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CrowdPrecision has decided to freeze its ITS for now.
The crypto market has indefinately weakened. After the hype swapped out of the crypto scene itself last autumn the percentage of fake ICOs has grown, spam and scam have grown bigger and thus investors became more aware and less trustful.
We are aware that due to lacking security, the scene is suffering, since too many ICOs simply vanquished with the money collected.
As a consequence the crypto market hype came to an utter end and now it seems to need a way to restore trust.
Our ambition was and still is to create a software that makes crowdsourcing more secure, transparent, easier to manage and more trustworthy, resulting in higher loans for workers and lower wages for employers. The costs were aimed to be gathered through the ITS.
Due to the bad market situation CrowdPrecision will not have the ITS run at this time. We'll rather develope a running system able to provide our ambition on private investments.
If we ever, at a later time, run an ITS, anyone who earned a bounty through our campaign will still be on our list of beneficials.
We thank all supporters for their efforts and hope for a better future for the crypto market.
Thomas Tran-Gia and the CrowdPrecision Team
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One approach to access this labor force consisting of global Internet users is Crowdsourcing. Instead of delegating large tasks to dedicated employees, the work is broken down into smaller jobs that can be accomplished independently and are distributed via marketplaces to a large number of online workers. On the one hand, do you consider these type of the tasks available to all freelancers?
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CrowdScience #12: Recommender systemsFor designing recommender systems it is necessary to understand the behavior and preferences of workers carrying out tasks via crowdsourcing platforms. Here's an empirical study to gain more insights: http://ow.ly/keWO50i4ato
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CrowdScience #11: Methods for CrowdtestingOur next chapter deals with different methods for QoE assessment with crowdsourcing. Matthias Hirth and Phuoc Tran-Gia present a collection of best practices: http://ow.ly/TtFd50i3wps
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CrowdScience #10: Network Measurements Matthias Hirth and Phuoc Tran-Gia research the impact of video traffic on access networks by comparing measurements of a global content delivery network (CDN): http://ow.ly/8gxF50i0pfz
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CrowdScience #9: Scientific Crowdsourcing Experiments Matthias Hirth summarizes “Best Practices and Recommendations for Crowdsourced QoE - Lessons learned from the Qualinet Task Force ‘Crowdsourcing‘“. http://ow.ly/cI1R50i0mz7
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CrowdScience #8: Application Layer Monitoring for Quality Assurance In “Predicting Result Quality in #Crowdsourcing Using Application Layer Monitoring“ you'll find out how an ALM can check the workers‘ interactions with the task interface: http://ow.ly/4D6050i0l3C
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CrowdScience #7: Assessment of Emotions through Crowdsourcing In this paper the significance of users‘ emotive scales in a #crowdsourcing context is analyzed: http://ow.ly/kYpL50hXbgO
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CrowdScience #6: External Tools for Subjective Quality Assessment In his paper “Survey of Web-based #Crowdsourcing Frameworks for Subjective Quality Assessment“ Matthias Hirth focuses on web-based frameworks for multimedia quality assessments: http://ow.ly/Nas450hXa8M
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Attention! We’ve decided to extend our KYC process to August 12th 2018. Until then everyone can still submit their personal data to the KYC process. Our Initial Token Sale will start on August 13th and end on August 19th 2018. Visit us on https://crowdprecision.io/ and join the KYC-Process.
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CrowdScience #5: Quality AssuranceIn this paper two crowd-based approaches are evaluated regarding fraud detection quality, expenses and scope of applicabilty: http://ow.ly/W2K150hX875
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CrowdScience #4: Development of Crowdsourcing PlatformsIn „ Modeling of Crowdsourcing Platforms and Granularity of Work Organization in Future Internet“ Matthias Hirth and Phuoc Tran-Gia analyse the dynamics of a crowdsourcing platform. https://t.co/WuDAvTJ5FN
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CrowdScience #3: User Valuations or Quality of ExperienceIn „Quantification of YouTube QoE via Crowdsourcing“ Matthias Hirth and Phuoc Tran-Gia present a QoE model for YouTube as well as a subjective QoE methodology based on #crowdsourcing: https://t.co/fDRXLR4yTO
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Prof. Phuoc Tran-Gia (Co-founder & Head of Finance of CrowdPrecision) presented CrowdPrecision at the Echelon Asia Summit in Singapore. http://ow.ly/H5Nd50hThhg
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CrowdScience #2: Analysis of Users. One of the critical success factors of a #crowdsourcing platform is a large and diverse user base. Phuoc Tran-Gia and Matthias Hirth performed an analysis of the users of our partner Microworkers: http://ow.ly/iFNh50hS6Iz
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CrowdScience #1: Crowdsourcing Basics.Learn more about applications and use cases of crowdsourcing in “Crowdsourcing and its Impact on Future Internet Usage“: http://ow.ly/xNys50hQI6t
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