No abstract
Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find, stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highlyrated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.
In this paper, an Internet of Things (IoT) based Crop Monitoring and Classification system is proposed for automated sensing, storing, and monitoring real-time parameters that play an important role in determining a crop鈥檚 quality and yield. Sensors are placed in-situ in the field and the warehouse to monitor the crop. The long-Range wide area network (LoRa) module is used for communication between the sensing unit placed at the field, warehouse, and data processing unit. The yield is classified based on qualitative analysis posed by the imperative sensor data. Further, to enable an equitable gateway of resource sharing between the distributor and the farmer, a Blockchain-based transaction is taught to enhance trust and security. This proposed method aims to eliminate intermediaries in the trade, thereby helping farmers get the price for their product details stored in an immutable database, which also displays the farmer鈥檚 quality of the crop reaped.
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