Publications
Search
1.
Wang, Changzhao; Aguilar, Stephen J.; Bankard, Jennifer S.; Bui, Eric; Nye, Benjamin
Writing with AI: What College Students Learned from Utilizing ChatGPT for a Writing Assignment Journal Article
In: Education Sciences, vol. 14, no. 9, pp. 976, 2024, ISSN: 2227-7102, (Publisher: MDPI AG).
@article{wang_writing_2024,
title = {Writing with AI: What College Students Learned from Utilizing ChatGPT for a Writing Assignment},
author = {Changzhao Wang and Stephen J. Aguilar and Jennifer S. Bankard and Eric Bui and Benjamin Nye},
url = {https://www.mdpi.com/2227-7102/14/9/976},
doi = {10.3390/educsci14090976},
issn = {2227-7102},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-17},
journal = {Education Sciences},
volume = {14},
number = {9},
pages = {976},
abstract = {To support the integration of AI in education, this empirical study investigated what lessons college students learned from using Generative AI for writing. We recruited 47 students in the United States from a university writing course. Students completed an assignment in which they used Generative AI tools (e.g., ChatGPT) to draft an application letter or personal statement. Data were collected using a survey of five open-ended questions about their writing process, what worked, what did not work, how to better write with AI, and general lessons learned. We applied thematic analysis and sentiment analysis methods to analyze students’ responses. Results show that (1) students went through multiple rounds of prompting; (2) students identified strengths of AI, such as connection to topic, template generation, and sentence quality; (3) the weaknesses of AI included general language, robotic tone and lacking emotion, lacking personal voice, and lacking critical thinking; (4) students wished to improve AI-generated writing by adding personal stories, connections to posting, feelings and thoughts, and deleting repetitive language; and (5) their overall attitudes toward AI tool were positive. We believe our findings can help relieve some concerns about cheating with AI. We also suggested strategies to regulate the use of AI.},
note = {Publisher: MDPI AG},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To support the integration of AI in education, this empirical study investigated what lessons college students learned from using Generative AI for writing. We recruited 47 students in the United States from a university writing course. Students completed an assignment in which they used Generative AI tools (e.g., ChatGPT) to draft an application letter or personal statement. Data were collected using a survey of five open-ended questions about their writing process, what worked, what did not work, how to better write with AI, and general lessons learned. We applied thematic analysis and sentiment analysis methods to analyze students’ responses. Results show that (1) students went through multiple rounds of prompting; (2) students identified strengths of AI, such as connection to topic, template generation, and sentence quality; (3) the weaknesses of AI included general language, robotic tone and lacking emotion, lacking personal voice, and lacking critical thinking; (4) students wished to improve AI-generated writing by adding personal stories, connections to posting, feelings and thoughts, and deleting repetitive language; and (5) their overall attitudes toward AI tool were positive. We believe our findings can help relieve some concerns about cheating with AI. We also suggested strategies to regulate the use of AI.
2.
Lucas, Gale M.; Becerik-Gerber, Burcin; Roll, Shawn C.
Calibrating workers’ trust in intelligent automated systems Journal Article
In: Patterns, vol. 5, no. 9, pp. 101045, 2024, ISSN: 2666-3899, (Publisher: Elsevier BV).
@article{lucas_calibrating_2024,
title = {Calibrating workers’ trust in intelligent automated systems},
author = {Gale M. Lucas and Burcin Becerik-Gerber and Shawn C. Roll},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2666389924001879},
doi = {10.1016/j.patter.2024.101045},
issn = {2666-3899},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-17},
journal = {Patterns},
volume = {5},
number = {9},
pages = {101045},
note = {Publisher: Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
3.
Zaizar, Eric D.; Gramlich, Michael A.; Rizzo, Albert “Skip”; Reger, Greg M.; Norr, Aaron M.
In: Training and Education in Professional Psychology, 2024, ISSN: 1931-3926, 1931-3918.
@article{zaizar_exploration_2024,
title = {Exploration of the impact of baseline clinician learner characteristics on motivational interviewing skill improvement following training with a virtual standardized patient.},
author = {Eric D. Zaizar and Michael A. Gramlich and Albert “Skip” Rizzo and Greg M. Reger and Aaron M. Norr},
url = {https://doi.apa.org/doi/10.1037/tep0000490},
doi = {10.1037/tep0000490},
issn = {1931-3926, 1931-3918},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-13},
journal = {Training and Education in Professional Psychology},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
4.
Bodner, Ehud; Mikulincer, Mario; McMahon, Elizabeth; Rizzo, Albert
Reviving life that has ceased on October the 7th: an attachment perspective on a virtual reality intervention Journal Article
In: Front. Virtual Real., vol. 5, pp. 1438663, 2024, ISSN: 2673-4192.
@article{bodner_reviving_2024,
title = {Reviving life that has ceased on October the 7th: an attachment perspective on a virtual reality intervention},
author = {Ehud Bodner and Mario Mikulincer and Elizabeth McMahon and Albert Rizzo},
url = {https://www.frontiersin.org/articles/10.3389/frvir.2024.1438663/full},
doi = {10.3389/frvir.2024.1438663},
issn = {2673-4192},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-15},
journal = {Front. Virtual Real.},
volume = {5},
pages = {1438663},
abstract = {Unfortunately, in recent years, wars have forced many civilians to evacuate their homes and move to safe zones. The event of October the seventh that took place in many Kibbutzim near the Gaza strip, exposed families who were on a Jewish holiday, to the murder of family and community members. They had to leave their burned houses and move to hotels and apartment buildings in other parts of Israel. Many people, also from the Northen parts of the country, are still in new safe zones, and have huge difficulties in returning to their houses (and not only because of objective security reasons). In this “perspective” article we propose a Virtual Reality (VR) application, which is based on past and current research in the fields of attachment theory and traumatic grief. We propose that in addition to the use of exposure therapy, a VR simulation which will activate the attachment system, can reorganize the evacuees’ figure and place attachment representations. We suggest that such a simulation will revive the evacuees’ sense of safe-haven and secure base and enable them to return to their home place, or to adjust to a new place, thereby leading to optimal adjustment. We start with a presentation of the theory of attachment, place attachment, attachment and loss and the two-track model of bereavement. Then, we describe the design of our VR intervention that aims to address this challenge from the attachment theory perspective with the evacuees. Finally, we discuss the challenges that need to be dealt with to implement the VR interventions through resilience centers in Israel.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Unfortunately, in recent years, wars have forced many civilians to evacuate their homes and move to safe zones. The event of October the seventh that took place in many Kibbutzim near the Gaza strip, exposed families who were on a Jewish holiday, to the murder of family and community members. They had to leave their burned houses and move to hotels and apartment buildings in other parts of Israel. Many people, also from the Northen parts of the country, are still in new safe zones, and have huge difficulties in returning to their houses (and not only because of objective security reasons). In this “perspective” article we propose a Virtual Reality (VR) application, which is based on past and current research in the fields of attachment theory and traumatic grief. We propose that in addition to the use of exposure therapy, a VR simulation which will activate the attachment system, can reorganize the evacuees’ figure and place attachment representations. We suggest that such a simulation will revive the evacuees’ sense of safe-haven and secure base and enable them to return to their home place, or to adjust to a new place, thereby leading to optimal adjustment. We start with a presentation of the theory of attachment, place attachment, attachment and loss and the two-track model of bereavement. Then, we describe the design of our VR intervention that aims to address this challenge from the attachment theory perspective with the evacuees. Finally, we discuss the challenges that need to be dealt with to implement the VR interventions through resilience centers in Israel.
5.
Han, Bin; Yau, Cleo; Lei, Su; Gratch, Jonathan
Knowledge-based Emotion Recognition using Large Language Models Miscellaneous
2024, (arXiv:2408.04123 [cs]).
@misc{han_knowledge-based_2024,
title = {Knowledge-based Emotion Recognition using Large Language Models},
author = {Bin Han and Cleo Yau and Su Lei and Jonathan Gratch},
url = {http://arxiv.org/abs/2408.04123},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-15},
publisher = {arXiv},
abstract = {Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.},
note = {arXiv:2408.04123 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.
6.
Parga, Madeline R.; Roll, Shawn C.; Lucas, Gale M.; Becerik-Gerber, Burcin; Naranayan, Shrikanth
Differences in Self-Rated Worker Outcomes Across Stress States: An Interim Analysis of Hybrid Worker Data Journal Article
In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2024, ISSN: 1071-1813, 2169-5067, (Publisher: SAGE Publications).
@article{parga_differences_2024,
title = {Differences in Self-Rated Worker Outcomes Across Stress States: An Interim Analysis of Hybrid Worker Data},
author = {Madeline R. Parga and Shawn C. Roll and Gale M. Lucas and Burcin Becerik-Gerber and Shrikanth Naranayan},
url = {https://journals.sagepub.com/doi/10.1177/10711813241275500},
doi = {10.1177/10711813241275500},
issn = {1071-1813, 2169-5067},
year = {2024},
date = {2024-08-01},
urldate = {2024-09-17},
journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
abstract = {Stress experiences can have dire consequences for worker performance and well-being, and the social environment of the workplace is a key contributor to worker experience. This study investigated the relationship between hybrid workers’ self-ratings of productivity, mood, and stress with perceptions of positive (eustress) and negative (distress) stress states. We hypothesized that self-ratings would vary across combinations of eustress and distress experiences and that these differences would differ based on the social context. Ecological momentary assessments (EMA) were used to obtain ecologically valid data at four data points each workday across a 4-month study period in a cohort of seven office workers. Findings aligned with the Yerkes–Dodson law, such that higher states of arousal were associated with greater self-perceived productivity, and higher stress magnitudes were found when distress existed. Compared to other states, eustress was associated with higher productivity in work-related activities and better mood across all activity types.},
note = {Publisher: SAGE Publications},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stress experiences can have dire consequences for worker performance and well-being, and the social environment of the workplace is a key contributor to worker experience. This study investigated the relationship between hybrid workers’ self-ratings of productivity, mood, and stress with perceptions of positive (eustress) and negative (distress) stress states. We hypothesized that self-ratings would vary across combinations of eustress and distress experiences and that these differences would differ based on the social context. Ecological momentary assessments (EMA) were used to obtain ecologically valid data at four data points each workday across a 4-month study period in a cohort of seven office workers. Findings aligned with the Yerkes–Dodson law, such that higher states of arousal were associated with greater self-perceived productivity, and higher stress magnitudes were found when distress existed. Compared to other states, eustress was associated with higher productivity in work-related activities and better mood across all activity types.
7.
Tak, Ala N.; Gratch, Jonathan
GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective Miscellaneous
2024, (arXiv:2408.13718 [cs]).
@misc{tak_gpt-4_2024,
title = {GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective},
author = {Ala N. Tak and Jonathan Gratch},
url = {http://arxiv.org/abs/2408.13718},
year = {2024},
date = {2024-08-01},
urldate = {2024-09-17},
publisher = {arXiv},
abstract = {This paper extends recent investigations on the emotional reasoning abilities of Large Language Models (LLMs). Current research on LLMs has not directly evaluated the distinction between how LLMs predict the self-attribution of emotions and the perception of others' emotions. We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity representing fine-grained inferred emotional attributions of others. We show that GPT-4 is especially accurate in reasoning about such stimuli. This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations. To further explore this, our second study utilizes a dataset containing annotations from both the author and a third-person perspective. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments. Notably, conventional computational models of emotion primarily rely on self-reported ground truth as the gold standard. However, an average observer's standpoint, which LLMs appear to have adopted, might be more relevant for many downstream applications, at least in the absence of individual information and adequate safety considerations.},
note = {arXiv:2408.13718 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This paper extends recent investigations on the emotional reasoning abilities of Large Language Models (LLMs). Current research on LLMs has not directly evaluated the distinction between how LLMs predict the self-attribution of emotions and the perception of others' emotions. We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity representing fine-grained inferred emotional attributions of others. We show that GPT-4 is especially accurate in reasoning about such stimuli. This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations. To further explore this, our second study utilizes a dataset containing annotations from both the author and a third-person perspective. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments. Notably, conventional computational models of emotion primarily rely on self-reported ground truth as the gold standard. However, an average observer's standpoint, which LLMs appear to have adopted, might be more relevant for many downstream applications, at least in the absence of individual information and adequate safety considerations.
8.
Owayyed, Mohammed Al; Tielman, Myrthe; Hartholt, Arno; Specht, Marcus; Brinkman, Willem-Paul
Agent-based social skills training systems: the ARTES architecture, interaction characteristics, learning theories and future outlooks Journal Article
In: Behaviour & Information Technology, pp. 1–28, 2024, ISSN: 0144-929X, 1362-3001.
@article{al_owayyed_agent-based_2024,
title = {Agent-based social skills training systems: the ARTES architecture, interaction characteristics, learning theories and future outlooks},
author = {Mohammed Al Owayyed and Myrthe Tielman and Arno Hartholt and Marcus Specht and Willem-Paul Brinkman},
url = {https://www.tandfonline.com/doi/full/10.1080/0144929X.2024.2374891},
doi = {10.1080/0144929X.2024.2374891},
issn = {0144-929X, 1362-3001},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-15},
journal = {Behaviour & Information Technology},
pages = {1–28},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
9.
Bell, Imogen H.; Pot-Kolder, Roos; Rizzo, Albert; Rus-Calafell, Mar; Cardi, Valentina; Cella, Matteo; Ward, Thomas; Riches, Simon; Reinoso, Martin; Thompson, Andrew; Alvarez-Jimenez, Mario; Valmaggia, Lucia
Advances in the use of virtual reality to treat mental health conditions Journal Article
In: Nat Rev Psychol, 2024, ISSN: 2731-0574.
@article{bell_advances_2024,
title = {Advances in the use of virtual reality to treat mental health conditions},
author = {Imogen H. Bell and Roos Pot-Kolder and Albert Rizzo and Mar Rus-Calafell and Valentina Cardi and Matteo Cella and Thomas Ward and Simon Riches and Martin Reinoso and Andrew Thompson and Mario Alvarez-Jimenez and Lucia Valmaggia},
url = {https://www.nature.com/articles/s44159-024-00334-9},
doi = {10.1038/s44159-024-00334-9},
issn = {2731-0574},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-11},
journal = {Nat Rev Psychol},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
10.
Han, Bin; Yau, Cleo; Lei, Su; Gratch, Jonathan
In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models Miscellaneous
2024, (arXiv:2408.00780 [cs]).
@misc{han_-depth_2024,
title = {In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models},
author = {Bin Han and Cleo Yau and Su Lei and Jonathan Gratch},
url = {http://arxiv.org/abs/2408.00780},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-15},
publisher = {arXiv},
abstract = {Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.},
note = {arXiv:2408.00780 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.
11.
Xiao, Hanyuan; Chen, Yingshu; Huang, Huajian; Xiong, Haolin; Yang, Jing; Prasad, Pratusha; Zhao, Yajie
Localized Gaussian Splatting Editing with Contextual Awareness Miscellaneous
2024, (arXiv:2408.00083 [cs]).
@misc{xiao_localized_2024,
title = {Localized Gaussian Splatting Editing with Contextual Awareness},
author = {Hanyuan Xiao and Yingshu Chen and Huajian Huang and Haolin Xiong and Jing Yang and Pratusha Prasad and Yajie Zhao},
url = {http://arxiv.org/abs/2408.00083},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-16},
publisher = {arXiv},
abstract = {Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to illumination mismatches within the environment. To bridge the gap, we introduce an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS) representation. Our key observation is that inpainting by the state-of-the-art conditional 2D diffusion model is consistent with background in lighting. To leverage the prior knowledge from the well-trained diffusion models for 3D object generation, our approach employs a coarse-to-fine objection optimization pipeline with inpainted views. In the first coarse step, we achieve image-to-3D lifting given an ideal inpainted view. The process employs 3D-aware diffusion prior from a view-conditioned diffusion model, which preserves illumination present in the conditioning image. To acquire an ideal inpainted image, we introduce an Anchor View Proposal (AVP) algorithm to find a single view that best represents the scene illumination in target region. In the second Texture Enhancement step, we introduce a novel Depth-guided Inpainting Score Distillation Sampling (DI-SDS), which enhances geometry and texture details with the inpainting diffusion prior, beyond the scope of the 3D-aware diffusion prior knowledge in the first coarse step. DI-SDS not only provides fine-grained texture enhancement, but also urges optimization to respect scene lighting. Our approach efficiently achieves local editing with global illumination consistency without explicitly modeling light transport. We demonstrate robustness of our method by evaluating editing in real scenes containing explicit highlight and shadows, and compare against the state-of-the-art text-to-3D editing methods.},
note = {arXiv:2408.00083 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to illumination mismatches within the environment. To bridge the gap, we introduce an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS) representation. Our key observation is that inpainting by the state-of-the-art conditional 2D diffusion model is consistent with background in lighting. To leverage the prior knowledge from the well-trained diffusion models for 3D object generation, our approach employs a coarse-to-fine objection optimization pipeline with inpainted views. In the first coarse step, we achieve image-to-3D lifting given an ideal inpainted view. The process employs 3D-aware diffusion prior from a view-conditioned diffusion model, which preserves illumination present in the conditioning image. To acquire an ideal inpainted image, we introduce an Anchor View Proposal (AVP) algorithm to find a single view that best represents the scene illumination in target region. In the second Texture Enhancement step, we introduce a novel Depth-guided Inpainting Score Distillation Sampling (DI-SDS), which enhances geometry and texture details with the inpainting diffusion prior, beyond the scope of the 3D-aware diffusion prior knowledge in the first coarse step. DI-SDS not only provides fine-grained texture enhancement, but also urges optimization to respect scene lighting. Our approach efficiently achieves local editing with global illumination consistency without explicitly modeling light transport. We demonstrate robustness of our method by evaluating editing in real scenes containing explicit highlight and shadows, and compare against the state-of-the-art text-to-3D editing methods.
12.
Liu, Ruying; Wu, Wanjing; Becerik-Gerber, Burcin; Lucas, Gale M.
2024, (arXiv:2407.10441 [cs]).
@misc{liu_enhancing_2024,
title = {Enhancing Building Safety Design for Active Shooter Incidents: Exploration of Building Exit Parameters using Reinforcement Learning-Based Simulations},
author = {Ruying Liu and Wanjing Wu and Burcin Becerik-Gerber and Gale M. Lucas},
url = {http://arxiv.org/abs/2407.10441},
year = {2024},
date = {2024-07-01},
urldate = {2024-09-17},
publisher = {arXiv},
abstract = {With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.},
note = {arXiv:2407.10441 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.
13.
Lu, Shuhong; Jin, Zhangyu; Rajendran, Vickram; Harari, Michal; Feng, Andrew; Melo, Celso M. De
Synthetic-to-real adaptation for complex action recognition in surveillance applications Proceedings Article
In: Manser, Kimberly E.; Melo, Celso De; Rao, Raghuveer M.; Howell, Christopher L. (Ed.): Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, pp. 14, SPIE, National Harbor, United States, 2024, ISBN: 978-1-5106-7388-5 978-1-5106-7389-2.
@inproceedings{lu_synthetic–real_2024,
title = {Synthetic-to-real adaptation for complex action recognition in surveillance applications},
author = {Shuhong Lu and Zhangyu Jin and Vickram Rajendran and Michal Harari and Andrew Feng and Celso M. De Melo},
editor = {Kimberly E. Manser and Celso De Melo and Raghuveer M. Rao and Christopher L. Howell},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13035/3012393/Synthetic-to-real-adaptation-for-complex-action-recognition-in-surveillance/10.1117/12.3012393.full},
doi = {10.1117/12.3012393},
isbn = {978-1-5106-7388-5 978-1-5106-7389-2},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-11},
booktitle = {Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II},
pages = {14},
publisher = {SPIE},
address = {National Harbor, United States},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
14.
Nurunnabi, Abdul; Teferle, Felicia; Laefer, Debra F.; Chen, Meida; Ali, Mir Masoom
Development of a Precise Tree Structure from LiDAR Point Clouds Journal Article
In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLVIII-2-2024, pp. 301–308, 2024, ISSN: 2194-9034.
@article{nurunnabi_development_2024,
title = {Development of a Precise Tree Structure from LiDAR Point Clouds},
author = {Abdul Nurunnabi and Felicia Teferle and Debra F. Laefer and Meida Chen and Mir Masoom Ali},
url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/301/2024/},
doi = {10.5194/isprs-archives-XLVIII-2-2024-301-2024},
issn = {2194-9034},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-11},
journal = {Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.},
volume = {XLVIII-2-2024},
pages = {301–308},
abstract = {Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.
15.
Zhang, Mingyuan; Cai, Zhongang; Pan, Liang; Hong, Fangzhou; Guo, Xinying; Yang, Lei; Liu, Ziwei
MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model Journal Article
In: IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 6, pp. 4115–4128, 2024, ISSN: 0162-8828, 2160-9292, 1939-3539.
@article{zhang_motiondiffuse_2024,
title = {MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model},
author = {Mingyuan Zhang and Zhongang Cai and Liang Pan and Fangzhou Hong and Xinying Guo and Lei Yang and Ziwei Liu},
url = {https://ieeexplore.ieee.org/document/10416192/},
doi = {10.1109/TPAMI.2024.3355414},
issn = {0162-8828, 2160-9292, 1939-3539},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-18},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
volume = {46},
number = {6},
pages = {4115–4128},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
16.
Yin, Yinxuan; Nayyar, Mollik; Holman, Daniel; Lucas, Gale; Holbrook, Colin; Wagner, Alan
Validation and Evacuee Modeling of Virtual Robot-guided Emergency Evacuation Experiments Miscellaneous
2024.
@misc{yin_validation_2024,
title = {Validation and Evacuee Modeling of Virtual Robot-guided Emergency Evacuation Experiments},
author = {Yinxuan Yin and Mollik Nayyar and Daniel Holman and Gale Lucas and Colin Holbrook and Alan Wagner},
url = {https://osf.io/mr78s},
doi = {10.31234/osf.io/mr78s},
year = {2024},
date = {2024-06-01},
urldate = {2024-09-17},
publisher = {Center for Open Science},
abstract = {Virtual Reality (VR) is an increasingly common tool for investigating human responses to emergency situations. Nonetheless, studies validating and comparing human subject behavior during real world emergencies to their responses in VR are notably rare, and no prior studies have validated whether human emergency responses to guidance from a robot are comparable in VR versus the real world. In the present pre-registered study, we used VR to replicate a previous robot- guided emergency evacuation study conducted in the real world and compared human subject behavior in matched physical and virtual environments. In both environments, human subjects were asked to follow a robot to a location and to then read an article. While reading, a fire alarm sounds. The robot then attempted to guide them to a distant, unfamiliar exit rather than nearby and familiar exits. We observed close correspondences between evacuee exit choice (the robot’s distant exit versus closer exits), evacuation time, and trust in the robot between the VR and physical environments. We further demonstrate that data collected in virtual reality can be used to create accurate motion models (mean error of 0.42 centimeters) predicting evacuee trajectories and locations in real life. Taken together, the results provide evidence for the ecological validity of VR approaches to studying human-robot interaction, particularly robot- guided emergency evacuation.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Virtual Reality (VR) is an increasingly common tool for investigating human responses to emergency situations. Nonetheless, studies validating and comparing human subject behavior during real world emergencies to their responses in VR are notably rare, and no prior studies have validated whether human emergency responses to guidance from a robot are comparable in VR versus the real world. In the present pre-registered study, we used VR to replicate a previous robot- guided emergency evacuation study conducted in the real world and compared human subject behavior in matched physical and virtual environments. In both environments, human subjects were asked to follow a robot to a location and to then read an article. While reading, a fire alarm sounds. The robot then attempted to guide them to a distant, unfamiliar exit rather than nearby and familiar exits. We observed close correspondences between evacuee exit choice (the robot’s distant exit versus closer exits), evacuation time, and trust in the robot between the VR and physical environments. We further demonstrate that data collected in virtual reality can be used to create accurate motion models (mean error of 0.42 centimeters) predicting evacuee trajectories and locations in real life. Taken together, the results provide evidence for the ecological validity of VR approaches to studying human-robot interaction, particularly robot- guided emergency evacuation.
17.
Saxon, Leslie; Faulk, Robert T; Boberg, Jill; Barrett, Trevor; McLelland, Steve
In: J. Spec. Oper. Med., 2024, ISSN: 1553-9768.
@article{saxon_continuous_2024,
title = {Continuous Assessment of Active-Duty Army Special Operations and Reconnaissance Marines Using Digital Devices and Custom Software: The Digital Comprehensive Operator Readiness Assessment (DcORA) Study},
author = {Leslie Saxon and Robert T Faulk and Jill Boberg and Trevor Barrett and Steve McLelland},
url = {https://www.jsomonline.org/Citations/PXKK-I23D.php},
doi = {10.55460/PXKK-I23D},
issn = {1553-9768},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-25},
journal = {J. Spec. Oper. Med.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
18.
Greenwald, Eric; Krakowski, Ari; Hurt, Timothy; Grindstaff, Kelly; Wang, Ning
It's like I'm the AI: Youth Sensemaking About AI through Metacognitive Embodiment Proceedings Article
In: Proceedings of the 23rd Annual ACM Interaction Design and Children Conference, pp. 789–793, ACM, Delft Netherlands, 2024, ISBN: 9798400704420.
@inproceedings{greenwald_its_2024,
title = {It's like I'm the AI: Youth Sensemaking About AI through Metacognitive Embodiment},
author = {Eric Greenwald and Ari Krakowski and Timothy Hurt and Kelly Grindstaff and Ning Wang},
url = {https://dl.acm.org/doi/10.1145/3628516.3659395},
doi = {10.1145/3628516.3659395},
isbn = {9798400704420},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-25},
booktitle = {Proceedings of the 23rd Annual ACM Interaction Design and Children Conference},
pages = {789–793},
publisher = {ACM},
address = {Delft Netherlands},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
19.
Chen, Meida; Lal, Devashish; Yu, Zifan; Xu, Jiuyi; Feng, Andrew; You, Suya; Nurunnabi, Abdul; Shi, Yangming
Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation Journal Article
In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLVIII-2-2024, pp. 49–54, 2024, ISSN: 2194-9034.
@article{chen_large-scale_2024,
title = {Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation},
author = {Meida Chen and Devashish Lal and Zifan Yu and Jiuyi Xu and Andrew Feng and Suya You and Abdul Nurunnabi and Yangming Shi},
url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/49/2024/},
doi = {10.5194/isprs-archives-XLVIII-2-2024-49-2024},
issn = {2194-9034},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-20},
journal = {Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.},
volume = {XLVIII-2-2024},
pages = {49–54},
abstract = {Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.
20.
Nye, Benjamin D.; Core, Mark G.; Chereddy, Sai V. R.; Young, Vivian; Auerbach, Daniel
Bootstrapping Assessments for Team Simulations: Transfer Learning Between First-Person-Shooter Game Maps Book Section
In: Sottilare, Robert A.; Schwarz, Jessica (Ed.): Adaptive Instructional Systems, vol. 14727, pp. 261–271, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-60608-3 978-3-031-60609-0, (Series Title: Lecture Notes in Computer Science).
@incollection{sottilare_bootstrapping_2024,
title = {Bootstrapping Assessments for Team Simulations: Transfer Learning Between First-Person-Shooter Game Maps},
author = {Benjamin D. Nye and Mark G. Core and Sai V. R. Chereddy and Vivian Young and Daniel Auerbach},
editor = {Robert A. Sottilare and Jessica Schwarz},
url = {https://link.springer.com/10.1007/978-3-031-60609-0_19},
doi = {10.1007/978-3-031-60609-0_19},
isbn = {978-3-031-60608-3 978-3-031-60609-0},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-18},
booktitle = {Adaptive Instructional Systems},
volume = {14727},
pages = {261–271},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Filter
2024
Wang, Changzhao; Aguilar, Stephen J.; Bankard, Jennifer S.; Bui, Eric; Nye, Benjamin
Writing with AI: What College Students Learned from Utilizing ChatGPT for a Writing Assignment Journal Article
In: Education Sciences, vol. 14, no. 9, pp. 976, 2024, ISSN: 2227-7102, (Publisher: MDPI AG).
Abstract | Links | BibTeX | Tags:
@article{wang_writing_2024,
title = {Writing with AI: What College Students Learned from Utilizing ChatGPT for a Writing Assignment},
author = {Changzhao Wang and Stephen J. Aguilar and Jennifer S. Bankard and Eric Bui and Benjamin Nye},
url = {https://www.mdpi.com/2227-7102/14/9/976},
doi = {10.3390/educsci14090976},
issn = {2227-7102},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-17},
journal = {Education Sciences},
volume = {14},
number = {9},
pages = {976},
abstract = {To support the integration of AI in education, this empirical study investigated what lessons college students learned from using Generative AI for writing. We recruited 47 students in the United States from a university writing course. Students completed an assignment in which they used Generative AI tools (e.g., ChatGPT) to draft an application letter or personal statement. Data were collected using a survey of five open-ended questions about their writing process, what worked, what did not work, how to better write with AI, and general lessons learned. We applied thematic analysis and sentiment analysis methods to analyze students’ responses. Results show that (1) students went through multiple rounds of prompting; (2) students identified strengths of AI, such as connection to topic, template generation, and sentence quality; (3) the weaknesses of AI included general language, robotic tone and lacking emotion, lacking personal voice, and lacking critical thinking; (4) students wished to improve AI-generated writing by adding personal stories, connections to posting, feelings and thoughts, and deleting repetitive language; and (5) their overall attitudes toward AI tool were positive. We believe our findings can help relieve some concerns about cheating with AI. We also suggested strategies to regulate the use of AI.},
note = {Publisher: MDPI AG},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To support the integration of AI in education, this empirical study investigated what lessons college students learned from using Generative AI for writing. We recruited 47 students in the United States from a university writing course. Students completed an assignment in which they used Generative AI tools (e.g., ChatGPT) to draft an application letter or personal statement. Data were collected using a survey of five open-ended questions about their writing process, what worked, what did not work, how to better write with AI, and general lessons learned. We applied thematic analysis and sentiment analysis methods to analyze students’ responses. Results show that (1) students went through multiple rounds of prompting; (2) students identified strengths of AI, such as connection to topic, template generation, and sentence quality; (3) the weaknesses of AI included general language, robotic tone and lacking emotion, lacking personal voice, and lacking critical thinking; (4) students wished to improve AI-generated writing by adding personal stories, connections to posting, feelings and thoughts, and deleting repetitive language; and (5) their overall attitudes toward AI tool were positive. We believe our findings can help relieve some concerns about cheating with AI. We also suggested strategies to regulate the use of AI.
Lucas, Gale M.; Becerik-Gerber, Burcin; Roll, Shawn C.
Calibrating workers’ trust in intelligent automated systems Journal Article
In: Patterns, vol. 5, no. 9, pp. 101045, 2024, ISSN: 2666-3899, (Publisher: Elsevier BV).
@article{lucas_calibrating_2024,
title = {Calibrating workers’ trust in intelligent automated systems},
author = {Gale M. Lucas and Burcin Becerik-Gerber and Shawn C. Roll},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2666389924001879},
doi = {10.1016/j.patter.2024.101045},
issn = {2666-3899},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-17},
journal = {Patterns},
volume = {5},
number = {9},
pages = {101045},
note = {Publisher: Elsevier BV},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zaizar, Eric D.; Gramlich, Michael A.; Rizzo, Albert “Skip”; Reger, Greg M.; Norr, Aaron M.
In: Training and Education in Professional Psychology, 2024, ISSN: 1931-3926, 1931-3918.
Links | BibTeX | Tags: Virtual Humans
@article{zaizar_exploration_2024,
title = {Exploration of the impact of baseline clinician learner characteristics on motivational interviewing skill improvement following training with a virtual standardized patient.},
author = {Eric D. Zaizar and Michael A. Gramlich and Albert “Skip” Rizzo and Greg M. Reger and Aaron M. Norr},
url = {https://doi.apa.org/doi/10.1037/tep0000490},
doi = {10.1037/tep0000490},
issn = {1931-3926, 1931-3918},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-13},
journal = {Training and Education in Professional Psychology},
keywords = {Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Bodner, Ehud; Mikulincer, Mario; McMahon, Elizabeth; Rizzo, Albert
Reviving life that has ceased on October the 7th: an attachment perspective on a virtual reality intervention Journal Article
In: Front. Virtual Real., vol. 5, pp. 1438663, 2024, ISSN: 2673-4192.
Abstract | Links | BibTeX | Tags: MedVR
@article{bodner_reviving_2024,
title = {Reviving life that has ceased on October the 7th: an attachment perspective on a virtual reality intervention},
author = {Ehud Bodner and Mario Mikulincer and Elizabeth McMahon and Albert Rizzo},
url = {https://www.frontiersin.org/articles/10.3389/frvir.2024.1438663/full},
doi = {10.3389/frvir.2024.1438663},
issn = {2673-4192},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-15},
journal = {Front. Virtual Real.},
volume = {5},
pages = {1438663},
abstract = {Unfortunately, in recent years, wars have forced many civilians to evacuate their homes and move to safe zones. The event of October the seventh that took place in many Kibbutzim near the Gaza strip, exposed families who were on a Jewish holiday, to the murder of family and community members. They had to leave their burned houses and move to hotels and apartment buildings in other parts of Israel. Many people, also from the Northen parts of the country, are still in new safe zones, and have huge difficulties in returning to their houses (and not only because of objective security reasons). In this “perspective” article we propose a Virtual Reality (VR) application, which is based on past and current research in the fields of attachment theory and traumatic grief. We propose that in addition to the use of exposure therapy, a VR simulation which will activate the attachment system, can reorganize the evacuees’ figure and place attachment representations. We suggest that such a simulation will revive the evacuees’ sense of safe-haven and secure base and enable them to return to their home place, or to adjust to a new place, thereby leading to optimal adjustment. We start with a presentation of the theory of attachment, place attachment, attachment and loss and the two-track model of bereavement. Then, we describe the design of our VR intervention that aims to address this challenge from the attachment theory perspective with the evacuees. Finally, we discuss the challenges that need to be dealt with to implement the VR interventions through resilience centers in Israel.},
keywords = {MedVR},
pubstate = {published},
tppubtype = {article}
}
Unfortunately, in recent years, wars have forced many civilians to evacuate their homes and move to safe zones. The event of October the seventh that took place in many Kibbutzim near the Gaza strip, exposed families who were on a Jewish holiday, to the murder of family and community members. They had to leave their burned houses and move to hotels and apartment buildings in other parts of Israel. Many people, also from the Northen parts of the country, are still in new safe zones, and have huge difficulties in returning to their houses (and not only because of objective security reasons). In this “perspective” article we propose a Virtual Reality (VR) application, which is based on past and current research in the fields of attachment theory and traumatic grief. We propose that in addition to the use of exposure therapy, a VR simulation which will activate the attachment system, can reorganize the evacuees’ figure and place attachment representations. We suggest that such a simulation will revive the evacuees’ sense of safe-haven and secure base and enable them to return to their home place, or to adjust to a new place, thereby leading to optimal adjustment. We start with a presentation of the theory of attachment, place attachment, attachment and loss and the two-track model of bereavement. Then, we describe the design of our VR intervention that aims to address this challenge from the attachment theory perspective with the evacuees. Finally, we discuss the challenges that need to be dealt with to implement the VR interventions through resilience centers in Israel.
Han, Bin; Yau, Cleo; Lei, Su; Gratch, Jonathan
Knowledge-based Emotion Recognition using Large Language Models Miscellaneous
2024, (arXiv:2408.04123 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{han_knowledge-based_2024,
title = {Knowledge-based Emotion Recognition using Large Language Models},
author = {Bin Han and Cleo Yau and Su Lei and Jonathan Gratch},
url = {http://arxiv.org/abs/2408.04123},
year = {2024},
date = {2024-08-01},
urldate = {2024-08-15},
publisher = {arXiv},
abstract = {Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.},
note = {arXiv:2408.04123 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.
Parga, Madeline R.; Roll, Shawn C.; Lucas, Gale M.; Becerik-Gerber, Burcin; Naranayan, Shrikanth
Differences in Self-Rated Worker Outcomes Across Stress States: An Interim Analysis of Hybrid Worker Data Journal Article
In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2024, ISSN: 1071-1813, 2169-5067, (Publisher: SAGE Publications).
Abstract | Links | BibTeX | Tags:
@article{parga_differences_2024,
title = {Differences in Self-Rated Worker Outcomes Across Stress States: An Interim Analysis of Hybrid Worker Data},
author = {Madeline R. Parga and Shawn C. Roll and Gale M. Lucas and Burcin Becerik-Gerber and Shrikanth Naranayan},
url = {https://journals.sagepub.com/doi/10.1177/10711813241275500},
doi = {10.1177/10711813241275500},
issn = {1071-1813, 2169-5067},
year = {2024},
date = {2024-08-01},
urldate = {2024-09-17},
journal = {Proceedings of the Human Factors and Ergonomics Society Annual Meeting},
abstract = {Stress experiences can have dire consequences for worker performance and well-being, and the social environment of the workplace is a key contributor to worker experience. This study investigated the relationship between hybrid workers’ self-ratings of productivity, mood, and stress with perceptions of positive (eustress) and negative (distress) stress states. We hypothesized that self-ratings would vary across combinations of eustress and distress experiences and that these differences would differ based on the social context. Ecological momentary assessments (EMA) were used to obtain ecologically valid data at four data points each workday across a 4-month study period in a cohort of seven office workers. Findings aligned with the Yerkes–Dodson law, such that higher states of arousal were associated with greater self-perceived productivity, and higher stress magnitudes were found when distress existed. Compared to other states, eustress was associated with higher productivity in work-related activities and better mood across all activity types.},
note = {Publisher: SAGE Publications},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stress experiences can have dire consequences for worker performance and well-being, and the social environment of the workplace is a key contributor to worker experience. This study investigated the relationship between hybrid workers’ self-ratings of productivity, mood, and stress with perceptions of positive (eustress) and negative (distress) stress states. We hypothesized that self-ratings would vary across combinations of eustress and distress experiences and that these differences would differ based on the social context. Ecological momentary assessments (EMA) were used to obtain ecologically valid data at four data points each workday across a 4-month study period in a cohort of seven office workers. Findings aligned with the Yerkes–Dodson law, such that higher states of arousal were associated with greater self-perceived productivity, and higher stress magnitudes were found when distress existed. Compared to other states, eustress was associated with higher productivity in work-related activities and better mood across all activity types.
Tak, Ala N.; Gratch, Jonathan
GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective Miscellaneous
2024, (arXiv:2408.13718 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{tak_gpt-4_2024,
title = {GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective},
author = {Ala N. Tak and Jonathan Gratch},
url = {http://arxiv.org/abs/2408.13718},
year = {2024},
date = {2024-08-01},
urldate = {2024-09-17},
publisher = {arXiv},
abstract = {This paper extends recent investigations on the emotional reasoning abilities of Large Language Models (LLMs). Current research on LLMs has not directly evaluated the distinction between how LLMs predict the self-attribution of emotions and the perception of others' emotions. We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity representing fine-grained inferred emotional attributions of others. We show that GPT-4 is especially accurate in reasoning about such stimuli. This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations. To further explore this, our second study utilizes a dataset containing annotations from both the author and a third-person perspective. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments. Notably, conventional computational models of emotion primarily rely on self-reported ground truth as the gold standard. However, an average observer's standpoint, which LLMs appear to have adopted, might be more relevant for many downstream applications, at least in the absence of individual information and adequate safety considerations.},
note = {arXiv:2408.13718 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This paper extends recent investigations on the emotional reasoning abilities of Large Language Models (LLMs). Current research on LLMs has not directly evaluated the distinction between how LLMs predict the self-attribution of emotions and the perception of others' emotions. We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity representing fine-grained inferred emotional attributions of others. We show that GPT-4 is especially accurate in reasoning about such stimuli. This suggests LLMs agree with humans' attributions of others' emotions in stereotypical scenarios remarkably more than self-attributions of emotions in idiosyncratic situations. To further explore this, our second study utilizes a dataset containing annotations from both the author and a third-person perspective. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments. Notably, conventional computational models of emotion primarily rely on self-reported ground truth as the gold standard. However, an average observer's standpoint, which LLMs appear to have adopted, might be more relevant for many downstream applications, at least in the absence of individual information and adequate safety considerations.
Owayyed, Mohammed Al; Tielman, Myrthe; Hartholt, Arno; Specht, Marcus; Brinkman, Willem-Paul
Agent-based social skills training systems: the ARTES architecture, interaction characteristics, learning theories and future outlooks Journal Article
In: Behaviour & Information Technology, pp. 1–28, 2024, ISSN: 0144-929X, 1362-3001.
@article{al_owayyed_agent-based_2024,
title = {Agent-based social skills training systems: the ARTES architecture, interaction characteristics, learning theories and future outlooks},
author = {Mohammed Al Owayyed and Myrthe Tielman and Arno Hartholt and Marcus Specht and Willem-Paul Brinkman},
url = {https://www.tandfonline.com/doi/full/10.1080/0144929X.2024.2374891},
doi = {10.1080/0144929X.2024.2374891},
issn = {0144-929X, 1362-3001},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-15},
journal = {Behaviour & Information Technology},
pages = {1–28},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bell, Imogen H.; Pot-Kolder, Roos; Rizzo, Albert; Rus-Calafell, Mar; Cardi, Valentina; Cella, Matteo; Ward, Thomas; Riches, Simon; Reinoso, Martin; Thompson, Andrew; Alvarez-Jimenez, Mario; Valmaggia, Lucia
Advances in the use of virtual reality to treat mental health conditions Journal Article
In: Nat Rev Psychol, 2024, ISSN: 2731-0574.
@article{bell_advances_2024,
title = {Advances in the use of virtual reality to treat mental health conditions},
author = {Imogen H. Bell and Roos Pot-Kolder and Albert Rizzo and Mar Rus-Calafell and Valentina Cardi and Matteo Cella and Thomas Ward and Simon Riches and Martin Reinoso and Andrew Thompson and Mario Alvarez-Jimenez and Lucia Valmaggia},
url = {https://www.nature.com/articles/s44159-024-00334-9},
doi = {10.1038/s44159-024-00334-9},
issn = {2731-0574},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-11},
journal = {Nat Rev Psychol},
keywords = {MedVR},
pubstate = {published},
tppubtype = {article}
}
Han, Bin; Yau, Cleo; Lei, Su; Gratch, Jonathan
In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models Miscellaneous
2024, (arXiv:2408.00780 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{han_-depth_2024,
title = {In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models},
author = {Bin Han and Cleo Yau and Su Lei and Jonathan Gratch},
url = {http://arxiv.org/abs/2408.00780},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-15},
publisher = {arXiv},
abstract = {Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.},
note = {arXiv:2408.00780 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.
Xiao, Hanyuan; Chen, Yingshu; Huang, Huajian; Xiong, Haolin; Yang, Jing; Prasad, Pratusha; Zhao, Yajie
Localized Gaussian Splatting Editing with Contextual Awareness Miscellaneous
2024, (arXiv:2408.00083 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{xiao_localized_2024,
title = {Localized Gaussian Splatting Editing with Contextual Awareness},
author = {Hanyuan Xiao and Yingshu Chen and Huajian Huang and Haolin Xiong and Jing Yang and Pratusha Prasad and Yajie Zhao},
url = {http://arxiv.org/abs/2408.00083},
year = {2024},
date = {2024-07-01},
urldate = {2024-08-16},
publisher = {arXiv},
abstract = {Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to illumination mismatches within the environment. To bridge the gap, we introduce an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS) representation. Our key observation is that inpainting by the state-of-the-art conditional 2D diffusion model is consistent with background in lighting. To leverage the prior knowledge from the well-trained diffusion models for 3D object generation, our approach employs a coarse-to-fine objection optimization pipeline with inpainted views. In the first coarse step, we achieve image-to-3D lifting given an ideal inpainted view. The process employs 3D-aware diffusion prior from a view-conditioned diffusion model, which preserves illumination present in the conditioning image. To acquire an ideal inpainted image, we introduce an Anchor View Proposal (AVP) algorithm to find a single view that best represents the scene illumination in target region. In the second Texture Enhancement step, we introduce a novel Depth-guided Inpainting Score Distillation Sampling (DI-SDS), which enhances geometry and texture details with the inpainting diffusion prior, beyond the scope of the 3D-aware diffusion prior knowledge in the first coarse step. DI-SDS not only provides fine-grained texture enhancement, but also urges optimization to respect scene lighting. Our approach efficiently achieves local editing with global illumination consistency without explicitly modeling light transport. We demonstrate robustness of our method by evaluating editing in real scenes containing explicit highlight and shadows, and compare against the state-of-the-art text-to-3D editing methods.},
note = {arXiv:2408.00083 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacement tasks as they do not consider the background, leading to illumination mismatches within the environment. To bridge the gap, we introduce an illumination-aware 3D scene editing pipeline for 3D Gaussian Splatting (3DGS) representation. Our key observation is that inpainting by the state-of-the-art conditional 2D diffusion model is consistent with background in lighting. To leverage the prior knowledge from the well-trained diffusion models for 3D object generation, our approach employs a coarse-to-fine objection optimization pipeline with inpainted views. In the first coarse step, we achieve image-to-3D lifting given an ideal inpainted view. The process employs 3D-aware diffusion prior from a view-conditioned diffusion model, which preserves illumination present in the conditioning image. To acquire an ideal inpainted image, we introduce an Anchor View Proposal (AVP) algorithm to find a single view that best represents the scene illumination in target region. In the second Texture Enhancement step, we introduce a novel Depth-guided Inpainting Score Distillation Sampling (DI-SDS), which enhances geometry and texture details with the inpainting diffusion prior, beyond the scope of the 3D-aware diffusion prior knowledge in the first coarse step. DI-SDS not only provides fine-grained texture enhancement, but also urges optimization to respect scene lighting. Our approach efficiently achieves local editing with global illumination consistency without explicitly modeling light transport. We demonstrate robustness of our method by evaluating editing in real scenes containing explicit highlight and shadows, and compare against the state-of-the-art text-to-3D editing methods.
Liu, Ruying; Wu, Wanjing; Becerik-Gerber, Burcin; Lucas, Gale M.
2024, (arXiv:2407.10441 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{liu_enhancing_2024,
title = {Enhancing Building Safety Design for Active Shooter Incidents: Exploration of Building Exit Parameters using Reinforcement Learning-Based Simulations},
author = {Ruying Liu and Wanjing Wu and Burcin Becerik-Gerber and Gale M. Lucas},
url = {http://arxiv.org/abs/2407.10441},
year = {2024},
date = {2024-07-01},
urldate = {2024-09-17},
publisher = {arXiv},
abstract = {With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.},
note = {arXiv:2407.10441 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.
Lu, Shuhong; Jin, Zhangyu; Rajendran, Vickram; Harari, Michal; Feng, Andrew; Melo, Celso M. De
Synthetic-to-real adaptation for complex action recognition in surveillance applications Proceedings Article
In: Manser, Kimberly E.; Melo, Celso De; Rao, Raghuveer M.; Howell, Christopher L. (Ed.): Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, pp. 14, SPIE, National Harbor, United States, 2024, ISBN: 978-1-5106-7388-5 978-1-5106-7389-2.
@inproceedings{lu_synthetic–real_2024,
title = {Synthetic-to-real adaptation for complex action recognition in surveillance applications},
author = {Shuhong Lu and Zhangyu Jin and Vickram Rajendran and Michal Harari and Andrew Feng and Celso M. De Melo},
editor = {Kimberly E. Manser and Celso De Melo and Raghuveer M. Rao and Christopher L. Howell},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13035/3012393/Synthetic-to-real-adaptation-for-complex-action-recognition-in-surveillance/10.1117/12.3012393.full},
doi = {10.1117/12.3012393},
isbn = {978-1-5106-7388-5 978-1-5106-7389-2},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-11},
booktitle = {Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II},
pages = {14},
publisher = {SPIE},
address = {National Harbor, United States},
keywords = {DTIC},
pubstate = {published},
tppubtype = {inproceedings}
}
Nurunnabi, Abdul; Teferle, Felicia; Laefer, Debra F.; Chen, Meida; Ali, Mir Masoom
Development of a Precise Tree Structure from LiDAR Point Clouds Journal Article
In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLVIII-2-2024, pp. 301–308, 2024, ISSN: 2194-9034.
Abstract | Links | BibTeX | Tags: Narrative, VGL
@article{nurunnabi_development_2024,
title = {Development of a Precise Tree Structure from LiDAR Point Clouds},
author = {Abdul Nurunnabi and Felicia Teferle and Debra F. Laefer and Meida Chen and Mir Masoom Ali},
url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/301/2024/},
doi = {10.5194/isprs-archives-XLVIII-2-2024-301-2024},
issn = {2194-9034},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-11},
journal = {Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.},
volume = {XLVIII-2-2024},
pages = {301–308},
abstract = {Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.},
keywords = {Narrative, VGL},
pubstate = {published},
tppubtype = {article}
}
Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.
Zhang, Mingyuan; Cai, Zhongang; Pan, Liang; Hong, Fangzhou; Guo, Xinying; Yang, Lei; Liu, Ziwei
MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model Journal Article
In: IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 6, pp. 4115–4128, 2024, ISSN: 0162-8828, 2160-9292, 1939-3539.
@article{zhang_motiondiffuse_2024,
title = {MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model},
author = {Mingyuan Zhang and Zhongang Cai and Liang Pan and Fangzhou Hong and Xinying Guo and Lei Yang and Ziwei Liu},
url = {https://ieeexplore.ieee.org/document/10416192/},
doi = {10.1109/TPAMI.2024.3355414},
issn = {0162-8828, 2160-9292, 1939-3539},
year = {2024},
date = {2024-06-01},
urldate = {2024-07-18},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
volume = {46},
number = {6},
pages = {4115–4128},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
Yin, Yinxuan; Nayyar, Mollik; Holman, Daniel; Lucas, Gale; Holbrook, Colin; Wagner, Alan
Validation and Evacuee Modeling of Virtual Robot-guided Emergency Evacuation Experiments Miscellaneous
2024.
Abstract | Links | BibTeX | Tags:
@misc{yin_validation_2024,
title = {Validation and Evacuee Modeling of Virtual Robot-guided Emergency Evacuation Experiments},
author = {Yinxuan Yin and Mollik Nayyar and Daniel Holman and Gale Lucas and Colin Holbrook and Alan Wagner},
url = {https://osf.io/mr78s},
doi = {10.31234/osf.io/mr78s},
year = {2024},
date = {2024-06-01},
urldate = {2024-09-17},
publisher = {Center for Open Science},
abstract = {Virtual Reality (VR) is an increasingly common tool for investigating human responses to emergency situations. Nonetheless, studies validating and comparing human subject behavior during real world emergencies to their responses in VR are notably rare, and no prior studies have validated whether human emergency responses to guidance from a robot are comparable in VR versus the real world. In the present pre-registered study, we used VR to replicate a previous robot- guided emergency evacuation study conducted in the real world and compared human subject behavior in matched physical and virtual environments. In both environments, human subjects were asked to follow a robot to a location and to then read an article. While reading, a fire alarm sounds. The robot then attempted to guide them to a distant, unfamiliar exit rather than nearby and familiar exits. We observed close correspondences between evacuee exit choice (the robot’s distant exit versus closer exits), evacuation time, and trust in the robot between the VR and physical environments. We further demonstrate that data collected in virtual reality can be used to create accurate motion models (mean error of 0.42 centimeters) predicting evacuee trajectories and locations in real life. Taken together, the results provide evidence for the ecological validity of VR approaches to studying human-robot interaction, particularly robot- guided emergency evacuation.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Virtual Reality (VR) is an increasingly common tool for investigating human responses to emergency situations. Nonetheless, studies validating and comparing human subject behavior during real world emergencies to their responses in VR are notably rare, and no prior studies have validated whether human emergency responses to guidance from a robot are comparable in VR versus the real world. In the present pre-registered study, we used VR to replicate a previous robot- guided emergency evacuation study conducted in the real world and compared human subject behavior in matched physical and virtual environments. In both environments, human subjects were asked to follow a robot to a location and to then read an article. While reading, a fire alarm sounds. The robot then attempted to guide them to a distant, unfamiliar exit rather than nearby and familiar exits. We observed close correspondences between evacuee exit choice (the robot’s distant exit versus closer exits), evacuation time, and trust in the robot between the VR and physical environments. We further demonstrate that data collected in virtual reality can be used to create accurate motion models (mean error of 0.42 centimeters) predicting evacuee trajectories and locations in real life. Taken together, the results provide evidence for the ecological validity of VR approaches to studying human-robot interaction, particularly robot- guided emergency evacuation.
Saxon, Leslie; Faulk, Robert T; Boberg, Jill; Barrett, Trevor; McLelland, Steve
In: J. Spec. Oper. Med., 2024, ISSN: 1553-9768.
Links | BibTeX | Tags: CBC, DTIC
@article{saxon_continuous_2024,
title = {Continuous Assessment of Active-Duty Army Special Operations and Reconnaissance Marines Using Digital Devices and Custom Software: The Digital Comprehensive Operator Readiness Assessment (DcORA) Study},
author = {Leslie Saxon and Robert T Faulk and Jill Boberg and Trevor Barrett and Steve McLelland},
url = {https://www.jsomonline.org/Citations/PXKK-I23D.php},
doi = {10.55460/PXKK-I23D},
issn = {1553-9768},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-25},
journal = {J. Spec. Oper. Med.},
keywords = {CBC, DTIC},
pubstate = {published},
tppubtype = {article}
}
Greenwald, Eric; Krakowski, Ari; Hurt, Timothy; Grindstaff, Kelly; Wang, Ning
It's like I'm the AI: Youth Sensemaking About AI through Metacognitive Embodiment Proceedings Article
In: Proceedings of the 23rd Annual ACM Interaction Design and Children Conference, pp. 789–793, ACM, Delft Netherlands, 2024, ISBN: 9798400704420.
Links | BibTeX | Tags: AI, DTIC, Machine Learning
@inproceedings{greenwald_its_2024,
title = {It's like I'm the AI: Youth Sensemaking About AI through Metacognitive Embodiment},
author = {Eric Greenwald and Ari Krakowski and Timothy Hurt and Kelly Grindstaff and Ning Wang},
url = {https://dl.acm.org/doi/10.1145/3628516.3659395},
doi = {10.1145/3628516.3659395},
isbn = {9798400704420},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-25},
booktitle = {Proceedings of the 23rd Annual ACM Interaction Design and Children Conference},
pages = {789–793},
publisher = {ACM},
address = {Delft Netherlands},
keywords = {AI, DTIC, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Chen, Meida; Lal, Devashish; Yu, Zifan; Xu, Jiuyi; Feng, Andrew; You, Suya; Nurunnabi, Abdul; Shi, Yangming
Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation Journal Article
In: Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLVIII-2-2024, pp. 49–54, 2024, ISSN: 2194-9034.
Abstract | Links | BibTeX | Tags: DTIC, Graphics, VGL
@article{chen_large-scale_2024,
title = {Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation},
author = {Meida Chen and Devashish Lal and Zifan Yu and Jiuyi Xu and Andrew Feng and Suya You and Abdul Nurunnabi and Yangming Shi},
url = {https://isprs-archives.copernicus.org/articles/XLVIII-2-2024/49/2024/},
doi = {10.5194/isprs-archives-XLVIII-2-2024-49-2024},
issn = {2194-9034},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-20},
journal = {Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.},
volume = {XLVIII-2-2024},
pages = {49–54},
abstract = {Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.},
keywords = {DTIC, Graphics, VGL},
pubstate = {published},
tppubtype = {article}
}
Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by adopting a hybrid approach, combining Gaussian Splatting with photogrammetry-derived meshes. Through comprehensive experimentation covering varying terrain sizes and Gaussian densities, we evaluate scalability, performance, and limitations. Our findings contribute to advancing the use of advanced computer graphics techniques for enhanced 3D terrain visualization and simulation.
Nye, Benjamin D.; Core, Mark G.; Chereddy, Sai V. R.; Young, Vivian; Auerbach, Daniel
Bootstrapping Assessments for Team Simulations: Transfer Learning Between First-Person-Shooter Game Maps Book Section
In: Sottilare, Robert A.; Schwarz, Jessica (Ed.): Adaptive Instructional Systems, vol. 14727, pp. 261–271, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-60608-3 978-3-031-60609-0, (Series Title: Lecture Notes in Computer Science).
Links | BibTeX | Tags: DTIC, Learning Sciences, Machine Learning, UARC
@incollection{sottilare_bootstrapping_2024,
title = {Bootstrapping Assessments for Team Simulations: Transfer Learning Between First-Person-Shooter Game Maps},
author = {Benjamin D. Nye and Mark G. Core and Sai V. R. Chereddy and Vivian Young and Daniel Auerbach},
editor = {Robert A. Sottilare and Jessica Schwarz},
url = {https://link.springer.com/10.1007/978-3-031-60609-0_19},
doi = {10.1007/978-3-031-60609-0_19},
isbn = {978-3-031-60608-3 978-3-031-60609-0},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-18},
booktitle = {Adaptive Instructional Systems},
volume = {14727},
pages = {261–271},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {DTIC, Learning Sciences, Machine Learning, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Core, Mark G.; Nye, Benjamin D.; Fegley, Brent D.
Trend-Aware Scenario Authoring: Adapting Training Toward Patterns from Real Operations Book Section
In: Sottilare, Robert A.; Schwarz, Jessica (Ed.): Adaptive Instructional Systems, vol. 14727, pp. 15–24, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-60608-3 978-3-031-60609-0, (Series Title: Lecture Notes in Computer Science).
Links | BibTeX | Tags: DTIC, Learning Sciences, UARC
@incollection{sottilare_trend-aware_2024,
title = {Trend-Aware Scenario Authoring: Adapting Training Toward Patterns from Real Operations},
author = {Mark G. Core and Benjamin D. Nye and Brent D. Fegley},
editor = {Robert A. Sottilare and Jessica Schwarz},
url = {https://link.springer.com/10.1007/978-3-031-60609-0_2},
doi = {10.1007/978-3-031-60609-0_2},
isbn = {978-3-031-60608-3 978-3-031-60609-0},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-18},
booktitle = {Adaptive Instructional Systems},
volume = {14727},
pages = {15–24},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {DTIC, Learning Sciences, UARC},
pubstate = {published},
tppubtype = {incollection}
}
Bohy, Hugo; Tran, Minh; Haddad, Kevin El; Dutoit, Thierry; Soleymani, Mohammad
Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice Proceedings Article
In: 2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pp. 1–5, IEEE, Istanbul, Turkiye, 2024, ISBN: 9798350394948.
@inproceedings{bohy_social-mae_2024,
title = {Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice},
author = {Hugo Bohy and Minh Tran and Kevin El Haddad and Thierry Dutoit and Mohammad Soleymani},
url = {https://ieeexplore.ieee.org/document/10581940/},
doi = {10.1109/FG59268.2024.10581940},
isbn = {9798350394948},
year = {2024},
date = {2024-05-01},
urldate = {2024-07-18},
booktitle = {2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)},
pages = {1–5},
publisher = {IEEE},
address = {Istanbul, Turkiye},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Liu, Rong; Xu, Rui; Hu, Yue; Chen, Meida; Feng, Andrew
AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field Miscellaneous
2024, (Version Number: 2).
Abstract | Links | BibTeX | Tags: Graphics, VGL
@misc{liu_atomgs_2024,
title = {AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field},
author = {Rong Liu and Rui Xu and Yue Hu and Meida Chen and Andrew Feng},
url = {https://arxiv.org/abs/2405.12369},
doi = {10.48550/ARXIV.2405.12369},
year = {2024},
date = {2024-05-01},
urldate = {2024-07-11},
publisher = {arXiv},
abstract = {3D Gaussian Splatting (3DGS) has recently advanced radiance field reconstruction by offering superior capabilities for novel view synthesis and real-time rendering speed. However, its strategy of blending optimization and adaptive density control might lead to sub-optimal results; it can sometimes yield noisy geometry and blurry artifacts due to prioritizing optimizing large Gaussians at the cost of adequately densifying smaller ones. To address this, we introduce AtomGS, consisting of Atomized Proliferation and Geometry-Guided Optimization. The Atomized Proliferation constrains ellipsoid Gaussians of various sizes into more uniform-sized Atom Gaussians. The strategy enhances the representation of areas with fine features by placing greater emphasis on densification in accordance with scene details. In addition, we proposed a Geometry-Guided Optimization approach that incorporates an Edge-Aware Normal Loss. This optimization method effectively smooths flat surfaces while preserving intricate details. Our evaluation shows that AtomGS outperforms existing state-of-the-art methods in rendering quality. Additionally, it achieves competitive accuracy in geometry reconstruction and offers a significant improvement in training speed over other SDF-based methods. More interactive demos can be found in our website (https://rongliu-leo.github.io/AtomGS/).},
note = {Version Number: 2},
keywords = {Graphics, VGL},
pubstate = {published},
tppubtype = {misc}
}
3D Gaussian Splatting (3DGS) has recently advanced radiance field reconstruction by offering superior capabilities for novel view synthesis and real-time rendering speed. However, its strategy of blending optimization and adaptive density control might lead to sub-optimal results; it can sometimes yield noisy geometry and blurry artifacts due to prioritizing optimizing large Gaussians at the cost of adequately densifying smaller ones. To address this, we introduce AtomGS, consisting of Atomized Proliferation and Geometry-Guided Optimization. The Atomized Proliferation constrains ellipsoid Gaussians of various sizes into more uniform-sized Atom Gaussians. The strategy enhances the representation of areas with fine features by placing greater emphasis on densification in accordance with scene details. In addition, we proposed a Geometry-Guided Optimization approach that incorporates an Edge-Aware Normal Loss. This optimization method effectively smooths flat surfaces while preserving intricate details. Our evaluation shows that AtomGS outperforms existing state-of-the-art methods in rendering quality. Additionally, it achieves competitive accuracy in geometry reconstruction and offers a significant improvement in training speed over other SDF-based methods. More interactive demos can be found in our website (https://rongliu-leo.github.io/AtomGS/).
Chang, Di; Shi, Yichun; Gao, Quankai; Fu, Jessica; Xu, Hongyi; Song, Guoxian; Yan, Qing; Zhu, Yizhe; Yang, Xiao; Soleymani, Mohammad
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion Miscellaneous
2024, (arXiv:2311.12052 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{chang_magicpose_2024,
title = {MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion},
author = {Di Chang and Yichun Shi and Quankai Gao and Jessica Fu and Hongyi Xu and Guoxian Song and Qing Yan and Yizhe Zhu and Xiao Yang and Mohammad Soleymani},
url = {http://arxiv.org/abs/2311.12052},
year = {2024},
date = {2024-05-01},
urldate = {2024-07-18},
publisher = {arXiv},
abstract = {In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting. Specifically, given a reference image, we aim to generate a person's new images by controlling the poses and facial expressions while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of (1) the pre-training of an appearance-control block and (2) learning appearance-disentangled pose control. Our novel design enables robust appearance control over generated human images, including body, facial attributes, and even background. By leveraging the prior knowledge of image diffusion models, MagicPose generalizes well to unseen human identities and complex poses without the need for additional fine-tuning. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. The code is available at: https://github.com/Boese0601/MagicDance},
note = {arXiv:2311.12052 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting. Specifically, given a reference image, we aim to generate a person's new images by controlling the poses and facial expressions while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of (1) the pre-training of an appearance-control block and (2) learning appearance-disentangled pose control. Our novel design enables robust appearance control over generated human images, including body, facial attributes, and even background. By leveraging the prior knowledge of image diffusion models, MagicPose generalizes well to unseen human identities and complex poses without the need for additional fine-tuning. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. The code is available at: https://github.com/Boese0601/MagicDance
Koresh, Caleb; Ustun, Volkan; Kumar, Rajay; Aris, Tim
Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Machine Learning
@article{koresh_improving_2024,
title = {Improving Reinforcement Learning Experiments in Unity through Waypoint Utilization},
author = {Caleb Koresh and Volkan Ustun and Rajay Kumar and Tim Aris},
url = {https://journals.flvc.org/FLAIRS/article/view/135571},
doi = {10.32473/flairs.37.1.135571},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {Multi-agent Reinforcement Learning (MARL) models teams of agents that learn by dynamically interacting with an environment and each other, presenting opportunities to train adaptive models for team-based scenarios. However, MARL algorithms pose substantial challenges due to their immense computational requirements. This paper introduces an automatically generated waypoint-based movement system to abstract and simplify complex environments in Unity while allowing agents to learn strategic cooperation. To demonstrate the effectiveness of our approach, we utilized a simple scenario with heterogeneous roles in each team. We trained this scenario on variations of realistic terrains and compared learning between fine-grained (almost) continuous and waypoint-based movement systems. Our results indicate efficiency in learning and improved performance with waypoint-based navigation. Furthermore, our results show that waypoint-based movement systems can effectively learn differentiated behavior policies for heterogeneous roles in these experiments. These early exploratory results point out the potential of waypoint-based navigation for reducing the computational costs of developing and training MARL models in complex environments. The complete project with all scenarios and results is available on GitHub: https://github.com/HATS-ICT/ml-agents-dodgeball-env-ICT.},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Multi-agent Reinforcement Learning (MARL) models teams of agents that learn by dynamically interacting with an environment and each other, presenting opportunities to train adaptive models for team-based scenarios. However, MARL algorithms pose substantial challenges due to their immense computational requirements. This paper introduces an automatically generated waypoint-based movement system to abstract and simplify complex environments in Unity while allowing agents to learn strategic cooperation. To demonstrate the effectiveness of our approach, we utilized a simple scenario with heterogeneous roles in each team. We trained this scenario on variations of realistic terrains and compared learning between fine-grained (almost) continuous and waypoint-based movement systems. Our results indicate efficiency in learning and improved performance with waypoint-based navigation. Furthermore, our results show that waypoint-based movement systems can effectively learn differentiated behavior policies for heterogeneous roles in these experiments. These early exploratory results point out the potential of waypoint-based navigation for reducing the computational costs of developing and training MARL models in complex environments. The complete project with all scenarios and results is available on GitHub: https://github.com/HATS-ICT/ml-agents-dodgeball-env-ICT.
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Training Reinforcement Learning Agents to React to an Ambush for Military Simulations Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Simulation, VR
@article{aris_training_2024,
title = {Training Reinforcement Learning Agents to React to an Ambush for Military Simulations},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/135578},
doi = {10.32473/flairs.37.1.135578},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {There is a need for realistic Opposing Forces (OPFOR)behavior in military training simulations. Current trainingsimulations generally only have simple, non-adaptivebehaviors, requiring human instructors to play the role ofOPFOR in any complicated scenario. This poster addressesthis need by focusing on a specific scenario: trainingreinforcement learning agents to react to an ambush. Itproposes a novel way to check for occlusion algorithmically.It shows vector fields showing the agent’s actions throughthe course of a training run. It shows that a single agentswitching between multiple goals is possible, at least in asimplified environment. Such an approach could reduce theneed to develop different agents for different scenarios.Finally, it shows a competent agent trained on a simplifiedReact to Ambush scenario, demonstrating the plausibility ofa scaled-up version.},
keywords = {Simulation, VR},
pubstate = {published},
tppubtype = {article}
}
There is a need for realistic Opposing Forces (OPFOR)behavior in military training simulations. Current trainingsimulations generally only have simple, non-adaptivebehaviors, requiring human instructors to play the role ofOPFOR in any complicated scenario. This poster addressesthis need by focusing on a specific scenario: trainingreinforcement learning agents to react to an ambush. Itproposes a novel way to check for occlusion algorithmically.It shows vector fields showing the agent’s actions throughthe course of a training run. It shows that a single agentswitching between multiple goals is possible, at least in asimplified environment. Such an approach could reduce theneed to develop different agents for different scenarios.Finally, it shows a competent agent trained on a simplifiedReact to Ambush scenario, demonstrating the plausibility ofa scaled-up version.
Liu, Lixing; Ustun, Volkan; Kumar, Rajay
Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning Journal Article
In: FLAIRS, vol. 37, 2024, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: Machine Learning
@article{liu_leveraging_2024,
title = {Leveraging Organizational Hierarchy to Simplify Reward Design in Cooperative Multi-agent Reinforcement Learning},
author = {Lixing Liu and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/135588},
doi = {10.32473/flairs.37.1.135588},
issn = {2334-0762},
year = {2024},
date = {2024-05-01},
urldate = {2024-08-13},
journal = {FLAIRS},
volume = {37},
abstract = {The effectiveness of multi-agent reinforcement learning (MARL) hinges largely on the meticulous arrangement of objectives. Yet, conventional MARL methods might not completely harness the inherent structures present in environmental states and agent relationships for goal organization. This study is conducted within the domain of military training simulations, which are typically characterized by complexity, heterogeneity, non-stationary and doctrine-driven environments with a clear organizational hierarchy and a top-down chain of command. This research investigates the approximation and integration of the organizational hierarchy into MARL for cooperative training scenarios, with the goal of streamlining the processes of reward engineering and enhancing team coordination. In the preliminary experiments, we employed two-tiered commander-subordinate feudal hierarchical (CSFH) networks to separate the prioritized team goal and individual goals. The empirical results demonstrate that the proposed framework enhances learning efficiency. It guarantees the learning of a prioritized policy for the commander agent and encourages subordinate agents to explore areas of interest more frequently, guided by appropriate soft constraints imposed by the commander.},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {article}
}
The effectiveness of multi-agent reinforcement learning (MARL) hinges largely on the meticulous arrangement of objectives. Yet, conventional MARL methods might not completely harness the inherent structures present in environmental states and agent relationships for goal organization. This study is conducted within the domain of military training simulations, which are typically characterized by complexity, heterogeneity, non-stationary and doctrine-driven environments with a clear organizational hierarchy and a top-down chain of command. This research investigates the approximation and integration of the organizational hierarchy into MARL for cooperative training scenarios, with the goal of streamlining the processes of reward engineering and enhancing team coordination. In the preliminary experiments, we employed two-tiered commander-subordinate feudal hierarchical (CSFH) networks to separate the prioritized team goal and individual goals. The empirical results demonstrate that the proposed framework enhances learning efficiency. It guarantees the learning of a prioritized policy for the commander agent and encourages subordinate agents to explore areas of interest more frequently, guided by appropriate soft constraints imposed by the commander.
Lukin, Stephanie M; Bonial, Claire; Marge, Matthew; Hudson, Taylor; Hayes, Cory J.; Pollard, Kimberly; Baker, Anthony L.; Foots, Ashley; Artstein, Ron; Gervits, Felix; Abrams, Mitchell; Cassidy, Henry; Donatelli, Lucia; Leuski, Anton; Hill, Susan G.; Traum, David; Voss, Clare
SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus Journal Article
In: pp. 14445 - 144458, 2024.
Abstract | Links | BibTeX | Tags:
@article{lukin-etal-2024-scout-situated,
title = {SCOUT: A Situated and Multi-Modal Human-Robot Dialogue Corpus},
author = {Stephanie M Lukin and Claire Bonial and Matthew Marge and Taylor Hudson and Cory J. Hayes and Kimberly Pollard and Anthony L. Baker and Ashley Foots and Ron Artstein and Felix Gervits and Mitchell Abrams and Henry Cassidy and Lucia Donatelli and Anton Leuski and Susan G. Hill and David Traum and Clare Voss},
url = {https://aclanthology.org/2024.lrec-main.1259},
year = {2024},
date = {2024-05-01},
pages = {14445 - 144458},
abstract = {We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker’s intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker’s intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered.
West, Taylor Nicole; Prinzing, Michael; Garton, Catherine; Berman, Catherine J.; Zhou, Jieni; Hale, James; Gratch, Jonathan; Fredrickson, Barbara
2024.
Abstract | Links | BibTeX | Tags: Emotions, Virtual Humans
@misc{west_improving_2024,
title = {Improving Social Connection with Weak Ties and Strangers: Effects of a New Micro-Intervention on Interaction Quality and Social Behavior},
author = {Taylor Nicole West and Michael Prinzing and Catherine Garton and Catherine J. Berman and Jieni Zhou and James Hale and Jonathan Gratch and Barbara Fredrickson},
url = {https://osf.io/ytjr6},
doi = {10.31234/osf.io/ytjr6},
year = {2024},
date = {2024-05-01},
urldate = {2024-06-25},
abstract = {We propose that the emotional quality of people’s interactions with acquaintances (i.e., weak ties) and strangers contributes to well-being. We test whether a new micro-intervention can raise the quality of these interactions. We randomized young adults (N = 335) to this connectedness micro-intervention or a control intervention. Both interventions were delivered via a psychoeducational video followed by a brief conversation with a virtual human, with whom participants developed if-then plans to carry out their assigned behavioral goal. Pre-intervention, high-quality weak-tie and stranger interactions were associated with lower loneliness and greater mental health independent of strong-tie interaction quality. Experimental data showed the connectedness intervention improved the emotional quality of participants' interactions with weak ties and strangers over two days, evident in participants’ episodic self-reports and faster in-lab conversational response time. Discussion centers on implications for developing scalable behavioral interventions to improve well-being.},
keywords = {Emotions, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
We propose that the emotional quality of people’s interactions with acquaintances (i.e., weak ties) and strangers contributes to well-being. We test whether a new micro-intervention can raise the quality of these interactions. We randomized young adults (N = 335) to this connectedness micro-intervention or a control intervention. Both interventions were delivered via a psychoeducational video followed by a brief conversation with a virtual human, with whom participants developed if-then plans to carry out their assigned behavioral goal. Pre-intervention, high-quality weak-tie and stranger interactions were associated with lower loneliness and greater mental health independent of strong-tie interaction quality. Experimental data showed the connectedness intervention improved the emotional quality of participants' interactions with weak ties and strangers over two days, evident in participants’ episodic self-reports and faster in-lab conversational response time. Discussion centers on implications for developing scalable behavioral interventions to improve well-being.
Zhang, Hao; Chang, Di; Li, Fang; Soleymani, Mohammad; Ahuja, Narendra
MagicPose4D: Crafting Articulated Models with Appearance and Motion Control Miscellaneous
2024, (Version Number: 1).
Abstract | Links | BibTeX | Tags: VGL, Virtual Humans
@misc{zhang_magicpose4d_2024,
title = {MagicPose4D: Crafting Articulated Models with Appearance and Motion Control},
author = {Hao Zhang and Di Chang and Fang Li and Mohammad Soleymani and Narendra Ahuja},
url = {https://arxiv.org/abs/2405.14017},
doi = {10.48550/ARXIV.2405.14017},
year = {2024},
date = {2024-05-01},
urldate = {2024-06-25},
publisher = {arXiv},
abstract = {With the success of 2D and 3D visual generative models, there is growing interest in generating 4D content. Existing methods primarily rely on text prompts to produce 4D content, but they often fall short of accurately defining complex or rare motions. To address this limitation, we propose MagicPose4D, a novel framework for refined control over both appearance and motion in 4D generation. Unlike traditional methods, MagicPose4D accepts monocular videos as motion prompts, enabling precise and customizable motion generation. MagicPose4D comprises two key modules:
i) Dual-Phase 4D Reconstruction Modulevphantom which operates in two phases. The first phase focuses on capturing the model's shape using accurate 2D supervision and less accurate but geometrically informative 3D pseudo-supervision without imposing skeleton constraints. The second phase refines the model using more accurate pseudo-3D supervision, obtained in the first phase and introduces kinematic chain-based skeleton constraints to ensure physical plausibility. Additionally, we propose a Global-local Chamfer loss that aligns the overall distribution of predicted mesh vertices with the supervision while maintaining part-level alignment without extra annotations.
ii) Cross-category Motion Transfer Modulevphantom leverages the predictions from the 4D reconstruction module and uses a kinematic-chain-based skeleton to achieve cross-category motion transfer. It ensures smooth transitions between frames through dynamic rigidity, facilitating robust generalization without additional training.
Through extensive experiments, we demonstrate that MagicPose4D significantly improves the accuracy and consistency of 4D content generation, outperforming existing methods in various benchmarks.},
note = {Version Number: 1},
keywords = {VGL, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
With the success of 2D and 3D visual generative models, there is growing interest in generating 4D content. Existing methods primarily rely on text prompts to produce 4D content, but they often fall short of accurately defining complex or rare motions. To address this limitation, we propose MagicPose4D, a novel framework for refined control over both appearance and motion in 4D generation. Unlike traditional methods, MagicPose4D accepts monocular videos as motion prompts, enabling precise and customizable motion generation. MagicPose4D comprises two key modules:
i) Dual-Phase 4D Reconstruction Modulevphantom which operates in two phases. The first phase focuses on capturing the model's shape using accurate 2D supervision and less accurate but geometrically informative 3D pseudo-supervision without imposing skeleton constraints. The second phase refines the model using more accurate pseudo-3D supervision, obtained in the first phase and introduces kinematic chain-based skeleton constraints to ensure physical plausibility. Additionally, we propose a Global-local Chamfer loss that aligns the overall distribution of predicted mesh vertices with the supervision while maintaining part-level alignment without extra annotations.
ii) Cross-category Motion Transfer Modulevphantom leverages the predictions from the 4D reconstruction module and uses a kinematic-chain-based skeleton to achieve cross-category motion transfer. It ensures smooth transitions between frames through dynamic rigidity, facilitating robust generalization without additional training.
Through extensive experiments, we demonstrate that MagicPose4D significantly improves the accuracy and consistency of 4D content generation, outperforming existing methods in various benchmarks.
i) Dual-Phase 4D Reconstruction Modulevphantom which operates in two phases. The first phase focuses on capturing the model's shape using accurate 2D supervision and less accurate but geometrically informative 3D pseudo-supervision without imposing skeleton constraints. The second phase refines the model using more accurate pseudo-3D supervision, obtained in the first phase and introduces kinematic chain-based skeleton constraints to ensure physical plausibility. Additionally, we propose a Global-local Chamfer loss that aligns the overall distribution of predicted mesh vertices with the supervision while maintaining part-level alignment without extra annotations.
ii) Cross-category Motion Transfer Modulevphantom leverages the predictions from the 4D reconstruction module and uses a kinematic-chain-based skeleton to achieve cross-category motion transfer. It ensures smooth transitions between frames through dynamic rigidity, facilitating robust generalization without additional training.
Through extensive experiments, we demonstrate that MagicPose4D significantly improves the accuracy and consistency of 4D content generation, outperforming existing methods in various benchmarks.
Jones, Brennan; Xu, Yan; Li, Qisheng; Scherer, Stefan
Designing a Proactive Context-Aware AI Chatbot for People's Long-Term Goals Proceedings Article
In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pp. 1–7, ACM, Honolulu HI USA, 2024, ISBN: 9798400703317.
Links | BibTeX | Tags: AI, Simulation
@inproceedings{jones_designing_2024,
title = {Designing a Proactive Context-Aware AI Chatbot for People's Long-Term Goals},
author = {Brennan Jones and Yan Xu and Qisheng Li and Stefan Scherer},
url = {https://dl.acm.org/doi/10.1145/3613905.3650912},
doi = {10.1145/3613905.3650912},
isbn = {9798400703317},
year = {2024},
date = {2024-05-01},
urldate = {2024-06-25},
booktitle = {Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
pages = {1–7},
publisher = {ACM},
address = {Honolulu HI USA},
keywords = {AI, Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Chemburkar, Ankur; Gordon, Andrew; Feng, Andrew
Evaluating Vision-Language Models on the TriangleCOPA Benchmark Journal Article
In: FLAIRS-37, vol. 37, 2024.
Abstract | BibTeX | Tags: DTIC, Narrative
@article{chemburkar_evaluating_2024,
title = {Evaluating Vision-Language Models on the TriangleCOPA Benchmark},
author = {Ankur Chemburkar and Andrew Gordon and Andrew Feng},
year = {2024},
date = {2024-05-01},
journal = {FLAIRS-37},
volume = {37},
abstract = {The TriangleCOPA benchmark consists of 100 textual questions with videos depicting the movements of simple shapes in the style of the classic social-psychology film created by Fritz Heider and Marianne Simmel in 1944. In our experiments, we investigate the performance of current vision-language models on this challenging benchmark, assessing the capability of these models for visual anthropomorphism and abstract interpretation.},
keywords = {DTIC, Narrative},
pubstate = {published},
tppubtype = {article}
}
The TriangleCOPA benchmark consists of 100 textual questions with videos depicting the movements of simple shapes in the style of the classic social-psychology film created by Fritz Heider and Marianne Simmel in 1944. In our experiments, we investigate the performance of current vision-language models on this challenging benchmark, assessing the capability of these models for visual anthropomorphism and abstract interpretation.
Mozgai, Sharon A; Kaurloto, Cari; Winn, Jade G; Leeds, Andrew; Beland, Sarah; Sookiassian, Arman; Hartholt, Arno
Accelerating Scoping Reviews: A Case Study in the User-Centered Design of an AI-Enabled Interdisciplinary Research Tool Proceedings Article
In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pp. 1–8, ACM, Honolulu HI USA, 2024, ISBN: 9798400703317.
Links | BibTeX | Tags: AI, DTIC, UARC, Virtual Humans
@inproceedings{mozgai_accelerating_2024,
title = {Accelerating Scoping Reviews: A Case Study in the User-Centered Design of an AI-Enabled Interdisciplinary Research Tool},
author = {Sharon A Mozgai and Cari Kaurloto and Jade G Winn and Andrew Leeds and Sarah Beland and Arman Sookiassian and Arno Hartholt},
url = {https://dl.acm.org/doi/10.1145/3613905.3637110},
doi = {10.1145/3613905.3637110},
isbn = {9798400703317},
year = {2024},
date = {2024-05-01},
urldate = {2024-06-18},
booktitle = {Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
pages = {1–8},
publisher = {ACM},
address = {Honolulu HI USA},
keywords = {AI, DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {inproceedings}
}
Soleymani, Mohammad; Kumano, Shiro; Provost, Emily Mower; Bianchi-Berthouze, Nadia; Sano, Akane; Suzuki, Kenji
Guest Editorial Best of ACII 2021 Journal Article
In: IEEE Trans. Affective Comput., vol. 15, no. 2, pp. 376–379, 2024, ISSN: 1949-3045, 2371-9850.
@article{soleymani_guest_2024,
title = {Guest Editorial Best of ACII 2021},
author = {Mohammad Soleymani and Shiro Kumano and Emily Mower Provost and Nadia Bianchi-Berthouze and Akane Sano and Kenji Suzuki},
url = {https://ieeexplore.ieee.org/document/10542496/},
doi = {10.1109/TAFFC.2024.3389249},
issn = {1949-3045, 2371-9850},
year = {2024},
date = {2024-04-01},
urldate = {2024-06-25},
journal = {IEEE Trans. Affective Comput.},
volume = {15},
number = {2},
pages = {376–379},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zhang, Hui; Kuang, Bingran; Zhao, Yajie
Camera Calibration using a Single View of a Symmetric Object Proceedings Article
In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2705–2709, IEEE, Seoul, Korea, Republic of, 2024, ISBN: 9798350344851.
Links | BibTeX | Tags: Graphics, VGL
@inproceedings{zhang_camera_2024,
title = {Camera Calibration using a Single View of a Symmetric Object},
author = {Hui Zhang and Bingran Kuang and Yajie Zhao},
url = {https://ieeexplore.ieee.org/document/10446005/},
doi = {10.1109/ICASSP48485.2024.10446005},
isbn = {9798350344851},
year = {2024},
date = {2024-04-01},
urldate = {2024-06-25},
booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {2705–2709},
publisher = {IEEE},
address = {Seoul, Korea, Republic of},
keywords = {Graphics, VGL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizzo, Albert Skip; Hartholt, Arno; Mozgai, Sharon
Settling the Score: Virtual Reality as a Tool to Enhance Trauma-Focused Therapy for PTSD Book Section
In: Rich, Grant J.; Kumar, V. K.; Farley, Frank H. (Ed.): Handbook of Media Psychology, pp. 187–213, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-56536-6 978-3-031-56537-3.
Links | BibTeX | Tags: DTIC, MedVR, Simulation, VR
@incollection{rich_settling_2024,
title = {Settling the Score: Virtual Reality as a Tool to Enhance Trauma-Focused Therapy for PTSD},
author = {Albert Skip Rizzo and Arno Hartholt and Sharon Mozgai},
editor = {Grant J. Rich and V. K. Kumar and Frank H. Farley},
url = {https://link.springer.com/10.1007/978-3-031-56537-3_14},
doi = {10.1007/978-3-031-56537-3_14},
isbn = {978-3-031-56536-6 978-3-031-56537-3},
year = {2024},
date = {2024-04-01},
urldate = {2024-06-18},
booktitle = {Handbook of Media Psychology},
pages = {187–213},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {DTIC, MedVR, Simulation, VR},
pubstate = {published},
tppubtype = {incollection}
}
Goh, Crystal; Ma, Yu; Rizzo, Albert
Normative performance data on visual attention in neurotypical children: virtual reality assessment of cognitive and psychomotor development Journal Article
In: Front. Virtual Real., vol. 5, pp. 1309176, 2024, ISSN: 2673-4192.
Abstract | Links | BibTeX | Tags: MedVR
@article{goh_normative_2024,
title = {Normative performance data on visual attention in neurotypical children: virtual reality assessment of cognitive and psychomotor development},
author = {Crystal Goh and Yu Ma and Albert Rizzo},
url = {https://www.frontiersin.org/articles/10.3389/frvir.2024.1309176/full},
doi = {10.3389/frvir.2024.1309176},
issn = {2673-4192},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-16},
journal = {Front. Virtual Real.},
volume = {5},
pages = {1309176},
abstract = {Introduction:
Virtual Reality (VR) is revolutionizing healthcare research and practice by offering innovative methodologies across various clinical conditions. Advances in VR technology enable the creation of controllable, multisensory 3D environments, making it an appealing tool for capturing and quantifying behavior in realistic scenarios. This paper details the application of VR as a tool for neurocognitive evaluation, specifically in attention process assessment, an area of relevance for informing the diagnosis of childhood health conditions such as Attention Deficit Hyperactivity Disorder (ADHD).
Methods:
The data presented focuses on attention performance results from a large sample (
n = 837) of neurotypical male and female children (ages 6–13) tested on a visual continuous performance task, administered within an immersive VR classroom environment. This data was collected to create a normative baseline database for use to inform comparisons with the performances of children with ADHD to support diagnostic decision-making in this area.
Results:
Results indicate systematic improvements on most metrics across the age span, and sex differences are noted on key variables thought to reflect differential measures of hyperactivity and inattention in children with ADHD. Results support VR technology as a safe and viable option for testing attention processes in children, under stimulus conditions that closely mimic ecologically relevant challenges found in everyday life.
Discussion:
In response to these stimulus conditions, VR can support advanced methods for capturing and quantifying users’ behavioral responses. VR offers a more systematic and objective approach for clinical assessment and intervention and provides conceptual support for its use in a wide variety of healthcare contexts.},
keywords = {MedVR},
pubstate = {published},
tppubtype = {article}
}
Introduction:
Virtual Reality (VR) is revolutionizing healthcare research and practice by offering innovative methodologies across various clinical conditions. Advances in VR technology enable the creation of controllable, multisensory 3D environments, making it an appealing tool for capturing and quantifying behavior in realistic scenarios. This paper details the application of VR as a tool for neurocognitive evaluation, specifically in attention process assessment, an area of relevance for informing the diagnosis of childhood health conditions such as Attention Deficit Hyperactivity Disorder (ADHD).
Methods:
The data presented focuses on attention performance results from a large sample (
n = 837) of neurotypical male and female children (ages 6–13) tested on a visual continuous performance task, administered within an immersive VR classroom environment. This data was collected to create a normative baseline database for use to inform comparisons with the performances of children with ADHD to support diagnostic decision-making in this area.
Results:
Results indicate systematic improvements on most metrics across the age span, and sex differences are noted on key variables thought to reflect differential measures of hyperactivity and inattention in children with ADHD. Results support VR technology as a safe and viable option for testing attention processes in children, under stimulus conditions that closely mimic ecologically relevant challenges found in everyday life.
Discussion:
In response to these stimulus conditions, VR can support advanced methods for capturing and quantifying users’ behavioral responses. VR offers a more systematic and objective approach for clinical assessment and intervention and provides conceptual support for its use in a wide variety of healthcare contexts.
Virtual Reality (VR) is revolutionizing healthcare research and practice by offering innovative methodologies across various clinical conditions. Advances in VR technology enable the creation of controllable, multisensory 3D environments, making it an appealing tool for capturing and quantifying behavior in realistic scenarios. This paper details the application of VR as a tool for neurocognitive evaluation, specifically in attention process assessment, an area of relevance for informing the diagnosis of childhood health conditions such as Attention Deficit Hyperactivity Disorder (ADHD).
Methods:
The data presented focuses on attention performance results from a large sample (
n = 837) of neurotypical male and female children (ages 6–13) tested on a visual continuous performance task, administered within an immersive VR classroom environment. This data was collected to create a normative baseline database for use to inform comparisons with the performances of children with ADHD to support diagnostic decision-making in this area.
Results:
Results indicate systematic improvements on most metrics across the age span, and sex differences are noted on key variables thought to reflect differential measures of hyperactivity and inattention in children with ADHD. Results support VR technology as a safe and viable option for testing attention processes in children, under stimulus conditions that closely mimic ecologically relevant challenges found in everyday life.
Discussion:
In response to these stimulus conditions, VR can support advanced methods for capturing and quantifying users’ behavioral responses. VR offers a more systematic and objective approach for clinical assessment and intervention and provides conceptual support for its use in a wide variety of healthcare contexts.
Soleymani, Mohammad; Rahmani, Mehdi; Bigdeli, Nooshin
Robust Tube-Based Reference Tracking Nonlinear Model Predictive Control for Wind Turbines Journal Article
In: IEEE Trans. Automat. Sci. Eng., pp. 1–13, 2024, ISSN: 1545-5955, 1558-3783.
@article{soleymani_robust_2024,
title = {Robust Tube-Based Reference Tracking Nonlinear Model Predictive Control for Wind Turbines},
author = {Mohammad Soleymani and Mehdi Rahmani and Nooshin Bigdeli},
url = {https://ieeexplore.ieee.org/document/10495787/},
doi = {10.1109/TASE.2024.3385714},
issn = {1545-5955, 1558-3783},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-16},
journal = {IEEE Trans. Automat. Sci. Eng.},
pages = {1–13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gurney, Nikolos; Loewenstein, George; Chater, Nick
Conversational technology and reactions to withheld information Journal Article
In: PLoS ONE, vol. 19, no. 4, pp. e0301382, 2024, ISSN: 1932-6203.
Abstract | Links | BibTeX | Tags: DTIC, Social Simulation, UARC
@article{gurney_conversational_2024,
title = {Conversational technology and reactions to withheld information},
author = {Nikolos Gurney and George Loewenstein and Nick Chater},
editor = {Petre Caraiani},
url = {https://dx.plos.org/10.1371/journal.pone.0301382},
doi = {10.1371/journal.pone.0301382},
issn = {1932-6203},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-16},
journal = {PLoS ONE},
volume = {19},
number = {4},
pages = {e0301382},
abstract = {People frequently face decisions that require making inferences about withheld information. The advent of large language models coupled with conversational technology, e.g., Alexa, Siri, Cortana, and the Google Assistant, is changing the mode in which people make these inferences. We demonstrate that conversational modes of information provision, relative to traditional digital media, result in more critical responses to withheld information, including: (1) a reduction in evaluations of a product or service for which information is withheld and (2) an increased likelihood of recalling that information was withheld. These effects are robust across multiple conversational modes: a recorded phone conversation, an unfolding chat conversation, and a conversation script. We provide further evidence that these effects hold for conversations with the Google Assistant, a prominent conversational technology. The experimental results point to participants’ intuitions about why the information was withheld as the driver of the effect.},
keywords = {DTIC, Social Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
People frequently face decisions that require making inferences about withheld information. The advent of large language models coupled with conversational technology, e.g., Alexa, Siri, Cortana, and the Google Assistant, is changing the mode in which people make these inferences. We demonstrate that conversational modes of information provision, relative to traditional digital media, result in more critical responses to withheld information, including: (1) a reduction in evaluations of a product or service for which information is withheld and (2) an increased likelihood of recalling that information was withheld. These effects are robust across multiple conversational modes: a recorded phone conversation, an unfolding chat conversation, and a conversation script. We provide further evidence that these effects hold for conversations with the Google Assistant, a prominent conversational technology. The experimental results point to participants’ intuitions about why the information was withheld as the driver of the effect.
Harris, Vera; Braggs, Robert; Traum, David
I’m not sure I heard you right, but I think I know what you mean – investigations into the impact of speech recognition errors on response selection for a virtual human. Proceedings Article
In: Sapporo Japan, 2024.
Links | BibTeX | Tags: Machine Learning
@inproceedings{harris_im_2024,
title = {I’m not sure I heard you right, but I think I know what you mean – investigations into the impact of speech recognition errors on response selection for a virtual human.},
author = {Vera Harris and Robert Braggs and David Traum},
url = {chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://people.ict.usc.edu/~traum/Papers/23-harris-iwsds2024.pdf},
year = {2024},
date = {2024-03-01},
address = {Sapporo Japan},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Brixey, Jacqueline; Traum, David
Why should a dialogue system speak more than one language? Proceedings Article
In: Sapporo Japan, 2024.
Links | BibTeX | Tags: Natural Language
@inproceedings{brixey_why_2024,
title = {Why should a dialogue system speak more than one language?},
author = {Jacqueline Brixey and David Traum},
url = {chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://people.ict.usc.edu/~traum/Papers/24-Why%20should%20a%20dialogue%20system%20speak%20more%20than%20one%20language.pdf},
year = {2024},
date = {2024-03-01},
address = {Sapporo Japan},
keywords = {Natural Language},
pubstate = {published},
tppubtype = {inproceedings}
}
Chen, Haiwei; Zhao, Yajie
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting Miscellaneous
2024, (arXiv:2403.18186 [cs]).
Abstract | Links | BibTeX | Tags: VGL
@misc{chen_dont_2024,
title = {Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting},
author = {Haiwei Chen and Yajie Zhao},
url = {http://arxiv.org/abs/2403.18186},
year = {2024},
date = {2024-03-01},
urldate = {2024-08-15},
publisher = {arXiv},
abstract = {We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors, discretized as tokens, by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block, a bidirectional transformer that infers the missing labels by only looking at these tokens, and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics.},
note = {arXiv:2403.18186 [cs]},
keywords = {VGL},
pubstate = {published},
tppubtype = {misc}
}
We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors, discretized as tokens, by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block, a bidirectional transformer that infers the missing labels by only looking at these tokens, and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics.
Singh, Ishika; Traum, David; Thomason, Jesse
TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models Miscellaneous
2024, (arXiv:2403.17246 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{singh_twostep_2024,
title = {TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models},
author = {Ishika Singh and David Traum and Jesse Thomason},
url = {http://arxiv.org/abs/2403.17246},
year = {2024},
date = {2024-03-01},
urldate = {2024-08-15},
publisher = {arXiv},
abstract = {Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, for example that two agents in the domain can execute an action simultaneously if postconditions of each do not interfere with preconditions of the other. A human expert can decompose a goal into largely independent constituent parts and assign each agent to one of these subgoals to take advantage of simultaneous actions for faster execution of plan steps, each using only single agent planning. By contrast, large language models (LLMs) used for directly inferring plan steps do not guarantee execution success, but do leverage commonsense reasoning to assemble action sequences. We combine the strengths of classical planning and LLMs by approximating human intuitions for two-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success. Additionally, we find that LLM-based approximations of subgoals can achieve similar multi-agent execution steps than those specified by human experts. Website and resources at https://glamor-usc.github.io/twostep},
note = {arXiv:2403.17246 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, for example that two agents in the domain can execute an action simultaneously if postconditions of each do not interfere with preconditions of the other. A human expert can decompose a goal into largely independent constituent parts and assign each agent to one of these subgoals to take advantage of simultaneous actions for faster execution of plan steps, each using only single agent planning. By contrast, large language models (LLMs) used for directly inferring plan steps do not guarantee execution success, but do leverage commonsense reasoning to assemble action sequences. We combine the strengths of classical planning and LLMs by approximating human intuitions for two-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success. Additionally, we find that LLM-based approximations of subgoals can achieve similar multi-agent execution steps than those specified by human experts. Website and resources at https://glamor-usc.github.io/twostep
Gordon, Andrew S.; Feng, Andrew
Combining the Predictions of Out-of-Domain Classifiers Using Etcetera Abduction Proceedings Article
In: 2024 58th Annual Conference on Information Sciences and Systems (CISS), pp. 1–6, IEEE, Princeton, NJ, USA, 2024, ISBN: 9798350369298.
Links | BibTeX | Tags: DTIC, Narrative, The Narrative Group, UARC
@inproceedings{gordon_combining_2024,
title = {Combining the Predictions of Out-of-Domain Classifiers Using Etcetera Abduction},
author = {Andrew S. Gordon and Andrew Feng},
url = {https://ieeexplore.ieee.org/document/10480194/},
doi = {10.1109/CISS59072.2024.10480194},
isbn = {9798350369298},
year = {2024},
date = {2024-03-01},
urldate = {2024-04-16},
booktitle = {2024 58th Annual Conference on Information Sciences and Systems (CISS)},
pages = {1–6},
publisher = {IEEE},
address = {Princeton, NJ, USA},
keywords = {DTIC, Narrative, The Narrative Group, UARC},
pubstate = {published},
tppubtype = {inproceedings}
}
Frummet, Alexander; Speggiorin, Alessandro; Elsweiler, David; Leuski, Anton; Dalton, Jeff
Cooking with Conversation: Enhancing User Engagement and Learning with a Knowledge-Enhancing Assistant Journal Article
In: ACM Trans. Inf. Syst., pp. 3649500, 2024, ISSN: 1046-8188, 1558-2868.
Abstract | Links | BibTeX | Tags: DTIC, Natural Language, UARC
@article{frummet_cooking_2024,
title = {Cooking with Conversation: Enhancing User Engagement and Learning with a Knowledge-Enhancing Assistant},
author = {Alexander Frummet and Alessandro Speggiorin and David Elsweiler and Anton Leuski and Jeff Dalton},
url = {https://dl.acm.org/doi/10.1145/3649500},
doi = {10.1145/3649500},
issn = {1046-8188, 1558-2868},
year = {2024},
date = {2024-03-01},
urldate = {2024-04-16},
journal = {ACM Trans. Inf. Syst.},
pages = {3649500},
abstract = {We present two empirical studies to investigate users’ expectations and behaviours when using digital assistants, such as Alexa and Google Home, in a kitchen context: First, a survey (N=200) queries participants on their expectations for the kinds of information that such systems should be able to provide. While consensus exists on expecting information about cooking steps and processes, younger participants who enjoy cooking express a higher likelihood of expecting details on food history or the science of cooking. In a follow-up Wizard-of-Oz study (N = 48), users were guided through the steps of a recipe either by an
active
wizard that alerted participants to information it could provide or a
passive
wizard who only answered questions that were provided by the user. The
active
policy led to almost double the number of conversational utterances and 1.5 times more knowledge-related user questions compared to the
passive
policy. Also, it resulted in 1.7 times more knowledge communicated than the
passive
policy. We discuss the findings in the context of related work and reveal implications for the design and use of such assistants for cooking and other purposes such as DIY and craft tasks, as well as the lessons we learned for evaluating such systems.},
keywords = {DTIC, Natural Language, UARC},
pubstate = {published},
tppubtype = {article}
}
We present two empirical studies to investigate users’ expectations and behaviours when using digital assistants, such as Alexa and Google Home, in a kitchen context: First, a survey (N=200) queries participants on their expectations for the kinds of information that such systems should be able to provide. While consensus exists on expecting information about cooking steps and processes, younger participants who enjoy cooking express a higher likelihood of expecting details on food history or the science of cooking. In a follow-up Wizard-of-Oz study (N = 48), users were guided through the steps of a recipe either by an
active
wizard that alerted participants to information it could provide or a
passive
wizard who only answered questions that were provided by the user. The
active
policy led to almost double the number of conversational utterances and 1.5 times more knowledge-related user questions compared to the
passive
policy. Also, it resulted in 1.7 times more knowledge communicated than the
passive
policy. We discuss the findings in the context of related work and reveal implications for the design and use of such assistants for cooking and other purposes such as DIY and craft tasks, as well as the lessons we learned for evaluating such systems.
active
wizard that alerted participants to information it could provide or a
passive
wizard who only answered questions that were provided by the user. The
active
policy led to almost double the number of conversational utterances and 1.5 times more knowledge-related user questions compared to the
passive
policy. Also, it resulted in 1.7 times more knowledge communicated than the
passive
policy. We discuss the findings in the context of related work and reveal implications for the design and use of such assistants for cooking and other purposes such as DIY and craft tasks, as well as the lessons we learned for evaluating such systems.
Lu, Liupei; Yin, Yufeng; Gu, Yuming; Wu, Yizhen; Prasad, Pratusha; Zhao, Yajie; Soleymani, Mohammad
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection Miscellaneous
2024, (arXiv:2403.10737 [cs]).
Abstract | Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@misc{lu_leveraging_2024,
title = {Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection},
author = {Liupei Lu and Yufeng Yin and Yuming Gu and Yizhen Wu and Pratusha Prasad and Yajie Zhao and Mohammad Soleymani},
url = {http://arxiv.org/abs/2403.10737},
year = {2024},
date = {2024-03-01},
urldate = {2024-04-16},
publisher = {arXiv},
abstract = {Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is costly. The limited labeled data are also not diverse in terms of gender which can affect model fairness. In this paper, we propose to use synthetically generated data and multi-source domain adaptation (MSDA) to address the problems of the scarcity of labeled data and the diversity of subjects. Specifically, we propose to generate a diverse dataset through synthetic facial expression re-targeting by transferring the expressions from real faces to synthetic avatars. Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset. Instead of aligning the overall distributions of different domains, we propose Paired Moment Matching (PM2) to align the features of the paired real and synthetic data with the same facial expression. To further improve gender fairness, PM2 matches the features of the real data with a female and a male synthetic image. Our results indicate that synthetic data and the proposed model improve both AU detection performance and fairness across genders, demonstrating its potential to solve AU detection in-the-wild.},
note = {arXiv:2403.10737 [cs]},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is costly. The limited labeled data are also not diverse in terms of gender which can affect model fairness. In this paper, we propose to use synthetically generated data and multi-source domain adaptation (MSDA) to address the problems of the scarcity of labeled data and the diversity of subjects. Specifically, we propose to generate a diverse dataset through synthetic facial expression re-targeting by transferring the expressions from real faces to synthetic avatars. Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset. Instead of aligning the overall distributions of different domains, we propose Paired Moment Matching (PM2) to align the features of the paired real and synthetic data with the same facial expression. To further improve gender fairness, PM2 matches the features of the real data with a female and a male synthetic image. Our results indicate that synthetic data and the proposed model improve both AU detection performance and fairness across genders, demonstrating its potential to solve AU detection in-the-wild.
Tran, Minh; Chang, Di; Siniukov, Maksim; Soleymani, Mohammad
Dyadic Interaction Modeling for Social Behavior Generation Miscellaneous
2024, (arXiv:2403.09069 [cs]).
Abstract | Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@misc{tran_dyadic_2024,
title = {Dyadic Interaction Modeling for Social Behavior Generation},
author = {Minh Tran and Di Chang and Maksim Siniukov and Mohammad Soleymani},
url = {http://arxiv.org/abs/2403.09069},
year = {2024},
date = {2024-03-01},
urldate = {2024-03-19},
publisher = {arXiv},
abstract = {Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.},
note = {arXiv:2403.09069 [cs]},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {misc}
}
Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.
Marti, Deniz; Hanrahan, David; Sanchez-Triana, Ernesto; Wells, Mona; Corra, Lilian; Hu, Howard; Breysse, Patrick N.; Laborde, Amalia; Caravanos, Jack; Bertollini, Roberto; Porterfield, Kate; Fuller, Richard
Structured Expert Judgement Approach of the Health Impact of Various Chemicals and Classes of Chemicals Miscellaneous
2024.
Abstract | Links | BibTeX | Tags:
@misc{marti_structured_2024-1,
title = {Structured Expert Judgement Approach of the Health Impact of Various Chemicals and Classes of Chemicals},
author = {Deniz Marti and David Hanrahan and Ernesto Sanchez-Triana and Mona Wells and Lilian Corra and Howard Hu and Patrick N. Breysse and Amalia Laborde and Jack Caravanos and Roberto Bertollini and Kate Porterfield and Richard Fuller},
url = {http://medrxiv.org/lookup/doi/10.1101/2024.01.30.24301863},
doi = {10.1101/2024.01.30.24301863},
year = {2024},
date = {2024-02-01},
urldate = {2024-08-13},
pages = {2024.01.30.24301863},
abstract = {ABSTRACT
Introduction
Chemical contamination and pollution are an ongoing threat to human health and the environment. The concern over the consequences of chemical exposures at the global level continues to grow. Because resources are constrained, there is a need to prioritize interventions focused on the greatest health impact. Data, especially related to chemical exposures, are rarely available for most substances of concern, and alternate methods to evaluate their impact are needed.
Structured Expert Judgment (SEJ) Process
A Structured Expert Judgment
3
process was performed to provide plausible estimates of health impacts for 16 commonly found pollutants: asbestos, arsenic, benzene, chromium, cadmium, dioxins, fluoride, highly hazardous pesticides (HHPs), lead, mercury, polycyclic-aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), Per- and Polyfluorinated Substances (PFAs), phthalates, endocrine disrupting chemicals (EDCs), and brominated flame retardants (BRFs). This process, undertaken by sector experts, weighed individual estimations of the probable global health scale health impacts of each pollutant using objective estimates of the expert opinions’ statistical accuracy and informativeness.
Main Findings
The foremost substances, in terms of mean projected annual total deaths, were lead, asbestos, arsenic, and HHPs. Lead surpasses the others by a large margin, with an estimated median value of 1.7 million deaths annually. The three other substances averaged between 136,000 and 274,000 deaths per year. Of the 12 other chemicals evaluated, none reached an estimated annual death count exceeding 100,000. These findings underscore the importance of prioritizing available resources on reducing and remediating the impacts of these key pollutants.
Range of Health Impacts
Based on the evidence available, experts concluded some of the more notorious chemical pollutants, such as PCBs and dioxin, do not result in high levels of human health impact from a global scale perspective. However, the chemical toxicity of some compounds released in recent decades, such as Endocrine Disrupters and PFAs, cannot be ignored, even if current impacts are limited. Moreover, the impact of some chemicals may be disproportionately large in some geographic areas. Continued research and monitoring are essential; and a preventative approach is needed for chemicals.
Future Directions
These results, and potential similar analyses of other chemicals, are provided as inputs to ongoing discussions about priority setting for global chemicals and pollution management. Furthermore, we suggest that this SEJ process be repeated periodically as new information becomes available.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
ABSTRACT
Introduction
Chemical contamination and pollution are an ongoing threat to human health and the environment. The concern over the consequences of chemical exposures at the global level continues to grow. Because resources are constrained, there is a need to prioritize interventions focused on the greatest health impact. Data, especially related to chemical exposures, are rarely available for most substances of concern, and alternate methods to evaluate their impact are needed.
Structured Expert Judgment (SEJ) Process
A Structured Expert Judgment
3
process was performed to provide plausible estimates of health impacts for 16 commonly found pollutants: asbestos, arsenic, benzene, chromium, cadmium, dioxins, fluoride, highly hazardous pesticides (HHPs), lead, mercury, polycyclic-aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), Per- and Polyfluorinated Substances (PFAs), phthalates, endocrine disrupting chemicals (EDCs), and brominated flame retardants (BRFs). This process, undertaken by sector experts, weighed individual estimations of the probable global health scale health impacts of each pollutant using objective estimates of the expert opinions’ statistical accuracy and informativeness.
Main Findings
The foremost substances, in terms of mean projected annual total deaths, were lead, asbestos, arsenic, and HHPs. Lead surpasses the others by a large margin, with an estimated median value of 1.7 million deaths annually. The three other substances averaged between 136,000 and 274,000 deaths per year. Of the 12 other chemicals evaluated, none reached an estimated annual death count exceeding 100,000. These findings underscore the importance of prioritizing available resources on reducing and remediating the impacts of these key pollutants.
Range of Health Impacts
Based on the evidence available, experts concluded some of the more notorious chemical pollutants, such as PCBs and dioxin, do not result in high levels of human health impact from a global scale perspective. However, the chemical toxicity of some compounds released in recent decades, such as Endocrine Disrupters and PFAs, cannot be ignored, even if current impacts are limited. Moreover, the impact of some chemicals may be disproportionately large in some geographic areas. Continued research and monitoring are essential; and a preventative approach is needed for chemicals.
Future Directions
These results, and potential similar analyses of other chemicals, are provided as inputs to ongoing discussions about priority setting for global chemicals and pollution management. Furthermore, we suggest that this SEJ process be repeated periodically as new information becomes available.
Introduction
Chemical contamination and pollution are an ongoing threat to human health and the environment. The concern over the consequences of chemical exposures at the global level continues to grow. Because resources are constrained, there is a need to prioritize interventions focused on the greatest health impact. Data, especially related to chemical exposures, are rarely available for most substances of concern, and alternate methods to evaluate their impact are needed.
Structured Expert Judgment (SEJ) Process
A Structured Expert Judgment
3
process was performed to provide plausible estimates of health impacts for 16 commonly found pollutants: asbestos, arsenic, benzene, chromium, cadmium, dioxins, fluoride, highly hazardous pesticides (HHPs), lead, mercury, polycyclic-aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), Per- and Polyfluorinated Substances (PFAs), phthalates, endocrine disrupting chemicals (EDCs), and brominated flame retardants (BRFs). This process, undertaken by sector experts, weighed individual estimations of the probable global health scale health impacts of each pollutant using objective estimates of the expert opinions’ statistical accuracy and informativeness.
Main Findings
The foremost substances, in terms of mean projected annual total deaths, were lead, asbestos, arsenic, and HHPs. Lead surpasses the others by a large margin, with an estimated median value of 1.7 million deaths annually. The three other substances averaged between 136,000 and 274,000 deaths per year. Of the 12 other chemicals evaluated, none reached an estimated annual death count exceeding 100,000. These findings underscore the importance of prioritizing available resources on reducing and remediating the impacts of these key pollutants.
Range of Health Impacts
Based on the evidence available, experts concluded some of the more notorious chemical pollutants, such as PCBs and dioxin, do not result in high levels of human health impact from a global scale perspective. However, the chemical toxicity of some compounds released in recent decades, such as Endocrine Disrupters and PFAs, cannot be ignored, even if current impacts are limited. Moreover, the impact of some chemicals may be disproportionately large in some geographic areas. Continued research and monitoring are essential; and a preventative approach is needed for chemicals.
Future Directions
These results, and potential similar analyses of other chemicals, are provided as inputs to ongoing discussions about priority setting for global chemicals and pollution management. Furthermore, we suggest that this SEJ process be repeated periodically as new information becomes available.
Ustun, Volkan; Jorvekar, Ronit; Gurney, Nikolos; Pynadath, David; Wang, Yunzhe
Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent: Proceedings Article
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, pp. 313–320, SCITEPRESS - Science and Technology Publications, Rome, Italy, 2024, ISBN: 978-989-758-680-4.
Links | BibTeX | Tags: AI, Cognitive Architecture, Social Simulation
@inproceedings{ustun_assessing_2024,
title = {Assessing Routing Decisions of Search and Rescue Teams in Service of an Artificial Social Intelligence Agent:},
author = {Volkan Ustun and Ronit Jorvekar and Nikolos Gurney and David Pynadath and Yunzhe Wang},
url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0012388100003636},
doi = {10.5220/0012388100003636},
isbn = {978-989-758-680-4},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-19},
booktitle = {Proceedings of the 16th International Conference on Agents and Artificial Intelligence},
pages = {313–320},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Rome, Italy},
keywords = {AI, Cognitive Architecture, Social Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
Gurney, Nikolos; Morstatter, Fred; Pynadath, David V.; Russell, Adam; Satyukov, Gleb
Operational Collective Intelligence of Humans and Machines Journal Article
In: 2024, (Publisher: [object Object] Version Number: 1).
Abstract | Links | BibTeX | Tags: DTIC, Social Simulation, UARC
@article{gurney_operational_2024,
title = {Operational Collective Intelligence of Humans and Machines},
author = {Nikolos Gurney and Fred Morstatter and David V. Pynadath and Adam Russell and Gleb Satyukov},
url = {https://arxiv.org/abs/2402.13273},
doi = {10.48550/ARXIV.2402.13273},
year = {2024},
date = {2024-02-01},
urldate = {2024-03-14},
abstract = {We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements acting alone.'' Collective Intelligence emerges from new ways of connecting humans and AI to enable decision-advantage, in part by creating and leveraging additional sources of information that might otherwise not be included. Aggregative crowdsourced forecasting (ACF) is a recent key advancement towards Collective Intelligence wherein predictions (Xtextbackslash% probability that Y will happen) and rationales (why I believe it is this probability that X will happen) are elicited independently from a diverse crowd, aggregated, and then used to inform higher-level decision-making. This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios (i.e., sequences of events with defined agents, components, and interactions) and decision-making, and considers whether such a capability could provide novel operational capabilities to enable new forms of decision-advantage.},
note = {Publisher: [object Object]
Version Number: 1},
keywords = {DTIC, Social Simulation, UARC},
pubstate = {published},
tppubtype = {article}
}
We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements acting alone.'' Collective Intelligence emerges from new ways of connecting humans and AI to enable decision-advantage, in part by creating and leveraging additional sources of information that might otherwise not be included. Aggregative crowdsourced forecasting (ACF) is a recent key advancement towards Collective Intelligence wherein predictions (Xtextbackslash% probability that Y will happen) and rationales (why I believe it is this probability that X will happen) are elicited independently from a diverse crowd, aggregated, and then used to inform higher-level decision-making. This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios (i.e., sequences of events with defined agents, components, and interactions) and decision-making, and considers whether such a capability could provide novel operational capabilities to enable new forms of decision-advantage.