Abstract: This paper is about the introduction of netcentric work in the public safety sector in the Netherlands. The idea behind netcentric work is that a common operational picture will help the professionals to overcome problems with sharing information during crisis. In this WIP paper we will pay attention to netcentric work principles and the dilemma of standardization of technologies versus local adaptation. In the Netherlands the government has chosen to introduce netcentric work via a Platform in which various options are discussed among members of Dutch safety regions. The outcome is a process of negotiation in what we call trading zones. In these trading zones netcentric work is (re)defined. Using theoretical concepts like soft-bureaucracy we show in this paper how netcentric work eventually is not about technology in the first place but a negotiated new way of working and organizing. Further research is needed to understand the full implications of netcentric work for the administration and organization of safety.

Abstract: User-generated content (UGC) on social media can act as a key source of information for emergency responders incrisis situations. However, due to the volume concerned, computational techniques are needed to effectively filter and prioritise this content as it arises during emerging events. In the literature, these techniques are trained using annotated content from previous crises. In this paper, we investigate how this prior knowledge can be best leveraged for new crises by examining the extent to which crisis events of a similar type are more suitable for adaptation tonew events (cross-domain adaptation). Given the recent successes of transformers in various language processing tasks, we propose CAST: an approach for Crisis domain Adaptation leveraging Sequence-to-sequence Transformers. We evaluate CAST using two major crisis-related message classification datasets. Our experiments show that ourCAST-based best run without using any target data achieves the state of the art performance in both in-domain and cross-domain contexts. Moreover, CAST is particularly effective in one-to-one cross-domain adaptation when trained with a larger language model. In many-to-one adaptation where multiple crises are jointly used as the source domain, CAST further improves its performance. In addition, we find that more similar events are more likely to bring better adaptation performance whereas fine-tuning using dissimilar events does not help for adaptation. To aid reproducibility, we open source our code to the community.

Abstract: Information is a critical need during disasters such as hurricanes. Increasingly, people are relying upon cellular and internet-based technology to communicate that information--modalities that are acutely vulnerable to the disruptions to telecommunication infrastructure that are common during disasters. Focusing on Hurricane Maria (2017) and its long-term impacts on Puerto Rico, this research examines how people affected by severe and sustained disruptions to telecommunications services adapt to those disruptions. Leveraging social media trace data as a window into the real-time activities of people who were actively adapting, we use a primarily qualitative approach to identify and characterize how people changed their telecommunications practices and routines--and especially how they changed their locations--to access Wi-Fi and cellular service in the weeks and months after the hurricane. These findings have implications for researchers seeking to better understand human responses to disasters and responders seeking to identify strategies to support affected populations.

Abstract: Significant progress has been made towards automated classification of disaster or crisis related tweets using machine learning approaches. Deep learning models, such as Convolutional Neural Networks (CNN), domain adaptation approaches based on self-training, and approaches based on pre-trained language models, such as BERT, have been proposed and used independently for disaster tweet classification. In this paper, we propose to combine self-training with CNN and BERT models, respectively, to improve the performance on the task of identifying crisis related tweets in a target disaster where labeled data is assumed to be unavailable, while unlabeled data is available. We evaluate the resulting self-training models on three crisis tweet collections and find that: 1) the pre-trained language model BERTweet is better than the standard BERT model, when fine-tuned for downstream crisis tweets classification; 2) self-training can help improve the performance of the CNN and BERTweet models for larger unlabeled target datasets, but not for smaller datasets.

Abstract: Effective command and control (C2) is necessary to achieve and maintain superiority in military engagements. C2 is well documented in the literature and is a major focus in the military arena; however, the conventional military network topology is increasingly becoming a liability and ineffective in the new age of asymmetric warfare. The beyond-line-of-sight command and control (BLOS C2) concept is a radical shift towards a seamless joint network topology, which will dramatically increase tactical C2 across military service branches, equipment types, and geographical locations. Though BLOS C2 is still in its testing phase, this paper examines the systems thinking approach of BLOS C2 with respect to layered models, adaptation, and synergy. The implementation of the BLOS C2 “tactical Wi-Fi” concept helps fill a Central Command (CENTCOM) capability gap in support of a Contingency Plan (CONPLAN) that provides Navy Forces Central Command (NAVCENT) with a robust force protection system. © 2012 ISCRAM.

Abstract: For recent years, disaster response management is considered as a promising field for applying methods and tools from business process management. Especially the development of adaptive workflow management systems (WfMS) brought a process-oriented management of highly dynamic disaster response processes (DRP) within tangible reach. However, time criticality, unpredictability or complex and changing disaster reality make it impossible to analyze and adapt ongoing DRP within reasonable time manually. Hence, to foster the application of disaster response WfMS in practice, it becomes mandatory to develop methods supporting an (semi-)automated analyses and adaption of ongoing DRP. Addressing this research gap, we present a novel method called DRP-ADAPT which analyzes given DRP models with respect to place-related conflicts and resolves inoperable response activities (semi-)automatically by process adaptation.

Abstract: Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.

Abstract: Huge amounts of data that are generated on social media during emergency situations are regarded as troves of critical information. The use of supervised machine learning techniques in the early stages of a disaster is challenged by the lack of labeled data for that particular disaster. Furthermore, supervised models trained on labeled data from a prior disaster may not produce accurate results, given the inherent variation between the current and the prior disasters. To address the challenges posed by the lack of labeled data for a target disaster, we propose to use a hybrid feature-instance adaptation approach based on matrix factorization and the k nearest neighbors algorithm, respectively. The proposed hybrid adaptation approach is used to select a subset of the source disaster data that is representative for the target disaster. The selected subset is subsequently used to learn accurate Naive Bayes classifiers for the target disaster.

Abstract: Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters,
in real time, are greatly needed. While many studies have focused on filtering textual information, the research
on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain
adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster.
To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks
(DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone,
and uses the adversarial training to find a transformation that makes the source and target data indistinguishable.
Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better
results as compared to the VGG-19 model fine-tuned on the source labeled data.