Dunn, M. M. (2023). Aphorme: An Intralingual Translation Tool for Emergency Management and Disaster Response. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 1033–1041). Omaha, USA: University of Nebraska at Omaha.
Abstract: While multilingual translation needs (from one or more language(s) to one or more others) in disaster events are a “perennial issue” among responders in crisis-affected communities (Crowley & Chan, 2011) and calls are being made to consider the access to (and translation of) information during crisis a human right (Greenwood et al., 2017), the literature that deals with intralingual translation in disaster is limited in places where it should thrive, such as crisis communication, translation studies, and rhetoric. Intralingual translation is of increasing relevance in disaster not only because of potential variability in literacy levels among those affected (O’Brien, 2020) but because responding to/planning for disaster requires an understanding of the ‘operational’ terms used (but not always shared) by other responding agencies in the field. This paper calls for increased attention to intralingual translation needs in disaster and introduces a translation technology (“Aphorme”) designed to mitigate those needs.
|
|
Cossentino, M., Guastella, D. A., Lopes, S., & Sabatucci, L. (2023). Adaptive Execution of Workflows in Emergency Response. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 784–796). Omaha, USA: University of Nebraska at Omaha.
Abstract: In emergencies, preparation is of paramount importance but it is not sufficient. As we know, emergency agencies develop extensive (text) plans to deal with accidents that could occur in their territories; their personnel train to enact such procedures, but, despite that, the unpredictable conditions that occur during an emergency require the ability to adapt the plan promptly. This paper deals with the last mile of a process we defined for enabling the adaptive execution of such emergency plans. In previous works, we discussed how to convert a free-text plan into a structured-text form, represent this plan using standard modelling notations, and extract goals that plans prescribe to be fulfilled. In this paper, we propose an approach for executing these plans with a workflow execution engine enriched by the capability to support runtime adaptation.
|
|
Dilini Rajapaksha, Kacper Sokol, Jeffrey Chan, Flora Salim, Mukesh Prasad, & Mahendra Samarawickrama. (2023). Analysing Donors’ Behaviour in Non-profit Organisations for Disaster Resilience. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 258–267). Palmerston North, New Zealand: Massey Unversity.
Abstract: With the advancement and proliferation of technology, non-profit organisations have embraced social media platforms to improve their operational capabilities through brand advocacy, among many other strategies. The effect of such social media campaigns on these institutions, however, remains largely underexplored, especially during disaster periods. This work introduces and applies a quantitative investigative framework to understand how social media influence the behaviour of donors and their usage of these platforms throughout (natural) disasters. More specifically, we explore how on-line engagement – as captured by Facebook interactions and Google search trends – corresponds to the donors’ behaviour during the catastrophic 2019–2020 Australian bushfire season. To discover this relationship, we analyse the record of donations made to the Australian Red Cross throughout this period. Our exploratory study reveals that social media campaigns are effective in encouraging on-line donations made via a dedicated website. We also compare this mode of giving to more regular, direct deposit gifting.
|
|
Massimo Cossentino, Davide, rea Guastella, Salvatore Lopes, Luca Sabatucci, & Mario Tripiciano. (2022). From Textual Emergency Procedures to Executable Plans. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 200–212). Tarbes, France.
Abstract: Crisis response and management often involve joint actions among different actors. This is particularly true in cross border cooperation, i.e. when actors belong to different countries. This is the operative context of the NETTUNIT research project, which long-term objective is to provide automatic support to emergency management. Modelling emergency plans is challenging because they are usually written in free-form text, thus in a form that is very far from being automatically processed and executed. In other words, it is non-trivial to define workflows capable of managing and monitoring emergency plans. To complicate the problem, typically an emergency evolves in a highly dynamic environment, so there is the need for run-time adaptation. In this paper, we propose a roadmap for producing executable workflows from emergency free-text plans. We set up our current progress in the project and focus on the sub-problem of identifying a suitable modelling notation. We also propose two improvements with respect to the state of the art: 1) a specific diagram focusing on events, roles and responsibilities in a goal-oriented fashion; 2) some guidelines for depicting the emergency plan at hand with a modelling notation.
|
|
Koki Asami, Shono Fujita, Kei Hiroi, & Michinori Hatayama. (2022). Data Augmentation with Synthesized Damaged Roof Images Generated by GAN. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 256–265). Tarbes, France.
Abstract: The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
|
|