Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Efficient Collaborative Crowdsourcing
Authors: Zhengxiang Pan, Han Yu, Chunyan Miao, Cyril Leung
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 9,140 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,926 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL, EMAIL |
| 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. |