| Records |
| Author |
Ben Ortiz; Laura Kahn; Marc Bosch; Philip Bogden; Viveca Pavon-Harr; Onur Savas; Ian McCulloh |
| Title |
Improving Community Resiliency and Emergency Response With Artificial Intelligence |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
35-41 |
| Keywords |
Emergency Management, Semantic Segmentation, Inland Flood Modeling, Route Optimization. |
| Abstract |
New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina. |
| Address |
Accenture Federal Services; Accenture Federal Services; Accenture Federal Services; Accenture Federal Services; Accenture Federal Services; Accenture Federal Services |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-4 |
ISBN |
2411-3390 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
Laura.kahn@accenturefederal.com |
Approved |
no |
| Call Number |
|
Serial |
2205 |
| Share this record to Facebook |
| |
|
| |
| Author |
Cheng Wang; Benjamin Bowes; Arash Tavakoli; Stephen Adams; Jonathan Goodall; Peter Beling |
| Title |
Smart Stormwater Control Systems: A Reinforcement Learning Approach |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
2-13 |
| Keywords |
Reinforcement Learning, Stormwater, Flooding Control. |
| Abstract |
Flooding poses a significant and growing risk for many urban areas. Stormwater systems are typically used to control flooding, but are traditionally passive (i.e. have no controllable components). However, if stormwater systems are retrofitted with valves and pumps, policies for controlling them in real-time could be implemented to enhance system performance over a wider range of conditions than originally designed for. In this paper, we propose an autonomous, reinforcement learning (RL) based, stormwater control system that aims to minimize flooding during storms. With this approach, an optimal control policy can be learned by letting an RL agent interact with the system in response to received reward signals. In comparison with a set of static control rules, RL shows superior performance on a wide range of artificial storm events. This demonstrates RL's ability to learn control actions based on observation and interaction, a key benefit for dynamic and ever-changing urban areas. |
| Address |
Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia; Department of Engineering Systems and Environment, University of Virginia |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-1 |
ISBN |
2411-3387 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
cw8xk@virginia.edu |
Approved |
no |
| Call Number |
|
Serial |
2202 |
| Share this record to Facebook |
| |
|
| |
| Author |
Dashley K. Rouwendal van Schijndel; Jo E. Hannay; Audun Stolpe |
| Title |
Simulation Vignette Generation from Answer Set Specifications |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
110-121 |
| Keywords |
Exercise Management; Answer Set Programming; Mixed Reality Simulations; Vignette Generation |
| Abstract |
We investigate an approach that allows exercise managers to design simulations with an explicit focus on building skills, rather than having to focus on all the objects and interactions that a simulation must have. Exercise managers may design exercises at various levels of abstraction and always independently of how those sessions are implemented in simulations, while simulation components that implement the design are assembled and to some extent, automatically, behind the scenes. We outline (1) how Answer Set Programming can assist exercise managers in exercise planning and (2) how automated stage and content generation may be used to invoke appropriate simulation components to realize the design. For deliberate and recurrent training of decision-making skills, stages and content must vary to avoid familiarity (testing effects). We conclude by distilling a main research hypothesis that stipulates how (1) and (2) represent two modes of automated reasoning (so-called deductive versus abductive) and how that distinction clarifies the planning task. |
| Address |
University of Oslo, Department of Technology Systems; Norwegian Computing Center, Department of Applied Research in Information Technology; University of Oslo, Department of Technology Systems, Norwegian Computing Center, Department of Applied Research in Information Technology |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-11 |
ISBN |
2411-3397 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
d.k.rouwendal@its.uio.no |
Approved |
no |
| Call Number |
|
Serial |
2212 |
| Share this record to Facebook |
| |
|
| |
| Author |
Dipak Singh; Shayan Shams; Joohyun Kim; Seung-jong Park; Seungwon Yang |
| Title |
Fighting for Information Credibility: AnEnd-to-End Framework to Identify FakeNews during Natural Disasters |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
90-99 |
| Keywords |
Neural Networks, Social Network, Natural Disaster, Fake News, Deep Learning. |
| Abstract |
Fast-spreading fake news has become an epidemic in the post-truth world of politics, the stock market, or even during natural disasters. A large amount of unverified information may reach a vast audience quickly via social media. The effect of misinformation (false) and disinformation (deliberately false) is more severe during the critical time of natural disasters such as flooding, hurricanes, or earthquakes. This can lead to disruptions in rescue missions and recovery activities, costing human lives and delaying the time needed for affected communities to return to normal. In this paper, we designed a comprehensive framework which is capable of developing a training set and trains a deep learning model for detecting fake news events occurring during disasters. Our proposed framework includes infrastructure to collect Twitter posts which spread false information. In our model implementation, we utilized the Transfer Learning scheme to transfer knowledge gained from a large and general fake news dataset to relatively smaller fake news events occurring during disasters as a means of overcoming the limited size of our training dataset. Our detection model was able to achieve an accuracy of 91.47\% and F1 score of 90.89 when it was trained with the first 28 hours of Twitter data. Our vision for this study is to help emergency managers during disaster response with our framework so that they may perform their rescue and recovery actions effectively and efficiently without being distracted by false information. |
| Address |
Louisiana State University; University of Texas; Louisiana State University; Louisiana State University;Louisiana State University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-9 |
ISBN |
2411-3395 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
dsingh8@lsu.edu |
Approved |
no |
| Call Number |
|
Serial |
2210 |
| Share this record to Facebook |
| |
|
| |
| Author |
Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris |
| Title |
A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
75-89 |
| Keywords |
Crisis Management, Real-Time Fire Severity Assessment, Image Recognition, Object Detection, Semantic Segmentation. |
| Abstract |
Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability. |
| Address |
Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH); Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH); Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH);Information Technologies Institute (ITI) – Centre for Research and Technology Hellas (CERTH) |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-8 |
ISBN |
2411-3394 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
gantzoulatos@iti.gr |
Approved |
no |
| Call Number |
|
Serial |
2209 |
| Share this record to Facebook |
| |
|
| |
| Author |
Julien Coche; Aurelie Montarnal; Andrea Tapia; Frederick Benaben |
| Title |
Automatic Information Retrieval from Tweets: A Semantic Clustering Approach |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
134-141 |
| Keywords |
Information Retrieval; Word Embedding; BERT |
| Abstract |
Much has been said about the value of social media messages for emergency services. The new uses related to these platforms bring users to share information, otherwise unknown in crisis events. Thus, many studies have been performed in order to identify tweets relating to a crisis event or to classify these tweets according to certain categories. However, determining the relevant information contained in the messages collected remains the responsibility of the emergency services. In this article, we introduce the issue of classifying the information contained in the messages. To do so, we use classes such as those used by the operators in the call centers. Particularly we show that this problem is related to named entities recognition on tweets. We then explain that a semi-supervised approach might be beneficial, as the volume of data to perform this task is low. In a second part, we present some of the challenges raised by this problematic and different ways to answer it. Finally, we explore one of them and its possible outcomes. |
| Address |
IMT Mines Albi; IMT Mines Albi; Penn State University; IMT Mines Albi |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-13 |
ISBN |
2411-3399 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
julien.coche@mines-albi.fr |
Approved |
no |
| Call Number |
|
Serial |
2214 |
| Share this record to Facebook |
| |
|
| |
| Author |
Md Fitrat Hossain; Thomas Kissane; Priyanka Annapureddy; Wylie Frydrychowicz; Sheikh Iqbal Ahamed; Naveen Bansal; Praveen Madiraju; Niharika Jain; Mark Flower; Katinka Hooyer; Lisa Rein; Zeno Franco |
| Title |
Implementing Algorithmic Crisis Alerts in mHealth Systems for Veterans with PTSD |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
122-133 |
| Keywords |
Crisis; Machine Learning Algorithms; mHealth; PTSD |
| Abstract |
This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer mentors (who are also veterans) need to observe the behavior of veterans to make a decision about conducting an intervention. Three different machine learning algorithms were used to predict long term crisis using acute crisis and early warning signs within the determined time point. |
| Address |
Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Marquette University; Mental Health America; Medical College of Wisconsin; Medical College of Wisconsin; Medical College of Wisconsin |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-12 |
ISBN |
2411-3398 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
mdfitrat.hossain@marquette.edu |
Approved |
no |
| Call Number |
|
Serial |
2213 |
| Share this record to Facebook |
| |
|
| |
| Author |
Mehdi Ben Lazreg; Usman Anjum; Vladimir Zadorozhny; Morten Goodwin |
| Title |
Semantic Decay Filter for Event Detection |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
14-26 |
| Keywords |
String Metric, Event Detection, Crisis Management. |
| Abstract |
Peaks in a time series of social media posts can be used to identify events. Using peaks in the number of posts and keyword bursts has become the go-to method for event detection from social media. However, those methods suffer from the random peaks in posts attributed to the regular daily use of social media. This paper proposes a novel approach to remedy that problem by introducing a semantic decay filter (SDF). The filter's role is to eliminate the random peaks and preserve the peak related to an event. The filter combines two relevant features, namely the number of posts and the decay in the number of similar tweets in an event-related peak. We tested the filter on three different data sets corresponding to three events: the STEM school shooting, London bridge attacks, and Virginia beach attacks. We show that, for all the events, the filter can eliminate random peaks and preserve the event-related peaks. |
| Address |
Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Informatics and Networked Systems, University of Pittsburgh, Pittsburgh, USA; Dept. of Information and Communication Technology, University of Agder,Grimstad, Norway |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-2 |
ISBN |
2411-3388 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
mehdi.ben.lazreg@uia.no |
Approved |
no |
| Call Number |
|
Serial |
2203 |
| Share this record to Facebook |
| |
|
| |
| Author |
Mirko Zaffaroni; Claudio Rossi |
| Title |
Water Segmentation with Deep Learning Models for Flood Detection and Monitoring |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
66-74 |
| Keywords |
Deep Learning, Water Segmentation, Data Validation. |
| Abstract |
Flooding is a natural hazard that causes a lot of deaths every year and the number of flood events is increasing worldwide because of climate change effects. Detecting and monitoring floods is of paramount importance in order to reduce their impacts both in terms of affected people and economic losses. Automated image analysis techniques capable to extract the amount of water from a picture can be used to create novel services aimed to detect floods from fixed surveillance cameras, drones, crowdsourced in-field observations, as well as to extract meaningful data from social media streams. In this work we compare the accuracy and the prediction performances of recent Deep Learning algorithms for the pixel-wise water segmentation task. Moreover, we release a new dataset that enhances well-know benchmark datasets used for multi-class segmentation with specific flood-related images taken from drones, in-field observations and social media. |
| Address |
LINKS Foundation, University of Turin, Computer Science Department; LINKS Foundation |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-7 |
ISBN |
2411-3393 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
mirko.zaffaroni@linksfoundation.com |
Approved |
no |
| Call Number |
|
Serial |
2208 |
| Share this record to Facebook |
| |
|
| |
| Author |
Nilani Algiriyage; Raj Prasanna; Emma E H Doyle; Kristin Stock; David Johnston |
| Title |
Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
100-109 |
| Keywords |
CCTV Big Data, YOLOv3, Traffic Flow Estimation. |
| Abstract |
Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps. |
| Address |
Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Joint Centre for Disaster Research, Massey University; Institute of Natural and Mathematical Sciences, Massey University; Joint Centre for Disaster Research, Massey University; |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-10 |
ISBN |
2411-3396 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
r.nilani@massey.ac.nz |
Approved |
no |
| Call Number |
|
Serial |
2211 |
| Share this record to Facebook |
| |
|
| |
| Author |
Paulina Potemski; Nada Matta; Patrick Laclémence |
| Title |
Modelling Women's Living Conditions' in Violence using KM techniques |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
27-34 |
| Keywords |
Women Conditions Life, Violence, Integrism Women Recruitment, Knowledge Management, Living Condition's Improvement, Tradition. |
| Abstract |
According to the United Nations Secretary General, gender equality has advanced in recent decades we are leaving in unprecedented global efforts to advance on women' empowerment. For example, girls' access to education has improved, the rate of child marriage declined and progress was made in the area of sexual and reproductive health and reproductive rights, including fewer maternal deaths. Nevertheless, gender equality remains a persistent challenge for countries worldwide and the lack of such equality is a major obstacle to sustainable development (Golombok et al, 1994, UNSG report, 2017). There are various inequity factors women confront. Women are the population that suffers most from different forms of discrimination. All of them root women's inferiority, women's dependence and as a matter of consequence, create a vicious circle of a domination system. Domination systems of men over women are all the more pernicious and harsher when combined with extreme poverty, remote living areas and conflicts. We discuss in this paper the fact that women are the population which underlive most difficult living conditions especially when violence and tradition are combined. Modelling life conditions put on the main factors of this violence and its consequences. |
| Address |
ICD, University of Technologie of Troyes, Troyes, France; ICD, University of Technologie of Troyes, Troyes, France; ICD, University of Technologie of Troyes, Troyes, France |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-3 |
ISBN |
2411-3389 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
paulina.potemski@utt.fr |
Approved |
no |
| Call Number |
|
Serial |
2204 |
| Share this record to Facebook |
| |
|
| |
| Author |
Rahul Pandey; Brenda Bannan; Hemant Purohit |
| Title |
CitizenHelper-training: AI-infused System for Multimodal Analytics to assist Training Exercise Debriefs at Emergency Services |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
42-53 |
| Keywords |
Training Exercise, Emergency Preparedness, AI system, Learning Analytics, Responder Training. |
| Abstract |
The adoption of Artificial Intelligence (AI) technologies across various real-world applications for human performance augmentation demonstrates an unprecedented opportunity for emergency management. However, the current exploration of AI technologies such as computer vision and natural language processing is highly focused on emergency response and less investigated for the preparedness and mitigation phases. The training exercises for emergency services are critical to preparing responders to perform effectively in the real-world, providing a venue to leverage AI technologies. In this paper, we demonstrate an application of AI to address the challenges in augmenting the performance of instructors or trainers in such training exercises in real-time, with the explicit aim of reducing cognitive overload in extracting relevant knowledge from the voluminous multimodal data including video recordings and IoT sensor streams. We present an AI-infused system design for multimodal stream analytics and lessons from its use during a regional training exercise for active violence events. |
| Address |
George Mason University; George Mason University; George Mason University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-5 |
ISBN |
2411-3391 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
rpandey4@gmu.edu |
Approved |
no |
| Call Number |
|
Serial |
2206 |
| Share this record to Facebook |
| |
|
| |
| Author |
Samer Chehade; Nada Matta; Jean-Baptiste Pothin; Remi Cogranne |
| Title |
Ontology-Based Approach for Designing User Interfaces: Application for Rescue Actors |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
54-65 |
| Keywords |
Interactions, Modelling, Ontologies, Rescue Operations, User Interface Design. |
| Abstract |
Nowadays, rescue actors still lack backing to exchange information effectively and ensure a common operational picture. Several studies report a low adoption of communication systems in rescue operations as well as a negative position of actors to such systems. The real needs of stakeholders, simply put, are not satisfied by the offered systems. Observing this circumstance through a user-centred design focal point, we notice that such issues ordinarily originate from inadequate design techniques. For this reason, we aim to implement Rescue MODES, a communication system oriented to support awareness amongst French actors in rescue operations based on their needs. In this paper, we propose an approach and introduce a platform that allows final users to design system interfaces in a customised way. This approach is based on an application ontology and an interaction model. |
| Address |
Department of Research and Development, DataHertz, Troyes, France; Institut Charles Delaunay, TechCICO, Université de Technologie de Troyes, Troyes, France; Department of Research and Development, DataHertz, Troyes, France; Institut Charles Delaunay, M2S, Université de Technologie de Troyes, Troyes, France |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-6 |
ISBN |
2411-3392 |
Medium |
|
| Track |
AI Systems for Crisis and Risks |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
Samer.chehade@datahertz.fr |
Approved |
no |
| Call Number |
|
Serial |
2207 |
| Share this record to Facebook |
