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Ai And Future-Making: Design, Biases, And Human-Plant Interactions, Maliheh Ghajargar

Art Faculty Articles and Research

Design researchers and practitioners are turning to generative AI (genAI) to support activities such as ideation and concept development in pursuit of preferred futures. At the same time, genAI is known to have biases, which prompts questions about how these biases might adversely affect design practices. In the domain of sustainable HCI, with its recent trends in human-nature interactions and more-than-human design, the question can be further refined into whether and how genAI biases might perpetuate anthropocentric biases that these practices are increasingly seeking to confront. In the present research, we conducted three workshops, focusing on genAI for human-plant interactions; …

Bi-Directional Transformers Vs. Word2vec: Discovering Vulnerabilities In Lifted Compiled Code, Gary Mccully, John Hastings, Shengjie Xu, Adam Fortier

Research & Publications

Detecting vulnerabilities within compiled binaries is challenging due to lost high-level code structures and other factors such as architectural dependencies, compilers, and optimization options. To address these obstacles, this research explores vulnerability detection using natural language processing (NLP) embedding techniques with word2vec, BERT, and RoBERTa to learn semantics from intermediate representation (LLVM IR) code. Long short-term memory (LSTM) neural networks were trained on embeddings from encoders created using approximately 48k LLVM functions from the Juliet dataset. This study is pioneering in its comparison of word2vec models with multiple bidirectional transformers (BERT, RoBERTa) embeddings built using LLVM code to train neural …

Immigrant Integration In Akureyri, Iceland: Identifying Ways To Cultivate A ‘Sense Of Belonging,’ From The Perspective Of Foreigners, Eliana Goldstein

Iceland: Climate Change and The Arctic

Iceland has experienced significant demographic changes since the 1990s. As climate change intensifies, raising the global temperature, migration to cooler climates such as Iceland will increase. To minimize ‘othering’ and rhetoric conducive to anti-immigrant environments, Iceland must work on integration issues, especially while opportunities and perceptions of other groups are still manageable. Through a series of interviews and discussions with foreigners in Akureyri and a focus group with employees from the University Centre of the Westfjords, integration was defined as a ‘sense of belonging’ and the ability to communicate with the native majority. To aid integration, this study identified a …

Exploring The Roles Of Genetics, Gender, Nurturing, And Learning Methods In Mathematical Skills Development, Laura Alinani, Jana Shyti

Honors Projects

This research explores the multifaceted factors influencing the development of mathematical competence through a combination of cognitive, psychological, and sociocultural frameworks. The study aims to address how genetic predispositions, nurturing environments, teaching methods, and societal influences interact to shape mathematical abilities. Using insights from genetic research, cognitive psychology, and education theory, we analyze the role of effective teaching methods, such as active knowledge construction and differentiated instruction, in fostering mathematical skills. The study also examines how societal biases, particularly gender stereotypes, impact students' confidence and interest in math. The findings highlight the importance of promoting gender-inclusive practices and early exposure …

Investigating Public Acceptance Of Responses To Public Emergencies: Based On Text Transparency And Empathy Sentiment Analysis, Xuefeng Zhang, Yelin Huang

Journal of Scientific Information Research

[Purpose/significance]Regarding the responses to public emergencies published by official agencies on social media, this study aims to measure information transparency and empathy in the response text, and further to investigate their influence on public acceptance of responses. [Method/process]This study used public emergency responses published on Sina Weibo, a Chinese popular social media, as data source. Through carefully collecting and filtering, we finally acquired 170 public emergency responses released from 2021 to 2023. Furthermore, by using methods of content analysis, manual coding, and natural language processing, we measured information transparency of responses from three aspects: information disclosure, information accuracy, and information …

Festival Of Research Abstracts, Fall 2024, College Of Science And Mathematics, Wright State University

Festival of Research

The collection of abstracts accepted for the Fall 2024 Festival of Research hosted by the Wright State University College of Science and Mathematics.

Making Plans Findable, Accessible, Interoperable, And Reusable With Data Infrastructure: A Search Engine For Constructing, Analyzing, And Visualizing Planning Documents, Lindsay Poirier, Dexter Antonio, Makenna Dettmann, Tiffany Eng, Jennifer Ganata, Sujoy Ghosh, Mirthala Lopez, Ranesh Karma, Asiya Natekal, Catherine Brinkley

Statistical and Data Sciences: Faculty Publications

Local land-use plans help guide future development, but it is often difficult to compare content across jurisdictions, making regional coordination and plan evaluation challenging. This research reviews federal, state, and local data infrastructure guidance for land-use plans and compares such guidance to compliance with a California use-case. Findings indicate a number of obstacles to fostering data sharing and comparative analysis of plans: there is currently no central repository of land-use plans; plans are not uniform in format and are often out of date; many plans are not machine-readable thereby inhibiting text extraction, and planning language varies so greatly that there …

A Survey Of Unikernel Security: Insights And Trends From A Quantitative Analysis, Alex Wollman, John Hastings

Research & Publications

Unikernels, an evolution of LibOSs, are emerging as a virtualization technology to rival those currently used by cloud providers. Unikernels combine the user and kernel space into one ``uni''fied memory space and omit functionality that is not necessary for its application to run, thus drastically reducing the required resources. The removed functionality is significant however, and includes components that have become common security technologies such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and Non-executable bits (NX bits). This raises questions about the security of unikernels. This research presents a quantitative methodology using TF-IDF to analyze the focus …

Self-Adaptive Fine-Grained Multi-Modal Data Augmentation For Semi-Supervised Muti-Modal Coreference Resolution, Li Zheng, Boyu Chen, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Donghong Ji

Research Collection School Of Computing and Information Systems

Coreference resolution, an essential task in natural language processing, is particularly challenging in multi-modal scenarios where data comes in various forms and modalities. Despite advancements, limitations due to scarce labeled data and underleveraged unlabeled data persist. We address these issues with a self-adaptive fine-grained multi-modal data augmentation framework for semi-supervised MCR, focusing on enriching training data from labeled datasets and tapping into the untapped potential of unlabeled data. Regarding the former issue, we first leverage text coreference resolution datasets and diffusion models,to perform fine-grained text-to-image generation with aligned text entities and image bounding boxes. We then introduce a self-adaptive selection …

Drinking Water And Wastewater In Socioeconomically Disadvantaged Communities In New Mexico: The Utilization Of Geographic Information Systems To Identify Communities Most In Need Of Technical Assistance, Alejandra Gaytan Tellez

Water Resources Professional Project Reports

The goal of this study is to identify the most burdened New Mexico community water systems (CWSs) and publicly owned treatment works (POTWs) with National Pollutant Discharge Elimination System (NPDES) permits to be able to provide them with technical assistance to address their issues, such as health-based and monitoring and reporting violations. Additionally, technical assistance can help them access federal or state funding to improve the infrastructure within their systems. The study evaluated multiple factors to determine the systems with the highest level of burden and reported the results as value numbers. The CWSs were evaluated on 10 factors that …

Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu

Research Collection School Of Computing and Information Systems

Despite efforts to mitigate aggressive financial reporting, earnings management remains challenging to parties interested in inhibiting its dysfunctional effects. Using linguistic algorithms to assess CEO agreeableness personality from their unscripted texts in conference calls, we find that it is a determinant that mitigates a firm's real earnings management. Furthermore, such an effect is more pronounced when firms confront intensive market competition and financial distress and have weaker managerial entrenchment or when CEOs face stronger internal governance. Our findings persist even after we utilize several alternative real earnings management metrics and control other confounding personalities in prior earnings management studies. The …

Collaborative Cross-Modal Fusion With Large Language Model For Recommendation, Zhongzhou Liu, Hao Zhang, Kuicai Dong, Yuan Fang

Research Collection School Of Computing and Information Systems

Despite the success of conventional collaborative filtering (CF) approaches for recommendation systems, they exhibit limitations in leveraging semantic knowledge within the textual attributes of users and items. Recent focus on the application of large language models for recommendation (LLM4Rec) has highlighted their capability for effective semantic knowledge capture. However, these methods often overlook the collaborative signals in user behaviors. Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, …

Joint Weakly Supervised Image Emotion Analysis Based On Interclass Discrimination And Intraclass Correlation, Xinyue Zhang, Zhaoxia Wang, Guitao Cao, Seng-Beng Ho

Research Collection School Of Computing and Information Systems

Regional information-based image emotion analysis has recently garnered significant attention. However, existing methods often focus on identifying region proposals through layered steps or merely rely on visual saliency. These approaches may lead to an underestimation of emotional categories and a lack of comprehensive interclass discrimination perception and emotional intraclass contextual mining. To address these limitations, we propose a novel approach named InterIntraIEA, which combines interclass discrimination and intraclass correlation joint learning capabilities for image emotion analysis. The proposed method not only employs category-specific dictionary learning for class adaptation, but also models intraclass contextual relationships and perceives correlations at the channel …

Temporal Relational Graph Convolutional Network Approach To Financial Performance Prediction, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang

Research Collection School Of Computing and Information Systems

Accurately predicting financial entity performance remains a challenge due to the dynamic nature of financial markets and vast unstructured textual data. Financial knowledge graphs (FKGs) offer a structured representation for tackling this problem by representing complex financial relationships and concepts. However, constructing a comprehensive and accurate financial knowledge graph that captures the temporal dynamics of financial entities is non-trivial. We introduce FintechKG, a comprehensive financial knowledge graph developed through a three-dimensional information extraction process that incorporates commercial entities and temporal dimensions and uses a financial concept taxonomy that ensures financial domain entity and relationship extraction. We propose a temporal and …

Exploring Conversations Between A Practitioner And A Person With Dementia, Kotaro Hara, Rosiana Natalie, Wei Soon Cheong, Jingjing Gu, Qianli Xu

Research Collection School Of Computing and Information Systems

In social service centers, practitioners engage in conversations with clients with dementia to facilitate their daily activities and provide support when they are distressed. However, the nature of the care demands the practitioner’s active engagement, which becomes difficult to deliver as the number of people who need care expands. Researchers have been investigating the efficacy of developing agents that assume conversational tasks to alleviate this work. To contribute to the future design of agents for caregiving, we collected and analyzed ten conversations between clients with mild dementia and practitioners who provide care. Our analyses of turn-taking dynamics and dialogue acts …

Unveiling Similarities In The Code Of Life: A Detailed Exploration Of Dna Sequence Matching Algorithm, Mahmoud Y. Shams, Romany M. Farag, Dalia A. Aldawody, Huda E. Khalid, Ahmed K. Essa, Hazem M. El-Bakry, A. A. Salama

Neutrosophic Systems with Applications

Identifying similar DNA sequences is crucial in various biological research endeavors. This paper delves into the intricate workings of a specific algorithm designed for this purpose. We provide a systematic explanation, exploring how the algorithm handles user input, reads stored DNA sequences, utilizes the Word2Vec model for vector representation, and calculates sequence similarity using diverse metrics like Cosine Similarity and Neutrosophic Distance. Additionally, the paper explores the incorporation of neutrosophic values to account for uncertainty in the comparisons. Finally, we discuss the extraction of results, including matched sequences, similarity scores, and accuracy measures. This in-depth exploration provides a clear understanding …

Nigerian Software Engineer Or American Data Scientist? Github Profile Recruitment Bias In Large Language Models, Takashi Nakano, Kazumasa Shimari, Raula Gaikovina Kula, Christoph Treude, Marc Cheong, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have taken the world by storm, demonstrating their ability not only to automate tedious tasks, but also to show some degree of proficiency in completing software engineering tasks. A key concern with LLMs is their “black-box” nature, which obscures their internal workings and could lead to societal biases in their outputs. In the software engineering context, in this early results paper, we empirically explore how well LLMs can automate recruitment tasks for a geographically diverse software team. We use OpenAI's ChatGPT to conduct an initial set of experiments using GitHub User Profiles from four regions to …

An Approach To Multi-Attribute Decision-Making Based On Single-Valued Neutrosophic Hesitant Fuzzy Aczel-Alsina Aggregation Operator, Raiha Imran, Kifayat Ullah, Zeeshan Ali, Maria Akram

Neutrosophic Systems with Applications

A single-valued Neutrosophic hesitant fuzzy set (SVNHFS) is a combination of a single-valued neutrosophic set (SVNS) and hesitant fuzzy set (HFS) that has been developed to address insufficient, unreliable, and vague environments in which each element has several possible options determined by the truthiness, indeterminacy and falsity value. By considering this, in this paper, we have proposed the Aczel-Alsina aggregation operator (AAAO) for SVNHFS, which is more flexible t-norm and t-conorm than the other and due to the flexible nature of parameters to solve Multi-Attribute decision making (MADM) problems. Further, the score function, accuracy function, and certainty function of SVNHFS …

Evaluation Of Renewable Energy Sources For A Sustainable Future: A Multi-Criteria Decision-Making Approach, Mai Mohamed, Asmaa Elsayed

Neutrosophic Systems with Applications

The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change …

Exploring How Uncertain Labels From Non-Consensus Panels Affect Machine Learning, Amal Almansour

College of Computing and Digital Media Dissertations

A dataset becomes meaningful for analysis when it contains more representative features. Machine and deep learning models rely on annotated instances for training. The annotation process is usually done either by humans (experts or crowdsourcing) or by models. In many cases, the variability between humans (the inter-observer variability) in evaluation leads to uncertainty in the learning process. Due to the lack of reliable labels in large datasets, the inter-observer variability can be quantified with different methods to estimate the ground truth label (i.e., referenced standard label) for model learning.

In health care, with the rise of artificial intelligence in clinical …

Mention Detection In Turkish Coreference Resolution, Şeni̇z Demi̇r, Hani̇fi̇ İbrahi̇m Akdağ

Turkish Journal of Electrical Engineering and Computer Sciences

A crucial step in understanding natural language is detecting mentions that refer to real-world entities in a text and correctly identifying their boundaries. Mention detection is commonly considered a preprocessing step in coreference resolution which is shown to be helpful in several language processing applications such as machine translation and text summarization. Despite recent efforts on Turkish coreference resolution, no standalone neural solution to mention detection has been proposed yet. In this article, we present two models designed for detecting Turkish mentions by using feed-forward neural networks. Both models extract all spans up to a fixed length from input text …

Enhancing Dtc Control Of Im Using Fuzzy Logic And Three-Level Inverter: A Comparative Study, Siham Mencou, Majid Benyakhlef, Elbachir Tazi

Turkish Journal of Electrical Engineering and Computer Sciences

Direct torque control is the most appropriate strategy for induction motor drive systems, due to its considerable ability to reduce the impact of of machine parameter variations, while offering fast dynamic response and simplified control implementation. However, persistent problems associated with high torque ripple and variable switching frequencies prevent its widespread adoption. To overcome these limitations, several techniques have been developed, in particular the use of multi-level inverters and fuzzy logic algorithms. This article proposes an in-depth evaluation of these techniques in a MATALB/Simulink environment, under various operational conditions. The main objective is to provide a detailed performance analysis of …

Business Failure Mode And Effects Analysis: A Risk Based Holistic Strategy To Improve Success Rates Of Small Technology Businesses, Krishnakumar R. Nair

USF Tampa Graduate Theses and Dissertations

Multiple US and global statistics show that small businesses are vital to economic development but fail at a startling rate. Small technology businesses (STBs) are around 35% of this small business segment. The central problem addressed in this research is to reduce their short lives and enable growth and stability. The solution is a significant business management strategy iteratively developed by working with business owners and practitioners. A holistic approach to view the business as a whole to address premature failures is not available, and this research presents a comprehensive approach to solving that problem. The study developed a novel …

Detecting Lgbtq+ Instances Of Cyberbullying, Arslan Bisharat, Manuel Madrigal, Mohammed Abuhamad, Deborah Hall, Yasin Silva

Computer Science: Faculty Publications and Other Works

Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. …

A Preliminary Fuzzy Inference System For Predicting Atmospheric Ozone In An Intermountain Basin, John R. Lawson, Seth N. Lyman

Mathematics and Statistics Faculty Publications

High concentrations of ozone in the Uinta Basin, Utah, can occur after sufficient snowfall and a strong atmospheric anticyclone creates a persistent cold pool that traps emissions from oil and gas operations, where sustained photolysis of the precursors builds ozone to unhealthy concentrations. The basin's winter-ozone system is well understood by domain experts and supported by archives of atmospheric observations. Rules of the system can be formulated in natural language ("sufficient snowfall and high pressure leads to high ozone"), lending itself to analysis with a fuzzy-logic inference system. This method encodes human expertise as machine intelligence in a more prescribed …

Investigating The Intersection Of Ai And Cybercrime: Risks, Trends, And Countermeasures, Sanaika Shetty, Kyung-Shick Choi, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Exposure And Connectedness To Natural Environments: An Examination Of The Measurement Invariance Of The Nature Exposure Scale (Nes) And Connectedness To Nature Scale (Cns) Across 65 Nations, 40 Languages, Gender Identities, And Age Groups, Viren Swami, Mathew P. White, Martin Voracek, Ulrich S. Tran, Toivo Aavik, Hamed Abdollahpour Ranjbar, Sulaiman Olanrewaju Adebayo, Reza Afhami, Oli Ahmed, David A. Frederick

Psychology Faculty Articles and Research

Detachment from nature is contributing to the environmental crisis and reversing this trend requires detailed monitoring and targeted interventions to reconnect people to nature. Most tools measuring nature exposure and attachment were developed in high-income countries and little is known about their robustness across national and linguistic groups. Therefore, we used data from the Body Image in Nature Survey to assess measurement invariance of the Nature Exposure Scale (NES) and the Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups (N = 56,968). While multi-group confirmatory factor analysis (MG-CFA) of the NES supported …

Translating Scientific Latin Texts With Artificial Intelligence: The Works Of Euler And Contemporaries, Sylvio R. Bistafa

Euleriana

The major hindrance in the study of earlier scientific literature is the availability of Latin translations into modern languages. This is particularly true for the works of Euler who authored about 850 manuscripts and wrote a thousand letters and received back almost two thousand more. The translation of many of these manuscripts, books and letters have been published in various sources over the last two centuries, but many more have not yet appeared. Fortunately, nowadays, artificial intelligence (AI) translation can be used to circumvent the challenges of translating such substantial number of texts. To validate this tool, benchmark tests have …

A Case Demonstration Of The Open Health Natural Language Processing Toolkit From The National Covid-19 Cohort Collaborative And The Researching Covid To Enhance Recovery Programs For A Natural Language Processing System For Covid-19 Or Postacute Sequelae Of Sars Cov-2 Infection: Algorithm Development And Validation, Andrew Wen, Liwei Wang, Huan He, Sunyang Fu, Sijia Liu, David A Hanauer, Daniel R Harris, Ramakanth Kavuluru, Rui Zhang, Karthik Natarajan, Nishanth P Pavinkurve, Janos Hajagos, Sritha Rajupet, Veena Lingam, Mary Saltz, Corey Elowsky, Richard A Moffitt, Farrukh M Koraishy, Matvey B Palchuk, Jordan Donovan, Lora Lingrey, Garo Stone-Derhagopian, Robert T Miller, Andrew E Williams, Peter J Leese, Paul I Kovach, Emily R Pfaff, Mikhail Zemmel, Robert D Pates, Nick Guthe, Melissa A Haendel, Christopher G Chute, Hongfang Liu, National Covid Cohort Collaborative, Recover Initiative

Faculty, Staff and Student Publications

BACKGROUND: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC).

OBJECTIVE: This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and …

Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media, Jay Ratican, James Hutson

Faculty Scholarship

Generative AI is revolutionizing the field of game design, introducing unprecedented adaptability and personalization in gameplay. The latest advancements in AI-driven engines enable real-time content creation, offering dynamic, player-driven experiences that diverge from traditional pre-programmed narratives. This shift marks a transition toward "choose your own adventure" formats, with an unlimited number of variations in levels, enemies, collectibles, and weaponry, tailored to each player's decisions. Google's GameNGen, for example, showcases AI's capacity to recreate classic games like DOOM, learning and generating gameplay in real time. These innovations are not restricted to gaming alone; they extend to edutainment, television, and film, where …