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In this paper, we describe our system for the shared task on Fighting the COVID-19 Infodemic in the English Language. Our proposed architecture consists of a multi-output classification model for the seven tasks, with a taskwise multi-head attention layer for inter-task information aggregation. This was built on top of the Bidirectional Encoder Representations obtained from the RoBERTa Transformer. Our team, dunder_mifflin, was able to achieve a mean F1 score of 0.891 on the test data, leading us to the second position on the test-set leaderboard.

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Abstract:The field of robotics is largely unexplored by the underdeveloped and the developing countries. Robotics have been a staple of advanced manufacturing for over half a century. Over the past decade, we have seen increasing levels of investment in autonomous vehicles used for surveillance and security. In domestic side the robots are employed only for detecting the human motion in the area and in capturing the motion and storing it in the database but in the defence side the robots are used for capturing the live motion and sending the live motion to the control room and capturing the person in the field. The proposed system focuses on designing a robot that can used for both defence and domestic systems. In this project embedded platform is used for making the robot, the robot which are we designing captures the live motion and the position of the live human using the camera and sends the live video and the position to the control room via wireless communication technique.

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