Physical Sciences and Mathematics Commons^™

Providing Beginners With Interactive Exploration Of Error Messages In Clojure, John Walbran, Elena Machkasova

Undergraduate Research Symposium 2024

Programmers are imperfect, and will often make mistakes when programming and create a program error, for example, attempting to divide by zero. When a computer tries to run a program with an error, the program will halt and present the details of the error to the user in the form of an error message. These error messages are often very jargon-heavy, and are not designed to be palatable to a novice programmer. This creates significant friction for new programmers trying to learn programming languages. This work is a part of an ongoing project (called Babel) led by Elena Machkasova in …

Enhancing Evolutionary Computation: Optimizing Phylogeny-Informed Fitness Estimation Through Strategic Modifications, Chenfei Peng, Nic Mcphee

Undergraduate Research Symposium 2024

In evolutionary computation, programs are developed using evolution's basic principles, such as selection, mutation, and recombination, to iteratively improve problem solutions towards optimal outcomes in a reasonable amount of time. To save time and be more efficient, we are currently exploring a modified version of phylogeny-informed fitness estimation. The original version evaluates each individual program on a subset of the training cases and estimates the performance everywhere else according to its parent's performance. Our approach involves comprehensive evaluation of promising programs across all training cases, increasing computational investment where the sub-sampled results indicated potential gains. This method led to our …

Unmc Ai Task Force Report, Emily Glenn, Rachel Lookadoo, Unmc Ai Task Force

Reports: University of Nebraska Medical Center

In July 2023, University of Nebraska Medical Center and Nebraska Medicine leadership charged a task force with investigating facets of artificial intelligence (AI) in an academic health center setting. What must we know, do and plan for regarding generative artificial intelligence in the domains of enhancing education, research, clinical care, business functions and in combating misinformation/disinformation? Task force members were allocated into five subcommittees to investigate key points to inform strategic planning—Enhance Learning, Enhance Research, Enhance Clinical Care, Enhance Business Function and Combat Dis-/Mis-Information and Bias. This work was aligned with the UNMC Strategic Planning process as a “big rock” …

Generative Ai-Based Non-Person Character (Npc) For Navigating Virtual Worlds, Ananth Ramaseri-Chandra

Computer Science Posters and Presentations

An innovative approach to virtual world interactions through generative AI-based Non-person Characters (NPCs). These AI-driven NPCs significantly advance over traditional, scripted characters by providing more realistic, adaptive, and dynamic interactions in various virtual environments. The work details the development process of these NPCs, from algorithm design to data integration and iterative refinement, ensuring their seamless integration into game environments. Additionally, the poster explores the wide-ranging applications of these AI NPCs, including enhancing gaming experiences, offering realistic training environments, and facilitating personalized virtual learning experiences. This research marks a substantial leap in virtual interaction, pushing the boundaries of immersion and realism …

Big Data Analysis And Programming For Engineers, Josh Steimel

Pacific Open Texts

This text serves to cover critical programming, data analysis, statistical analysis, and mathematical skills for engineers. In particular fundamental programming skills are demonstrated using Mathematica specifically the importing of data sets, loop structures, plotting and statistically analyzing data, image analysis, and machine learning. Critical engineering topics such as solid mechanics, vibrations, and engineering problems which require solving ODEs and PDEs are covered.

Investigating The Use Of Conversational Agents As Accountable Buddies To Support Health And Lifestyle Change, Ekaterina Uetova, Dympna O'Sullivan, Lucy Hederman, Robert J. Ross

Academic Posters Collection

The poster focuses on the role of conversational agents in promoting health and well-being. Results of the literature review indicate that negative emotions can hinder individuals from taking necessary actions related to their health. The study concludes that understanding and addressing emotional barriers is essential to facilitating early access to health services and improving well-being. The poster outlines plans to investigate motivation strategies, develop a prototype conversational agent based on user study insights and chat log data, and incorporate emotion regulation to effectively manage users' emotional experiences.

Attention-Based Gender-Stereotype Detection, Manuela Nayantara, Sarah Jane Delany

Academic Posters Collection

Gender stereotypes are perceptions about the typical physical, emotional, and social characteristics of individuals. Unlike gender bias which can result in the systematic and unfair treatment of individuals based on their gender, gender stereotypes do not always perpetuate a negative impact. Hence, there is no solid definition that frames what is considered a gender-stereotype in text. In addition, there is also a lack of labelled gender-stereotype datasets. This has led to most of the work in literature being about gender bias and not gender stereotypes. Therefore, in our research, we frame a clear definition of what constitutes a gender-stereotype in …

The Proof Is In The Pudding – Using Perceived Stress To Measure Short-Term Impact In Initiatives To Enhance Gender Balance In Computing Education, Alina Berry, Sarah Jane Delany

Academic Posters Collection

The problem of gender imbalance in computing higher education has forced academics and professionals to implement a wide range of initiatives. Many initiatives use recruitment or retention numbers as their most obvious evidence of impact. This type of evidence of impact is, however, more resource heavy to obtain, as well as often requires a longitudinal approach. There are many shorter term initiatives that use other ways to measure their success.

First, this poster presents with a review of existing evaluation measures in interventions to recruit and retain women in computing education across the board. Three main groups of evaluation come …

A Mode Sum Regularization Prescription In Quantum Field Theory In Curved Spacetimes In Higher Dimensions And For Charged Scalar Fields, Eoin Scanlon

Academic Posters Collection

Semi-classical gravity combines classical treatment of the gravitational field with quantum mechanical treatment of matter fields. A significant challenge however is the divergence contained within the stress-energy tensor when solving the semi-classical Einstein equations. This work extends to higher dimensions an extremely efficient method for renormalizing the stress-energy tensor of a quantum scalar field in spherically-symmetric black hole spacetimes, thereby removing the divergences. The method applies to a scalar field with arbitrary field parameters. The utility of the method is demonstrated by computing the renormalized stress-energy tensor for a scalar field in the Schwarzschild black hole spacetime for odd dimensions.

Framework For Trustworthy Ai In The Health Sector, Mykhailo Danilevskyi

Academic Posters Collection

The European Commission defines that Trustworthy AI should be lawful, ethical and robust. The ethical component and its technical methods are the main focus of the research. According to this, the initial research goal is to create a methodology for evaluating datasets for ML modeling using ethical principles in the healthcare domain. Ethical risk assessment will help to ensure compliance with principles such as privacy, fairness, safety and transparency which are especially important for the Health Care sector. At the same time, risks must be evaluated with respect to AI model performance and possible scenarios of risk mitigation. Ethical risk …

Spoken Language To Irish Sign Language Machine Translation: A Linguistically Informed Approach, Jesus Aguilar Lopez, Irene Murtagh, Sheila Castilho

Academic Posters Collection

Many deaf and hard-of-hearing individuals rely on sign language (SL) on a daily basis as a preferred language. Although nowadays there are significant advances on spoken language research, current approaches are often neither linguistically motivated nor tailored to the unique features of SLs. Further research and development are necessary to enhance Sign Language Machine Translation (SLMT) and bring it to a similar level as spoken language MT. This research will endeavour to improve the accuracy and efficiency of SLMT systems, making them more accessible to the Deaf community and empowering deaf and hard of hearing individuals to communicate more effectively …

The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá

Academic Posters Collection

Recent developments, guidelines, and acts by the European Commission have started to frame policy for AI and related areas such as ML and data, not only for the broader community, but in the context of education specifically. This poster presents a succinct overview of these developments. Specifically, we look to bring together all publications that might impact the teaching of AI (for example, teacher expectations in the coming years around AI competencies) and publications that affect the use of AI in the classroom. We mean using tools and systems that incorporate both ‘Good Old Fashioned’ AI and those that can …

Unmasking Deception In Vanets: A Decentralized Approach To Verifying Truth In Motion, Susan Zehra, Syed R. Rizvi, Steven Olariu

College of Sciences Posters

VANET, which stands for "Vehicular Ad Hoc Network," is a wireless network that allows vehicles to communicate with each other and with infrastructure, such as Roadside Units (RSUs), with the aim of enhancing road safety and improving the overall driving experience through real-time exchange of information and data. VANET has various applications, including traffic management, road safety alerts, and navigation. However, the security of VANET can be compromised if a malicious user alters the content of messages transmitted, which can harm both individual vehicles and the overall trust in VANET technology. Ensuring the correctness of messages is crucial for the …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

College of Sciences Posters

To identify robots and human users in web archives, we conducted a study using the access logs from the Internet Archive’s (IA) Wayback Machine in 2012 (IA2012), 2015 (IA2015), and 2019 (IA2019), and the Portuguese Web Archive (PT) in 2019 (PT2019). We identified user sessions in the access logs and classified them as human or robot based on their browsing behavior. In 2013, AlNoamany et al. [1] studied the user access patterns using IA access logs from 2012. They established four web archive user access patterns: single-page access (Dip), access to the same page at multiple archive times (Dive), access …

Adhd Prediction Through Analysis Of Eye Movements With Graph Convolution Network, Gavindya Jayawardena, Sampath Jayarathna, Yi He

College of Sciences Posters

Processing speech with background noise requires appropriate parsing of the distorted auditory signal, fundamental language abilities as well as higher signal-to-noise ratio. Adolescents with ADHD have difficulty processing speech with background noise due to reduced inhibitory control and working memory capacity. In this study we utilize Audiovisual Speech-In-Noise performance and eye-tracking measures of young adults with ADHD compared to age-matched controls, and generate graphs for ADHD evaluation using the eye-tracking data. We form graphs utilizing the eight eye-tracking features (fixation count, average, total, and standard deviation of fixation duration, max and min saccade peak velocity, min, average, and standard deviation …

Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Future particle accelerators will exceed by far the current data size (10¹⁵) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …

Ml-Based Surrogates And Emulators, Tareq Alghamdi, Yaohang Li, Nobuo Sato

College of Sciences Posters

No abstract provided.

A Novel Parking Management In Smart City Vehicular Datacenters, Syed Rizvi, Susan Zehra, Steven Olariu

College of Sciences Posters

Researchers have shown that most vehicles spend the majority of their time parked in parking garages, lots, or driveways. During this time, their computing resources are unused and untapped. This has led to substantial interest in Vehicular Cloud, an area of research in which each vehicle acts as a computation node. The main difference between traditional cloud computing and vehicular cloud computing is the availability of nodes. In traditional clouds, nodes are available 24/7, while in vehicular clouds, nodes (vehicles) are only available while parked in parking lots. This creates a dynamic environment as vehicles enter and exit parking garages …

Analysis Of Ab Initio Protein Structure Prediction Methods, Maytha Alshammari, Jing He

College of Sciences Posters

Protein structure prediction produces atomic models of three-dimensional structure of a protein from its amino acid sequence. Understanding the function mechanism of proteins requires knowledge of three-dimensional structures. When developing new enzymes and drugs, it's essential to understand the structure of the target protein. In this study, we analyze models predicted using two ab initio protein structure prediction methods, trRosetta and Quark. A set of thirty protein chains was used to evaluate the effectiveness of the methods. The thirty chains were collected from Protein Data Bank (June – November, 2020). The length and the relative position of the predicted secondary …

Exploring C++, Alice E. Fischer

Electrical & Computer Engineering and Computer Science Book Series

This book is intended for use by students who hope to deepen their understanding of C++ and learn about advanced features. It is also useful for C or Java programmers who want to learn C++ and OO style . . . fast. It assumes that the reader knows basic programming including types, type-matching rules, control structures, functions, arrays, pointers, and simple data structures. The material should help you develop a deeper understanding of the implementation of C++, of clean program design and of the features that make C++ a powerful and flexible language.

Using Open-Source To Enhance Teaching And Scholarship, Steven Clontz, Michael Black, Ricky Green, Carlos Montalvo, Rebecca Macdonald, Sean Stalley

CoTL 2023 Panel

The adoption of open-source resources (software, hardware, educational content, and more) that are freely licensed for use, sharing, repurposing, and remixing has grown dramatically in recent years, within both academia and industry. This panel features several faculty and staff who will share and discuss their experiences using open-source solutions to enhance teaching and scholarship (both SoTL and discipline research) at their institutions.

Scalable Quantum Edge Detection Method For D-Nisq Imaging Simulations: Use Cases From Nuclear Physics And Medical Image Computing, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Edge Detection is one of the computationally intensive modules in image analysis. It is used to find important landmarks by identifying a significant change (or “edge”) between pixels and voxels. We present a hybrid Quantum Edge Detection method by improving three aspects of an existing widely referenced implementation, which for our use cases generates incomprehensible results for the type and size of images we are required to process. Our contributions are in the pre- and post-processing (i.e., classical phase) and a quantum edge detection circuit: (1) we use space- filling curves to eliminate image artifacts introduced by the image decomposition, …

Exploring Human Perception While Reading Fake And Real News Articles, Yasasi Abeysinghe, Gavindya Jayawardana, Autumn Woodson, Efe Bozkir, Enkelejda Kasneci, Andrew Duchowski, Sampath Jayarathna

College of Sciences Posters

With the increased spread of misinformation on online platforms and the popularity of AI-generated text, there is a critical need to detect human perception regarding the truthfulness of news. Users’ believability in a news item influences the reading and sharing of that news. Hence, in order to reduce the spread of fake news online, it is important to understand how users' engagement with fake and real news and users' perceived believability impact their behavioral and physiological factors. In this work, we study human eye movements based on the truthfulness of news and their perceived believability. Using the publicly available FakeNewsPerception …

Metaenhance: Metadata Quality Improvement For Electronic Theses And Dissertations, Muntabir H. Choudhury, Lamia Salsabil, Himarsha R. Jayanetti, Jian Wu

College of Sciences Posters

Metadata quality is crucial for digital objects to be discovered through digital library interfaces. Although DL systems have adopted Dublin Core to standardize metadata formats (e.g., ETD-MS v1.1¹), the metadata of digital objects may contain incomplete, inconsistent, and incorrect values [1]. Most existing frameworks to improve metadata quality rely on crowdsourced correction approaches, e.g., [2]. Such methods are usually slow and biased toward documents that are more discoverable by users. Artificial intelligence (AI) based methods can be adopted to overcome this limit by automatically detecting, correcting, and canonicalizing the metadata, featuring quick and unbiased responses to document metadata. …

X-Disetrac: Distributed Eye-Tracking With Extended Realities, Bhanuka Mahanama, Sampath Jayarathna

College of Sciences Posters

Humans use heterogeneous collaboration mediums such as in-person, online, and extended realities for day-to-day activities. Identifying patterns in viewpoints and pupillary responses (a.k.a eye-tracking data) provide informative cues on individual and collective behavior during collaborative tasks. Despite the increasing ubiquity of these different mediums, the aggregation and analysis of eye-tracking data in heterogeneous collaborative environments remain unexplored. Our study proposes X-DisETrac: Extended Distributed Eye Tracking, a versatile framework for eye tracking in heterogeneous environments. Our approach tackles the complexity by establishing a platform-agnostic communication protocol encompassing three data streams to simplify data aggregation and …

Classifying Recaptured Identity Documents Using The Biomedical Meijering And Sato Algorithms, John Magee, Stephen Sheridan, Christina Thorpe

Academic Posters Collection

Recaptured identity documents are a low-cost, high-risk threat to modern eKYC systems. Bad actors can easily manipulate images and print them. Existing solutions typically demand manual review of remotely captured identity documents, this is expensive and does not scale. In 2022, the UK National Crime Agency estimated fraud cost business hundreds of billion pounds per year and document forgery is an area of investigation by Europol.

Evaluation Of Gender-Based Differences In Primary School Maths Education: The Potential Of Digital Games, Maíra Amaral

Academic Posters Collection

Digital Game-Based Learning is shown to be a more effective instructional method than traditional instruction, however less effective than other technology-supported instruction according to Byun and Joung (2018). Regarding gender aspects, according to findings by Mclaren and colleagues in 2022, girls may learn more mathematics from digital learning games than boys. In their study, even reporting greater behavioural and cognitive engagement, boys did not learn more with the game than girls.

Feedback, Learning Outcomes And Mathematics Anxiety In A Digital Game Based Learning Approach In Mathematics Education, André Almo

Academic Posters Collection

Feedback is a crucial part of learning, and an essential element in digital game-based learning approaches, in which digital games - known as 'serious games' - are used to deliver educational content. Feedback features respond to players' actions within the game, providing them with information and guidance, as well as potentially impacting their learning, motivation and engagement. However, these features may be designed differently, since they include various distinct characteristics and dimensions. This work proposes a new taxonomy for feedback features in serious games, with an emphasis in game design aspects, in order to provide clearer descriptions and distinctions of …

Using Machine Learning For Web Accessibility, Tlamelo Makati

Academic Posters Collection

This research will explore the potential of machine learning to enhance web accessibility. Web accessibility is typically defined in terms of Web Accessibility Guidelines (WCAG), which states that everyone should be able to perceive, operate, understand and interpret the web regardless of disability or use of assistive technology. We would like to consult digital accessibility experts through interviews and focus groups to understand the web accessibility auditing and remediation processes in detail, with a focus on web navigation. An important goal of this work is to establish development processes where all stakeholders can leverage machine-learning tools to produce more accessible …

Identifying Gendered Language, Shweta Soundararajan, Sarah Jane Delany

Academic Posters Collection

Gendered language refers to the use of words that indicate the gender of an individual. It can be explicit, where the gender is directly implied by the specific words used (e.g., mother, she, man), or it can be implicit, where societal roles and behaviors convey a person's gender. For example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The presence of gendered language in natural language processing (NLP) systems can reinforce gender stereotypes and bias. Our work introduces an approach to creating gendered language datasets using ChatGPT. These datasets …