Efficient Collaborative Crowdsourcing

Authors: Zhengxiang Pan, Han Yu, Chunyan Miao, Cyril Leung

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical Analysis proves that Crowd Asm can achieve close to optimal profit for workers in a given crowdsourcing system if they follow the recommendations. The Crowd Asm Approach... Thus, the objective function can be re-expressed as: ... we have proven that the time averaged profit achievable for a given crowdsourcing system following Crowd Asm is within O( 1 ρ) of the optimal profit, subject to the physical limitations of the crowdsourcing system. Theoretical analysis has shown that Crowd Asm can achieve close to optimal profit for workers in a collaborative crowdsourcing system if they follow the recommendations.
Researcher Affiliation Academia 1Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Tech. Univ., Singapore 2Department of Electrical and Computer Engineering, the University of British Columbia, Vancouver, BC, Canada {panz0012, han.yu, ascymiao}@ntu.edu.sg, cleung@ece.ubc.ca
Pseudocode No The paper presents mathematical equations and a derivation of its approach but does not include pseudocode or an algorithm block.
Open Source Code No The paper does not mention providing open-source code for the described methodology.
Open Datasets No The paper is purely theoretical and does not involve experimental training on a dataset, therefore no public dataset information for training is provided.
Dataset Splits No The paper is theoretical and does not involve data splits for training, validation, or testing, thus no such information is provided.
Hardware Specification No The paper is theoretical and does not describe experiments, therefore no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not specify any software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe experiments, thus no experimental setup details like hyperparameters or training settings are provided.