A Model for Aggregating Contributions of Synergistic Crowdsourcing Workflows
Authors: Yili Fang, Hailong Sun, Richong Zhang, Jinpeng Huai, Yongyi Mao
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies confirms the superiority of our proposed model. Experiment Setup To evaluate the effectiveness and time needed of our proposed model, we give simulation. Figure 3 illustrates that the accuracy of the assembly model is higher than the single choice model from 0th iteration to 90th. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Beihang University, Beijing,China 2School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository links, explicit statements of code release) for the source code of the methodology described. |
| Open Datasets | No | The paper uses a simulated environment for its experiments rather than a publicly available dataset. It states: "A CST with M subtasks is simulated with a pool of M objects, each of which represents a subtask." and "Each worker generates a set of objects to simulate the output of CST". |
| Dataset Splits | No | The paper describes a simulation setup and compares models but does not provide specific details on dataset splits (e.g., percentages or counts for training, validation, or testing sets). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the "ZMDP package" but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | Experiment Setup To evaluate the effectiveness and time needed of our proposed model, we give simulation. A CST with M subtasks is simulated with a pool of M objects, each of which represents a subtask. Each object contains ID and difficulty. Each worker generates a set of objects to simulate the output of CST, which contains the ID of subtask processed by workers and the skill excellence of workers, where the ID follows a Gaussian distribution N(µ, σi2), and the skill excellence represents whether the output of the subtask is correct. we make Rs = Z M as the rewarding function based on the accuracy. The progress of the CST consisting of 300 subtasks through the assembly model and the single choice model is simulated. |