46,762 research outputs found
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
"Last-Mile" preparation for a potential disaster
Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity
DAHiTrA: Damage Assessment Using a Novel Hierarchical Transformer Architecture
This paper presents DAHiTrA, a novel deep-learning model with hierarchical
transformers to classify building damages based on satellite images in the
aftermath of hurricanes. An automated building damage assessment provides
critical information for decision making and resource allocation for rapid
emergency response. Satellite imagery provides real-time, high-coverage
information and offers opportunities to inform large-scale post-disaster
building damage assessment. In addition, deep-learning methods have shown to be
promising in classifying building damage. In this work, a novel
transformer-based network is proposed for assessing building damage. This
network leverages hierarchical spatial features of multiple resolutions and
captures temporal difference in the feature domain after applying a transformer
encoder on the spatial features. The proposed network achieves
state-of-the-art-performance when tested on a large-scale disaster damage
dataset (xBD) for building localization and damage classification, as well as
on LEVIR-CD dataset for change detection tasks. In addition, we introduce a new
high-resolution satellite imagery dataset, Ida-BD (related to the 2021
Hurricane Ida in Louisiana in 2021, for domain adaptation to further evaluate
the capability of the model to be applied to newly damaged areas with scarce
data. The domain adaptation results indicate that the proposed model can be
adapted to a new event with only limited fine-tuning. Hence, the proposed model
advances the current state of the art through better performance and domain
adaptation. Also, Ida-BD provides a higher-resolution annotated dataset for
future studies in this field
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
eStorys: A visual storyboard system supporting back-channel communication for emergencies
This is the post-print version of the final paper published in Journal of Visual Languages & Computing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.In this paper we present a new web mashup system for helping people and professionals to retrieve information about emergencies and disasters. Today, the use of the web during emergencies, is confirmed by the employment of systems like Flickr, Twitter or Facebook as demonstrated in the cases of Hurricane Katrina, the July 7, 2005 London bombings, and the April 16, 2007 shootings at Virginia Polytechnic University. Many pieces of information are currently available on the web that can be useful for emergency purposes and range from messages on forums and blogs to georeferenced photos. We present here a system that, by mixing information available on the web, is able to help both people and emergency professionals in rapidly obtaining data on emergency situations by using multiple web channels. In this paper we introduce a visual system, providing a combination of tools that demonstrated to be effective in such emergency situations, such as spatio/temporal search features, recommendation and filtering tools, and storyboards. We demonstrated the efficacy of our system by means of an analytic evaluation (comparing it with others available on the web), an usability evaluation made by expert users (students adequately trained) and an experimental evaluation with 34 participants.Spanish Ministry of Science and Innovation and Universidad Carlos III de Madrid and
Banco Santander
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