62,275 research outputs found
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health. Prior
works have shown that using Computer Vision (CV) techniques to identify smoke
as visual evidence can influence the attitude of regulators and empower
citizens to pursue environmental justice. However, existing datasets are not of
sufficient quality nor quantity to train the robust CV models needed to support
air quality advocacy. We introduce RISE, the first large-scale video dataset
for Recognizing Industrial Smoke Emissions. We adopted a citizen science
approach to collaborate with local community members to annotate whether a
video clip has smoke emissions. Our dataset contains 12,567 clips from 19
distinct views from cameras that monitored three industrial facilities. These
daytime clips span 30 days over two years, including all four seasons. We ran
experiments using deep neural networks to establish a strong performance
baseline and reveal smoke recognition challenges. Our survey study discussed
community feedback, and our data analysis displayed opportunities for
integrating citizen scientists and crowd workers into the application of
Artificial Intelligence for social good.Comment: Technical repor
User-driven design of decision support systems for polycentric environmental resources management
Open and decentralized technologies such as the Internet provide increasing opportunities to create knowledge and deliver computer-based decision support for multiple types of users across scales. However, environmental decision support systems/tools (henceforth EDSS) are often strongly science-driven and assuming single types of decision makers, and hence poorly suited for more decentralized and polycentric decision making contexts. In such contexts, EDSS need to be tailored to meet diverse user requirements to ensure that it provides useful (relevant), usable (intuitive), and exchangeable (institutionally unobstructed) information for decision support for different types of actors. To address these issues, we present a participatory framework for designing EDSS that emphasizes a more complete understanding of the decision making structures and iterative design of the user interface. We illustrate the application of the framework through a case study within the context of water-stressed upstream/downstream communities in Lima, Peru
A Storm in an IoT Cup: The Emergence of Cyber-Physical Social Machines
The concept of social machines is increasingly being used to characterise
various socio-cognitive spaces on the Web. Social machines are human
collectives using networked digital technology which initiate real-world
processes and activities including human communication, interactions and
knowledge creation. As such, they continuously emerge and fade on the Web. The
relationship between humans and machines is made more complex by the adoption
of Internet of Things (IoT) sensors and devices. The scale, automation,
continuous sensing, and actuation capabilities of these devices add an extra
dimension to the relationship between humans and machines making it difficult
to understand their evolution at either the systemic or the conceptual level.
This article describes these new socio-technical systems, which we term
Cyber-Physical Social Machines, through different exemplars, and considers the
associated challenges of security and privacy.Comment: 14 pages, 4 figure
The Development of Citizen Oriented Informatics
We define the concept of citizen-oriented computer application. Quality characteristics are set for computer applications developed in the conditions of citizen-oriented computing and outline the development cycle for these applications. It defines the conditions of existence for citizen-oriented applications. Average and long-term strategies are elaborated.Distributed Applications, Metrics, Citizen-Orientation, Strategies
Linked open government data: lessons from Data.gov.uk
The movement to publish government data is an opportunity to populate the linked data Web with data of good provenance. The benefits range from transparency to public service improvement, citizen engagement to the creation of social and economic value. There are many challenges to be met before the vision is implemented, and this paper describes the efforts of the EnAKTing project to extract value from data.gov.uk, through the stages of locating data sources, integrating data into the linked data Web, and browsing and querying it
‘Probing with the prototype’:using a prototype e-participation platform as a digital cultural probe to investigate youth engagement with the environment
This study describes how we used a prototype e-participation plat-form as a digital cultural probe to investigate youth motivation and engagement strategies. This is a novel way of considering digital cultural probes which can contribute to the better creation of e-participation platforms. This probe has been conducted as part of the research project STEP which aims at creating an e-participation platform to engage young European Citizens in environmental decision making. Our probe technique has given an insight into the environ-mental issues concerning young people across Europe as well as possible strat-egies for encouraging participation. How the e-participation platform can be utilised to support youth engagement through opportunities for social interac-tion and leadership is discussed. This study leads to a better understanding of how young people can co-operate with each other to provide collective intelli-gence and how this knowledge could contribute to effective e-participation of young people
Mathematical practice, crowdsourcing, and social machines
The highest level of mathematics has traditionally been seen as a solitary
endeavour, to produce a proof for review and acceptance by research peers.
Mathematics is now at a remarkable inflexion point, with new technology
radically extending the power and limits of individuals. Crowdsourcing pulls
together diverse experts to solve problems; symbolic computation tackles huge
routine calculations; and computers check proofs too long and complicated for
humans to comprehend.
Mathematical practice is an emerging interdisciplinary field which draws on
philosophy and social science to understand how mathematics is produced. Online
mathematical activity provides a novel and rich source of data for empirical
investigation of mathematical practice - for example the community question
answering system {\it mathoverflow} contains around 40,000 mathematical
conversations, and {\it polymath} collaborations provide transcripts of the
process of discovering proofs. Our preliminary investigations have demonstrated
the importance of "soft" aspects such as analogy and creativity, alongside
deduction and proof, in the production of mathematics, and have given us new
ways to think about the roles of people and machines in creating new
mathematical knowledge. We discuss further investigation of these resources and
what it might reveal.
Crowdsourced mathematical activity is an example of a "social machine", a new
paradigm, identified by Berners-Lee, for viewing a combination of people and
computers as a single problem-solving entity, and the subject of major
international research endeavours. We outline a future research agenda for
mathematics social machines, a combination of people, computers, and
mathematical archives to create and apply mathematics, with the potential to
change the way people do mathematics, and to transform the reach, pace, and
impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent
Computer Mathematics, CICM 2013, July 2013 Bath, U
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