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Author Cendrella Chahine; Thierry Vidal; Mohamad El Falou; François Pérès
Title Multi-Agent Dynamic Planning Architectures for Crisis Rescue Plans Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 243-255
Keywords Multi-agent systems; planning and scheduling; uncertainty; coordination
Abstract We are interested in rescue management in crises such as in terrorist attacks. Today, there are emergency plans that take into account all the stakeholders involved in a crisis depending on the event type, magnitude and place. Unfortunately, they do not anticipate the evolution of the crisis situation such as traffic and hospital overcrowding. In addition, decisions are taken after the information has been passed from the operational level to higher levels. This work focuses on the operational level of the emergency plan. What will happen if the actors at this level, can make certain decisions without escalating the information to higher levels? To answer this question, a multi-agent dynamic planning approach is proposed and it will be tested in two different architectures in order to see how much autonomy can be given to an agent and how they coordinate to save the victims.
Address ULF Liban/LGP-ENIT; LGP-ENIT; ULF Tripoli; LGP-ENIT
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2414
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Author Koki Asami; Shono Fujita; Kei Hiroi; Michinori Hatayama
Title Data Augmentation with Synthesized Damaged Roof Images Generated by GAN Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 256-265
Keywords disaster response; generative adversarial networks; data augmentation; damage classification
Abstract The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
Address Kyoto University; Kyoto University; Kyoto University; Kyoto University
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2415
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Author Rocco Sergio Palermo; Antonio De Nicola
Title A Simulation Framework for Epidemic Spreading in Semantic Social Networks Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 266-273
Keywords Epidemics; Simulation; Semantic Social Network; Ontology; Crisis
Abstract Epidemic spreading simulation in social networks denotes a set of techniques that allow to assess the temporal evolution and the consequences of a pandemic. They were largely used by governments and International health organizations during the COVID-19 world crisis to decide the appropriate countermeasures to limit the diffusion of the disease. Among them, the existing simulation techniques based on a network model aimed at studying the infectious disease dynamics have a prominent role and are widely adopted. However, even if they leverage the topological structure of a social network, they disregard the intrinsic and individual features of its members. A semantic social network is defined as a structure consisting of interlinking layers, which include a social network layer, to represent people and their relationships and a concept network layer, to represent concepts, their ontological relationships and implicit similarities. Here, we propose a novel epidemic simulation framework that allows to describe a community of people as a semantic social network, to adopt the most commonly used compartmental models for describing epidemic spreading, such as Susceptible-Infected-Susceptible (SIS) or Susceptible-Infected-Removed (SIR), and to enable semantic reasoning to increase the accuracy of the simulation. Finally, we show how to use the framework to simulate the impact of a pandemic in a community where the job of each member is known in advance.
Address Università Guglielmo Marconi; ENEA
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2416
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Author Aïdin Sumic; Emna Amdouni; Thierry Vidal; Hedi Karray
Title Towards Flexibility Sharing in Multi-agent Dynamic Planning: The Case of the Health Crisis Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 274-284
Keywords crisis management; flexibility; multi-agent system; decision making under uncertainty; negotiation
Abstract Planning problems in a crisis context are a highly uncertain environment where health facilities must cooperate in providing health services to their patients. We focus on the health crisis in France due to the COVID19 pandemic. In fact, the lack of appropriate scheduling tools, resources, and communication leads hospitals to be submerged by infected patients and forced to transfer them to other hospitals. In this work we aim to provide a global solution to such planning problems to improve the current French health system. We introduce a cooperative approach called OPPIC (Operational Planning Platform for Inter-healthcare Coordination). OPPIC is based on a decentralized system, where health facilities plan is dynamic, flexible, robust to uncertainty, and respond to goals and optimization criteria. This paper proposed a first planning model to OPPIC and provided a first way of negotiation between health facilities based on their plan’s local and global flexibility.
Address Laboratoire Génie de Production Tarbes; Laboratoire Génie de Production Tarbes; Laboratoire Génie de Production Tarbes; Laboratoire Génie de Production Tarbes
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2417
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Author Christian Iasio; Ingrid Canovas; Elie Chevillot-Miot; Tendry Randramialala
Title A New Approach to Structured Processing of Feedback for Discovering and Investigating Interconnections, Cascading Events and Disaster Chains Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 285-298
Keywords Knowledge management; multiperspectivity; lessons learning; crisis management
Abstract Post-disaster information processing is relevant for the continuous improvement of operations and the reductionof risks. The current methodologies for post-disaster review suffer from several limitations, which reduce their use as a way of translating narrative in data for qualitative and quantitative analysis. Learning or effective knowledge sharing need a common formalism and method. Ontologies are the reference tool for structuring information in a “coded” data structure. Using the investigation of disaster management during the 2017 hurricane season in the French West Indies within the scope of the ANR “APRIL” project, this contribution introduces a methodology and a tool for providing a graphical representation of experiences for post-disaster review and lessons learning, based on a novel approach to case-based ontology development.
Address BRGM; LATTS – Université Gustave Eiffel,Marne la Vallée; Institut des Hautes Etudes du Ministère de l’Intérieur; BRGM
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2418
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Author Simon Mille; Gerard Casamayor; Jens Grivolla; Alexander Shvets; Leo Wanner
Title Automatic Multilingual Incident Report Generation for Crisis Management Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 299-309
Keywords natural language generation; multilingual; ontology; incidents; crisis management
Abstract Successful and effucient crisis management depends on the availability of all accessible relevant information on the incidents during a crisis. The sources of this information are very often multiple and manifold – in particular in the case of environmental crises such as wild fires, floods, drought, etc. For the staff of the control centres it can be a challenge to follow up on all of them. In this paper, we present work in progress on an automatic multilingual incident report generator that produces summaries of all environmental incidents communicated by citizens or authorities in a given time range for a given region in terms of a text message, an audio, a video or an image and analyzed by dedicated modules into uniform knowledge representation structures.
Address NLP Group Pompeu Fabra University, Barcelona; NLP Group Pompeu Fabra University, Barcelona; NLP Group Pompeu Fabra University, Barcelona; NLP Group Pompeu Fabra University, Barcelona; Catalan Institute for Research and Advanced Studies (ICREA) and NLP Gr
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2419
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Author Nada Matta; Paul Henri Richard; Alain Hugerot; Theo Lebert
Title Experience Feedback Capitalization of Covid-19 Management in Troyes city Type Conference Article
Year (down) 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 311-319
Keywords Experience feedback; MASK method; COVID19 crisis Management; actors’ relations formalization
Abstract All countries have to face the COVID’19 pandemic and its heavy consequences. This sanitary crisis differs from all others in terms of the quick spread of contaminations, the high number of deaths (more than 5,5 Million globally and 123,893 in France) and the accrued number of patients hospitalized and induced in intensive care units. All sanitary procedures have proven to be inadequate. Several actors at different levels, whether international, European, national and local, as well as at the level of public and private organizations have been involved in the management of this type of crisis. These actors deal with different aspects of it, i.e., health, people protection, and economic and social situations. Existing procedures revealed a big lack in the relationships between different local and departmental actors. We did a number of interviews with strategic actors addressing the COVID’19 crisis in the City of Troyes. The objective of these interviews is to identify lessons learned from their experience feedback about relational problems and modifications needed. We present in this paper the first results of this study.
Address University of Technology of Troyes; University of Technology of Troyes; Hospital of Simon Weil of Troyes; Orange Lab
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2420
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Author Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang
Title A Question Answering System Applied to Disasters Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 2-16
Keywords Emergency Management, Disaster, Natural Language Processing, Deep Learning
Abstract In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph.
Address Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University
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 gxj19@mails.tsinghua.edu.cn Approved no
Call Number ISCRAM @ idladmin @ Serial 2308
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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 (down) 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 Audun Stolpe; Jo Hannay
Title On the Adaptive Delegation and Sequencing of Actions Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 28-39
Keywords Decision support, AI Planning, Delegation, Sequencing, Adaptivity, Cognitive processes
Abstract Information systems support to crisis response and management relies crucially on presenting actionable information in a manner that supports cognitive processes, and does not overwhelm them. We outline how AI Planning can be used viably to support the \emph{delegation and sequencing} of tasks. The idea is to use standard operating procedures as initial specifications of plans in terms of actors, actions and delegation rules. When expressed in the AI planning language \textit{Answer set Programming} (ASP), machine reasoning can be used in a \textit{pre-incident review} to display relevant delegation and sequencing inherent in a plan. % together with measures of goal achievement. The purpose of this is to uncover weaknesses in the initial plan and to optimize sequencing and delegation to increase the likelihood of achieving goals. Further, adaptive planning can be supported in \textit{during-incident reviews} by updating the current status, upon which ASP will then compute new alternatives. % and corresponding goal achievement measures. At this point, initial goals may no longer be viable and the explicit suggestion of prior sub-optimal goals now worth pursuing can be a game-changer under stress. The conceptual basis we lay out in terms of delegation and sequencing can be readily extended with further planning factors, such as resource requirements, role transfer and goal achievement.
Address Norwegian Computing Center; Norwegian Computing 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 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 audun.stolpe@its.uio.no Approved no
Call Number ISCRAM @ idladmin @ Serial 2310
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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 (down) 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 Kenneth Johnson; Javier Cámara; Roopak Sinha; Samaneh Madanian; Dave Parry
Title Towards Self-Adaptive Disaster Management Systems Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 49-61
Keywords disaster management, self-adaptive systems, formal verification, probabilistic model checking, constraint solving
Abstract Disasters often occur without warning and despite extensive preparation, disaster managers must take action to respond to changes critical resource allocations to support existing health-care facilities and emergency triages. A key challenge is to devise sound and verifiable resourcing plans within an evolving disaster scenario. Our main contribution is the development of a conceptual self-adaptive system featuring a monitor-analyse-plan-execute (MAPE) feedback loop to continually adapt resourcing within the disaster-affected region in response to changing usage and requirements. We illustrate the system's use on a case study based on Auckland city (New Zealand). Uncertainty arising from partial knowledge of infrastructure conditions and outcomes of human participant's actions are modelled and automatically analysed using formal verification techniques. The analysis inform plans for routing resources to where they are needed in the region. Our approach is shown to readily support multiple model and verification techniques applicable to a range of disaster scenarios.
Address Auckland University of Technology; University of York; Auckland University of Technology; AUT university; Auckland University 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 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 kenneth.johnson@aut.ac.nz Approved no
Call Number ISCRAM @ idladmin @ Serial 2312
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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 (down) 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
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 tina.mioch@tno.nl Approved no
Call Number ISCRAM @ idladmin @ Serial 2313
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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 (down) 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 Antonio De Nicola; Maria Luisa Villani; Francesco Costantino; Andrea Falegnami; Riccardo Patriarca
Title Knowledge Fusion for Distributed Situational Awareness driven by the WAx Conceptual Framework Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 79-85
Keywords distributed situational awareness, knowledge fusion, WAx framework, crisis management, cyber-socio-technical systems
Abstract Large crisis scenarios involve several actors, acting at the blunt-end of the process, such as rescue team directors, and at the sharp-end, such as firefighters. All of them have different perspectives on the crisis situation, which could be either coherent, alternative or complementary. This heterogeneity of perceptions hinders situational awareness, which is defined as the achievement of an overall picture on the above-mentioned crisis situation. We define knowledge fusion as the process of integrating multiple knowledge entities to produce actionable knowledge, which is consistent, accurate, and useful for the purpose of the analysis. Hence, we present a conceptual modelling approach to gather and integrate knowledge related to large crisis scenarios from locally-distributed sources that can make it actionable. The approach builds on the WAx framework for cyber-socio-technical systems and aims at classifying and coping with the different knowledge entities generated by the involved operators. The conceptual outcomes of the approach are then discussed in terms of open research challenges for knowledge fusion in crisis scenarios.
Address ENEA; ENEA – CR Casaccia; Sapienza University of Rome; Sapienza University of Rome; Sapienza University of Rome
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 antonio.denicola@enea.it Approved no
Call Number ISCRAM @ idladmin @ Serial 2315
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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 (down) 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 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 rouba.iskandar@univ-grenoble-alpes.fr Approved no
Call Number ISCRAM @ idladmin @ Serial 2316
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Author Yasas Senarath; Jennifer Chan; Hemant Purohit; Ozlem Uzuner
Title Evaluating the Relevance of UMLS Knowledge Base for Public Health Informatics during Disasters Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 97-105
Keywords Public Health, Disaster Informatics, Health Informatics, UMLS, Metathesaurus
Abstract During disasters public health organizations increasingly face challenges in acquiring and transforming real-time data into knowledge about the dynamic public health needs. Resources on the internet can provide valuable information for extracting knowledge that can help improve decisions which will ultimately result in targeted and efficient health services. Digital content such as online articles, blogs, and social media are some of such information sources that could be leveraged to improve the health care systems during disasters. To efficiently and accurately identify relevant disaster health information, extraction tools require a common vocabulary that is aligned to the health domain so that the knowledge from these unstructured digital sources can be accurately structured and organized. In this paper, we study the degree to which the Unified Medical Language System (UMLS) contains relevant disaster, public health, and medical concepts for which public health information in disaster domain could be extracted from digital sources.
Address George Mason University; Northwestern University; George Mason University; George Mason University
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 ywijesu@gmu.edu Approved no
Call Number ISCRAM @ idladmin @ Serial 2317
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Author Dashley Rouwendal van Schijndel; Audun Stolpe; Jo Erskine Hannay
Title Toward an AI-based external scenario event controller for crisis response simulations Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 106-117
Keywords Scenario event controller, AI Planning, Modelling and Simulation, Simulation controller
Abstract There is a need for tool support for structured planning, execution and analysis of simulation-based training for crisisresponse and management. As a central component of an architecture for such tool support, we outline the design ofan AI-based scenario event controller. The event controller is a component that uses machine reasoning to computethe next state in a scenario, given the actions performed in the corresponding simulation (execution of the scenario).Scenarios are specified in Answer Set Programming, which is a logic programming language we use for automatedplanning of training scenarios. A plan encoding in ASP adds expressivity in scenario specification and enablesmachine reasoning. For exercise managers this gives AI-based tool support for before-action and during-actionreviews to optimize learning. In line with Modelling and Simulation as as Service, our approach externalizes eventcontrol from any particular simulation platform. The scenario, and its unfolding in terms of events, is externalizedas a service. This increases interoperability and enables scenarios to be designed and modified readily and rapidlyto adapt to new training requirements.
Address University of Oslo; Norsk Regnesentral; Norsk Regnesentral
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 d.k.rouwendal@its.uio.no Approved no
Call Number ISCRAM @ idladmin @ Serial 2318
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Author Fiona McNeill; Andriana Gkaniatsou; Alan Bundy
Title Dynamic data sharing for facilitating communication during emergency responses Type Conference Article
Year (down) 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014
Volume Issue Pages 369-373
Keywords Chains; Communication; Data interpretation; Dynamic data sharing; Emergency response; Intelligent queries; Matching; Proof of concept; Query; Query answering; Information systems
Abstract This paper describes the CHAIn system, which is designed to facilitate data sharing between disparate organisations during emergency response situations by resolving mismatches in their data. It uses structured data matching to reformulate failed queries in cases where these failed because of incompatibilities between the query (derived from the source schema) and the schema of the queried datasource (the target schema). This reformulation is done by developing matches between the source schema and the target schema. These matches are then used to reformulate the query and retrieve responses relevant to those expected by the original query. Despite the growing interest in intelligent query answering, integration of data matching into query answering is novel, and allows users to successfully query datasources even if they do not know how the data in that source is organized, which is often the case during emergency responses. We describe the proof-of-concept system we have developed and an encouraging initial evaluation.
Address Heriot-Watt University, United Kingdom; University of Edinburgh, United Kingdom
Corporate Author Thesis
Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780692211946 Medium
Track Intelligent Systems Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 758
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Author Jaziar Radianti; Julie Dugdale; Jose J. Gonzalez; Ole-Christoffer Granmo
Title Smartphone sensing platform for emergency management Type Conference Article
Year (down) 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014
Volume Issue Pages 379-383
Keywords Civil defense; Disasters; Hazards; Information systems; Intelligent systems; Learning systems; Mobile phones; Pattern recognition; Risk management; Sensors; Signal encoding; Emergency management; Human Tracking; Human-centered computing; Mobile sensing; Publish-subscribe; Smartphones
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.
Address CIEM, University of Agder, Norway; University Pierre Mendès France, Grenoble, France
Corporate Author Thesis
Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780692211946 Medium
Track Intelligent Systems Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 864
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Author Krispijn Scholte; Leon J.M. Rothkrantz
Title Personal warning system for vessels under bad weather conditions Type Conference Article
Year (down) 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014
Volume Issue Pages 359-368
Keywords Alarm systems; Automation; Information systems; Meteorology; Waterway transportation; Weather forecasting; Automatic identification system; Bayesian reasoning; Context sensitive; Early Warning System; Maritime surveillance; Ships
Abstract Many services provide weather forecasts, including severe weather alerts for the marine. It proves that many ships neglect the warnings because they expect to be able to handle the bad weather conditions. In order to identify possible unsafe situations the Coast Guard needs to observe marine vessel traffic 24 hours, 7 days a week. In this paper we propose a system that is able to support the Coast Guard. Ships can be localized and tracked individually using the Automatic Identification System (AIS). We present a system which is able to send a personal alert to ships expected to be in danger now or the near future. Ships will be monitored in the dangerous hours and routed to safe areas in the shortest time. The system is based on AIS data, probabilistic reasoning and expertise from the Coast Guard. A first prototype will be presented for open waters around the Netherlands.
Address Staff Regulations at Royal Netherlands Navy, Netherlands; Delft University of Technology, Netherlands
Corporate Author Thesis
Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780692211946 Medium
Track Intelligent Systems Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 922
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Author Corine H.G. Horsch; Nanja J. J. M. Smets; Mark A. Neerincx; Raymond H. Cuijpers
Title Revealing unexpected effects of rescue robots' team-membership in a virtual environment Type Conference Article
Year (down) 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 627-631
Keywords Human robot interaction; Information systems; Situation awareness; Team identification; Team performance; Teamwork; Usar; Virtual reality
Abstract In urban search and rescue (USAR) situations resources are limited and workload is high. Robots that act as team players instead of tools could help in these situations. A Virtual Reality (VR) experiment was set up to test if team performance of a human-robot team increases when the robot act as such a team player. Three robot settings were tested ranging from the robot as a tool to the robot as a team player. Unexpectedly, team performance seemed to be the best for the tool condition. Two side-effects of increasing robot's teammembership could explain this result: Mental workload increased for the humans who had to work with the team-playing robot, whereas the tendency to share information was reduced between these humans. Future research should, thus, focus on team-memberships that improve communication and reduce cognitive workload.
Address Delft University of Technology, Netherlands; TNO, Delft University of Technology, Netherlands; Eindhoven University of Technology, Netherlands
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Intelligent Systems Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 594
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Author Svend-Anjes Pahl; Thomas Thiel-Clemen
Title KIS – A crisis team information system Type Conference Article
Year (down) 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 632-637
Keywords Information systems; Knowledge management; Management information systems; Crisis management systems; Crisis team; Disaster assistance; Manage information; On demands; Ontology-based; Human resource management
Abstract Widespread crises require the deployment of a crisis team, to coordinate the disaster assistance. Because of their low frequency of occurrence and the extensive assignment of volunteers, often only less practical knowledge in managing widespread crises are available on demand. If such a crisis occurs, the gained knowledge must be quickly shared within the team. Current crisis management systems are designed to manage big amounts of situation facts, crisis teams based their work on. But very often these systems are not able to manage information about the linkage of these facts causing the problems. KIS is the first prototype of a crisis team information system, able to combine an ontology based data model for situation representation with the ability to forecast causal chained and spatially related problems derived on situation facts. KIS is able to store and manage this knowledge so that it can easily be shared with others.
Address Hamburg University of Applied Sciences, Germany
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Intelligent Systems Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 828
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Author Leon J.M. Rothkrantz
Title Crisis management using multiple camera surveillance systems Type Conference Article
Year (down) 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 617-626
Keywords Disasters; Information systems; Object recognition; Crisis management; License plate recognition; Multiple cameras; Semi-automatics; Surveillance systems; Tracking algorithm; Video surveillance; Video surveillance systems; Security systems
Abstract During recent disasters such as tsunami, flooding, hurricanes, nuclear disaster, earthquake people have to leave their living areas for their own safety. But it proves that some people are not informed about the evacuation, or are not willing or able to leave or don't know how to leave the hazardous areas. The topic of the paper is how to adapt current video surveillance systems along highway and streets to semi-automatic surveillance systems. When a suspicious event is detected a human operator in the control room has to be alerted to take appropriate actions. The architecture of the system and main modules are presented in the paper. Different algorithms to detect localize and track people are published by the authors elsewhere but are summarized in the current paper. The system has been tested in a real life environment and the test results are presented in the paper.
Address Delft University of Technology, Netherlands Defense Academy, Netherlands
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Intelligent Systems Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 892
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Author Alexander Smirnov; Tatiana Levashova; Nikolay Shilov
Title Context-based knowledge fusion patterns in decision support system for emergency response Type Conference Article
Year (down) 2013 Publication ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2013
Volume Issue Pages 597-606
Keywords Artificial intelligence; Decision support systems; Information systems; Context-Aware; Context-based; Decision supports; Emergency response; Internal structure; Knowledge fusion; Knowledge sources; Operational stages; Emergency services
Abstract The purpose of this paper is discovery of context-based knowledge fusion patterns. Knowledge fusion is considered as an appearance of new knowledge in consequence of processes ongoing in decision support systems. The knowledge fusion processes are considered within a system intended to support decisions on planning emergency response actions. The knowledge fusion patterns are generalized with regard to preservation of internal structures and autonomies of information and knowledge sources involved in the knowledge fusion and to knowledge fusion results. The found patterns give a general idea of knowledge fusion processes taking place at the operational stage of decision support system functioning, i.e. the stage where context-aware functions of the system come into operation. As a practical application, such patterns can support engineers with making choice of knowledge sources to be used in the systems they design.
Address St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences (SPIIRAS), St.-Petersburg, Russian Federation; SPIIRAS, St.-Petersburg, Russian Federation
Corporate Author Thesis
Publisher Karlsruher Institut fur Technologie Place of Publication KIT; Baden-Baden Editor T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9783923704804 Medium
Track Intelligent Systems Expedition Conference 10th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 960
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