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Full-Text Articles in Engineering
Cyberinet: Integrated Semi-Modular Sensors For The Computer-Augmented Clarinet, Matthew Bardin
Cyberinet: Integrated Semi-Modular Sensors For The Computer-Augmented Clarinet, Matthew Bardin
LSU Doctoral Dissertations
The Cyberinet is a new Augmented instrument designed to easily and intuitively provide a method of computer-enhanced performance to the Clarinetist to allow for greater control and expressiveness in a performance. A performer utilizing the Cyberinet is able to seamlessly switch between a traditional performance setting and an augmented one. Towards this, the Cyberinet is a hardware replacement for a portion of a Clarinet containing a variety of sensors embedded within the unit. These sensors collect various real time data motion data of the performer and air fow within the instrument. Additional sensors can be connected to the Cyberinet to …
An Investigation On The Resilience Of Long Short-Term Memory Deep Neural Networks, Christopher Vasquez
An Investigation On The Resilience Of Long Short-Term Memory Deep Neural Networks, Christopher Vasquez
LSU Master's Theses
In a world of continuously advancing technology, the reliance on these technologies continues to increase. Recently, transformer networks [22] have been implemented through various projects such as ChatGPT. These networks are extremely computationally demanding and require cutting-edge hardware to explore. However, with the growing increase and popularity of these neural networks, a question of reliability and resilience comes about, especially as the dependency and research on these networks grow. Given the computational demand of transformer networks, we investigate the resilience of the weights and biases of the predecessor of these networks, i.e. the Long Short-Term (LSTM) neural network, through four …
Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha
Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha
LSU Doctoral Dissertations
Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …