A Study Of Virtuous Cycle Of Service Participation On Crowdsourcing Platforms

Competition has undoubtedly increased substantially over the last decade for several reasons. The Internet has been far and away the largest contributor to the rise in competitive markets due to establishing an online business has lower operating costs and greater flexibility. Companies must have internet business ideas to survive to stay competitive in today’s markets. Crowdsourcing is a phenomenon receiving highly attention both inside and outside of academia. With the rapid development of Web2.0 and social media, an emerging business model like a raging fire impacts on the market: a crowdsourcing platform. Crowdsourcing platforms provide a good environment to fulfill people’s needs and seize value from providing products and services. It is important to understand what drives people to deliver and capture values from a crowdsourcing platform. The purpose of this paper is to explore how service participation works on successful crowdsourcing platforms in their cycles. We focus on why do participants (both sides of supply and demand) are willing to join into the platform to provide services and request services, finding out their virtuous cycles on the platforms in different applications. This study is the first of its kind to explore how service participation works on successful crowdsourcing platforms in their cycles. We will use a qualitative multiple case study, which facilitated an exploration of the phenomenon in an area that has received little theoretical development and allowed us to study the cycle of service participation on crowdsourcing platform in a real-life context. The results may reveal us some significant driving factors on why people are willing to provide and request services on crowdsourcing platforms and what important strategies should be taken while running a crowdsourcing platform. This not only gives us a more broaden view of crowdsourcing and platform operating, but also provides companies, which use crowdsourcing platform to run their business, a more realistic decision making references

ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes

The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data. In this paper, we curate a large-scale and complementary dataset extending both the aforementioned ones by associating all objects mentioned in a referential sentence to their underlying instances inside a 3D scene. Specifically, our Scan Entities in 3D (ScanEnts3D) dataset provides explicit correspondences between 369k objects across 84k natural referential sentences, covering 705 real-world scenes. Crucially, we show that by incorporating intuitive losses that enable learning from this novel dataset, we can significantly improve the performance of several recently introduced neural listening architectures, including improving the SoTA in both the Nr3D and ScanRefer benchmarks by 4.3% and 5.0%, respectively. Moreover, we experiment with competitive baselines and recent methods for the task of language generation and show that, as with neural listeners, 3D neural speakers can also noticeably benefit by training with ScanEnts3D, including improving the SoTA by 13.2 CIDEr points on the Nr3D benchmark. Overall, our carefully conducted experimental studies strongly support the conclusion that, by learning on ScanEnts3D, commonly used visio-linguistic 3D architectures can become more efficient and interpretable in their generalization without needing to provide these newly collected annotations at test time. The project's webpage is https://scanents3d.github.io/ .Comment: The project's webpage is https://scanents3d.github.io