COVID-19 is a global pandemic afflicting our so ciety. We propose covloT, a novel Arduino-based automatic hand sanitizer dispenser, integrated with an oximeter, a heart rate monitor, a non-contact body temperature sensor, and voice assistant feedback. This system can be deployed as an end-to end COVID patient monitoring system and also for automated sanitization. The system was tested on 100 people to evaluate its performance. The mean absolute error and root mean square error values were found to be 0. 79 and 1.03 for the oximeter, 1.22 and 0.70 for the heart rate monitor and 1.07 and 1.28 for the body temperature monitor, respectively, compared to the industry-standard devices. These low error values indicate the high accuracy of our proposed system. We believe this is the first low-cost integrated patient monitoring and sanitization system with vocal feedback, to increase accessibility and ultimately helps combat the virus.
This paper describes the systems built by our team for the "Emotion Analysis in Tamil" shared task at the Second Workshop on Speech and Language Technologies for Dravidian Languages at ACL 2022. There were two multi-class classification sub-tasks as a part of this shared task. The dataset for sub-task A contained 11 types of emotions while sub-task B was more fine-grained with 31 emotions. We fine-tuned an XLM-RoBERTa and DeBERTA base model for each sub-task. For sub-task A, the XLM-RoBERTa model achieved an accuracy of 0.46 and the DeBERTa model achieved an accuracy of 0.45. We had the best classification performance out of 11 teams for sub-task A. For sub-task B, the XLM-RoBERTa model's accuracy was 0.33 and the DeBERTa model had an accuracy of 0.26. We ranked 2 nd out of 7 teams for sub-task B.
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