Artificial Intelligence and Robotics Commons^™

Requiring Students To Integrate Chatgptinto Course Assignments, Mark Small, Venera Balidemaj

Clemson Teaching Excellence Conference 2024: Teaching in the Age of AI

No abstract provided.

Toxic Comment Classification Project, Brandon Solon

Symposium of Student Scholars

The digital landscape has blossomed thanks to the surge of online platforms, boosting the variety and volume of user-created content. But it's not without its shadows; cyberbullying and hate speech have also proliferated, making web spaces less safe. At our project centerstage, we work on creating a machine learning model skilled at spotting toxic comments with precision - this way contributing towards an internet society free from fear or discomfort. We put well-documented datasets to good use along with careful preprocessing maneuvers while trialing diverse machina-learning protocols as part of constructing solid classification architecture for usages beyond current limitations within …

Artificial Intelligence History, And Libraries: History And Legacy Of Library Contributions To Machine Learning, Wilhelmina Randtke

Library Faculty Presentations

Machine learning seems to be newly everywhere. It's not new, so much as faster processing makes it newly useful. Imagine an automated cataloging program that takes 300 years to run, versus one that takes a week to run. Increased processing speed is a substantive change. This presentation overviews the history of libraries and artificial intelligence. First, teasing out past applications of machine learning in libraries. High quality results and concrete applications of artificial intelligence in libraries have been explored and published for decades. Over time, faster processing allows use at scale. Second, how library and metadata work contributes to machine …

Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed …

Autonomous Watercraft Simulation And Programming, Nicholas J. Savino

Student Scholar Showcase

Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades, advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats, and an increasing popularity of self-driving cars. We predicted the motion of an autonomous vehicle using simulations in Python. The simulation models the motion of a small scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as …

Automatic Program Rewriting In Non-Ground Answer Set Programs, Yuliya Lierler

Yuliya Lierler

No abstract provided.

Forecasting Smart Meter Energy Usage Using Distributed Systems And Machine Learning, Feiran Ji, Chris Dong, Lingzhi Du, Zizhen Song, Yuedi Zheng, Paul Intrevado

Creative Activity and Research Day - CARD

In this research, we explore the technical and computational merits of a machine learning algorithm on a large data set, employing distributed systems. Using 167 million(10 GB) energy consumption observations collected by smart meters from residential consumers in London, England, we predict future residential energy consumption using a Random Forest machine learning algorithm. Distributed systems such as AWS S3 and EMR, MongoDB and Apache Spark are used. Computational times and predictive accuracy are evaluated. We conclude that there are significant computational advantages to using distributed systems when applying machine learning algorithms on large-scale data. We also observe that distributed systems …

An Optimization Approach To Automate The Generation Of Radiotherapy Treatment Plans, Qian Li

Creative Activity and Research Day - CARD

The main goal of radiotherapy is to deliver a specified dose of radiation directly to the tumor while minimizing radiation damage to healthy tissues. Currently, the treatment plan is being developed by professional planners using a commercial treatment planning system. In this treatment planning system, the planner modifies the objectives and weights of the objectives until an ideal combination of doses is achieved. This arbitrary process can cost a few hours or a day to finish. My research aims to automate the generation of the plans by implementing an optimization algorithm on top of the treatment planning system using gradient …

Topical Analysis Of The Enron Emails Using Graph Theory, Casey Kalinowski

Student Scholar Showcase

The Enron Scandal of the early 2000s shook the financial world. The subsequent investigation of the Enron Corporation resulted in the arrests of many top-level executives, but are these employees the only ones responsible for the wide scale fraud in the company? A topical analysis of a social network of over 150 employees of the Enron Corporation using Graph Theory could result in new findings or prove that the investigators were correct in their original findings. The research is a retrospective analysis of a corpus of over 500,000 emails from more than 150 employees and top-level executives of the Enron …

Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra

UNO Student Research and Creative Activity Fair

Modular Self-reconfigurable Robots (MSRs) are robots that can adapt their shape and mobility while performing their operations. We are developing an MSR called MARIO (Modular Robots for Assistance in Robust and Intelligent Operations) to assist patients with spinal cord injury in performing daily living tasks. In this research, we are investigating computational techniques that will enable MARIO to autonomously adapt its shape while performing an assistive task, and, while remaining aware of the human user’s satisfaction in receiving assistance from MARIO. We are developing semi-autonomous decision making techniques within a computational framework called shared autonomy that will adapt MARIO’s movements …

A Modular Robotic System For Assessment And Exercise Of Human Movement, Mohan Sai Ambati

UNO Student Research and Creative Activity Fair

This project targets the problem of developing a wearable modular robotic system, for assessing human movement and providing different types of exercises for the user. The system attempts to provide not only a variety of exercises (concentric, eccentric, assisted and resisted), but also to assess the change in variability of the movement as the subject shows functional improvement. The system will not only be useful for patients with sensorimotor problem such as stroke, Parkinson’s, cerebral palsy, but also for special populations such as astronauts who spend long periods of time in space and experience muscle atrophy. In this work, a …

The Critical Role Of Cubesat Spacecraft In A Multi-Tier Mission For Mars Exploration, Jeremy Straub

Jeremy Straub

A multi-tier architecture is under development (with similar craft heterogeneity to Fink's work on ‘tier scalable’ missions) which will facilitate autonomous local control of multiple heterogeneous craft. This mission architecture has been developed with a Mars mission in mind and has included CubeSats in a variety of critical mission roles.

Two concepts will be presented: the addition of CubeSats to a larger-scale multi-tier mission, where the CubeSats serve a supporting role and a mission driven by CubeSat orbital capabilities. In the first, CubeSats are utilized to augment the area of spatial coverage that can be obtained and the temporal coverage …

Forced Displacement In Colombia, Fernando Estrada

Fernando Estrada

No abstract provided.

Imagining Emergent Metadata, Realizing The Emergent Web, Jason A. Bengtson

Jason A Bengtson

Current metadata schemas are largely analog technology grafted onto the digital format. They have three inherent limitations that need to be transcended: they generate a static product which must be changed manually, they revolve around the needs of human, rather than mechanistic agents, and they are limited by the imagination and organizational capabilities of human agency. The author argues that to meet future challenges metadata will have to take a more flexible, adaptive form that centers on the needs of the machine in searching, interpretation and organization until the information it proxies enters into the human sphere. The author further …

The Art Of Redirection: Putting Mobile Devices Where You Want Them, Jason A. Bengtson

Jason A Bengtson

Mobile technology has exploded, with many libraries experiencing a surge in access to their resources through mobile devices. In response, many institutions have created or are creating mobile sites designed to accommodate themselves to the unique strictures of these devices. One hurdle faced by these organizations, however, is getting mobile users to those sites. One solution is mobile redirect scripts, which automatically redirect mobile users from a regular page to a mobile page. These scripts come in various forms and present unique challenges to libraries. How are these scripts created? What triggers can or should be used to activate them? …

Imagining Emergent Metadata, Realizing The Emergent Web, Jason A. Bengtson

Jason A Bengtson

Current metadata schemas are largely analog technology grafted onto the digital format. They have three inherent limitations that need to be transcended: they generate a static product which must be changed manually, they revolve around the needs of human, rather than mechanistic agents, and they are limited by the imagination and organizational capabilities of human agency. The author argues that to meet future challenges metadata will have to take a more flexible, adaptive form that centers on the needs of the machine in searching, interpretation and organization until the information it proxies enters into the human sphere. The author further …