61 citations · 80 across the 7 of their papers we have counts for
7 papers
Detection of Hate and Threat in Digital Forensics: A Case-Driven Multimodal Approach
Ponkoj Chandra Shill
Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts may contain explicit or impli…
Undergraduate Robotics Education with General Instructors using a Student-Centered Personalized Learning Framework
Rui Wu, David J Feil-Seifer, Ponkoj C Shill +7
Recent advancements in robotics, including applications like self-driving cars, unmanned systems, and medical robots, have had a significant impact on the job market. On one hand,…
WIP: A Unit Testing Framework for Self-Guided Personalized Online Robotics Learning
Ponkoj Chandra Shill, David Feil-Seifer, Jiullian-Lee Vargas Ruiz +1
Our ongoing development and deployment of an online robotics education platform highlighted a gap in providing an interactive, feedback-rich learning environment essential for mast…
Human-Robot Interaction and Perceived Irrationality: A Study of Trust Dynamics and Error Acknowledgment
Ponkoj Chandra Shill, Md Azizul Hakim, Shawon Dey
As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study systematically examines trust dynamic…
A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm
Hossein Jamali, Ponkoj Chandra Shill, David Feil-Seifer +2
Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being develope…
WIP: Development of a Student-Centered Personalized Learning Framework to Advance Undergraduate Robotics Education
Ponkoj Chandra Shill, Rui Wu, Hossein Jamali +4
This paper presents a work-in-progress on a learn-ing system that will provide robotics students with a personalized learning environment. This addresses both the scarcity of skill…
Multi-modal Hate Speech Detection using Machine Learning
Fariha Tahosin Boishakhi, Ponkoj Chandra Shill, Md. Golam Rabiul Alam
With the continuous growth of internet users and media content, it is very hard to track down hateful speech in audio and video. Converting video or audio into text does not detect…