Colloquium Archive

Short presentations of research carried out by Sonoma State Computer Science Students.

• Carson Whitt
  Title: Human Audio Emotional Classification
  Research Mentor: Dr. Nina Marhamati
  Abstract: Natural language processing in the realm of computer science has many facets, with one of the most difficult being human vocal classification. Many techniques have been developed to address challenges such as voice recognition and language classification, but one area that has been growing with the rise of deep learning is classification of human emotion. Many techniques in the pursuit of extracting and abstracting useful data from human audio have been addressed in past research papers. The goal of our research is to use those techniques such as spectrogram analysis and vocal embedding to design and complete a working model for taking raw human audio and classifying the existing emotion using Robert Plutchicks’ wheel of emotions as a reference. For audio data we have been using the RAVDESS database, which contains over 2000 samples and eight emotional categories all using a Northern American accent. We use a basic deep learning model to train and classify based on vocal embeddings extracted from YAMNet. Combined with that we have used multiple techniques and augmentations to overcome the lack of audio data readily available. Classifying to three basic classes (neutral, pleasant, unpleasant) has given poor accuracy and convergence of the model but overall has made good strides towards a working solution to the emotional classification challenge.
   
• Ari Encarnacion
  Title: Machine Learning in Geology: A Pipeline for Automatic Classification of Shear-Sense Indicating Clasts
  Research Mentor: Dr. Gurman Gill
  Abstract: We are constructing a machine learning (ML) powered, automated pipeline for classifications and detections of shear-sense indicating clasts in photomicrographs. Classifications include Sinistral (Counter-Clockwise aka CCW) and Dextral (Clockwise aka CW) shearing. Detections refer to the location of clasts in photomicrographs. Current efforts involve improving final classification results, gathering more data, and experimentation with different combinations of object detectors and classifiers. This presentation focuses on the current pipeline structure and how detections could improve classification results. Future work includes pipeline assembly and providing user access to the model via an app. This app will employ our pipeline to provide automatic classification & detections to the user. This will provide users with vital data, and feedback on app-generated results will benefit our pipeline.
   
• Brandon Fong
  Title: Elliptic Curve Cryptography
  Research Mentor: Dr. Mark Gondree
  Abstract: I will summarize select topics covered in a recent directed study course on the topic of modern cryptography. In particular, I will focus on some well-known cryptographic schemes and the practical consideration of key lengths for those systems.   Suggestions on key lengths are based on best known attacks against cryptographic systems. Some problems yield new schemes that outperform current schemes.  In this presentation, I discuss Elliptic Curve (EC) cryptosystems and compare these against the well-known Rivest-Shamir-Adleman (RSA) cryptosystem.