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..
Action Recognition From Skeleton Data via Analogical Generalization Over Qualitative Representations
Authors: Kezhen Chen, Kenneth Forbus
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on three public datasets illustrate its utility. |
| Researcher Affiliation | Academia | Kezhen Chen, Kenneth D. Forbus Northwestern University Kezhen EMAIL |
| Pseudocode | No | The paper describes the pipeline and steps in prose and with diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We evaluated dynamic feature selection on the Florence 3D Action dataset (Seidenari et al. 2013) and The University of Texas at Dallas Multimodal Human Action Dataset (UTD-MHAD) was collected as part of research on human action recognition by Chen et al. (Chen et al. 2015). To further evaluate our method, we ran an experiment on the UTKinect-Action3D dataset (Xia, Chen, and Aggarwal 2012). |
| Dataset Splits | Yes | Cross-subject validation was used. We follow the leave one out cross validation protocol (LOOCV) (Seidenari et al. 2013) to better compare with other methods. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like Cog Sketch and QSRlib, but does not provide specific version numbers for any of its software dependencies. |
| Experiment Setup | Yes | For all experiments reported here, we used an assimilation threshold of 0.7. We used a probability cutoff of 0.2 in all experiments. The search stops either when a cutoff is reached (here, the cutoff is four optional features, which provides a reasonable tradeoff between accuracy and efficiency) |