Records |
Author |
Masahiro Watanabe; Yu Ozawa; Kenichi Takahashi; Eri Takane; Tetsuya Kimura; Soichiro Suzuki; Kenjiro Tadakuma; Giancarlo Marafioti; Terje Mugaas; Miltiadis Koutsokeras; Satoshi Tadokoro |
Title |
Hardware Design and Tests of SMURF V1 Platform for Searching Survivors in Debris Cones |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
849-866 |
Keywords |
Search and rescue (SaR), miniaturized mobile robot, wheeled robot, remotely operated vehicle (ROV), emergency responder |
Abstract |
When a large-scale disaster such as earthquake occurs, a huge number of victims will be trapped under debris in a wide area. Rescue activities in debris are technically not easy and endanger the first responders. There are several methods for improving safety and efficiency of rescue operation, but their availability is limited to a certain area or short operating time. Our project called CURSOR is developing tools to comprehensively search victims using a large number of ground-based robots entering debris transported by aerial drones. Here we show the development of the exploration robot collecting information with several sensors. The robot system was designed based on the requirements and performance was evaluated by ruggedization tests and mobility tests. No critical problem was found in the durability, and the mobility showed as the same as the ordinary wheel. To improve the mobility, we are planning to apply a proposed unique track mechanism. |
Address |
Tough Cyberphysical AI Research Center, Tohoku University; Graduate School of Information Sciences, Tohoku University; Tough Cyberphysical AI Research Center, Tohoku University; Graduate School of Engineering, Tohoku University; Department of System Safet |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Technologies for First Responders |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
watanabe.masahiro@rm.is.tohoku.ac.jp |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2378 |
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|
Author |
Nada Matta; Thomas Godard; Guillaume Delatour; Ludovic Blay; Franck Pouzet; Audrey Senator |
Title |
Analyzing Social Media in Crisis Management Using Expertise Feedback Modelling |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
17-27 |
Keywords |
Social Media analysis, TextMining, sentiment analysis, crisis management, decision making |
Abstract |
Currently social media are largely used in interactions, especially in crisis situations. We note a big volume of interactions around events. Observing these interactions give information even to alert the existence of an incident, event, or to understand the expansion of a problem. Crisis management actors observe social media to be aware about this type of information in order to consider them in their decisions. Specific organizations are founded in order to observe social media interactions and send their analysis to rescue and crisis management actors. In our work, an experience feedback of this type of organizations (VISOV, a crisis social media analysis association) is capitalized in order to emphasize from one side, main dimensions of this analysis and from another side, to simulate some aspects using TextMining that help to explore big volume of data. |
Address |
University of Technology of Troyes; University of Technology of Troyes; University of Technology of Troyes; VISOV; CS Group; ENSOSP |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
nada.matta@utt.fr |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2309 |
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Author |
Nathan Elrod; Pranav Mahajan; Monica Katragadda; Shane Halse; Jess Kropczynski |
Title |
An Exploration of Methods Using Social Media to Examine Local Attitudes Towards Mask-Wearing During a Pandemic |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
345-358 |
Keywords |
Social media analytics, situational awareness, sentiment analysis, n-grams, social network analysis |
Abstract |
During the COVID-19 health crisis, local public offcials expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health offcials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health offcials' situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results oer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness. |
Address |
University of Cincinnati; University of Cincinnati; University of Cincinnati; University of Cincinnati; University of Cincinnati |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Disaster Public Health & Healthcare Informatics in the Pandemic |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
elrodnj@ucmail.uc.edu |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2338 |
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|
Author |
Nilani Algiriyage; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston; Minura Punchihewa; Santhoopa Jayawardhana |
Title |
Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
40-48 |
Keywords |
Computer Vision, Traffic Flow, YOLOv4, CCTV Big Data |
Abstract |
Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions. |
Address |
Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington; University of Kelaniya; Univerity of Kelaniya |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
rangika.nilani@gmail.com |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2311 |
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|
|
Author |
Nilani Algiriyage; Rangana Sampath; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston |
Title |
Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
731-743 |
Keywords |
Tweet Classification, Machine Learning, Deep Learning, Disasters |
Abstract |
Social media applications such as Twitter and Facebook are fast becoming a key instrument in gaining situational awareness (understanding the bigger picture of the situation) during disasters. This has provided multiple opportunities to gather relevant information in a timely manner to improve disaster response. In recent years, identifying crisis-related social media posts is analysed as an automatic task using machine learning (ML) or deep learning (DL) techniques. However, such supervised learning algorithms require labelled training data in the early hours of a crisis. Recently, multiple manually labelled disaster-related open-source twitter datasets have been released. In this work, we create a large dataset with 186,718 tweets by combining a number of such datasets and evaluate the performance of multiple ML and DL algorithms in classifying disaster-related tweets in three settings, namely ``in-disaster'', ``out-disaster'' and ``cross-disaster''. Our results show that the Bidirectional LSTM model with Word2Vec embeddings performs well for the tweet classification task in all three settings. We also make available the preprocessing steps and trained weights for future research. |
Address |
Massey University; Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
rangika.nilani@gmail.com |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2368 |
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|
Author |
Oussema Ben Amara; Daouda Kamissoko; Frédérick Benaben; Ygal Fijalkow |
Title |
Hardware architecture for the evaluation of BCP robustness indicators through massive data collection and interpretation |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
71-78 |
Keywords |
Business Continuity Plan, Social sciences, Risk Management, Robustness, Embedded Hardware |
Abstract |
Recently, the concept of robustness measurement has become clearly important especially with the rise of risky events such as natural disasters and mortal pandemics. In this context, this paper proposes an overview of a hardware architecture for massive data collection in the aim of evaluating robustness indicators. This paper essentially addresses the theoretical and general problems that the scientific research is seeking to address in this area, offers a literature review of what already exists and, based on preliminary diagnosis of what the literature has, presents a new approach and some of the targeted findings with a focus on the leading aspects, having a primary objective of explaining the multiple aspects of this research work. |
Address |
IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; IMT Mines Albi, University of Toulouse; INU Champollion, University of Toulouse |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
oussema.ben_amara@mines-albi.fr |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2314 |
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|
Author |
Rouba Iskandar; Julie Dugdale; Elise Beck; Cécile Cornou |
Title |
PEERS: An integrated agent-based framework for simulating pedestrians' earthquake evacuation |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
86-96 |
Keywords |
Seismic risk, human behavior, interdisciplinarity, evacuation, agent-based model |
Abstract |
Traditional seismic risk assessment approaches focus on assessing the damages to the urban fabric and the resultant socio-economic consequences, without adequately incorporating the social component of risk. However, the human behavior is essential for anticipating the impacts of an earthquake, and should be included in quantitative risk assessment studies. This paper proposes an interdisciplinary agent-based modeling framework for simulating pedestrians' evacuation in an urban environment during and in the immediate aftermath of an earthquake. The model is applied to Beirut, Lebanon and integrates geo-spatial, socio-demographic, and quantitative behavioral data corresponding to the study area. Several scenarios are proposed to be explored using this model in order to identify the influence of relevant model parameters. These experiments could contribute to the development of improved of emergency management plans and prevention strategies. |
Address |
Université Grenoble Alpes, ISTerre, Pacte, LIG; Université Grenoble Alpes, LIG; Université Grenoble Alpes, Pacte; Université Grenoble Alpes, ISTerre |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
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Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
rouba.iskandar@univ-grenoble-alpes.fr |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2316 |
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Author |
Shalini Priya; Manish Bhanu; Sourav Kumar Dandapat; Joydeep Chandra |
Title |
Mirroring Hierarchical Attention in Adversary for Crisis Task Identification: COVID-19, Hurricane Irma |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
609-620 |
Keywords |
Covid-19, Hurricane, Adversarial, Hierarchical attention, Support, Infrastructure Damage |
Abstract |
A surge of instant local information on social media serves as the first alarming tone of need, supports, damage information, etc. during crisis. Identifying such signals primarily helps in reducing and suppressing the substantial impacts of the outbreak. Existing approaches rely on pre-trained models with huge historic information as well ason domain correlation. Additionally, existing models are often task specific and need auxiliary feature information.Mitigating these limitations, we introduce Mirrored Hierarchical Contextual Attention in Adversary (MHCoA2) model that is capable to operate under varying tasks of different crisis incidents. MHCoA2 provides attention by capturing contextual correlation among words to enhance task identification without relying on auxiliary information.The use of adversarial components and an additional feature extractor in MHCoA2 enhances its capability to achievehigher performance. MHCoA2 reports an improvement of 5-8% in terms of standard metrics on two real worldcrisis incidents over state-of-the-art. |
Address |
Indian Institute of Technology Patna; Indian Institute of Technology Patna; Indian Institute of Technology Patna; Indian Institute of Technology Patna |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
shalini.pcs16@iitp.ac.in |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2359 |
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Author |
Shangde Gao; Yan Wang; Lisa Platt |
Title |
Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
333-344 |
Keywords |
COVID-19, misinformation, social media, System Dynamics, vaccine hesitancy |
Abstract |
This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely “Susceptible”, “Infected”, or “Recovered” status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world. |
Address |
University of Florida; University of Florida; University of Florida |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
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Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Disaster Public Health & Healthcare Informatics in the Pandemic |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
gao.shangde@ufl.edu |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2337 |
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|
Author |
Shivam Sharma; Cody Buntain |
Title |
An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
808-815 |
Keywords |
covid19, twitter, trecis, cross-validation, machine learning, transfer learning |
Abstract |
In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. |
Address |
New Jersey Institute of Technology; New Jersey Institute of Technology |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
cbuntain@njit.edu |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2375 |
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Author |
Sindisiwe Magutshwa; Jaziar Radianti |
Title |
A Qualitative Risk Identification Framework for Cyber-Physical-Social Systems |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
377-390 |
Keywords |
Cyber physical systems, Cyber physical Social Systems, risk, vulnerability, mission critical |
Abstract |
As information and communication technologies, real-world physical systems, and people become interconnected in critical infrastructure, attention has shifted to the operations of Cyber-Physical-Social Systems (CPSS). CPSS are progressively integrated in core critical infrastructure organisational processes to achieve a combination of benefits. However, the high degree of integration of technology into human society and mission-critical processes leads to an increase in complexity and introduces novel risks and vulnerabilities. These novel constraints extend beyond what is known from previous cyber-physical and critical infrastructure systems studies and prompt the need for revised risk perception and identification methodologies. This paper aims to develop a novel qualitative risk identification framework that is used in the identification of risk and vulnerability in CPSS ecosystems deployed in critical infrastructure or mission-critical organisational processes. The framework emphasizes interactions between humans and the system making it possible to identify and under-stand how non-technical risk impacts the CPSS ecosystem. |
Address |
University of Agder; University of Agder |
Corporate Author |
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Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Enhancing Protection of Critical Infrastructures |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
sindisiwe.magutshwa@uia.no |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2340 |
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Author |
Sofia Kostakonti; Ramona Velea; Vassilis Papataxiarhis; Daniele Del Bianco; Uberto Delprato; Stathes Hadjiefthymiades |
Title |
A semantic approach for modeling vulnerability of communities |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
305-318 |
Keywords |
Community vulnerability, semantic modeling, community resilience, knowledge representation and reasoning |
Abstract |
In this paper, we propose the use of semantic technologies for the representation of concepts and relationships required for the modeling of vulnerability data for local communities. First, we discuss the concepts of vulnerability and resilience and we try to identify the relationship between the two. We provide some background knowledge and we present basic characteristics of the two concepts. Next, we discuss the motivation behind the use of semantic technologies, and we show how the proposed framework can address existing challenges in terms of vulnerability assessment. The core part of this paper focuses on the semantic representation of community vulnerability aspects. We give an overview of the layered semantic framework consisting of interconnected ontological models and we provide a set of use-cases where the use of semantic-based modeling and query answering can prove beneficial in terms of assessing vulnerability. |
Address |
National and Kapodistrian University of Athens; Institute of International Sociology of Gorizia; National and Kapodistrian University of Athens; Institute of International Sociology of Gorizia; Intelligence for Environment and Security; National and Kapod |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Data and Resilience: Opportunities and Challenges |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
sofkost@di.uoa.gr |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2335 |
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Author |
Sofie Pilemalm; Jaziar Radianti; Bjørn Erik Munkvold; Tim A. Majchrzak; Kristine Steen-Tveit |
Title |
Turning Common Operational Picture Data into Double-loop Learning from Crises – can Vision Meet Reality? |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
417-430 |
Keywords |
Common operational picture, situation awareness, double-loop learning, crisis management, map-based evaluation |
Abstract |
This study proposes a framework for double-loop learning from crises, using common operational pictures (COP). In most crises, a COP is of outmost importance to gain a common understanding among inter-organizational response. A COP is typically expressed through a map visualization. While the technologies to support COP progress rapidly, the corresponding practice of evaluating the COP and situational awareness is not yet established. Tools that enable responders to learn after the crisis, look back in time on the COP devel-opment and detect the barriers that prevent the COP establishment, still seem absent. Double-loop learning is an organizational practice to learn from previous actions widely adopted in the safety domain, and lately used in crisis management. This paper addresses the perceived gap by presenting the technical, organizational and structural requirements derived from document analysis, observation, and a workshop with multiple crisis management stakeholders, and integrating them to an initial framework. |
Address |
Linköping university; University of Agder; University of Agder; University of Agder; University of Agder |
Corporate Author |
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Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Enhancing Resilient Response in Inter-organizational Contexts |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
sofie.pilemalm@liu.se |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2343 |
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Author |
Stefan Schauer; Stefan Rass; Sandra König |
Title |
Simulation-driven Risk Model for Interdependent Critical Infrastructures |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
404-415 |
Keywords |
risk model, risk assessment, interdependent critical infrastructures, cross-domain simulation |
Abstract |
Critical infrastructures (CIs) in urban areas or municipalities have evolved into strongly interdependent and highly complex networks. To assess risks in this sophisticated environment, classical risk management approaches require extensions to reflect those interdependencies and include the consequences of cascading effects into the assessment. In this paper, we present a concept for a risk model specifically tailored to those requirements of interdependent CIs. We will show how the interdependencies can be reflected in the risk model in a generic way such that the dependencies among CIs on different levels of abstraction can be described. Furthermore, we will highlight how the simulation of cascading effects can be directly integrated to consistently represent the assessment of those effects in the risk model. In this way, the model supports municipalities' decision makers in improving their risk and resilience management of the CIs under their administration. |
Address |
AIT Austrian Institute of Technology GmbH; System Security Group, Department of Applied Informatics, Universitaet Klagenfurt; Austrian Institute of Technology |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Enhancing Protection of Critical Infrastructures |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
stefan.schauer@ait.ac.at |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2342 |
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Author |
Stella Polikarpus; Tobias Ley; Katrin Poom-Valickis |
Title |
Collaborative Authoring of Virtual Simulation Scenarios for Assessing Situational Awareness |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
229-237 |
Keywords |
situational awareness (SA), virtual simulation, virtual simulation scenario, process model, Effective Command Behavioral Marker Framework |
Abstract |
Situational awareness (SA), the ability to perceive, comprehend and predict situation around you and it is a key in attending any incident as critical foundation for successful decision-making. Because incidents are solitary events, development and assessment of SA presents a significant challenge. In this article we analyze the authoring process of twenty-two scenarios implemented in the XVR on-scene virtual simulation software used to assess rescue incident commanders' (ICs) SA. To allow the scenarios to be used by different assessors, the Collaborative Authoring Process Model for Virtual Simulation Scenarios (CAPM) was developed. In Estonia, 473 assessments were recorded in Effective Command database and analysed by all three levels of SA as recommended by Endsley (2000). Introduction of CAPM resulted in scenarios being re-used by different assessors for authentic SA measuring. In the last sections of this article, we introduce our suggestions to improve virtual scenario design and SA research. |
Address |
Tallinn University; Tallinn University; Tallinn University |
Corporate Author |
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Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Command & Control Studies |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
stella.polikarpus@gmail.com |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2328 |
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Author |
Stella van Esch; Marc van den Homberg; Kees Boersma |
Title |
Looking Beyond the Data: an Assessment of the Emerging Data Ecosystem of Nepal's Flood Early Warning Systems |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
282-293 |
Keywords |
Floods, early warning systems, Nepal, data ecosystem, social shaping of technology |
Abstract |
Increasingly, data-driven instruments are used in disaster risk reduction to foster more efficient, effective, and evidence-based decision-making. This data revolution brings along opportunities and challenges, which are sometimes related to the data itself, but more often seem related to the environment in which the data is put to use. To provide insight into such an emerging data ecosystem, this paper uses a qualitative case study to assess the use of data in flood early warning systems (EWS) in Nepal. In response to the research question 'How does the data ecosystem impact the opportunities and challenges regarding data use in flood early warning systems in Nepal?', this paper discusses the importance of considering the broader context instead of regarding data as an entity unto itself. It shows how actors, policies and other contextual factors impact the effectiveness of data use by either presenting opportunities, like the establishment of a national disaster data repository, or challenges, like inadequate human resources for working with data. |
Address |
Vrije Universiteit Amsterdam; 510 – an initiative of The Netherlands Red Cross; Vrije Universiteit Amsterdam |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Data and Resilience: Opportunities and Challenges |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
s.r.van.esch@student.vu.nl |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2333 |
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Author |
Therese Habig; Richard Lüke; Simon Gehlhar; Torben Sauerland; Daniel Tappe |
Title |
A Consolidated Understanding of Disaster Community Technologies |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
778-791 |
Keywords |
Disaster Community Technologies, social media and crowdsourcing, categorization and classification schema, knowledge base |
Abstract |
Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview. |
Address |
safety innovation center; safety innovation center; safety innovation center; safety innovation center; safety innovation center |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
habig@safetyinnovation.center |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2373 |
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Author |
Tiberiu Sosea; Iustin Sirbu; Cornelia Caragea; Doina Caragea; Traian Rebedea |
Title |
Using the Image-Text Relationship to Improve Multimodal Disaster Tweet Classification |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
691-704 |
Keywords |
Multi-modal disaster tweet classification, Image-text coherence relationship prediction, ViLBERT |
Abstract |
In this paper, we show that the text-image relationship of disaster tweets can be used to improve the classification of tweets from emergency situations. To this end, we introduce DisRel, a dataset which contains 4,600 multimodal tweets, collected during the disasters that hit the USA in 2017, and manually annotated with coherence image-text relationships, such as Similar and Complementary. We explore multiple models to detect these relationships and perform a comprehensive analysis into the robustness of these methods. Based on these models, we build a simple feature augmentation approach that can leverage the text-image relationship. We test our methods on 2 tasks in CrisisMMD: Humanitarian Categories and Damage Assessment, and observe an increase in the performance of the relationship-aware methods. |
Address |
University of Illinois at Chicago; University Politehnica of Bucharest; University of Illinois at Chicago; Kansas State University; University Politehnica of Bucharest |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
|
Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
tsosea2@uic.edu |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2365 |
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Author |
Tiina Ristmae; Dimitra Dionysiou; Miltiadis Koutsokeras; Athanasios Douklias; Eleftherios Ouzounoglou; Angelos Amditis; Anaxagoras Fotopoulos; George Diles; Pantelis Linardatos; Konstantinos Smanis; Pantelis Lappas; Marios Moutzouris; Manolis Tsogas; Dani |
Title |
The CURSOR Search and Rescue (SaR) Kit: an innovative solution for improving the efficiency of Urban SaR Operations |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
867-880 |
Keywords |
Urban Search and Rescue, Victim detection, Rescue robotics, Sensors, Situational awareness |
Abstract |
CURSOR (Coordinated Use of miniaturized Robotic equipment and advanced Sensors for search and rescue OpeRations) is an ongoing European H2020 project with the main objective to enhance the efficiency and safety of Urban Search and Rescue (USaR) operations on disaster sites. CURSOR's approach relies on the integration of multiple mature and emerging technologies offering complementary capabilities to an USaR system, so as to address several challenges and capability gaps currently encountered during first responder missions. The project's research and development are structured around an earthquake master scenario. CURSOR aspires to advance the state-of the-art in several key aspects, including reduced time for victim detection, increased victim localization accuracy, enhanced real-time worksite information management, improved situational awareness and rescue team safety. |
Address |
Federal Agency for Technical Relief (THW) – Headquarters Staff Unit Research & Innovation Management; Institute of Communication and Computer Systems – National Technical University of Athens; Institute of Communication and Computer Systems – National Tec |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Technologies for First Responders |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
Tiina.Ristmaee@thw.de |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2379 |
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Author |
Tina Mioch; Reinier Sterkenburg; Tatjana Beuker; Mark A. Neerincx |
Title |
Actionable Situation Awareness: Supporting Team Decisions in Hazardous Situations |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
62-70 |
Keywords |
Situation Awareness, Actionability, Decision support, Chemical hazard |
Abstract |
Situation Awareness (SA) has been recognized and studied as an important requirement for an effective task performance of first responders. The integration of increasingly advanced sensor, network and artificial intelligence technology into the work processes affects the building, maintenance and sharing of SA. Connecting SA to decision support models provides new possibilities for the development of actionable SA (aSA), entailing information that guides the momentary decision-making processes of the concerning actors. In the European ASSISTANCE project, we are developing an aSA module that displays information about gas distributions, its current and predicted future states (e.g., entailing risks of breathing-in of toxic gases), with references to effective decision-making patterns for this situation. The aSA model is continuously updated based on sensor data. This paper gives an overview of this aSA module for chemical hazard prediction and corresponding display, and presents initial team design patterns that will be integrated into this display to support its actionability. |
Address |
Tno; Tno; Tno; Tno |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
AI and Intelligent Systems for Crises and Risks |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
tina.mioch@tno.nl |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2313 |
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Author |
Tinghao Zhang; Lida Huang; Tao Chen; Shuo Bai |
Title |
GIS Based Emergency Management Framework for Large-scale Events: A Case Study of the Torch Relay Activity |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
503-514 |
Keywords |
Arcgis, large-scale event, emergency management, epidemic prevention |
Abstract |
Due to the high popular concentration of large-scale events, once an emergency (like a stampede) occurs, it will often cause severe casualties. Moreover, since the widespread of the COVID-19, the prevention of the novel coronavirus should also be considered during mass gatherings. How to reduce the probability and potential consequence of emergencies is of great significance. This research designs an emergency management framework using ArcGIS-based geographic information technology for large-scale events. To verify the effectiveness of our framework, we take the Winter Olympic torch relay in university as an example. The paper is mainly divided into two parts, emergency resource allocation and the emergency prevention model. The former part focuses on the site selection of emergency sentries and emergency hospitals during the torch relay. In the latter part, an emergency prevention model is designed for two significant emergencies: stampede and epidemic. |
Address |
Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University Hefei |
Corporate Author |
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Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
zth19@mails.tsinghua.edu.cn |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2351 |
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Author |
Tobias Hellmund; Jürgen Moßgraber; Manfred Schenk; Philipp Hertweck; Hylke van der Schaaf; Hans Springer |
Title |
The Design and Implementation of ZEUS: Novel Support in Managing Large-Scale Evacuations |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
1003-1014 |
Keywords |
Management of Large-Scale Evacuations, Emergency Accommodation Management, Evacuation Management |
Abstract |
This paper introduces ZEUS, a novel software tool for the management of large-scale evacuations. The tasks ZEUS supports were derived from two Standard Operating Procedures, developed on demand of the German federal states. To this date, the authors are not aware of another software tool that gives technical support to the management and control of large-scale evacuations as ZEUS does. It comprises functionalities to (pre-)plan a large-scale evacuation, as well as functions for the management of the flow of evacuees during an evacuation situation. This paper describes how the requirements of ZEUS were derived from the two named planning frameworks and how use-cases were developed to meet the requirements; these use-cases were conceptualized as different steps of a workflow. In an evaluation, the paper gives credit how ZEUS can provide technical support for the evaluation of large-scale evacuations. ZEUS will undergo a two-staged review process: first, a controlled theoretical scenario is tested and, upon successful completion, a practical test on a large scale will be executed. |
Address |
Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Fraunhofer IOSB; Ministry of Interior, Digitization, and Migration Baden-Württemberg |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Other |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
tobias.hellmund@iosb.fraunhofer.de |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2392 |
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Author |
Tobias Meuser; Lars Baumgärtner; Patrick Lieser |
Title |
Pandemic Skylines: Digital Twins for More Realism in Epidemic Simulations |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
133-145 |
Keywords |
Simulation, Disaster Communication, Pandemic |
Abstract |
In the recent months, many measures have been taken by governments to fight the COVID-19 pandemic. Due to the unknown properties of the disease and a lack of experience with handling pandemics, the effectiveness of measures taken was often hard to evaluate the effectiveness of measures, leading to inefficient measures and late execution of efficient measures. Many models have been proposed to evaluate the performance of these measures on the spreading of a pandemic, but these models are commonly vastly simplified and, thus, limited in expressiveness. To extend the expressiveness of the models, we developed a epidemic simulation inside of a flexible and scalable city simulation game to analyse the counter measures to a pandemic in this city and spot common places of infection on a microscopic level. The configurability of our developed epidemic simulation will also be useful for potential future pandemics. |
Address |
TU Darmstadt – KOM; TU Darmstadt – STG; TU Darmstadt – KOM |
Corporate Author |
|
Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
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Edition |
|
ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
Track |
Analytical Modeling and Simulation |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
tobias.meuser@kom.tu-darmstadt.de |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2320 |
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|
Author |
Usman Anjum; Vladimir Zadorozhny; Prashant Krishnamurthy |
Title |
TBAM: Towards An Agent-Based Model to Enrich Twitter Data |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
|
Issue |
|
Pages |
146-158 |
Keywords |
Agent-Based Model, Twitter, Modeling and Simulation, Event Detection |
Abstract |
Twitter is widely being used by researchers to understand human behavior, e.g. how people behave when an event occurs and how it changes their microblogging pattern. The changing microblogging behavior can have an important application in the form of detecting events. However, the Twitter data that is available has limitations in it has incomplete and noisy information and has irregular samples. In this paper we create a model, calledTwitter Behavior Agent-Based Model (TBAM)to simulate Twitter pattern and behavior using Agent-Based Modeling(ABM). The generated data can be used in place or to complement the real-world data and improve the accuracy of event detection. We confirm the validity of our model by comparing it with real data collected from Twitter |
Address |
University of Pittsburgh; University of Pittsburgh; University of Pittsburgh |
Corporate Author |
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Thesis |
|
Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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Track |
Analytical Modeling and Simulation |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
usa3@pitt.edu |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2321 |
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Author |
Valentin Barriere; Guillaume Jacquet |
Title |
How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing |
Type |
Conference Article |
Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
Volume |
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Issue |
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Pages |
766-771 |
Keywords |
Transformers, Contextual keywords, Humanitarian Computing, Tweets analysis |
Abstract |
In a classification task, dealing with text snippets and metadata usually requires to deal with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics. |
Address |
European Commission's Joint Research Center; European Commission's Joint Research Center |
Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language |
English |
Summary Language |
English |
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
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Medium |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
Notes |
valbarrierepro@gmail.com |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2371 |
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