Publications

@article{liu_gender_2023,

title = {Gender moderates the effects of ambient bergamot scent on stress restoration in offices},

author = {Ruying Liu and Mohamad Awada and Burcin Becerik Gerber and Gale M. Lucas and Shawn C. Roll},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0272494423001834},

doi = {10.1016/j.jenvp.2023.102135},

issn = {02724944},

year  = {2023},

date = {2023-11-01},

urldate = {2023-09-20},

journal = {Journal of Environmental Psychology},

volume = {91},

pages = {102135},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{seyedrezaei_interaction_2023,

title = {Interaction effects of indoor environmental quality factors on cognitive performance and perceived comfort of young adults in open plan offices in North American Mediterranean climate},

author = {Mirmahdi Seyedrezaei and Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn Roll},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0360132323007709},

doi = {10.1016/j.buildenv.2023.110743},

issn = {03601323},

year  = {2023},

date = {2023-10-01},

urldate = {2023-09-20},

journal = {Building and Environment},

volume = {244},

pages = {110743},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{mozgai_machine_2023,

title = {Machine learning for semi-automated scoping reviews},

author = {Sharon Mozgai and Cari Kaurloto and Jade Winn and Andrew Leeds and Dirk Heylen and Arno Hartholt and Stefan Scherer},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2667305323000741},

doi = {10.1016/j.iswa.2023.200249},

issn = {26673053},

year  = {2023},

date = {2023-09-01},

urldate = {2023-08-23},

journal = {Intelligent Systems with Applications},

volume = {19},

pages = {200249},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{liu_effectiveness_2023,

title = {Effectiveness of VR-based training on improving occupants’ response and preparedness for active shooter incidents},

author = {Ruying Liu and Burcin Becerik-Gerber and Gale M. Lucas},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0925753523001170},

doi = {10.1016/j.ssci.2023.106175},

issn = {09257535},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-22},

journal = {Safety Science},

volume = {164},

pages = {106175},

keywords = {Simulation, UARC, virtual reality},

pubstate = {published},

tppubtype = {article}

}

@article{chadalapaka_leveraging_2023,

title = {Leveraging Graph Networks to Model Environments in Reinforcement Learning},

author = {Viswanath Chadalapaka and Volkan Ustun and Lixing Liu},

url = {https://journals.flvc.org/FLAIRS/article/view/133118},

doi = {10.32473/flairs.36.133118},

issn = {2334-0762},

year  = {2023},

date = {2023-05-01},

urldate = {2023-08-04},

journal = {FLAIRS},

volume = {36},

abstract = {This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.},

keywords = {CogArch, Cognitive Architecture, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{aris_learning_2023,

title = {Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning},

author = {Timothy Aris and Volkan Ustun and Rajay Kumar},

url = {https://journals.flvc.org/FLAIRS/article/view/133348},

doi = {10.32473/flairs.36.133348},

issn = {2334-0762},

year  = {2023},

date = {2023-05-01},

urldate = {2023-08-04},

journal = {FLAIRS},

volume = {36},

abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.},

keywords = {CogArch, Cognitive Architecture, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{liu_be_2023,

title = {Be prepared: How training and emergency type affect evacuation behaviour},

author = {Ruying Liu and Runhe Zhu and Burcin Becerik‐Gerber and Gale M. Lucas and Erroll G. Southers},

url = {https://onlinelibrary.wiley.com/doi/10.1111/jcal.12812},

doi = {10.1111/jcal.12812},

issn = {0266-4909, 1365-2729},

year  = {2023},

date = {2023-04-01},

urldate = {2023-08-22},

journal = {Computer Assisted Learning},

pages = {jcal.12812},

abstract = {Abstract 

 Background 

 Video‐based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video‐based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness. 

 Objectives 

 This study examines whether video‐based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies. 

 Methods 

 We simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video‐based training. 

 Results and Conclusions 

 The results revealed that participants with video‐based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self‐efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations. 

 Implications 

 Our results suggested that video‐based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video‐based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type. 

 , 

 Lay Description 

 What is already known about this topic 

 People can behave differently in different types of building emergencies. Understanding human behaviours in building emergencies is essential for developing emergency preparedness strategies. 

 Emergency training is important for building occupants and video is a widely used media for emergency training. However, its training effectiveness needs to be evaluated. 

 What this paper adds 

 We used virtual environments to investigate evacuation behaviour. 

 The effectiveness of video‐based training and human responses in building emergencies were studied on both subjective responses and objective measurements. 

 Video‐based training significantly reduced the occurrence of following behaviours. 

 The different natures of the fire emergency and active shooter incidents shape the effectiveness of video‐based training. 

 Implications of study findings for practitioners 

 Video‐based training can improve building occupants' emergency preparedness to a certain extent. 

 Emergency training media should be designed considering the influence of emergency type.},

keywords = {Simulation, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{hsu_virtual_2023,

title = {A virtual reality program to assess cognitive function in multiple sclerosis: A pilot study},

author = {Wan-Yu Hsu and Joaquin A. Anguera and Albert Rizzo and Richard Campusano and Nancy D. Chiaravalloti and John DeLuca and Adam Gazzaley and Riley M. Bove},

url = {https://www.proquest.com/docview/2787027204/abstract/BEA88F7BB72B4623PQ/1},

doi = {10.3389/fnhum.2023.1139316},

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-31},

journal = {Frontiers in Human Neuroscience},

abstract = {Introduction: Cognitive impairment is a debilitating symptom in people with multiple sclerosis (MS). Most of the neuropsychological tasks have little resemblance to everyday life. There is a need for ecologically valid tools for assessing cognition in real-life functional contexts in MS. One potential solution would involve the use of virtual reality (VR) to exert finer control over the task presentation environment; however, VR studies in the MS population are scarce. Objectives: To explore the utility and feasibility of a VR program for cognitive assessment in MS. Methods: A VR classroom embedded with a continuous performance task (CPT) was assessed in 10 non-MS adults and 10 people with MS with low cognitive functioning. Participants performed the CPT with distractors (ie. WD) and without distractors (ie. ND). The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test – II (CVLT-II), and a feedback survey on the VR program were administered. Results: People with MS exhibited greater reaction time variability (RTV) compared to non-MS participants, and greater RTV in both WD and ND conditions was associated with lower SDMT. Conclusions: VR tools warrant further research to determine their value as an ecologically valid platform for assessing cognition and everyday functioning in people with MS.},

note = {Place: Lausanne, Switzerland 

Publisher: Frontiers Research Foundation 

Section: ORIGINAL RESEARCH article},

keywords = {UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{yu_transupr_2023,

title = {TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation},

author = {Zifan Yu and Meida Chen and Zhikang Zhang and Suya You and Fengbo Ren},

url = {https://arxiv.org/abs/2302.08594},

doi = {10.48550/ARXIV.2302.08594},

year  = {2023},

date = {2023-01-01},

urldate = {2023-08-24},

abstract = {In this work, we target the problem of uncertain points refinement for image-based LiDAR point cloud semantic segmentation (LiDAR PCSS). This problem mainly results from the boundary-blurring problem of convolution neural networks (CNNs) and quantitation loss of spherical projection, which are often hard to avoid for common image-based LiDAR PCSS approaches. We propose a plug-and-play transformer-based uncertain point refiner (TransUPR) to address the problem. Through local feature aggregation, uncertain point localization, and self-attention-based transformer design, TransUPR, integrated into an existing range image-based LiDAR PCSS approach (e.g., CENet), achieves the state-of-the-art performance (68.2% mIoU) on Semantic-KITTI benchmark, which provides a performance improvement of 0.6% on the mIoU.},

note = {Publisher: arXiv 

Version Number: 2},

keywords = {STG, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{yang_light_2023,

title = {Light Sampling Field and BRDF Representation for Physically-based Neural Rendering},

author = {Jing Yang and Hanyuan Xiao and Wenbin Teng and Yunxuan Cai and Yajie Zhao},

url = {https://arxiv.org/abs/2304.05472},

doi = {10.48550/ARXIV.2304.05472},

year  = {2023},

date = {2023-01-01},

urldate = {2023-08-22},

abstract = {Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural rendering, we want to develop a physically-based neural shader to eliminate device dependency and significantly boost performance. However, no existing lighting and material models in the current neural rendering approaches can accurately represent the comprehensive lighting models and BRDFs properties required by the PBR process. Thus, this paper proposes a novel lighting representation that models direct and indirect light locally through a light sampling strategy in a learned light sampling field. We also propose BRDF models to separately represent surface/subsurface scattering details to enable complex objects such as translucent material (i.e., skin, jade). We then implement our proposed representations with an end-to-end physically-based neural face skin shader, which takes a standard face asset (i.e., geometry, albedo map, and normal map) and an HDRI for illumination as inputs and generates a photo-realistic rendering as output. Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.},

note = {Publisher: arXiv 

Version Number: 1},

keywords = {Computer Vision and Pattern Recognition (cs.CV), UARC},

pubstate = {published},

tppubtype = {article}

}

@article{hale_risk_2023,

title = {Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation},

author = {James Hale and Peter Kim and Jonathan Gratch},

url = {https://escholarship.org/uc/item/7n01v4f9#main},

year  = {2023},

date = {2023-01-01},

journal = {Proceedings of the Annual Meeting of the Cognitive Science Society},

volume = {Volume 45},

abstract = {Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{lu_co-speech_2023,

title = {Co-Speech Gesture Synthesis using Discrete Gesture Token Learning},

author = {Shuhong Lu and Youngwoo Yoon and Andrew Feng},

url = {https://arxiv.org/abs/2303.12822},

doi = {10.48550/ARXIV.2303.12822},

year  = {2023},

date = {2023-01-01},

urldate = {2023-08-04},

abstract = {Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis by modeling the gesture segments as discrete latent codes. Our method utilizes RQ-VAE in the first stage to learn a discrete codebook consisting of gesture tokens from training data. In the second stage, a two-level autoregressive transformer model is used to learn the prior distribution of residual codes conditioned on input speech context. Since the inference is formulated as token sampling, multiple gesture sequences could be generated given the same speech input using top-k sampling. The quantitative results and the user study showed the proposed method outperforms the previous methods and is able to generate realistic and diverse gesture motions.},

note = {Publisher: arXiv 

Version Number: 1},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{de_melo_social_2023,

title = {Social Functions of Machine Emotional Expressions},

author = {Celso M. De Melo and Jonathan Gratch and Stacy Marsella and Catherine Pelachaud},

url = {https://ieeexplore.ieee.org/document/10093227/},

doi = {10.1109/JPROC.2023.3261137},

issn = {0018-9219, 1558-2256},

year  = {2023},

date = {2023-01-01},

urldate = {2023-08-04},

journal = {Proc. IEEE},

pages = {1–16},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {MedVR, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{awada_cognitive_2022,

title = {Cognitive performance, creativity and stress levels of neurotypical young adults under different white noise levels},

author = {Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn Roll},

url = {https://www.nature.com/articles/s41598-022-18862-w},

doi = {10.1038/s41598-022-18862-w},

issn = {2045-2322},

year  = {2022},

date = {2022-08-01},

urldate = {2023-03-31},

journal = {Sci Rep},

volume = {12},

number = {1},

pages = {14566},

abstract = {Noise is often considered a distractor; however recent studies suggest that sub-attentive individuals or individuals diagnosed with attention deficit hyperactivity disorder can benefit from white noise to enhance their cognitive performance. Research regarding the effect of white noise on neurotypical adults presents mixed results, thus the implications of white noise on the neurotypical population remain unclear. Thus, this study investigates the effect of 2 white noise conditions, white noise level at 45 dB and white noise level at 65 dB, on the cognitive performance, creativity, and stress levels of neurotypical young adults in a private office space. These conditions are compared to a baseline condition where participants are exposed to the office ambient noise. Our findings showed that the white noise level at 45 dB resulted in better cognitive performance in terms of sustained attention, accuracy, and speed of performance as well as enhanced creativity and lower stress levels. On the other hand, the 65 dB white noise condition led to improved working memory but higher stress levels, which leads to the conclusion that different tasks might require different noise levels for optimal performance. These results lay the foundation for the integration of white noise into office workspaces as a tool to enhance office workers’ performance.},

note = {Number: 1 

Publisher: Nature Publishing Group},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{adami_impact_2022,

title = {Impact of VR-Based Training on Human–Robot Interaction for Remote Operating Construction Robots},

author = {Pooya Adami and Patrick B. Rodrigues and Peter J. Woods and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale Lucas},

url = {https://ascelibrary.org/doi/10.1061/%28ASCE%29CP.1943-5487.0001016},

doi = {10.1061/(ASCE)CP.1943-5487.0001016},

issn = {0887-3801, 1943-5487},

year  = {2022},

date = {2022-05-01},

urldate = {2022-09-23},

journal = {J. Comput. Civ. Eng.},

volume = {36},

number = {3},

pages = {04022006},

keywords = {DTIC, UARC, Virtual Humans, VR},

pubstate = {published},

tppubtype = {article}

}

@article{rodrigues_ergonomic_2022,

title = {Ergonomic assessment of office worker postures using 3D automated joint angle assessment},

author = {Patrick B. Rodrigues and Yijing Xiao and Yoko E. Fukumura and Mohamad Awada and Ashrant Aryal and Burcin Becerik-Gerber and Gale Lucas and Shawn C. Roll},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1474034622000672},

doi = {10.1016/j.aei.2022.101596},

issn = {14740346},

year  = {2022},

date = {2022-04-01},

urldate = {2022-09-26},

journal = {Advanced Engineering Informatics},

volume = {52},

pages = {101596},

keywords = {DTIC, Machine Learning, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{chawla_social_2022,

title = {Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks},

author = {Kushal Chawla and Weiyan Shi and Jingwen Zhang and Gale Lucas and Zhou Yu and Jonathan Gratch},

url = {https://arxiv.org/abs/2210.05664},

doi = {10.48550/ARXIV.2210.05664},

year  = {2022},

date = {2022-01-01},

urldate = {2023-08-22},

abstract = {Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users' cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.},

note = {Publisher: arXiv 

Version Number: 2},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{herrick_using_2022,

title = {Using Augmented Reality (AR) to Bring the Past to Life in Informal Science Learning},

author = {Imogen Herrick and Gale Sinatra and Alana Kennedy and Benjamin Nye and William Swartout and Emily Lindsey},

url = {https://par.nsf.gov/biblio/10344989},

year  = {2022},

date = {2022-01-01},

journal = {NSF-PAR},

abstract = {A key mission for museums is to engage a large and diverse public audience in science learning (Macdonald, 1997). To that end, science museums attempt to use immersive technologies in entertaining, socially oriented, and innovative ways. An example is the use of augmented reality (AR) to overlay virtual objects onto the real-world (Azuma, Baillot, Behringer, Feiner, Julier, & MacIntyre, 2001).We used a Design Based Research (DBR) approach to develop and test four features of an AR experience to promote place-based science learning in an museum setting. While quantitative differences were not found among conditions in knowledge gained, significant learning gains were seen from pre to post, illustrating the potential for place-based informal science learning. Incorporating AR technology into museum exhibits can update them with 21st tools to support visitor engagement in the learning experience. This research contributes to understanding of usability and logistical issues for different AR designs for a public, outdoor informal settings.},

keywords = {Learning Sciences, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{awada_associations_2021,

title = {Associations Among Home Indoor Environmental Quality Factors and Worker Health While Working From Home During COVID-19 Pandemic},

author = {Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn C. Roll},

url = {https://asmedigitalcollection.asme.org/sustainablebuildings/article/2/4/041001/1122847/Associations-Among-Home-Indoor-Environmental},

doi = {10.1115/1.4052822},

issn = {2642-6641, 2642-6625},

year  = {2021},

date = {2021-11-01},

urldate = {2022-09-26},

journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},

volume = {2},

number = {4},

pages = {041001},

abstract = {Abstract The outbreak of SARS-CoV-2 virus forced office workers to conduct their daily work activities from home over an extended period. Given this unique situation, an opportunity emerged to study the satisfaction of office workers with indoor environmental quality (IEQ) factors of their houses where work activities took place and associate these factors with mental and physical health. We designed and administered a questionnaire that was open for 45 days during the COVID-19 pandemic and received valid data from 988 respondents. The results show that low satisfaction with natural lighting, glare, and humidity predicted eye-related symptoms, while low satisfaction with noise was a strong predictor of fatigue or tiredness, headaches or migraines, anxiety, and depression or sadness. Nose- and throat-related symptoms and skin-related symptoms were only uniquely predicted by low satisfaction with humidity. Low satisfaction with glare uniquely predicted an increase in musculoskeletal discomfort. Symptoms related to mental stress, rumination, or worry were predicted by low satisfaction with air quality and noise. Finally, low satisfaction with noise and indoor temperature predicted the prevalence of symptoms related to trouble concentrating, maintaining attention, or focus. Workers with higher income were more satisfied with humidity, air quality, and indoor temperature and had better overall mental health. Older individuals had increased satisfaction with natural lighting, humidity, air quality, noise, and indoor temperature. Findings from this study can inform future design practices that focus on hybrid home-work environments by highlighting the impact of IEQ factors on occupant well-being.},

keywords = {DTIC, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{adami_effectiveness_2021,

title = {Effectiveness of VR-based training on improving construction workers’ knowledge, skills, and safety behavior in robotic teleoperation},

author = {Pooya Adami and Patrick B. Rodrigues and Peter J. Woods and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale Lucas},

url = {https://linkinghub.elsevier.com/retrieve/pii/S147403462100183X},

doi = {10.1016/j.aei.2021.101431},

issn = {14740346},

year  = {2021},

date = {2021-10-01},

urldate = {2022-09-26},

journal = {Advanced Engineering Informatics},

volume = {50},

pages = {101431},

keywords = {DTIC, Learning Sciences, UARC, VR},

pubstate = {published},

tppubtype = {article}

}