Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms
Authors: Boyang Li, Yurong Cheng, Ye Yuan, Guoren Wang, Lei Chen
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct the experiments on real and synthetic datasets, experimental results show the effectiveness and efficiency of our algorithms. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Northeastern University, China 2School of Computer Science and Technology, Beijing Institute of Technology, China 3The Hong Kong University of Science and Technology, Hong Kong SAR, China |
| Pseudocode | Yes | Algorithm 1: Sliding Window; Algorithm 2: Threshold Scanning |
| Open Source Code | No | The paper does not provide any specific links or statements regarding the public availability of its source code. |
| Open Datasets | Yes | We conduct our algorithms over g Mission [Chen et al., 2014]. We also generate a synthetic dataset to test the algorithms. The details of real and synthetic datasets are illustrated in Table 2 and 3. |
| Dataset Splits | No | The paper mentions using real and synthetic datasets for experiments but does not explicitly provide details about train/validation/test splits (e.g., percentages, counts, or a specific splitting methodology). |
| Hardware Specification | Yes | We conduct the experiments in a machine with Intel Xeon Silver 4110 and 512GB main memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We generate the capacity of workers from 1 to 10, and the maximum waiting time is from 3 to 10 minutes. We set the travel speed of workers is 8 and the travel speed of users is 4. The maximum radius of workers as 80, and the maximum radius of users as 40. We select three different θ in the synthetic datasets (shorted as TS-1 , TS-4 and TS-10 ). |