Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Kernel Cross-Correlator
Authors: Chen Wang, Le Zhang, Lihua Xie, Junsong Yuan
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,348 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,409 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. |
| Researcher Affiliation | Collaboration | Chen Wang,1 Le Zhang,2 Lihua Xie,1 Junsong Yuan1 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 2Advanced Digital Sciences Center, Singapore |
| Pseudocode | No | The paper describes methods mathematically but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The source codes of both experiments are released at https://github.com/wang-chen/KCC. |
| Open Datasets | Yes | The well-known benchmark dataset that contains 51 sequences (Wu, Lim, and Yang 2013) is selected. The public wearable action recognition database (WARD) (Yang et al. 2009) is chosen. |
| Dataset Splits | Yes | The standard criterion of success plot curve is adopted as the performance criteria. In the experiments, short duration segments (50 instants, about 1.67 s) are randomly selected from the database for each trail. Then the cross-validation with single training sample (one segment) is performed among all 13 activities. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or processor types) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | In both correlators, the regularization term λ in (2) is set as 0.001 and the Gaussian kernel is used with sigma of 0.1. In the experiments, the regularization term λ in (2) is set as 0.0015 while the Gaussian kernel is used with sigma of 1. |