ISCRAM Digital Library -- Query Results

	ISCRAM Digital Library Home \| Show All \| Simple Search \| Advanced Search	Login Quick Search: Field: contains: ...
	1–8 of 8 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

<< 1 >>

List View

Citations

Details

	Records
	Author	Alessandro Farasin; Luca Colomba; Giulio Palomba; Giovanni Nini
	Title	Supervised Burned Areas Delineation by Means of Sentinel-2 Imagery and Convolutional Neural Networks			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	1060-1071
	Keywords	Burned Area Delineation, Sentinel-2, U-Net, CuMedVision1, Convolutional Neural Network, Deep Learning, Supervised Learning, Pixel-wise Segmentation.
	Abstract	Wildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods.
	Address	Politecnico di Torino – DAUIN dept., and LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept.; LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept.
	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	978-1-949373-27-96	ISBN	2411-3482	Medium
	Track	Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management	Expedition		Conference	17th International Conference on Information Systems for Crisis Response and Management
	Notes	alessandro.farasin@polito.it			Approved	no
	Call Number				Serial	2297
Share this record to Facebook



	Author	Li, H.; Caragea, D.; Mhatre, A.; Ge, J.; Liu, M.
	Title	Identifying COVID-19 Tweets Relevant to Low-Income Households Using Semi-supervised BERT and Zero-shot ChatGPT Models			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	953-963
	Keywords	COVID Low-income Households; Semi-Supervised Learning; Self-Training; Knowledge Distillation; ChatGPT
	Abstract	Understanding the COVID-19 pandemic impacts on low-income households can inform social services about the needs of vulnerable communities. Some recent works have studied such impacts through social media content analysis, and supervised machine learning models have been proposed to automatically classify COVID-19 tweets into different categories, such as income and economy impacts, social inequality and justice issues, etc. In this paper, we propose semi-supervised learning models based on BERT with Self-Training and Knowledge Distillation for identifying COVID-19 tweets relevant to low-income households by leveraging readily available unlabeled data in addition to limited amounts of labeled data. Furthermore, we explore ChatGPT’s potential for annotating COVID-19 data and the performance of fine-tuned GPT-3 models. Our semi-supervised BERT model with Knowledge Distillation showed improvements compared to a supervised baseline model, while zero-shot ChatGPT showed good potential as a tool for annotating crisis data. However, our study suggests that the cost of fine-tuning large and expensive GPT-3 models may not be worth for some tasks.
	Address	Department of Computer Science, California State University; Department of Computer Science,Kansas State University; Department of Computer Science, California State University; University of North Texas, Health Science Center; University of North Texas, Health Science Center
	Corporate Author				Thesis
	Publisher	University of Nebraska at Omaha	Place of Publication	Omaha, USA	Editor	Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi
	Language	English	Summary Language		Original Title
	Series Editor	Hosssein Baharmand	Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition	1
	ISSN		ISBN		Medium
	Track	AI for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/EFMA5735			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2579
Share this record to Facebook



	Author	Long, Z.; McCreadiem, R.; Imran, M.
	Title	CrisisViT: A Robust Vision Transformer for Crisis Image Classification			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	309-319
	Keywords	Social Media Classification; Crisis Management; Deep Learning; Vision Transformers; Supervised Learning
	Abstract	In times of emergency, crisis response agencies need to quickly and accurately assess the situation on the ground in order to deploy relevant services and resources. However, authorities often have to make decisions based on limited information, as data on affected regions can be scarce until local response services can provide first-hand reports. Fortunately, the widespread availability of smartphones with high-quality cameras has made citizen journalism through social media a valuable source of information for crisis responders. However, analyzing the large volume of images posted by citizens requires more time and effort than is typically available. To address this issue, this paper proposes the use of state-of-the-art deep neural models for automatic image classification/tagging, specifically by adapting transformer-based architectures for crisis image classification (CrisisViT). We leverage the new Incidents1M crisis image dataset to develop a range of new transformer-based image classification models. Through experimentation over the standard Crisis image benchmark dataset, we demonstrate that the CrisisViT models significantly outperform previous approaches in emergency type, image relevance, humanitarian category, and damage severity classification. Additionally, we show that the new Incidents1M dataset can further augment the CrisisViT models resulting in an additional 1.25% absolute accuracy gain.
	Address	University of Glasgow
	Corporate Author				Thesis
	Publisher	University of Nebraska at Omaha	Place of Publication	Omaha, USA	Editor	Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi
	Language	English	Summary Language		Original Title
	Series Editor	Hosssein Baharmand	Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition	1
	ISSN		ISBN		Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/SDSM9194			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2528
Share this record to Facebook



	Author	Matt Wolff
	Title	Unsupervised methods for detecting a malicious insider			Type	Conference Article
	Year	2010	Publication	ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings	Abbreviated Journal	ISCRAM 2010
	Volume		Issue		Pages
	Keywords	Information systems; Natural language processing systems; Network security; Unsupervised learning; Insider Threat; Malicious insiders; Masquerade attacks; Supervised algorithm; Unsupervised algorithms; Unsupervised method; User masquerades; Algorithms
	Abstract	One way a malicious insider can attack a network is by masquerading as a different user. Various algorithms have been proposed in an effort to detect when a user masquerade attack has occurred. In this paper, two unsupervised algorithms are proposed with the intended goal of detecting user masquerade attacks. The effectiveness of these two unsupervised algorithms are then compared against supervised algorithms.
	Address	University of Hawaii, United States
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Seattle, WA	Editor	S. French, B. Tomaszewski, C. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN		Medium
	Track	Special Session: Information Credibility, Trust, Privacy and Security in Information Systems for Emergency Management	Expedition		Conference	7th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1097
Share this record to Facebook



	Author	Savannah Thais; Shaine Leibowitz; Allie Saizan; Ashay Singh
	Title	Understanding Historical, Socio-Economic, and Policy Contributions to COVID-19 Health Inequities			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	481-494
	Keywords	Public Health; COVID-19; Economic Impact; Mobile Health; Unsupervised Learning; Longitudinal Analysis; Community Vulnerability Index; Proxy Outcomes; Health Policy; Social Determinants of Health; Equity
	Abstract	The COVID-19 pandemic has generated unprecedented, devastating impacts across the United States. However, some communities have disproportionately endured adverse health outcomes and socioeconomic injuries. Ascertaining the factors driving these inequities is crucial to determining how policy could mitigate the impacts of future public health crises. We have established research-driven metrics, aggregated as the Community Vulnerability Index (CVI), that quantify vulnerability to public health and economic impacts of COVID-19. We performed two analyses to better understand similarities between communities in terms of the vulnerabilities represented by the metrics. We performed an unsupervised k-means clustering analysis to understand whether communities can be grouped together based on their levels of negative social and health indicators. Our goal for this analysis is to determine whether attributes of the constructed clusters reveal areas of opportunity for potential policy impacts and future disaster response efforts. We also analyzed similarities between communities across time using time-sensitive clustering analysis to discover whether historical community vulnerabilities were persistent in the years preceding the pandemic and to better understand the historical factors associated with disparate COVID-19 impacts. In particular, we highlight where communities should invest based on their historical health and socioeconomic patterns and related COVID impacts. Through extensive interpretation of our findings, we uncover how health policy can advance equity and improve community resilience.
	Address	Princeton University; Community Insight and Impact; Community Insight and Impact; Community Insight and Impact
	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	Data and Resilience: Opportunities and Challenges	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2434
Share this record to Facebook



	Author	Schmidt-Colberg, A.; Löffler-Dauth, L.
	Title	A Human-Centric Evaluation Dataset for Automated Early Wildfire Detection from a Causal Perspective			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	933-943
	Keywords	Wildfire Detection; Supervised Learning; Causality; Evaluation
	Abstract	Insight into performance ability is crucial for successfully implementing AI solutions in real-world applications. Unanticipated input can lead to false positives (FP) and false negatives (FN), potentially resulting in false alarms in fire detection scenarios. Literature on fire detection models shows varying levels of complexity and explicability in evaluation practices; little supplementary information on performance ability outside of accuracy scores is provided. We advocate for a standardized evaluation dataset that prioritizes the end-user perspective in assessing performance capabilities. This leads us to ask what an evaluation dataset needs to constitute to enable a non-expert to determine the adequacy of a model's performance capabilities for their specific use case. We propose using data augmentation techniques that simulate interventions to remove the connection to the original target label, providing interpretable counterfactual explanations into a model's predictions.
	Address	Fraunhofer FOKUS
	Corporate Author				Thesis
	Publisher	University of Nebraska at Omaha	Place of Publication	Omaha, USA	Editor	Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi
	Language	English	Summary Language		Original Title
	Series Editor	Hosssein Baharmand	Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition	1
	ISSN		ISBN		Medium
	Track	AI for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/KHML7113			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2577
Share this record to Facebook



	Author	Zijun Long; Richard McCreadie
	Title	Is Multi-Modal Data Key for Crisis Content Categorization on Social Media?			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1068-1080
	Keywords	Social Media Classification; Multi-modal Learning; Crisis Management; Deep Learning, BERT; Supervised Learning
	Abstract	The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail.
	Address	University of Glasgow; University of Glasgow
	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	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2472
Share this record to Facebook



	Author	Zoha Sheikh; Hira Masood; Sharifullah Khan; Muhammad Imran
	Title	User-Assisted Information Extraction from Twitter During Emergencies			Type	Conference Article
	Year	2017	Publication	Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management	Abbreviated Journal	Iscram 2017
	Volume		Issue		Pages	684-691
	Keywords	social media; disaster response; query expansion; supervised learning
	Abstract	Disasters and emergencies bring uncertain situations. People involved in such situations look for quick answers to their rapid queries. Moreover, humanitarian organizations look for situational awareness information to launch relief operations. Existing studies show the usefulness of social media content during crisis situations. However, despite advances in information retrieval and text processing techniques, access to relevant information on Twitter is still a challenging task. In this paper, we propose a novel approach to provide timely access to the relevant information on Twitter. Specifically, we employee Word2vec embeddings to expand initial users queries and based on a relevance feedback mechanism we retrieve relevant messages on Twitter in real-time. Initial experiments and user studies performed using a real world disaster dataset show the significance of the proposed approach.
	Address	National University of Sciences and Technology, Islamabad, Pakistan; Qatar Computing Research Institute, HBKU Doha, Qatar
	Corporate Author				Thesis
	Publisher	Iscram	Place of Publication	Albi, France	Editor	Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN		Medium
	Track	Social Media Studies	Expedition		Conference	14th International Conference on Information Systems for Crisis Response And Management
	Notes				Approved	no
	Call Number				Serial	2056
Share this record to Facebook