Authors:
Gabriel Valentim
1
;
João Lima
1
;
Fernando Barbosa
2
;
João Marques
3
and
Fabrício Rubin
4
Affiliations:
1
Centro de Tecnologia, Universidade Federal de Santa Maria, Brazil
;
2
Reitora, Universidade Federal de Santa Maria, Brazil
;
3
Motorola Solutions, São Paulo, Brazil
;
4
Petroleo Brasileiro S.A., Rio de Janeiro, Brazil
Keyword(s):
Moral Harassment Detection, Ubiquitous Computing, Conversational Data Analysis, Artificial Intelligence, Textual Similarity, Natural Language Processing (NLP), Mobile Computing, Workplace Harassment, Pervasive Data Analysis, Ethical AI.
Abstract:
This study presents the development and evaluation of a moral harassment detection system focusing on mobile and pervasive computing, leveraging artificial intelligence, textual similarity analysis, and ubiquitous data generated from recorded audio. Implemented as a mobile application, the system allows users to record audio and identify inappropriate behaviors using models like Mistral AI and Cohere, while integrating a collaborative database that evolves with user contributions. Tests conducted ranged from simple phrases to complex dialogues and colloquial expressions, demonstrating the hybrid approach’s effectiveness in capturing cultural and linguistic nuances. By combining advanced technologies and user participation, the system adaptively identifies moral harassment, enhancing detection accuracy and continuous learning. This work underscores the potential of mobile devices and pervasive systems to monitor daily interactions in real-time, contributing to moral harassment prevent
ion, fostering ethical environments, and advancing the innovative use of ubiquitous data for social well-being.
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