The Effects of Performance-Contingent Financial Incentives in Online Labor Markets

Online labor markets such as Amazon Mechanical Turk (MTurk) have emerged as platforms that facilitate the allocation of productive effort across global economies. Many of these markets compensate workers with monetary payments. We study the effects of performance-contingent financial rewards on work quality and worker effort in MTurk via two experiments. We find that the magnitude of performance contingent financial rewards alone affects neither quality nor effort. However, when workers working on two tasks of the same type in a sequence, the change in the magnitude of the reward over the two tasks affects both. In particular, both work quality and worker effort increase (alternatively decrease) as the reward increases (alternatively decreases) for the second task. This suggests the existence of the anchoring effect on workers’ perception of incentives in MTurk and that this effect can be leveraged in workflow design to increasethe effectiveness of financial incentives.Engineering and Applied Science

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Database

Superradiant Solid in Cavity QED Coupled to a Lattice of Rydberg Gas

We study an optical cavity coupled to a lattice of Rydberg atoms, which can be represented by a generalized Dicke model. We show that the competition between the atomic interaction and atom-light coupling induces a rich phase diagram. A novel "superradiant solid" (SRS) phase is found, where both the superradiance and crystalline orders coexist. Different from the normal second order superradiance (SR) transition, here both the Solid-1/2 and SRS to SR phase transitions are first order. These results are confirmed by the large scale quantum Monte Carlo simulations.Comment: 5 pages,4 figure