Schmidt-Colberg, A., & Löffler-Dauth, L. (2023). A Human-Centric Evaluation Dataset for Automated Early Wildfire Detection from a Causal Perspective. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 933–943). Omaha, USA: University of Nebraska at Omaha.
Abstract: Insight into performance ability is crucial for successfully implementing AI solutions in real-world applications. Unanticipated input can lead to false positives (FP) and false negatives (FN), potentially resulting in false alarms in fire detection scenarios. Literature on fire detection models shows varying levels of complexity and explicability in evaluation practices; little supplementary information on performance ability outside of accuracy scores is provided. We advocate for a standardized evaluation dataset that prioritizes the end-user perspective in assessing performance capabilities. This leads us to ask what an evaluation dataset needs to constitute to enable a non-expert to determine the adequacy of a model's performance capabilities for their specific use case. We propose using data augmentation techniques that simulate interventions to remove the connection to the original target label, providing interpretable counterfactual explanations into a model's predictions.
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Lamsal, R., Read, M. R., & Karunasekera, S. (2023). A Twitter narrative of the COVID-19 pandemic in Australia. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 353–370). Omaha, USA: University of Nebraska at Omaha.
Abstract: Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.
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Samuel Auclair, Pierre Gehl, Mickael Delatre, Christophe Debray, & Philippe Méresse. (2022). In-depth Analysis of Practitioners' Perceptions about Seismic Early Warning Prior to Aftershocks: The Point of View of the USAR Community. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 740–754). Tarbes, France.
Abstract: Urban Search and Rescue (USAR) teams are particularly exposed to the risk of collapse of buildings due to aftershocks, making concept of earthquake early warning (EEW) particularly interesting. In addition to scientific advances in EEW, it is crucial to understand what are the real expectations and needs of USAR teams, and to what extent EEW solutions could meet them. In this study, we conduct a survey to collect insights from USAR rescuers: it highlights that aftershocks are a major concern for them. In this context, we find that the concept of EEW is very favorably received by the respondents, who consider different types of possible actions upon receipt of an early warning. This study provides a basis for the functional specifications of future solutions of EEW useful to all USAR teams, as well as for the definition of their modalities of engagement on the field.
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Kerrianne Morrison, Yee-Yin Choong, Shanee Dawkins, & Sandra Spickard Prettyman. (2021). Communication Technology Problems and Needs of Rural First Responders. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 817–834). Blacksburg, VA (USA): Virginia Tech.
Abstract: Although new technology may benefit rural first responders to help them serve their communities, to date little is known about what communication technology problems rural first responders most need addressed and what future technology they desire. To explore the context of use and communication technology problems and needs of rural first responders, semi-structured interviews were conducted with 63 rural first responders across four disciplines: Communications (Comm) Center & 9-1-1 Services, Emergency Medical Services, Fire Service, and Law Enforcement. Using qualitative data analysis, interview data were sorted into problems and needs categories. Rural first responders' greatest problems were with reliable coverage/connectivity, interoperability, implementation/information technology (IT) infrastructure, and physical ergonomics. Rural first responders' greatest need for new technology was to address their current problems, but they were interested in new technology that leverages real-time technology and location tracking. Implications for researchers and developers of public safety communication technology are discussed.
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Cecilia Hammar Wijkmark, & Ilona Heldal. (2020). Virtual and Live Simulation-Based Training for Incident Commanders. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 1154–1162). Blacksburg, VA (USA): Virginia Tech.
Abstract: Computer and virtual simulation-based training (CST) offer several benefits for emergency response and management preparedness. However, organizations responsible for training are often hesitant to use CST, based on cost and perceived lack of benefit when compared to live simulation training (LST). This paper investigates how CST can complement LST, and how it contributes to achieving the necessary learning objectives for level one fire and rescue service incident commanders (ICs). Data and examples come from an experimental study with students from different fire and rescue services trained in the role of the IC in LST and CST, in a similar scenario. Results show the cost and benefits of the CST implementation based on evaluations from learners, instructors and responsible managers. Participants had a positive attitude towards using virtual simulations, but the results also point to barriers regarding the suitable design of learning scenarios and implementation.
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