Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning

• URL: http://arxiv.org/abs/2503.05719v1
• Date: Mon, 17 Feb 2025 08:10:56 GMT
• Title: Investigating Role of Personal Factors in Shaping Responses to Active Shooter Incident using Machine Learning
• Authors: Ruying Liu, Burçin Becerik-Gerber, Gale M. Lucas,
• Abstract summary: This study bridges the knowledge gap on how personal factors affect building occupants' responses in active shooter situations.<n>The personal factors studied are training methods, prior training experience, sense of direction, and gender.
• Score: 1.4610685586329806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study bridges the knowledge gap on how personal factors affect building occupants' responses in active shooter situations by applying interpretable machine learning methods to data from 107 participants. The personal factors studied are training methods, prior training experience, sense of direction, and gender. The response performance measurements consist of decisions (run, hide, multiple), vulnerability (corresponding to the time a participant is visible to a shooter), and pre-evacuation time. The results indicate that the propensity to run significantly determines overall response strategies, overshadowing vulnerability, and pre-evacuation time. The training method is a critical factor where VR-based training leads to better responses than video-based training. A better sense of direction and previous training experience are correlated with a greater propensity to run and less vulnerability. Gender slightly influences decisions and vulnerability but significantly impacts pre-evacuation time, with females evacuating slower, potentially due to higher risk perception. This study underscores the importance of personal factors in shaping responses to active shooter incidents.

Related papers

• On the Effectiveness of Adversarial Training on Malware Classifiers [14.069462668836328]
  Adversarial Training (AT) has been widely applied to harden learning-based classifiers against adversarial evasive attacks.<n>Previous work seems to suggest robustness is a task-dependent property of AT.<n>We argue it is a more complex problem that requires exploring AT and the intertwined roles played by certain factors within data.
  arXiv  Detail & Related papers  (2024-12-24T06:55:53Z)
• Early Period of Training Impacts Adaptation for Out-of-Distribution Generalization: An Empirical Study [56.283944756315066]
  We investigate the relationship between learning dynamics, out-of-distribution generalization and the early period of neural network training.<n>We show that changing the number of trainable parameters during the early period of training can significantly improve OOD results.<n>Our experiments on both image and text data show that the early period of training is a general phenomenon that can improve ID and OOD performance with minimal complexity.
  arXiv  Detail & Related papers  (2024-03-22T13:52:53Z)
• Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies [0.9378955659006951]
  In complex industrial and chemical process control rooms, effective decision-making is crucial for safety andeffi- ciency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface.
  arXiv  Detail & Related papers  (2024-02-20T18:31:27Z)
• Protecting Split Learning by Potential Energy Loss [70.81375125791979]
  We focus on the privacy leakage from the forward embeddings of split learning. We propose the potential energy loss to make the forward embeddings become more 'complicated'
  arXiv  Detail & Related papers  (2022-10-18T06:21:11Z)
• Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study [6.076137037890219]
  We investigate how the interaction between a human and a continually learning prediction agent develops as the agent develops competency. We develop a virtual reality environment and a time-based prediction task wherein learned predictions from a reinforcement learning (RL) algorithm augment human predictions. Our findings suggest that human trust of the system may be influenced by early interactions with the agent, and that trust in turn affects strategic behaviour.
  arXiv  Detail & Related papers  (2021-12-14T22:46:44Z)
• Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning [114.9857000195174]
  A major challenge to widespread industrial adoption of deep reinforcement learning is the potential vulnerability to privacy breaches. We propose an adversarial attack framework tailored for testing the vulnerability of deep reinforcement learning algorithms to membership inference attacks.
  arXiv  Detail & Related papers  (2021-09-08T23:44:57Z)
• The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies [79.66833203975729]
  We conduct a vignette study in which laypersons are tasked with predicting future re-arrests. Our key findings are as follows: Participants often predict that an offender will be rearrested even when they deem the likelihood of re-arrest to be well below 50%. Judicial decisions, unlike participants' predictions, depend in part on factors that are to the likelihood of re-arrest.
  arXiv  Detail & Related papers  (2021-09-03T11:09:10Z)
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning [51.15273664903583]
  Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic and the LEAF benchmarks.
  arXiv  Detail & Related papers  (2021-02-01T06:06:21Z)
• Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries [74.59376038272661]
  We introduce sampling attack, a novel membership inference technique that unlike other standard membership adversaries is able to work under severe restriction of no access to scores of the victim model. We show that a victim model that only publishes the labels is still susceptible to sampling attacks and the adversary can recover up to 100% of its performance. For defense, we choose differential privacy in the form of gradient perturbation during the training of the victim model as well as output perturbation at prediction time.
  arXiv  Detail & Related papers  (2020-09-01T12:54:54Z)
• Towards Understanding Fast Adversarial Training [91.8060431517248]
  We conduct experiments to understand the behavior of fast adversarial training. We show the key to its success is the ability to recover from overfitting to weak attacks.
  arXiv  Detail & Related papers  (2020-06-04T18:19:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.