Microlearning in crowdsourcing and crowdtasking applicaitons
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The document discusses the Envirofying initiative aimed at integrating environmental observation into future internet applications through microlearning and crowdsourcing. It highlights scenarios such as biodiversity surveys, personal environmental information systems, and collaborative marine data usage, emphasizing citizen participation as sensors for real-time environmental data collection. The ongoing development, initially under the FP7 framework, is now evolving as a free and open-source software called Ubicity.
Microlearning in crowdsourcing and crowdtasking applicaitons
- 1. “ENVIROfying” the Future Internet THE ENVIRONMENTAL OBSERVATION WEB FOR THE CROSS-DOMAIN FI-PPP APPLICATIONS Microlearning in Crowdsourcing and Crowdtasking Applications Microlearning 7, Sept. 27-28 2013 Denis Havlik (AIT)
- 3. Copyright © ENVIROFI Project Consortium 3 Enviromatics meet Future Internet Future Internet • Networking technology • Infrastructure as a Service • Internet of Things, Content, People INSPIRE, GMES, SISE • Geospatial • Environmental Observations • Model Web, Sensor Web, • Data Fusion, Uncertainty ENVIROFI FI-PPP Environmental Usage Area • FI Requirements • Specific Enablers • Envirofied cross-area Applications
- 4. ENVIROFI Scenarios 1. Bringing Biodiversity into the Future Internet • Enabled biodiversity surveys with advanced ontologies • Analysis, quality assurance and dissemination of biodiversity data 1. Personal Information System for Air Pollutants, allergens and meteorological conditions • Enhance human to environment interaction • Atmospheric conditions and pollution in “the palm of your hand” 1. Collaborative Usage of Marine Data Assets • Assess needs of key marine user communities • Selection of representative marine use cases for further trial: leisure and tourism, ocean energy devices, aquaculture, oil spill alert Copyright © 2013 ENVIROFI Project Consortium 4
- 5. Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 5
- 6. People as sensors? Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. Illustration by Scoobay (http://www.flickr.com/photos/scoobay/224565711/) 6
- 7. Motivation matters! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 7
- 8. Balance taking and giving View existing knowledge •Map view •Table view •Detailed View •Areas of Interest View existing knowledge •Map view •Table view •Detailed View •Areas of Interest Receive information (events!) •Requests for more observations, •Warnings, e.g. “pollen warning” •Interests, e.g. “monumental tree in vicinity” Receive information (events!) •Requests for more observations, •Warnings, e.g. “pollen warning” •Interests, e.g. “monumental tree in vicinity” Report observations •“New” things, e.g. “here and now I see a tree” •Personal, e.g. “I have a headache” •Obs. on existing thing, e.g. “this tree currently blossoms Report observations •“New” things, e.g. “here and now I see a tree” •Personal, e.g. “I have a headache” •Obs. on existing thing, e.g. “this tree currently blossoms Inform Server Backend (or proxy) Alert! Request Action! Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 8
- 9. Observation DB Add value to observations 9 Plausibility/Confidence checks Consensus building Previous situation knowledge Habitat Informatio n Image Recognition Reporters Reputation Observ. on things (independent, conflicting, incomplete) Observations on observations (identification, plausibility, annotation) Application specific views (fusion, meaning uncertainty) Sensor Networks ENVIROFI observations ENVIROFI observations Integrate existing data Integrate existing data USE Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH.
- 10. (Crowdt)ask and thou shall be given? 10 Mobile Users Sensors Automated Tasking External Data Manual Tasking Decision maker Experts Algorithms Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH.
- 11. Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 11
- 12. Three learning strategies Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 12 Classical: large information intake, well in-advance to use Illustration from Flickr, by Dean+Barb Illustration from Flickr, by Tulane Public Relations Learning by doing: trial and error method Illustration from: The Black Cat Diaries Learning while doing: just in time intake of information in small portions “Danger, complex diagrams ahead” Illustration from Flickr, by Matthew Rogers
- 13. Information gained from using of the application… Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 13 Biodiversity Personal environmental Information Which species are common in my area? What is my current and cumulative exposure? What species is this? Am I allergic to pollen? Sensitive to weather changes? Ozone? … Is it dangerous? Is any of the factors I’m sensitive to likely to occur tomorrow? Is it edible? Will it fall and ruin my car?
- 14. Support „learning while doing“ Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH. 14 Objective Possible approach How to use the application? Tooltips or popup messages on first use (implemented) Training to recognise objects Scavenger hunt for known and tagged objects Learn to avoid misidentifications control questions & feedback A-posteriori feedback Notify user when more info on the object is available (implemented) Classify data & assess users knowledge Generalized re-capcha principle Other ideas?
- 15. “Generalized re-captcha“ example Photos from flickr.com. From left to right by Karl-Ludwig G. Poggemann, abby chicken & Marcy Reiford 15 Question: Which of these photos show maple leafs? (known, maple) (known, oak) (unknown) 1. System mixes known and unknown samples 2. User can choose yes/no/can‘t say for each photo 3. Correlate all answers to: (1) correlate known and unknown samples; and (2) determine users level of knowledge 4. Add feedback for training purposes Copyright © 2013 Denis Havlik, AIT Austrian Institute of Technology GmbH.
- 16. 1. The ideas presented today were developed and partially realized as Mobile Data Acquisition System (MDAF) in the scope of the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Number 284898 (ENVIROFI) 2. MDAF contributors: Eun Yu, Clemens Bernhard Geyer, Peter Kutschera, Markus Falgenhauer, Markus Cizek, Ralf Vamosi, Maria Egly, Hermann Huber and most recently Jan von Oort. • Currently active developers are underlined. Acknowledgements 16
- 17. Unless stated differently, the slides are © 2013 Denis Havlik and licensed under the terms of the Creative Commons ”Attribution-ShareAlike 3.0“ license. Re-use of resuls 17 MDAF development continues as FOSS under a new name: UBICITY. First demonstration on ISESS 2013 in 2 weeks; new users and partners are welcome!
- 18. Thank you for your attention Dr. Denis Havlik denis.havlik@ait.ac.at The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Number 284898 www.envirofi.eu
Editor's Notes
- #5: Two out of three ENVIROFI scenarios are strongly biased towards citizen scientists, mobile crowdsourcing and crowdtasking and local situation awareness.
- #7: In some senses, humans are „bad sensors“. They are non-standardized, difficult to calibrate, don‘t like the idea of working 24/7, easily bored and their accuracy and sensitivity erratically varies over time. However, they also excell at pattern recognition and interpretation of the results. This makes them complementary to hardware sensors and very valuable for some types of applications.
- #10: Unlike standard monitoring systems, the human sensors (and to a lesser extent also information from user-owned sensors) inevitably deliver conflicting and incomplete information. The Quality assurance of such data often relies on combination of peer review, expert opinions and various indicators. In MDAF, all these results can happily co-exist even if they are contradicting each other. The decision „what is the reality?“ is only made at the level of „application specific view“, taking into account the owners interests and trust in various data sources. As a result, it is perfectly possible to generate several conflicting „realities“ from the same data set. E.g. a Greanpeace applicaiton will show different reality than a fisherman association applicaiton simply because they make different assumprions concerning the relative importance and trustworthiness of the data. The „applicaiton specific view“ has not been fully implemented, but the technology is the same as the one used for the quality assurance part.
- #11: Tasking of volunteers and experts is a key to data collection and quality assurance. It is crucial to task the users which are both able and willing to perform this task, while avoiding the information overflow. In ENVIROFI, AIT was able to develop a concept and basic technology which will allow us to implement the context- and profile- specific tasking in the future projects.
- #18: I‘m not sure which is the license for the slides which I „inherited“ here, sorry. I‘m sure that *I* can use them, and I presume the right to re-use them will be granted to anyone who asks. Please contact the respective consortium leaders.