Seungwon Yang, Haeyong Chung, Xiao Lin, Sunshin Lee, Liangzhe Chen, Andrew Wood, et al. (2013). PhaseVis1: What, when, where, and who in visualizing the four phases of emergency management through the lens of social media. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 912–917). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The Four Phase Model of Emergency Management has been widely used in developing emergency/disaster response plans. However, the model has received criticism contrasting the clear phase distinctions in the model with the complex and overlapping nature of phases indicated by empirical evidence. To investigate how phases actually occur, we designed PhaseVis based on visualization principles, and applied it to Hurricane Isaac tweet data. We trained three classification algorithms using the four phases as categories. The 10-fold cross-validation showed that Multi-class SVM performed the best in Precision (0.8) and Naïve Bayes Multinomial performed the best in F-1 score (0.782). The tweet volume in each category was visualized as a ThemeRiver[TM], which shows the 'What' aspect. Other aspects – 'When', 'Where', and 'Who' – Are also integrated. The classification evaluation and a sample use case indicate that PhaseVis has potential utility in disasters, aiding those investigating a large disaster tweet dataset.
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Simon French, Emma Carter, & Carmen Niculae. (2006). When experts or models disagree. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 547–553). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: In managing crises, decision makers are confronted with a plethora of uncertainties. Many arise because the world is uncertain, particularly in the context of a crisis. But some arise because analyses based upon different, but seemingly equivalent models lead to different forecasts. Other times expert advisors differ in their explanations and predictions of the evolving situation. We argue that when handled correctly such conflict can alert the decision makers to the inherent complexity and uncertainty of the situation and improve their management of the crisis.
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Michael J. Chumer, & Murray Turoff. (2006). Command and control (C2): Adapting the distributed military model for emergency response and emergency management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 465–476). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: The military use of Command and Control (C2) has been refined over centuries of use and developed through years of combat situations. This C2 model is framed as process, function, and organization, suggesting that emergency response organizations and emergency management structure their non military C2 and subsequent response scenarios within the C2 framework established in this paper.
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Rajesh M. Hegde, B.S. Manoj, Bashkar D. Rao, & Ramesh R. Rao. (2006). Emotion detection from speech signals and its applications in supporting enhanced QoS in emergency response. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 82–91). Newark, NJ: Royal Flemish Academy of Belgium.
Abstract: Networking in the event of disasters requires new hybrid wireless architectures such as Wireless Mesh Networks (WMNs). Provisioning Quality of Service (QoS) in such networks which are quickly deployed during emergencies demand radical solutions. In this paper, we provide a new QoS approach for voice calls over a wireless mesh networks during emergency situations. According to our scheme, the contention and back-off parameters are modified based on the emotion content in the voice streams. This paper also looks at methods for detecting emotion from an incoming voice call using the speech signal. The issues of interest in such situations are whether the caller is in a state of extreme panic, moderate panic, or in a normal state of behavior. The communication network behavior should be modified to provide differentiated QoS for calls based on the degree of emotion. We use several features extracted from the speech signal like the range of pitch variation, energy in the critical bark band, range of the first three formant variations, and speaking rate among others to discriminate between the three emotional states. At the back end the Gaussian mixture modeling techniques is used to model the three emotional states of the speaker. Since a large number of features increase the computational complexity and time, a feature selection technique is employed based on the Bhattacharya distance, to select the set of features that give maximum discrimination between the classes. These set of features are employed to simulate an emotion recognition system. The results indicate a promising emotion detection rate for the three emotions. We also present the early results on detecting the emotion content in the speech and using this in the MAC layer differentiated QoS provisioning scheme. Our scheme provides an end-to-end delay performance improvement for panicked calls as high as 60% compared to normal calls.
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Murray Turoff, Connie White, Linda Plotnick, & Starr Roxanne Hiltz. (2008). Dynamic emergency response management for large scale decision making in extreme events. In B. V. de W. F. Fiedrich (Ed.), Proceedings of ISCRAM 2008 – 5th International Conference on Information Systems for Crisis Response and Management (pp. 462–470). Washington, DC: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: Effective management of a large-scale extreme event requires a system that can quickly adapt to changing needs of the users. There is a critical need for fast decision-making within the time constraints of an ongoing emergency. Extreme events are volatile, change rapidly, and can have unpredictable outcomes. Large, not predetermined groups of experts and decision makers need a system to prepare for a response to a situation never experienced before and to collaborate to respond to the actual event. Extreme events easily require a hundred or more independent agencies and organizations to be involved which usually results in two or more times the number of individuals. To accomplish the above objectives we present a philosophical view of decision support for Emergency Preparedness and Management that has not previously been made explicit in this domain and describe a number of the current research efforts at NJIT that fit into this framework.
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