Abstract: Detecting the occurrence of hydraulic accidents or contamination events in the shortest time has always been a significant but difficult task. The simple and efficient way is to identify the sudden changes or outliers hidden in the vast amounts of monitoring data produced minute by minute, which is unpractical for human. A new method, which employs a data self-recognition approach to achieve that automatically, has been proposed in this paper. The autoregressive moving average (ARMA) model was employed in this research to construct the self-recognition model. 56 months monitoring data from Changping water distribution network in Beijing, which was firstly cut into different time-slice series, was used to establish the ARMA model. This provided a prediction confidence interval in order to identify the outliers in the test data series. The results showed a good performance in outlier identification and the accuracy ranges from 90% to 95%.Thus, the ARMA model showed great potential in dealing with monitoring data and achieving the expected performance of data self-recognition technology.

Abstract: Crisis management (CM) is an aspect of command and control characterized by complexity, uncertainty, and severe time pressure. To address these challenges, CM teams can use collaborative work support systems (CWSS) to help plan their intervention and coordination activities. However, the use of CWSS is not necessarily beneficial and in some cases, can impede more than augment performance. Hence, it is essential to understand the impact of a CWSS on team performance and CM teamwork. We have developed a methodology to assess the effectiveness of CWSS by comparing the use of an interactive Smartboard with that of a traditional topographic map during team planning activities. To do so, a dynamic CM situation is simulated using a forest firefighting functional simulation – the C3Fire microworld. We compared two groups of participants on the basis of performance, communication, coordination efficiency, and planning quality. Based on a preliminary analysis, in comparison to maps, the use of a CWSS seems to be beneficial to planning activities and CM coordination. At this point the main contribution of the current on-going project is to provide a method and metrics for the objective assessment of new technology in the context of CM.

Abstract: In recent years, we have developed a framework of human-computer interaction that offers recognition of various communication modalities including speech, lip movement, facial expression, handwriting/drawing, gesture, text and visual symbols. The framework allows the rapid construction of a multimodal, multi-device, and multi-user communication system within crisis management. This paper reports the approaches used in multi-user information integration (input fusion) and multimodal presentation (output fission) modules, which can be used in isolation, but also as part of the framework. The latter is able to specify and produce contextsensitive and user-tailored output combining language, speech, visual-language and graphics. These modules provide a communication channel between the system and users with different communication devices. By the employment of ontology, the system's view about the world is constructed from multi-user observations and appropriate multimodal responses are generated.

Abstract: Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability.

Abstract: Unmanned aerial vehicles (UAVs) have increased in popularity in recent years and are now involved in many activities, professional and otherwise. First responders, those teams and individuals who are the first to respond in crisis situations, have been using UAVs to assist them in locating victims and identifying hazards without endangering human personnel needlessly. However, professional UAV controllers tend to be heavy and cumbersome, requiring both hands to operate. First responders, on the other hand, often need to carry other important equipment and need to keep their hands free during a mission. This work considers enabling first responders to control UAVs with single-handed gestures, freeing their other hand and reducing their encumbrance. Two sets of gesture UAV controls are presented and implemented in a simulated environment, and a two-part user study is conducted: the first part assesses the comfort of each gesture and their intuitive association with basic flight control concepts; and the second evaluates two different modes of gesture control in a population of users including both genders, and first responders as well as members of the general populace. The results, consisting of both objective and subjective measurements, are discussed, hindrances and problems are identified, and directions of future work and research are mapped out.

Abstract: This paper presents a study of the role of artefacts in sensemaking during emergency response. A qualitative study was conducted with two UK Police Forces, with a particular focus on the role of artefacts in the creation and modification of sensemaking frames. This research demonstrates that sensemaking is a key component of emergency response Command and Control and that this activity is distributed across the individuals within the system. Collaborative sensemaking is coordinated via social and organisational means, supported by a range of private (informal) and shared (formal) artefacts, which function as resources for action – cueing frame seeking and frame-defined data collection. The study also reveals the role of narrative in bridging the gap between these two parallel sensemaking processes and raises implications for the further digitisation of the emergency response environment, demonstrating the importance of balancing social and technical factors in the design of ICT for emergency response. © 2012 ISCRAM.

Abstract: In disaster situations like earthquakes and hurricanes, people have difficulties accessing shelter and requesting help. Many smartphone applications provide behavioral advice or means to communicate during such situations. However, to what extent a person is affected by a disaster is often unclear, as these applications rely on the user's subjective assessment. Therefore, detecting a user's situation is key to provide more meaningful information in such applications and to allows first responders to better assess incoming messages. We propose a predictive model that recognizes four normal and ten disaster-related activities achieving an average f1-score of up to 90.1\%, solely based on sensor readings of the subject's mobile device. We conduct an extensive measurement-based evaluation to assess the impact of individual model parameters on the prediction accuracy. Our model is orientation-independent, position-independent, and subject-independent, making it an ideal foundation for future context-aware emergency applications.

Abstract: The increasingly sophisticated sensors supported by modern smartphones open up novel research opportunities, such as mobile phone sensing. One of the most challenging of these research areas is context-aware and activity recognition. The Smart Rescue project takes advantage of smartphone sensing, processing and communication capabilities to monitor hazards and track people in a disaster. The goal is to help crisis managers and members of the public in early hazard detection, prediction, and in devising risk-minimizing evacuation plans when disaster strikes. In this paper we suggest a novel smartphone-based communication framework. It uses specific machine learning techniques that intelligently process sensor readings into useful information for the crisis responders. Core to the framework is a content-based publish-subscribe mechanism that allows flexible sharing of sensor data and computation results. We also evaluate a preliminary implementation of the platform, involving a smartphone app that reads and shares mobile phone sensor data for activity recognition.

Abstract: What role should operational risk leaders have in the executive suite? This paper argues that, when nervous CEOs ask “What can go wrong? How can we get ahead of the curve?”, they should look to their operational risk leaders. Those leaders oversee corporate and information security as well as business continuity, crisis management and disaster recovery programs inside companies. That makes them ideally qualified to take the process of crisis management, including analysis of aggregate risk across all silos – To the CEO and then into the boardroom when the need arises, before the corporate crisis is full-blown.

Abstract: Event recognition systems have high potential to support crisis management and emergency response. Given the vast amount of possible input channels, automatic processing of raw data is crucial. In this paper, we describe several components integrated in an overall intelligent resource management system, namely abnormal event detection in audio and video material, as well as automatic speech recognition within a public safety network. We elaborate on the challenges expected from real life data and the solutions that we applied. The overall system, based on Event-Driven Service-Oriented Architecture, has been implemented and partly integrated into the end users' infrastructures. The system is continuously running since almost two years, collecting data for research purposes. © 2012 ISCRAM.

Abstract: Technological advances in both distributed cooperative work and web-map services have the potential to support distributed and collaborative time-critical decision-making for crisis response. We address this potential through the theoretical perspective of distributed cognition and apply this perspective to development of a geocollaborationenabled web application that supports coordinated crisis management activities. An underlying goal of our overall research program is to understand how distributed cognition operates across groups working to develop both awareness of the geographic situation within which events unfold, and insights about the processes that have lead to that geographic situation over time. In this paper, we present our preliminary research on a web application that addresses these issues. Specifically, the application (key parts of which are implemented) enables online, asynchronous, map-based interaction between actors, thus supporting distributed spatial and temporal cognition, and, more specifically, situational awareness and subsequent action in the context of humanitarian disaster relief efforts.

Abstract: We report on the results of an investigation about the “informal, ” public-side communications that occurred in the aftermath of the April 16, 2007 Virginia Tech (VT) Shooting. Our on-going research reveals several examples of on-line social interaction organized around the goal of collective problem-solving. In this paper, we focus on specific instances of this distributed problem-solving activity, and explain, using an ethnomethodological lens, how a loosely connected group of people can work together on a grave topic to provide accurate results.