Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers’ expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano).

An emerging evacuation path planning technique that uses Low Power Wide Area Networks (LPWAN) to enable real-time danger prediction and user-oriented path planning can ensure the safe and timely navigation of evacuees in complex scenarios such as cruise ships. However, most existing LPWAN-based evacuation models assume pedestrians’ walking speed remains constant and ignore crowd congestion in corridors before exits, which is not appropriate for rocking ships. To overcome these issues, this paper proposes a congestion-relived guiding framework with dedicated path planning for emergency evacuation on passenger ships. The basic idea is to averagely minimize the total evacuation time while meeting the deadline for ship capsizing under all circumstances by selecting uncrowded paths for each passenger individually. First, we use probability distributions rather than constant numbers to represent walking time (also called delay) along passageways. A worst-case delay bound with a high level of trustworthiness is also estimated for each passageway under the boundary condition of ship capsizing. Next, we predict the congestion of corridors by modeling the spatiotemporal movement of passengers, and then distribute evacuation loads evenly among corridors to alleviate the congestion. The total expected evacuation time of all corridors is finally minimized based on the delay probability distribution and estimated congestion, and the deadline for ship evacuation under all circumstances is met with the worst-case delay bound. Simulation results show that our approach significantly reduces the total escaping time of crowd evacuation by $45\%$ and $34\%$ while improving the navigation success ratio by more than $20\%$ and $80\%$ compared with the state-of-the-art emergency evacuation systems, namely the look-up table guiding scheme and the group-based guiding evacuation scheme, respectively.

The problems in the requirement using natural language, such as incomplete, inconsistent, ambiguous, can lead to delays or even failures in software projects. The requirement using formal specification can solve these problems. However, translating software requirements from natural language to formal specification has always been a difficulty. This paper proposes a new method for the translation from the natural language to a formal specification, the automaton. Firstly, a constrained structure of natural language is used to specify the requirements, and then two tables are used to extract the characteristics of requirements. At last, the translation from natural language to automaton will finish with help of control structure chart of requirement.

Cyber-Physical System is (CPS) is a system that integrates computing and physical processes. It is considered to be the next computing revolution that will change the way we interact with the world. However, the identification and detection of interactive faults in CPS is a major challenge. To this end, this paper proposes a new fault detection method based on STPA for interactive faults in CPS. The method constructs a control model of the system through PHAVerLite, which constructs continuous physical processes and discrete computing logic into state machines. Then injecting interactive faults into the model, and verifying whether the interactive faults cause hazards through reachability analysis. Finally, the method is applied to a railway crossing control system, and the results show that the method can successfully detect interactive faults in CPS.