Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Incentive-Boosted Federated Crowdsourcing
Authors: Xiangping Kang, Guoxian Yu, Jun Wang, Wei Guo, Carlotta Domeniconi, Jinglin Zhang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results confirm that i Fed Crowd can complete secure crowdsourcing projects with high quality and efficiency. Extensive simulations are conducted to demonstrate that i Fed Crowd can motivate workers to complete secure crowdsourcing projects with high quality and efficiency. We conduct a comparison of its performance with two baselines, namely Random and MAX. Experiment with Real Crowdsourcing Project |
| Researcher Affiliation | Academia | 1School of Software, Shandong University, Jinan, China 2SDU-NTU Joint Centre for AI Research, Shandong University, Jinan, China 3Department of Computer Science, George Mason University, Fairfax, VA, USA 4School of Control Science and Engineering, Shandong University, Jinan, China |
| Pseudocode | Yes | Algorithm 1 summarizes the pseudo-code of i Fed Crowd. Algorithm 1: i Fed Crowd: incentive-boosted Federated Crowdsourcing |
| Open Source Code | Yes | The code of i Fed Crowd is shared at www.sduidea.cn/codes.php?name=i Fed Crowd. |
| Open Datasets | Yes | We used a real-world dataset called Fit Rec (Ni, Muhlstein, and Mc Auley 2019) for experiments. |
| Dataset Splits | No | The paper mentions using a dataset and training a model but does not specify any train/validation/test splits, percentages, or cross-validation methods to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory, cloud instance types). |
| Software Dependencies | No | The paper mentions 'We implement i Fed Crowd with the Mindspore deep learning framework.' but does not provide a version number for Mindspore or any other software dependencies. |
| Experiment Setup | No | The paper states model architecture ('A single layer LSTM followed by a fully connected layer'), and parameters for its game model (α, β, γ, δ ranges), but it lacks specific hyperparameters for the deep learning model training (e.g., learning rate, batch size, epochs, optimizer settings). |