Related papers: Artificial Intelligence for Emergency Response
The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization architecture will enable cloud computing at the…
Challenges, such as a lack of information for emergency decision-making, time pressure, and limited knowledge of experts acting as decision-makers (DMs), can result in the generation of poor or inconsistent indirect information regarding…
Enterprises are constantly under attack from sophisticated adversaries. These adversaries use a variety of techniques to first gain access to the enterprise, then spread laterally inside its networks, establish persistence, and finally…
Interest in emergent communication has recently surged in Machine Learning. The focus of this interest has largely been either on investigating the properties of the learned protocol or on utilizing emergent communication to better solve…
Pure machine-based solutions usually struggle in the challenging classification tasks such as entity resolution (ER). To alleviate this problem, a recent trend is to involve the human in the resolution process, most notably the…
Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However,…
Mental disorders are clinically significant patterns of behavior that are associated with stress and/or impairment in social, occupational, or family activities. People suffering from such disorders are often misjudged and poorly diagnosed…
In the United States alone accidental home deaths exceed 128,000 per year. Our work aims to enable home robots who respond to emergency scenarios in the home, preventing injuries and deaths. We introduce a new dataset of household…
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully…
We formulate and solve two resource allocation problems motivated by a preparedness question of emergency response services. First, we consider the assignment of vehicles to stations, and, in a second step, assign crews to vehicles. In both…
In case of a severe accident, the key to saving lives is the time between the incident and when the victim receives treatment from the first-responders. In areas with well designed emergency medical systems, the time for an ambulance to…
Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There…
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant…
As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These…
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things…
AI-enabled decision-support systems aim to help medical providers rapidly make decisions with limited information during medical emergencies. A critical challenge in developing these systems is supporting providers in interpreting the…
Incident Response Planning (IRP) is essential for effective cybersecurity management, requiring detailed documentation (or playbooks) to guide security personnel during incidents. Yet, creating comprehensive IRPs is often hindered by…
E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector. The depletion of fossil fuels, escalating greenhouse gas emissions, and the…
The problem of detecting the presence of a signal that can lead to a disaster is studied. A decision-maker collects data sequentially over time. At some point in time, called the change point, the distribution of data changes. This change…
Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must…