Life, Death, and Ecstasy: A Musical Ekphrasis on the Paintings of Caravaggio

Description
Life, Death, and Ecstasy is a series of 3 musical works to form a triptych which is an ekphrasis on the works of Caravaggio. This triptych seeks to cover Life, Death, and Ecstasy all in varied and complex methods. The

Life, Death, and Ecstasy is a series of 3 musical works to form a triptych which is an ekphrasis on the works of Caravaggio. This triptych seeks to cover Life, Death, and Ecstasy all in varied and complex methods. The painting that represents life is The Raising of Lazarus, and the corresponding song I wrote separates Lazarus in death and in life; the lyrics represent his thoughts before and after the transition back to the world. The painting that represents death is Judith and Holofernes, and the song is from the perspective of Judith; it shows her plot against holofernes and represents the cruelty of quick death, no matter how justified. The painting that represents ecstasy is Mary Magdalene in Ecstasy, and the song captures the sexual nature of representations of religious experiences, specifically the controversy of Mary Magdalene’s character. All of the lyrics to the songs in this project are sung from the perspective of what I believe to be the main character of the painting, and all of the musical choices of the instrumentation represent aspects of the paintings which I will be discussing within this paper. With this project, I hope to demonstrate that art transcends not only boundaries of form, but also generations. I hope my music enhances the listeners’ perspectives and interpretations of the paintings.
Date Created
2024-05
Agent

Lettuce Nutritional Deficiency and Disease Identification with ResNet-50 and CapsNet

Description
Manually determining the health of a plant requires time and expertise from a human. Automating this process utilizing machine learning could provide significant benefits to the agricultural field. The detection and classification of health defects in crops by analyzing visual

Manually determining the health of a plant requires time and expertise from a human. Automating this process utilizing machine learning could provide significant benefits to the agricultural field. The detection and classification of health defects in crops by analyzing visual data using computer vision tools can accomplish this. In this paper, the task is completed using two different types of existing machine learning algorithms, ResNet50 and CapsNet, which take images of crops as input and return a classification that denotes the health defect the crop suffers from. Specifically, the models analyze the images to determine if a nutritional deficiency or disease is present and, if so, identify it. The purpose of this project is to apply the proven deep learning architecture, ResNet50, to the data, which serves as a baseline for comparison of performance with the less researched architecture, CapsNet. This comparison highlights differences in the performance of the two architectures when applied to a complex dataset with a multitude of classes. This report details the data pipeline process, including dataset collection and validation, as well as preprocessing and application to the model. Additionally, methods of improving the accuracy of the models are recorded and analyzed to provide further insights into the comparison of the different architectures. The ResNet-50 model achieved an accuracy of 100% after being trained on the nutritional deficiency dataset. It achieved an accuracy of 88.5% on the disease dataset. The CapsNet model achieved an accuracy of 90% on the nutritional deficiency dataset but only 70% on the disease dataset. In comparing the performance of the two models, the ResNet model outperformed the other; however, the CapsNet model shows promise for future implementations. With larger, more complete datasets as well as improvements to the design of capsule networks, they will likely provide exceptional performance for complex image classification tasks.
Date Created
2024-05
Agent

divinedecisions_gameplay.mp4

Date Created
2024-05
Agent

Divine Decisions: A Study of Agency in Video Game Narrative

Description
This paper describes the Divine Decisions project, an experiment on the synthesis of physical and digital design techniques in the field of video game design and development. The project is inspired by unique types of video game input devices like

This paper describes the Divine Decisions project, an experiment on the synthesis of physical and digital design techniques in the field of video game design and development. The project is inspired by unique types of video game input devices like the Nintendo R.O.B, the digital twin technologies utilized in Activision Blizzard’s Skylanders series, and the narrative themes present in titles such as Undertale and Fear and Hunger, with the ultimate goal of creating a uniquely immersive experience that enhances the user’s sense of agency and responsibility for their choices. Divine Decisions examines how the use of physical, interactive elements can affect how an audience experiences a digital narrative and how they choose to interact with it.
Date Created
2024-05
Agent

Improving Enemy Intelligence in 3D Games

Description
My thesis focuses on improving enemy intelligence in 3D games. The development of reactive yet unpredictable agents is vital to the creation of interactive and immersive gameplay. I attempted to achieve this through two approaches: using a machine-learning model and integrating

My thesis focuses on improving enemy intelligence in 3D games. The development of reactive yet unpredictable agents is vital to the creation of interactive and immersive gameplay. I attempted to achieve this through two approaches: using a machine-learning model and integrating fuzzy logic to simulate enemy personalities. The machine learning model I developed aimed to create adaptive agents that learn from their environment, while the fuzzy logic state machine adds variance to enemy behaviors, creating more challenging opponents. My machine-learning approach involved the implementation of a Python-based machine-learning package within the Unity game engine to simulate the learning of various games. Fuzzy logic was integrated by giving each instance of an enemy a personality matrix that governs the flow of their state machine. I encountered a variety of problems when trying to train my machine-learning model but was still able to learn about the potential applications. My work with fuzzy logic showed great promise in creating a better gaming experience for players through more dynamic enemies. I conclude by emphasizing the potential of these approaches to enhance the gaming experience and the importance of continued research in improving enemy intelligence.
Date Created
2024-05
Agent

Founder's Lab - Nova Six

Description
This thesis paper outlines Nova-six company, an honors thesis project conducted through the Founder’s Lab program at Arizona State University. Nova-six is a multimedia company centered around the space industry. Nova-six’s mission is to ignite Generation Z’s passion for space

This thesis paper outlines Nova-six company, an honors thesis project conducted through the Founder’s Lab program at Arizona State University. Nova-six is a multimedia company centered around the space industry. Nova-six’s mission is to ignite Generation Z’s passion for space by reimagining it through the lens of contemporary culture. To this end, Nova-six has developed its brand to be a space-themed streetwear, pop art, and entertainment venture. Through its innovative approach, Nova-six aims to transform the space industry's narrative, making it a central part of today's cultural conversations and inspiring a new generation to explore the final frontier.
Date Created
2024-05
Agent

Using Machine Learning Algorithms for Privacy

Description
Machine learning algorithms have a wide variety of applications and use cases. They are robust in the sense that they can continue to learn and improve long after they have been deployed without much programmer supervision. One key

Machine learning algorithms have a wide variety of applications and use cases. They are robust in the sense that they can continue to learn and improve long after they have been deployed without much programmer supervision. One key area that machine learning has been used for is in the detection and classification of objects in images and videos. This so-called computer vision has typically been used by companies to extract user information from the images and videos that they post. Meta (formerly known as Facebook) had been using such algorithms to automatically tag users in pictures that were uploaded to the Facebook website up until November 2021 [1]. Although these algorithms have been used to exploit user’s privacy, they can also be used to help ensure this privacy. For this creative project, I developed a machine learning model that could detect faces in a given picture and identify the area of the picture that these faces took up. Training a model from scratch can take millions of images of data and hundreds of hours on powerful GPUs. Since I didn’t have access to those resources, I began with a pre-trained model known as VGG16 by Karen Simonyan & Andrew Zisserman. From there, I took 90 pictures of myself and annotated where in the image my face was located. Since 90 pictures wouldn’t be enough data for this algorithm, I used an image augmentation algorithm to randomly crop, flip, change brightness, change gamma, and recolor the images to expand the dataset. In total, I used 5400 images to train the algorithm. The machine learning model had a loss value that hovered around 0.1 thanks to the VGG16 model. It was able to accurately detect my face and also adapt whenever I moved my face horizontally and vertically across a camera. However, the model struggled to draw a bounding box whenever I moved my face forward or backward in the camera shot.
Date Created
2024-05
Agent