japanese Society for Artificial Intelligence},
volume = {36},
number = {1},
pages = {13},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Kawano, Seiya; Yoshino, Koichiro; Traum, David; Nakamura, Satoshi
Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning Proceedings Article
In: 1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction, pp. 21–29, ISCA, 2021.
Abstract | Links | BibTeX | Tags: ARL, Dialogue, DTIC, Natural Language, Virtual Humans
@inproceedings{kawano_dialogue_2021,
title = {Dialogue Structure Parsing on Multi-Floor Dialogue Based on Multi-Task Learning},
author = {Seiya Kawano and Koichiro Yoshino and David Traum and Satoshi Nakamura},
url = {http://www.isca-speech.org/archive/RobotDial_2021/abstracts/4.html},
doi = {10.21437/RobotDial.2021-4},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {1st RobotDial Workshop on Dialogue Models for Human-Robot Interaction},
pages = {21–29},
publisher = {ISCA},
abstract = {A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.},
keywords = {ARL, Dialogue, DTIC, Natural Language, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
A multi-floor dialogue consists of multiple sets of dialogue participants, each conversing within their own floor, but also at least one multicommunicating member who is a participant of multiple floors and coordinating each to achieve a shared dialogue goal. The structure of such dialogues can be complex, involving intentional structure and relations that are within or across floors. In this study, we propose a neural dialogue structure parser based on multi-task learning and an attention mechanism on multi-floor dialogues in a collaborative robot navigation domain. Our experimental results show that our proposed model improved the dialogue structure parsing performance more than those of single models, which are trained on each dialogue structure parsing task in multi-floor dialogues.
Mozgai, Sharon; Femminella, Brian; Hartholt, Arno; Rizzo, Skip
User-Centered Design Model for Mobile Health (mHealth) Applications: A Military Case Study in Rapid Assessment Process (RAP) Journal Article
In: pp. 10, 2021.
Abstract | Links | BibTeX | Tags: ARL, MedVR, VHTL
@article{mozgai_user-centered_2021,
title = {User-Centered Design Model for Mobile Health (mHealth) Applications: A Military Case Study in Rapid Assessment Process (RAP)},
author = {Sharon Mozgai and Brian Femminella and Arno Hartholt and Skip Rizzo},
url = {https://uploads-ssl.webflow.com/5f11f7e80d5a3b6dfdeeb614/5f9b3284d3d73e1da6a8f848_CHI_2021_Battle%20Buddy.pdf},
year = {2021},
date = {2021-01-01},
pages = {10},
abstract = {CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods; HCI design and evaluation methods; User centered design; • Applied computing → Military; • Computing methodologies → Intelligent agents.},
keywords = {ARL, MedVR, VHTL},
pubstate = {published},
tppubtype = {article}
}
CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods; HCI design and evaluation methods; User centered design; • Applied computing → Military; • Computing methodologies → Intelligent agents.
Melo, Celso M.; Marsella, Stacy; Gratch, Jonathan
Risk of Injury in Moral Dilemmas With Autonomous Vehicles Journal Article
In: Front. Robot. AI, vol. 7, pp. 572529, 2021, ISSN: 2296-9144.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{de_melo_risk_2021,
title = {Risk of Injury in Moral Dilemmas With Autonomous Vehicles},
author = {Celso M. Melo and Stacy Marsella and Jonathan Gratch},
url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.572529/full},
doi = {10.3389/frobt.2020.572529},
issn = {2296-9144},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-14},
journal = {Front. Robot. AI},
volume = {7},
pages = {572529},
abstract = {As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others’ behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others’ behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.
Gratch, Jonathan
The Promise and Peril of Automated Negotiators Journal Article
In: Negotiation Journal, vol. 37, no. 1, pp. 13–34, 2021, ISSN: 0748-4526, 1571-9979.
Links | BibTeX | Tags: ARO-Coop, Virtual Humans
@article{gratch_promise_2021,
title = {The Promise and Peril of Automated Negotiators},
author = {Jonathan Gratch},
url = {https://onlinelibrary.wiley.com/doi/10.1111/nejo.12348},
doi = {10.1111/nejo.12348},
issn = {0748-4526, 1571-9979},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-14},
journal = {Negotiation Journal},
volume = {37},
number = {1},
pages = {13–34},
keywords = {ARO-Coop, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Lee, Minha; Lucas, Gale; Gratch, Jonathan
Comparing mind perception in strategic exchanges: human-agent negotiation, dictator and ultimatum games Journal Article
In: J Multimodal User Interfaces, 2021, ISSN: 1783-7677, 1783-8738.
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{lee_comparing_2021,
title = {Comparing mind perception in strategic exchanges: human-agent negotiation, dictator and ultimatum games},
author = {Minha Lee and Gale Lucas and Jonathan Gratch},
url = {http://link.springer.com/10.1007/s12193-020-00356-6},
doi = {10.1007/s12193-020-00356-6},
issn = {1783-7677, 1783-8738},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
journal = {J Multimodal User Interfaces},
abstract = {Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In a comparative manner, we observed how perceived minds of agents shape people’s behavior in the dictator game, ultimatum game, and negotiation against artificial agents. To do so, we varied agents’ minds on two dimensions of the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude) via descriptions and dialogs. In our first study, agents with emotional capacity garnered more allocations in the dictator game, but in the ultimatum game, agents’ described agency and affective capacity, both led to greater offers. In the second study on negotiation, agents ascribed with low-agency traits earned more points than those with high-agency traits, though the negotiation tactic was the same for all agents. Although patiency did not impact game points, participants sent more happy and surprise emojis and emotionally valenced messages to agents that demonstrated emotional capacity during negotiations. Further, our exploratory analyses indicate that people related only to agents with perceived affective aptitude across all games. Both perceived agency and affective capacity contributed to moral standing after dictator and ultimatum games. But after negotiations, only agents with perceived affective capacity were granted moral standing. Manipulating mind dimensions of machines has differing effects on how people react to them in dictator and ultimatum games, compared to a more complex economic exchange like negotiation. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations, in contrast with simple economic games.},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In a comparative manner, we observed how perceived minds of agents shape people’s behavior in the dictator game, ultimatum game, and negotiation against artificial agents. To do so, we varied agents’ minds on two dimensions of the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude) via descriptions and dialogs. In our first study, agents with emotional capacity garnered more allocations in the dictator game, but in the ultimatum game, agents’ described agency and affective capacity, both led to greater offers. In the second study on negotiation, agents ascribed with low-agency traits earned more points than those with high-agency traits, though the negotiation tactic was the same for all agents. Although patiency did not impact game points, participants sent more happy and surprise emojis and emotionally valenced messages to agents that demonstrated emotional capacity during negotiations. Further, our exploratory analyses indicate that people related only to agents with perceived affective aptitude across all games. Both perceived agency and affective capacity contributed to moral standing after dictator and ultimatum games. But after negotiations, only agents with perceived affective capacity were granted moral standing. Manipulating mind dimensions of machines has differing effects on how people react to them in dictator and ultimatum games, compared to a more complex economic exchange like negotiation. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations, in contrast with simple economic games.
Gordon, Carla; Georgila, Kallirroi; Yanov, Volodymyr; Traum, David
Towards Personalization of Spoken Dialogue System Communication Strategies Book Section
In: D'Haro, Luis Fernando; Callejas, Zoraida; Nakamura, Satoshi (Ed.): Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 145–160, Springer Singapore, Singapore, 2021, ISBN: 9789811583940 9789811583957, (Series Title: Lecture Notes in Electrical Engineering).
Abstract | Links | BibTeX | Tags: Dialogue, Natural Language, UARC, Virtual Humans
@incollection{dharo_towards_2021,
title = {Towards Personalization of Spoken Dialogue System Communication Strategies},
author = {Carla Gordon and Kallirroi Georgila and Volodymyr Yanov and David Traum},
editor = {Luis Fernando D'Haro and Zoraida Callejas and Satoshi Nakamura},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_11},
doi = {10.1007/978-981-15-8395-7_11},
isbn = {9789811583940 9789811583957},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {145--160},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.},
note = {Series Title: Lecture Notes in Electrical Engineering},
keywords = {Dialogue, Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
This study examines the effects of 3 conversational traits – Register, Explicitness, and Misunderstandings – on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a “conversational” Register, while explicit confirmations were preferred for systems employing a “formal” Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Gervits, Felix; Leuski, Anton; Bonial, Claire; Gordon, Carla; Traum, David
A Classification-Based Approach to Automating Human-Robot Dialogue Book Section
In: Marchi, Erik; Siniscalchi, Sabato Marco; Cumani, Sandro; Salerno, Valerio Mario; Li, Haizhou (Ed.): Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems, pp. 115–127, Springer, Singapore, 2021, ISBN: 9789811593239.
Abstract | Links | BibTeX | Tags: Dialogue, DTIC
@incollection{gervits_classification-based_2021,
title = {A Classification-Based Approach to Automating Human-Robot Dialogue},
author = {Felix Gervits and Anton Leuski and Claire Bonial and Carla Gordon and David Traum},
editor = {Erik Marchi and Sabato Marco Siniscalchi and Sandro Cumani and Valerio Mario Salerno and Haizhou Li},
url = {https://doi.org/10.1007/978-981-15-9323-9_10},
doi = {10.1007/978-981-15-9323-9_10},
isbn = {9789811593239},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems},
pages = {115–127},
publisher = {Springer},
address = {Singapore},
series = {Lecture Notes in Electrical Engineering},
abstract = {We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.},
keywords = {Dialogue, DTIC},
pubstate = {published},
tppubtype = {incollection}
}
We present a dialogue system based on statistical classification which was used to automate human-robot dialogue in a collaborative navigation domain. The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We evaluate our system on several sets of source data from the corpus and find that response accuracy is generally high, even with very limited training data. Another contribution of this work is the novel demonstration of a dialogue manager that uses the classifier to engage in multi-floor dialogue with two different human roles. Overall, this approach is useful for enabling spoken dialogue systems to produce robust and accurate responses to natural language input, and for robots that need to interact with humans in a team setting.
Brixey, Jacqueline; Traum, David
Masheli: A Choctaw-English Bilingual Chatbot Book Section
In: D'Haro, Luis Fernando; Callejas, Zoraida; Nakamura, Satoshi (Ed.): Conversational Dialogue Systems for the Next Decade, vol. 704, pp. 41–50, Springer Singapore, Singapore, 2021, ISBN: 9789811583940 9789811583957, (Series Title: Lecture Notes in Electrical Engineering).
Abstract | Links | BibTeX | Tags: Natural Language, UARC, Virtual Humans
@incollection{dharo_masheli_2021,
title = {Masheli: A Choctaw-English Bilingual Chatbot},
author = {Jacqueline Brixey and David Traum},
editor = {Luis Fernando D'Haro and Zoraida Callejas and Satoshi Nakamura},
url = {http://link.springer.com/10.1007/978-981-15-8395-7_4},
doi = {10.1007/978-981-15-8395-7_4},
isbn = {9789811583940 9789811583957},
year = {2021},
date = {2021-01-01},
urldate = {2021-04-15},
booktitle = {Conversational Dialogue Systems for the Next Decade},
volume = {704},
pages = {41--50},
publisher = {Springer Singapore},
address = {Singapore},
abstract = {We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to practice. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.},
note = {Series Title: Lecture Notes in Electrical Engineering},
keywords = {Natural Language, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {incollection}
}
We present the implementation of an autonomous Choctaw-English bilingual chatbot. Choctaw is an American indigenous language. The intended use of the chatbot is for Choctaw language learners to practice. The system’s backend is NPCEditor, a response selection program that is trained on linked questions and answers. The chatbot’s answers are stories and conversational utterances in both languages. We experiment with the ability of NPCEditor to appropriately respond to language mixed utterances, and describe a pilot study with Choctaw-English speakers.
He, Zihao; Tavabi, Leili; Lerman, Kristina; Soleymani, Mohammad
Speaker Turn Modeling for Dialogue Act Classification Proceedings Article
In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2150–2157, Association for Computational Linguistics, Punta Cana, Dominican Republic, 2021.
Links | BibTeX | Tags: Dialogue, DTIC, UARC
@inproceedings{he_speaker_2021,
title = {Speaker Turn Modeling for Dialogue Act Classification},
author = {Zihao He and Leili Tavabi and Kristina Lerman and Mohammad Soleymani},
url = {https://aclanthology.org/2021.findings-emnlp.185},
doi = {10.18653/v1/2021.findings-emnlp.185},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
pages = {2150–2157},
publisher = {Association for Computational Linguistics},
address = {Punta Cana, Dominican Republic},
keywords = {Dialogue, DTIC, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Cheng, Junyan; Fostiropoulos, Iordanis; Boehm, Barry; Soleymani, Mohammad
Multimodal Phased Transformer for Sentiment Analysis Proceedings Article
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 2447–2458, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 2021.
Links | BibTeX | Tags: DTIC, UARC
@inproceedings{cheng_multimodal_2021,
title = {Multimodal Phased Transformer for Sentiment Analysis},
author = {Junyan Cheng and Iordanis Fostiropoulos and Barry Boehm and Mohammad Soleymani},
url = {https://aclanthology.org/2021.emnlp-main.189},
doi = {10.18653/v1/2021.emnlp-main.189},
year = {2021},
date = {2021-01-01},
urldate = {2022-09-23},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
pages = {2447–2458},
publisher = {Association for Computational Linguistics},
address = {Online and Punta Cana, Dominican Republic},
keywords = {DTIC, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Bell, Benjamin; Bennett, Winston “Wink”; Kelsey, Elaine; Nye, Benjamin
Attention and Engagement in Virtual Environments: Measuring the Unobservable Proceedings Article
In: 2021.
Links | BibTeX | Tags: AR, DTIC, Machine Learning, UARC, VR
@inproceedings{bell_attention_2021,
title = {Attention and Engagement in Virtual Environments: Measuring the Unobservable},
author = {Benjamin Bell and Winston “Wink” Bennett and Elaine Kelsey and Benjamin Nye},
url = {https://www.xcdsystem.com/iitsec/proceedings/index.cfm?Year=2021&AbID=95758&CID=862#View},
year = {2021},
date = {2021-01-01},
keywords = {AR, DTIC, Machine Learning, UARC, VR},
pubstate = {published},
tppubtype = {inproceedings}
}
Barnes, Michael J.; Wang, Ning; Pynadath, David V.; Chen, Jessie Y. C.
Human-agent bidirectional transparency Book Section
In: Trust in Human-Robot Interaction, pp. 209–232, Elsevier, 2021, ISBN: 978-0-12-819472-0.
@incollection{barnes_human-agent_2021,
title = {Human-agent bidirectional transparency},
author = {Michael J. Barnes and Ning Wang and David V. Pynadath and Jessie Y. C. Chen},
url = {https://linkinghub.elsevier.com/retrieve/pii/B9780128194720000101},
doi = {10.1016/B978-0-12-819472-0.00010-1},
isbn = {978-0-12-819472-0},
year = {2021},
date = {2021-01-01},
urldate = {2022-10-24},
booktitle = {Trust in Human-Robot Interaction},
pages = {209–232},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Awada, Mohamad; Zhu, Runhe; Becerik-Gerber, Burcin; Lucas, Gale; Southers, Erroll
An integrated emotional and physiological assessment for VR-based active shooter incident experiments Journal Article
In: Advanced Engineering Informatics, vol. 47, pp. 101227, 2021, ISSN: 14740346.
Links | BibTeX | Tags: DTIC, VR
@article{awada_integrated_2021,
title = {An integrated emotional and physiological assessment for VR-based active shooter incident experiments},
author = {Mohamad Awada and Runhe Zhu and Burcin Becerik-Gerber and Gale Lucas and Erroll Southers},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1474034620301968},
doi = {10.1016/j.aei.2020.101227},
issn = {14740346},
year = {2021},
date = {2021-01-01},
urldate = {2022-10-24},
journal = {Advanced Engineering Informatics},
volume = {47},
pages = {101227},
keywords = {DTIC, VR},
pubstate = {published},
tppubtype = {article}
}
2020
Adami, Pooya; Becerik-Gerber, Burcin; Soibelman, Lucio; Doleck, Tenzin; Copur-Gencturk, Yasemin; Lucas, Gale
An Immersive Virtual Learning Environment for Worker-Robot Collaboration on Construction Sites Proceedings Article
In: 2020 Winter Simulation Conference (WSC), pp. 2400–2411, IEEE, Orlando, FL, USA, 2020, ISBN: 978-1-72819-499-8.
Links | BibTeX | Tags: Learning Sciences
@inproceedings{adami_immersive_2020,
title = {An Immersive Virtual Learning Environment for Worker-Robot Collaboration on Construction Sites},
author = {Pooya Adami and Burcin Becerik-Gerber and Lucio Soibelman and Tenzin Doleck and Yasemin Copur-Gencturk and Gale Lucas},
url = {https://ieeexplore.ieee.org/document/9383944/},
doi = {10.1109/WSC48552.2020.9383944},
isbn = {978-1-72819-499-8},
year = {2020},
date = {2020-12-01},
urldate = {2022-10-24},
booktitle = {2020 Winter Simulation Conference (WSC)},
pages = {2400–2411},
publisher = {IEEE},
address = {Orlando, FL, USA},
keywords = {Learning Sciences},
pubstate = {published},
tppubtype = {inproceedings}
}