Several fairness concepts have been proposed recently in attempts to approximate envy-
freeness in settings with indivisible goods. Among them, the concept of envy-freeness up to …

Mobile crowdsensing is a novel paradigm to collect sensing data and extract useful
information about regions of interest. It widely employs incentive mechanisms to recruit a …

Crowdsourcing allows requesters to allocate tasks to a group of workers on the Internet to
make use of their collective intelligence. Quality control is a key design objective in incentive …

Crowdsourcing has gained immense popularity in machine learning applications for
obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from …

We develop an auction framework for privacy-preserving data aggregation in mobile
crowdsensing, where the platform plays the role as an auctioneer to recruit workers for …

The growing need for labeled training data has made crowdsourcing a vital tool for
developing machine learning applications. Here, workers on a crowdsourcing platform are …

There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a
large number of labels can be often quickly gathered at low cost. However, the labels …

Mobile crowd sensing (MCS) arises as a promising data collection paradigm that leverages
the power of ubiquitous mobile devices to acquire rich information regarding their …

We revisit the classical problem of designing Budget-Feasible Mechanisms (BFMs) for
submodular valuation functions, which has been extensively studied since the seminal …

The study explores the direct and mediated impacts of customers' perception of purchase
budget (BGT) on purchase intention (PIT) through perceived quality (PPQ), perceived price …