ICT and Artificial Intelligence for Crisis and Emergency Management

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Now showing 1 - 7 of 7
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    An IoT Software Architecture for an Evacuable Building Architecture
    ( 2019-01-08) Muccini, Henry ; Arbib, Claudio ; Davidsson, Paul ; Tourchi Moghaddam, Mahyar
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    Multi-Factor Model and Simulation of Social Cohesion and Its Effect on Evacuation
    ( 2019-01-08) Adam, Carole ; Garbay, Catherine ; Dugdale, Julie
    In this paper we propose an analysis of social cohesion in terms of 3 factors: emotions, social norms, and mutual knowledge. These factors have previously been analysed separately in terms of Beliefs, Desires, Intentions (BDI) logics, but have not been merged into a unified model. The goal of this paper is to provide a unified agent-based model and describe its implementation in the GAMA simulation platform. The simulator is applied to an evacuation case study and different scenarios are run to evaluate the impact of the 3 factors on cohesion, as well as the effect of cohesion on evacuation. This paper describes first results and concludes with interesting future prospects.
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    A Highly Effective Deep Learning Based Escape Route Recognition Module for Autonomous Robots in Crisis and Emergency Situations
    ( 2019-01-08) Buettner, Ricardo ; Baumgartl, Hermann
    Using convolutional neural networks we extend the work by Dugdale's group on socially relevant multi-agent systems in crisis and emergency situations by giving the artificial agent the ability to precisely recognize escape signs, doors and stairs for escape route planning. We build an efficient recognition module consisting of three blocks of a depth-wise separable convolutional layer, a max-pooling layer, and a batch-normalization layer before dense, dropout and classifying the image. A rigorous evaluation based on the MCIndoor20000 dataset shows excellent performance values (e.g. over 99.81 percent accuracy). In addition, our module architecture is 78 times smaller than the MCIndoor20000 benchmark - making it suitable for embedding in operational drones and robots.
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    To Alert or Not to Alert? That Is the Question
    ( 2019-01-08) Arru, Maude ; Negre, Elsa ; Rosenthal-Sabroux, Camille
    Most of crises, environmental, humanitarian, economic or even social, occur after different presaging signals that permit to trigger warnings. These warnings can help to prevent damages and harm if they are issued timely and provide information that helps responders and population to adequately prepare for the disaster to come. Today, there are many systems based on Information and Communication Technologies that are designed to recognize foreboding signals of crises to limit their consequences. Warning system are part of them, they have proved to be effective, but as for all systems including human beings, a part of unpredictable remains. In this article, we provide a method of data analysis that allows decision makers in crisis cells to have answer elements to the question of alerting or not populations in a given geographical area. This method is based on a selection of factors that influence population behaviors, for which we establish a list of relevant indicators that can be informed in the preliminary phase of a crisis into warning systems. From these indicators, we propose a tool for decision support (based on a decision tree as a possible representation).
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    Crisis Warning Apps: Investigating the Factors Influencing Usage and Compliance with Recommendations for Action
    ( 2019-01-08) Fischer, Diana ; Putzke-Hattori, Johannes ; Fischbach, Kai
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    Vulnerability Analysis of Interdependent Critical Infrastructures upon a Cyber-attack
    ( 2019-01-08) Abdelgawad, Ahmed ; Farstad, Tor-Edin ; Gonzalez, Jose
    There is an extensive literature on modelling cascading effects in Critical Infrastructures (CIs). Concerning the cascading impacts of a cyber-attack upon other CIs, a detailed scenario analysis done by the Norwegian Directorate of Civil Protection concludes that a considerable impact could be achieved. However, the analysis admits that the probability of the attack would be very low, since it would require considerable expertise and resources. We argue that a smart attacker could exploit existing knowledge on cascading impacts to plan for perfidiously-timed cyber-attacks requiring low resources that would achieve a significant disruption of CIs. To illustrate our point, we build and simulate a highly-aggregated system dynamics model using estimates of disruptions effects across CIs taken from the literature.
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