Category: Publications

Carousel by ArtSciLab

The ArtSciLab (A.S.L) Carousel was created by a previous ArtSciLab member, Mahmoud Elkarmalawy. This project is intended to be a multi-modal curation system that showcases works created by members and colleagues of the ArtSciLab. Along with a variety of schools outside of ATEC within the University of Texas at Dallas. Collaborations can range from white papers, artwork, videos, sound, etc. If done well, the lab’s multidisciplinary approach to research projects will be on prominent display. The goal for the carousel is to help collaborators and members of the lab to have a network with others of the same interest.

The ArtSciLab Carousel is a project created by a previous lab member named, Mahmoud Elkarmalawy. The idea for the project is to have art, science, and art/science work rotating within the Edith O’ Donnell building. Eventually, extending out to a variety of areas throughout the campus. There are a group of individuals that organize this project called, The Carousel Collaborators (CACO). Including Taylor Green, Swati Anwesha and Caroline Trotter. Group members will have a chance to create a piece of work in their field of interest, if they would like. After completing their work, an artist’s presentation is allowed to display the completion in the Dean’s lobby. The results of this operation are to exemplify new projects to give onlookers different perspectives on art and science work.

Taylor Green is graphic design artist born and raised in Texas. She has done logo designing, branding art, and editorial design while continuing her studies at the University of Texas at Dallas. Her values within my work are accountability, attention to detail, quality, transparency, collaboration, and continuous improvement. The visuals she create present a significant amount of detail and attention. Designing visuals can create an impact on those that come across the illustrations. The impact that she want to have on the community is positive and informative.

Version 1.0 Theoretical Approach and Initial Methodology

In this paper we begin an attempt to understand and explain the ontological origins,
epistemological structure, and both social and neurological mapping of concepts. Through mapping out
complex networks of concepts, people, and books from small scale local environments to intercontinental links, we attempt to analyze connections in new ways. The aim of this is to gain insight into
how distributed cognitive networks unfold and form, along with finding novel ways of revealing latent or
hidden structure in such networks. In analyzing the network of concepts, people, and books that
constitute the Art-Sci lab at UT Dallas and expanding our scope to an ever-evolving international web of
concepts and people, we seek to reveal any patterns that emerge across levels of analysis within these
distributed cognitive networks.

To explore this in an applied way, the conceptual basis and initial steps
of three different experimental projects will be detailed. Brain of Books (B.o.B.) seeks to find the
conceptual links between books on the shelves in our lab. Further, it aims to find the relevance of these
connections for interlinking people in the lab, authors of titles and those cited within titles. Constellation
Mapper is an online forum for facilitating the formation of a widely distributed network of people and
concepts as a social media platform. Digital Iterative Glossary (D.I.G.) focuses on forming evolving
glossaries of concepts and context-relevant definitions for groups or networks of people (e.g., the ArtSci Lab).

In this paper, we will consider the area of 4E cognitive science as the contextual landscape.
We will lay out this perspective and its relevance and various implications on the current topic and set
of projects. As well, we will detail the reasons for the object of study (books) in the experimental
approach of the B.o.B. project and have a discussion on fractality and spreading activation in
connection to complex networks. Finally, preliminary steps, methods, and results will be detailed, along
with a discussion on the potential steps to come.

A stumble into Text-to-Image Neural Networks

This white paper covers how to get the images you want, how to tell if an image is generated, how to use it in the real world, and ethical and moral issues that arise from this technology.

If you ever wanted to express your ideas really fast, usually you’d make what we call a napkin sketch, something to write down the overall concepts so we can see if the idea is worth pursuing. The technology which he used helps create what he would consider very refined napkin sketches. Here is an AI model called DALL-E 2. It was developed by a company called OpenAI and the model generates images you want from a text or image input. What I have been trying to do is explore the limits as to what the tool can do, and the functionality of this. What are its implications?

One thing he noticed about this is that there are mainly two types of people who generate images. One from the first type would keep his prompts vague and use it to generate new ideas. The second type he call the shopping list prompters. One from this group would have an image in his head and use the AI to create it, listing as much detail as possible.

But how is this useful? He had a friend who he met from this interest get to the New York Times because he submitted one of his generations to an art contest and won. Inspired by this, he teamed up with a friend to make a demo game to show that you could get ideas for game assets with AI-generated art. He also plan on printing out stickers based off of AI generations but as of right now he do not have access to that technology.

Clement Lee likes to make illustrations, design, use artificial intelligence, and type. He is collaborating in the ArtSciLab and is studying in the field of Arts, Humanities, and Technology at UT Dallas.

Know more about how Music Therapy helps patients of Alzheimers/Dementia in faster recovery.

Alzheimers disease is a developing disease during middle or old age that demolishes memory and important mental functions due to the decline of the brain. Alzheimers disease, also known as Dementia, is one of the most common diseases in people over the age of sixty five. It can be caused by a combination of genetics, lifestyle, and environmental factors which can effect the brain over time. The main symptoms of Alzheimers are confusion and memory loss. Many people experience a combination of symptoms that are cognitive, behavioral, mood related, and psychological. Treatment for this disease can involve cognitive-enhancing medication. However, this can only temporarily improve the symptoms. Although, there is no cure for people that have Alzheimers, music has been shown to be a very powerful tool for minimizing symptoms and recalling memories for these patients.

Music therapy has been shown to be one of the most effective ways of helping patients with Alzheimers disease. It is one of the types of active aging programs which are offered to elderly people. Pharmacological treatment for symptoms of this disease requires very high doses of medication. These drugs actually worsen motor function causing a decline in cognitive function. A natural treatment for Alzheimers/Dementia includes music therapy. Music therapy uses music to improve communication, learning, mobility, and other mental and physical functions.

Devi Kasturi graduated from the University of Texas at Dallas in May of 2022 with a Bachelor of Science degree in Psychology and a minor in Vocal Music. She has been passionate about music her entire life and can testify that music has made a major difference in her life. Devi has won several awards for singing both state and district wide. She likes to spend her time volunteering at nursing homes and with hospice patients. Devi thinks music is a calling and a gift that will benefit all of mankind. Science and music are a powerful combination and she hopes to continue her research in this field. Her future plans include starting pharmacy school and she will continue to practice music as a way of life.

Diving deep into the analysis of financial talks on Reddit that made the fluctuation in the stock prices.

The following document by Omkar Ajnadkar shows that the social media has become an important part of digital web life, the effect it has on financial decisions is also increasing. The social media conversations on websites like Twitter, Reddit and Facebook are having an ever-increasing effect on stock prices as well as the way in which companies make decisions. This has made it important to analyze this data to make accurate predictions of stock prices in future. In this paper, we try to analyze financial discussions among users of Reddit by extracting hidden patterns, themes and user characters to predict future actions and consequences on the market.

We explore techniques based on natural language processing to pass conversations through data pipeline along with extracting stock tickers, manual as well as automatic theme extraction and word clouds. We also discuss common text processing techniques which can also be applied to other problems involving text analysis along with correlation model focusing on sentiment analysis as a predictor of stock movement.

Omkar Ajnadkar is a MSCS student at The University of Texas at Dallas in Machine and Deep learning . He has worked in the domain of Data Science, Machine Learning and Full Stack Web Development in various startups. He also likes to research in the domain of Computer Vision and Natural Language Processing and have published papers.

LinkedIn: https://www.linkedin.com/in/omkar-ajnadkar/

Portfolio: https://blackbird71sr.github.io/

Word Cloud – Words our lab members think we “don’t” use in the lab!

Solving an Image with Sudoku 9×9 grid using image-processing techniques and backtracking algorithm

This project aims to assist users during the sudoku’s solving process, which could help users know if they are solving the puzzle correctly. The user needs to input an image of a 9×9 Sudoku grid which the model will use to solve the puzzle. The model is developed in Python using image processing, optical character recognition, and backtracking algorithm. These techniques are used in cutting edge technologies like the autonomous vehicle for parking assistant, detecting lanes enabling security, enabling virtual advertisement in sports, etc.

Akash is a graduate student at The University of Texas at Dallas, and currently, he is pursuing a Master’s in Computer Science with a specialization in Intelligent Systems. His areas of interest can be broadly classified as Computer Vision, Robotics & Software Development. 

Please feel free to connect with Akash over LinkedIn.