The UTSA Journal of Undergraduate Research & Scholarly Work
Permanent URI for this communityhttps://hdl.handle.net/20.500.12588/5
The University of Texas at San Antonio Journal of Undergraduate Research and Scholarly Works (JURSW) is a peer-reviewed academic journal published by the Office of the Vice President for Research. The JURSW publishes scholarly inquiry from a wide variety of disciplines and from interdisciplinary and multidisciplinary frameworks.
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Browsing The UTSA Journal of Undergraduate Research & Scholarly Work by Subject "Artificial Intelligence"
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Item Quantum Computation, Quantum Algorithms and Implications on Data Science(UTSA Office of Undergraduate Research, 2020-12) Kim, Nathan; Garcia, Jeremy; Han, DavidQuantum computing is a new revolutionary computing paradigm, first theorized in 1981. It is based on quantum physics and quantum mechanics, which are fundamentally stochastic in nature with inherent randomness and uncertainty. The power of quantum computing relies on three properties of a quantum bit: superposition, entanglement, and interference. Quantum algorithms are described by the quantum circuits, and they are expected to solve decision problems, functional problems, oracular problems, sampling tasks and optimization problems so much faster than the classical silicon-based computers. They are expected to have a tremendous impact on the current Big Data technology, machine learning and artificial intelligence. Despite the theoretical and physical advancements, there are still several technological barriers for successful applications of quantum computation. In this work, we review the current state of quantum computation and quantum algorithms, and discuss their implications on the practice of Data Science in the near future. There is no doubt that quantum computing will accelerate the process of scientific discoveries and industrial advancements, having a transformative impact on our society.Item Statistical Perspectives in Teaching Deep Learning from Fundamentals to Applications(UTSA Office of Undergraduate Research, 2020-12) Kim, Nathan; Han, DavidThe use of Artificial Intelligence, machine learning and deep learning have gained a lot of attention and become increasingly popular in many areas of application. Historically machine learning and theory had strong connections to statistics; however, the current deep learning context is mostly in computer science perspectives and lacks statistical perspectives. In this work, we address this research gap and discuss how to teach deep learning to the next generation of statisticians. We first describe some backgrounds and how to get motivated. We discuss different terminologies in computer science and statistics, and how deep learning procedures work without getting into mathematics. In response to a question regarding what to teach, we address organizing deep learning contents and focus on the statistician’s view; form basic statistical understandings of the neural networks to the latest hot topics on uncertainty quantifications for prediction of deep learning, which has been studied in the Bayesian frameworks. Further, we discuss how to choose computational environments and help develop programming skills for the students. We also discuss how to develop homework incorporating the idea of experimental design. Finally, we discuss how to expose students to the domain knowledge and help to build multi-discipline collaborations.Item Voice Activation Control with Digital Assistant for Humanoid Robot Torso(Office of the Vice President for Research, 2018) Wallace, Conor; Erol, Berat A.Digital Voice Assistants are an emerging technology due to improvements on mobile communication and computing technologies and are becoming more popular in recent years due to growing marketing strategies on new smart home devices by cloud service providers. Most of the applications on smart environments and assistive robotics are relying on these digital assistants, or Internet of Things (IoT) devices, based on the nature of the system, namely voice activation and control. Smart home assistants, such as Amazon Echo with Alexa and Google Home are the most well-known examples on this manner, relying on the base of processing verbal requests by looking for key words in the conversation as well as the structure of a natural language source of communication. Later, these key words are used to trigger the predefined skills for fulfilling the users’ request. A simple structure of the process works both ways between the user and the device by providing a verbal request/order and verbal feedback. In this project, we have built a system, a humanoid robot torso and an IoT device, to control and simulate the process of an interactive functioning assistive robot and tried to improve the effects of Human Robot Interactions.