English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We study a problem of optimal information gathering from multiple dataproviders that need to be incentivized to provide accurate information. Thisproblem arises in many real world applications that rely on crowdsourced datasets, but where the process of obtaining data is costly. A notable example ofsuch a scenario is crowd sensing. To this end, we formulate the problem ofoptimal information gathering as maximization of a submodular function under abudget constraint, where the budget represents the total expected payment todata providers. Contrary to the existing approaches, we base our payments onincentives for accuracy and truthfulness, in particular, {\em peer-prediction}methods that score each of the selected data providers against its best peer,while ensuring that the minimum expected payment is above a given threshold. Wefirst show that the problem at hand is hard to approximate within a constantfactor that is not dependent on the properties of the payment function.However, for given topological and analytical properties of the instance, weconstruct two greedy algorithms, respectively called PPCGreedy andPPCGreedyIter, and establish theoretical bounds on their performance w.r.t. theoptimal solution. Finally, we evaluate our methods using a realistic crowdsensing testbed.

Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints