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Abstract Active shooter incidents represent an increasing threat to American society, especially in commercial and educational buildings. In recent years, a wide variety of security countermeasures have been recommended by public and governmental agencies. Many of these countermeasures are aimed to increase building security, yet their impact on human behavior when an active shooter incident occurs remains underexplored. To fill this research gap, we conducted virtual experiments to evaluate the impact of countermeasures on human behavior during active shooter incidents. A total of 162 office workers and middle/high school teachers were recruited to respond to an active shooter incident in virtual office and school buildings with or without the implementation of multiple countermeasures. The experiment results showed countermeasures significantly influenced participants’ response time and decisions (e.g., run, hide, fight). Participants’ responses and perceptions of the active shooter incident were also contingent on their daily roles, as well as building and social contexts. Teachers had more concerns for occupants’ safety than office workers. Moreover, teachers had more positive perceptions of occupants in the school, whereas office workers had more positive perceptions of occupants in the office.
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Advanced Federated Learning-Empowered Edge-Cloud Framework for School Safety Prediction and Emergency Alert System
The safety and security of educational environments are paramount concerns for communities worldwide. Recent incidents of violence in schools underscore the urgent need for innovative and proactive safety measures that extend beyond traditional reactive approaches. In response to this imperative, we propose an Advanced Federated Learning- Empowered Edge-Cloud Framework for School Safety Prediction and Emergency Alert System, which is a groundbreaking solution designed to address the pressing challenges of ensuring school safety. In a world where educational institutions face escalating threats, this framework leverages the innovative approach of federated learning, enabling real-time threat detection and proactive alert generation while preserving data privacy. Challenges such as delayed response times, false alarms, and limited threat assessment protocols are met head-on through the integration of predictive algorithms, sensors, and edge computing. This transformative system not only revolutionizes security but also prioritizes the psychological well-being of students, staff, and visitors, fostering an environment conducive to learning. Its significance lies in its potential to prevent incidents, minimize harm, and bolster community confidence in school safety measures, ultimately contributing to the well- being and growth of future generations. Through this pioneering work, we aim to redefine school safety paradigms, making educational institutions safer and more secure for all.
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- Award ID(s):
- 2219741
- PAR ID:
- 10535374
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-1306-2
- Page Range / eLocation ID:
- 507 to 512
- Format(s):
- Medium: X
- Location:
- Hoboken, NJ, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CloudNet59005.2023.10490054
