Query: disaster information acl

title: Bayesian Estimation Method of Transportation Frequency for Disaster Information Sharing System
abstract: In large scale disasters such as earthquakes, sharing of disaster information is required without relying on the existing communication infrastructure. In this paper, we propose a disaster information sharing system using passive portable storages such as a USB memory stick as for communication media. Our pro posed system assumes a serious disaster that requires the physical transport of communication media. In this system, the portable storages are transported by local citizens. In such situations, each sending node cannot know both the destination node of each message and the receiver node of portable storages. Therefore, an efficiency content selection method is required for our proposed system. We also propose a content selection method based on priority. The proposed method calculates the content priority based on the transportation frequency information between nodes estimated from the transportation history of portable storages and the content diffusion information in the network. We have revealed the effectiveness of our proposed method by simulation using actual geographical points of evacuation centers.

title: Effects and Issues of the Disaster Information Tweeting and Mapping System for Residents
abstract: Using social media for information sharing during and after disasters has been gaining attention. To facilitate information sharing during disasters, we developed a web system called Disaster Information Tweeting and Mapping System (DITS/DIMS) We organized a disaster evacuation drill in July 2019 and participated in a disaster prevention fair in August 2019 using DITS/DIMS in Hiratsuka City, Kanagawa prefecture, Japan. In this paper, we describe the results from the questionnaire surveys at the two events. The results showed that participants recognized that DITS/DIMS could be useful during disasters. Therefore, the use of this system has the potential to make an important contribution to disaster prevention and mitigation. However, some issues exist with the system when it is actually used by individuals; therefore, future improvements are required.

title: A Proposal on Disaster Information and Rescue Request Sharing Application Using Sina Weibo
abstract: During large scale disasters, a large number of posts about damage reports and rescue requests are shared on social media. The utilization of the posts has become important for disaster response. Sina Weibo is the most famous microblogging service in China. In this study, we propose a web system to facilitate sharing disaster related information on Sina Weibo. Using the system, users can easily share the information on Sina Weibo. Moreover, the posted information is listed on a Message board on the system to support rescue and assist activities by local governments and rescue experts.

title: RALE ACL A Language for Information Exchange between Case Based Agents as Alternative to the FIPA ACL Based Communication

title: Telegram Based Chatbot Application for Foreign People in Japan to Share Disaster Related Information in Real Time
abstract: When a disaster occurs, early evacuation of residents is one of the effective ways for the reduction of human damage. Then, the rapid collection and delivery of accurate disaster related information are essential. In this study, we develop a chatbot application as an add on to the Telegram platform interface. This chatbot provides emergency information to foreign people in Japan at the time of disasters. The information provided by the proposed application is, for example, the nearby evacuation points such as the evacuation centers and train stations, and the real time disaster information based on the user's current location. People can also share disaster related pictures via the proposed application. Such pictures may be useful not only for disaster affected people but also for governmental agencies to gain a better understanding of current conditions and thus help them make informed decisions.

title: An Information centric NFV based System Implementation for Disaster Management Services
abstract: When disasters occur, they not only affect the human life. Therefore, communication in disaster management is very important. During the disaster recovery phase, the network infrastructure may be partially fragmented and mobile rescue operations may involve many teams with different roles which can dynamically change. Therefore, disaster management services require high flexibility both in terms of network infrastructure management and rescue group communication. Existing studies have shown that IP based or traditional telephony solutions are not well suited to deal with such flexible group communication and network management due to their connection oriented communication, no built in support for mobile devices, and no mechanism for network fragmentation. Recent studies show that information centric networking offers scalable and flexible communication based on its name based interest oriented communication approach. However, considering the difficulty of deploying a new service on the existing network, the programmability and virtualization of the network are required. This paper presents our implementation of an information centric disaster management system based on network function virtualization (vICSNF) We show a proof of concept system with a case study for Seoul disaster management services. The system achieves flexibility both in terms of network infrastructure management and rescue group communication. Obtained testbed results show that vICSNF achieves a low communication overhead compared to the IP based approach and the auto configuration of vICSNFs enables the quick deployment for disaster management services in disaster scenarios.

title: A Crowdsourcing based Information System Framework for Coordinated Disaster Management and Building Community Resilience
abstract: Disaster management involves intensive coordination among multiple agencies like police, fire departments, public health, non govt. agencies, including local volunteers/field workers. Accurate situational information about damage, resource needs, available resources etc. in the affected areas help the disaster management agencies in proper damage and need assessment and prepare suitable resource deployment plan. Crowdsourcing has become a popular approach for information collection where open crowds of people share multimodal situational information (text, images, audio, video etc. about any event through social media posts. However, the authenticity and reliability of such posts are still debatable. Gathering situational data directly from the affected community (community sourcing) can supplement social media posts to generate effective insights. In this paper, we attempt to design and develop a multiplatform disaster management information system where both social media based crowdsourcing and community sourcing techniques are used to accumulate location specific situational information. Subsequently, a coherent picture of the disaster situation is evolved through the integration of these local snapshots. Here, we explore how community participation, in the context of disaster management, can be enhanced through collaborative knowledge transaction, which eventually will lead towards the development of a resilient community. A field trial of our system is conducted involving a remote village community at Namkhana, West Bengal.

title: Designing an Information Bot for an Opportunistic Mobile Environment in Disaster Response Scenario
abstract: This work primarily comprises of designing a framework of an information bot for an opportunistic mobile environment in post disaster situation. The bot would collect the required classes information periodically through questioning various mobile nodes by considering their roles (i.e. Volunteer, Local People and Victims) in disaster situation. Besides, it would validate the collected information through sharing with other nodes of same area of interest during information exchange. A portion of the designed framework has been considered for emulation in AIML platform using python. From observations, it is evident that the assistance of an information bot during data acquisition and validation is worthwhile for preservation of consistent disaster intent information.

title: Augmented Information Discovery using NFC Technology within a Platform for Disaster Monitoring
abstract: Catastrophic and accidental natural events (e.g. earthquakes, sudden floods, fires, etc. and deficiencies in appropriate management activities require proper plans for disaster management and real time definition of safe paths and escape routes. Emergencies can affect the structural health status of structures and infrastructures and, consequently, the safety in highly populated areas (e.g. urban contexts, occasional assembly points for crowds, theme parks, etc. is compromised. Consequently, the use of a decision platform for the management of structures and infrastructures, which is able to gather real time data from sensors and provide alerts and augmented information about safe paths, paves the way to the adoption of a proactive form of risk management. In this work, objectives were confined into the validation of a Near Field Communication (NFC) system. Data coming from user smartphones using the NFC technology and sensor data deriving from the decision platform were used as input in a Machine Learning (ML) algorithm. Results show that the ML algorithm mentioned above is able to refine the safe path recognition strategy defined from the platform. In addition, the bidirectional and touch less NFC technology allows the delivery of alerts and disaster plan dissemination, also in the case of connectivity shutdown.

title: STIMULATE: A System for Real time Information Acquisition and Learning for Disaster Management
abstract: Real time information sharing and propagation using social media such as Twitter has proven itself as a potential resource to improve situational awareness in a timely manner for disaster management. Traditional disaster management systems work well for analyzing static and historical information. However, they cannot process dynamic streams of data that are being generated in real time. This paper presents STIMULATE a System for Real time Information Acquisition and Learning for Disaster Management that can (1) fetch and process tweets in real time, (2) classify those tweets into FEMA defined categories for rescue priorities using pre trained deep learning models and generate useful insights, (3) find FEMA defined stranded people for rescue missions of varying priorities, and (4) provide an interactive web interface for rescue management given the available resources. The STIMULATE prototype is primarily built using the Python Flask framework for web interaction. Additionally, it is deployed in the cloud environment using Hadoop and MongoDB for scalable storage, and on demand computing for processing extensive social media data. The deep learning models in the STIMULATE prototype use Python Keras and the TensorFlow library. We use Bi directional Long Short Term Memory (BLSTM) and Convolutional Neural Network (CNN) for developing the tweet classifier. Further, we use the Python PyWSGI WebSocket server for rescue scheduling operations. We present a deep learning system trained on hurricane Harvey and Irma datasets only. The tweet classifier is evaluated using 15 different disaster datasets. Finally, we present the results of multiple simulations using synthetic data with different sizes to measure the performance and effectiveness of the tweets processor and rescue scheduling algorithm.