Multi-agent Attentional Activity Recognition
Authors: Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, Zhiwen Yu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on four real-world datasets demonstrate that the proposed model outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | 1University of New South Wales 2Northwestern Polytechnical University |
| Pseudocode | Yes | The detailed procedure is shown in Algorithm 1. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or explicitly state that the code is publicly available. |
| Open Datasets | Yes | The experiments are performed by Leave-One-Subject-Out (LOSO) on four datasets, MHEALTH [Banos et al., 2014], PAMAP2 [Reiss and Stricker, 2012], UCI HAR [Anguita et al., 2013] and MARS. |
| Dataset Splits | Yes | The experiments are performed by Leave-One-Subject-Out (LOSO) on four datasets... The time window of inputs is 20 with 50% overlap. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The time window of inputs is 20 with 50% overlap. The size of each observation patch is set to K/8, where K P is the size of the inputs. In the partial observation part, the sizes of θe, θtl, θo are 128, 128, 220, respectively. The filter size of the convolutional layer in the shared observation module is 1 M and the number of feature maps is 40, where M denotes the width of os g. The size of LSTM cells is 220, and the length of episodes is 40. The Gaussian distribution that defines the selection policies is with a variance of 0.22. |