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Haoqi Zhang

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Publications by Haoqi Zhang (bibliography)

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2012
 
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Kash, Ian A., Lai, John K., Zhang, Haoqi and Zohar, Aviv (2012): Economics of BitTorrent communities. In: Proceedings of the 2012 International Conference on the World Wide Web 2012. pp. 221-230.

Over the years, private file-sharing communities built on the BitTorrent protocol have developed their own policies and mechanisms for motivating members to share content and contribute resources. By requiring members to maintain a minimum ratio between uploads and downloads, private communities effectively establish credit systems, and with them full-fledged economies. We report on a half-year-long measurement study of DIME -- a community for sharing live concert recordings -- that sheds light on the economic forces affecting users in such communities. A key observation is that while the download of files is priced only according to the size of the file, the rate of return for seeding new files is significantly greater than for seeding old files. We find via a natural experiment that users react to such differences in resale value by preferentially consuming older files during a 'free leech' period. We consider implications of these finding on a user's ability to earn credits and meet ratio enforcements, focusing in particular on the relationship between visitation frequency and wealth and on low bandwidth users. We then share details from an interview with DIME moderators, which highlights the goals of the community based on which we make suggestions for possible improvement.

© All rights reserved Kash et al. and/or ACM Press

2011
 
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Noronha, Jon, Hysen, Eric, Zhang, Haoqi and Gajos, Krzysztof Z. (2011): PlateMate: crowdsourcing nutritional analysis from food photographs. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 1-12.

We introduce PlateMate, a system that allows users to take photos of their meals and receive estimates of food intake and composition. Accurate awareness of this information can help people monitor their progress towards dieting goals, but current methods for food logging via self-reporting, expert observation, or algorithmic analysis are time-consuming, expensive, or inaccurate. PlateMate crowdsources nutritional analysis from photographs using Amazon Mechanical Turk, automatically coordinating untrained workers to estimate a meal's calories, fat, carbohydrates, and protein. We present the Management framework for crowdsourcing complex tasks, which supports PlateMate's nutrition analysis workflow. Results of our evaluations show that PlateMate is nearly as accurate as a trained dietitian and easier to use for most users than traditional self-reporting.

© All rights reserved Noronha et al. and/or ACM Press

 
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