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..
Optimal Region Search with Submodular Maximization
Authors: Xuefeng Chen, Xin Cao, Yifeng Zeng, Yixiang Fang, Bin Yao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two applications using three real-world datasets. The results demonstrate that our algorithms can achieve high-quality solutions and are faster than a state-of-the-art method by orders of magnitude. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, University of New South Wales, Australia, 2 Department of Computer & Information Sciences, Northumbria University, UK, 3 Department of Computer Science and Engineering, Shanghai Jiaotong University, China |
| Pseudocode | Yes | Algorithm 1: App ORS Algorithm |
| Open Source Code | No | The paper does not include an unambiguous statement where the authors state they are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We use two real-world datasets SG and AS crawled from Four Square (also used in the work [Zeng et al., 2015]), in which SG has 189,306 check-ins made by 2,321 users at 5,412 locations in Singapore, and AS contains 201,525 check-ins made by 4,630 users at 6,176 locations in Austin. [...] We use the road network in California (CA) from a public website1. We then utilized the Foursquare APIs to fill in the missing keywords for nodes (categories of locations)2. CA contains 21,048 nodes and 22,830 edges [Li et al., 2005]. |
| Dataset Splits | No | The paper mentions using real-world datasets for experiments but does not provide specific details on how the data was split into training, validation, or test sets, nor does it reference predefined splits. |
| Hardware Specification | Yes | We implement all the algorithms in JAVA on Windows 10, and run on a server with an Intel(R)Xeon(R) W-2155 3.3GHz CPU and 256 GB memory. |
| Software Dependencies | No | The paper states that algorithms are implemented 'in JAVA on Windows 10' but does not provide specific version numbers for Java or any other software libraries or dependencies used. |
| Experiment Setup | Yes | We compare three proposed algorithms with GCBAll in terms of efficiency (the run time) and effectiveness (the objective score) by varying Δ from 20km to 60km. [...] Next, we run the three algorithms on SY by varying the number of nodes from 10,000 to 30,000, and set Δ = 60km. |