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..
A Decomposition Approach for Urban Anomaly Detection Across Spatiotemporal Data
Authors: Mingyang Zhang, Tong Li, Hongzhi Shi, Yong Li, Pan Hui
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method using both real-world and synthetic datasets. The results show our method can detect meaningful events and outperforms state-of-the-art anomaly detecting methods by a large margin. |
| Researcher Affiliation | Academia | 1The Hong Kong University of Science and Technology 2Tsinghua University 3University of Helsinki |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The first dataset is the NYC taxi trip records and the second one is the NYC bike trip records1... 1http://www.nyc.gov/html/tlc/html/about/trip record data.shtml. Besides, we crawled the weather data of New York during the data collection period from Wunder Ground2. 2https://www.wunderground.com |
| Dataset Splits | No | The paper explicitly mentions a train/test split for the synthetic dataset ('We use the data of the first 19 weeks for training and the data of the last week for testing.') but does not specify a separate validation split or explicit cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'neural network' but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Refer to Figure 3, we set both FC network I and II as twolayer networks and FC network III as a three-layer network. λ in (5) is set to 10 3 in the experiments. ... We set λ = {0, 10 4, 10 3, 10 2, 10 1, 1} and run our model on the synthetic dataset. |