Personality-Aware Personalized Emotion Recognition from Physiological Signals
Authors: Sicheng Zhao, Guiguang Ding, Jungong Han, Yue Gao
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method. |
| Researcher Affiliation | Academia | Sicheng Zhao , Guiguang Ding , Jungong Han and Yue Gao School of Software, Tsinghua University, China Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA School of Computing and Communications, Lancaster University, UK Corresponding author: Guiguang Ding. schzhao@gmail.com, {dinggg,gaoyue}@tsinghua.edu.cn, jungong.han@lancaster.ac.uk |
| Pseudocode | No | The paper describes the proposed method mathematically and textually but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | To the best of our knowledge, ASCERTAIN [Subramanian et al., 2016] is the only published and released dataset to date that connects personality and emotional states via physiological responses. |
| Dataset Splits | Yes | 50% of stimuli and corresponding physiological signals and emotions of each subject are randomly selected as the training set and the rest constitute the testing set. The parameters of the baselines are selected by 10-fold cross validation on the training set. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions methods like SVM, Naive Bayes, and hypergraph learning but does not specify any software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Unless otherwise specified, parameter K in hyperedge generation is set to 10, and regularizer parameters λ = 0.1 and η = 100 are adopted in experiment. |