Understanding Social Interpersonal Interaction via Synchronization Templates of Facial Events
Authors: Rui Li, Jared Curhan, Mohammed Hoque
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our model on two different dyadic conversations of negotiation and job-interview. Based on the discovered facial event coordination, we are able to predict their conversation outcomes with higher accuracy than HMMs and GMMs. |
| Researcher Affiliation | Academia | Rui Li College of Computing & Info. Sciences Rochester Institute of Technology Rochester, New York 14623 Email: rxlics@rit.edu Jared Curhan Sloan School of Management Massachusetts Institute of Technology Cambridge, Boston 02139 Email: curhan@mit.edu Mohammed Ehsan Hoque Department of Computer Science University of Rochester Rochester, New York 14627 Email: mehoque@cs.rochester.edu |
| Pseudocode | No | The paper contains figures depicting the approach and probabilistic graphical model (Figure 1 and Figure 2), but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | No | The paper describes using custom datasets collected from 'Mechanical Turkers' and 'student participants' but does not provide concrete access information (link, DOI, or proper citation for a publicly available dataset). |
| Dataset Splits | Yes | Their cardinality numbers are determined via ļ¬vefold cross-validation. We use cross-validation scheme to recursively assign random 60% conversation videos of the two datasets into the training set and the rest into the testing set for the performance comparison. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud instances) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'Computer Expression Recognition Toolbox (CERT)' and comparison models like 'HMMs and GMMs', but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We specify a noninformative uniform base measure B0 as in Figure 2, and compute the posterior by initializing 4 chains of 10,000 sampling iterations on the training data. |