UC3M4Safety Database - List of Audiovisual Stimuli
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the most optimal way possible.The emotional labelling list contains the distribution of the participants by gender and age group and the emotional indicators related to each clip' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Affective computing","Audiovisual stimuli","Basic emotions","Gender-based violence","Gender perspective","Machine Learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the most optimal way possible.The emotional labelling list contains the distribution of the participants by gender and age group and the emotional indicators related to each clip' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
EMPATIA-CM (Comprehensive Protection of Gender-based Violence Victims through Multimodal Affective Computing) is a research project that aims to generally understand the reactions of Gender-based Violence Victims to situations of danger, generate mechanisms for automatic detection of these situations and study how to react in a comprehensive, coordinated and effective way to protect them in the most optimal way possible.The emotional labelling list contains the distribution of the participants by gender and age group and the emotional indicators related to each clip
Affective computing; Audiovisual stimuli; Basic emotions; Gender-based violence; Gender perspective; Machine Learning