Social and Behavioral Sciences Commons^™

Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn

Electrical & Computer Engineering Faculty Publications

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. …

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

A Review Of Emerging Sensor Technologies For Tank Inspection: A Focus On Lidar And Hyperspectral Imaging And Their Automation And Deployment, Sergio Pallas Enguita, Chung-Hao Chen, Samuel Kovacic

Electrical & Computer Engineering Faculty Publications

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.

Beyond Spatial Materiality, Towards Inter- And Intra-Subjectivity: Conceptualizing Exclusion In Education As Internalized Ableism And Psycho-Emotional Disablement, Anthony J. Maher, Justin A. Haegele

Human Movement Studies & Special Education Faculty Publications

Of the little written about educational exclusion, much of it considers exclusion as disabled students experiencing less access, opportunities and participation in education when compared to their nondisabled same-aged peers. Our article aims to move beyond these narrow, parochial, and reductive postulates by centering the inter- and intra-subjectivities of disabled students to conceptualize exclusion as experiences with internalized ableism and psycho-emotional disablement that may (or may not) be experienced in any or all material and social spaces in education. We cast light on ableism and psycho-emotional disablement in education so that we and others can challenge, disrupt, and transform it …

Predictors Of Occupational Distress Of Catholic Priests On The Eastern Seaboard Of The United States, Michael D. Kostick, Xihe Zhu, Justin A. Haegele, Pete Baker

Human Movement Studies & Special Education Faculty Publications

With ever-increasing demands placed upon active priests in the United States, insight into protecting their mental health may help strengthen vocational resilience for individual priests. The purpose of this study was to examine the association of individual variables, workplace characteristics, and physical activity participation with occupational distress levels among Catholic priests. A 22-question survey consisting of a demographic questionnaire, the Clergy Occupational Distress Index, and the International Physical Activity Questionnaire was employed to collect individual variables, workplace characteristics, physical activity participation, and occupational distress levels of Catholic priests from the Eastern seaboard of the United States. Regression analyses showed that …

The Associations Of Physical Activity And Sedentary Behavior With Self-Rated Health In Chinese Children And Adolescents, Yahan Liang, Youzhi Ke, Yang Liu

Human Movement Studies & Special Education Faculty Publications

Objective

The study aimed to analyze the independent and joint associations of physical activity (PA) and sedentary behavior (SB) with self-rated health (SRH) among Chinese children and adolescents.

Methods

Cross-sectional data on moderate-to-vigorous physical activity (MVPA), school-based PA, extracurricular physical activity (EPA), screen time (ST), homework time, and SRH were assessed through a self-report questionnaire in the sample of 4227 Chinese children and adolescents aged 13.04 ± 2.62 years. Binary logistic regression was used to compare gender differences in PA, SB, and SRH among children and adolescents, and analyses were adjusted for age and ethnicity.

Results

In independent associations, boys …

Wild Sun Bears (Helarctos Malayanus) Exhibit Aseasonality In Parturition, Zachary A. David, Brian Crudge, Matt Hunt, Kirsty Officer, Vuthy Choun, Barbara Durrant, Megan A. Owen, Morokot Long, John P. Whiteman

Biological Sciences Faculty Publications

Seasonal reproduction can provide species with fitness advantages by allowing the birth of young to coincide with favorable environmental conditions, particularly in environments with high seasonal and intra-annual variation in these conditions. Seven of the eight species in the bear family (Ursidae) reproduce seasonally in both managed care and the wild; however, data for the eighth species, the sun bear (Helarctos malayanus), is unclear. Sun bears have been observed to reproduce throughout the year in managed care, yet currently there are no clear data of birth timing for wild sun bear cubs. Here we investigate the seasonality of …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

How Should Focus Be Shifted From Individual Preference To Collective Wisdom For Patients At The End Of Life With Antimicrobial-Resistant Infections?, Jeannie P. Cimiotti, Kimberly Adams Tufts, Lucia D. Wocial, Elizabeth Peter

Ellmer School of Nursing Faculty Publications

Despite growth in numbers of organizational antimicrobial stewardship programs, antimicrobial resistance continues to escalate. Interprofessional education and collaboration are needed to make these programs appropriately responsive to the ethically and clinically complex needs of patients at the end of life whose care plans still require antimicrobial management.

Can Large Language Models Discern Evidence For Scientific Hypotheses? Case Studies In The Social Sciences, Sai Koneru, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies …

Short: Can Citations Tell Us About A Paper's Reproducibility? A Case Study Of Machine Learning Papers, Rochana R. Obadage, Sarah M. Rajtmajer, Jian Wu

Computer Science Faculty Publications

The iterative character of work in machine learning (ML) and artificial intelligence (AI) and reliance on comparisons against benchmark datasets emphasize the importance of reproducibility in that literature. Yet, resource constraints and inadequate documentation can make running replications particularly challenging. Our work explores the potential of using downstream citation contexts as a signal of reproducibility. We introduce a sentiment analysis framework applied to citation contexts from papers involved in Machine Learning Reproducibility Challenges in order to interpret the positive or negative outcomes of reproduction attempts. Our contributions include training classifiers for reproducibility-related contexts and sentiment analysis, and exploring correlations between …

Forecasting Crude Oil Price Using Multiple Factors, Hind Aldabagh, Xianrong Zheng, Mohammad Najand, Ravi Mukkamala

Computer Science Faculty Publications

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical …

Understaning Low Vision Graphical Perception Of Bar Charts, Yash Prakash, Akshay Kolgar Nayak, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Bar charts are widely used for their simplicity in data representation, prompting numerous studies to explore and model how users interact with and perceive bar chart information. However, these studies have predominantly focused on sighted users, with a few also targeting blind screen-reader users, whereas the graphical perception of low-vision screen magnifier users is still an uncharted research territory. We fill this knowledge gap in this paper by designing four experiments for a laboratory study with 25 low-vision participants to examine their graphical perception while interacting with bar charts. For our investigation, we built a custom screen magnifier-based logger that …

You Shall Know A Forum By The Words They Keep: Analyzing Language Use In Accessibility Forums For Blind Users, Nithiya Venkatraman, Anand Aiyer, Yash Prakash, Vikas Ashok

Computer Science Faculty Publications

Discussion forums are one of the favored platforms for knowledge sharing. Given their popularity, copious research exists on understanding linguistic and behavioral characteristics of forum conversations. However, prior investigations have mainly focused on general forums designed primarily for sighted users, and as such the applicability of their findings to the dedicated accessibility discussion forums frequented by blind individuals remains unanswered. To bridge this knowledge gap, and facilitate the development of better-informed assistive technologies for blind people, we investigated language use and identified the key semantic and cognitive characteristics of online accessibility forums. To aid our investigation, we collected a dataset …

Retrogressive Document Manipulation Of Us Federal Environmental Websites, Lesley Frew, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

Changes made to webpages can affect their retrievability. Often this is done with the intention of increasing the page's search engine ranking to improve overall access to information on the page. The Environmental Data and Governance Initiative (EDGI) created a dataset that describes changes on US federal environmental webpages between 2016 and 2020. EDGI noted that many environmental terms were deleted from the pages, but without user data, claims that page retrievability and public information access were lowered are only anecdotal. The Open Resource for Click Analysis in Search (ORCAS) dataset was created during the same time frame, from 2017 …

Evaluating Social Media Reach Via Mainstream Media Discourse, Himarsha R. Jayanetti

Computer Science Faculty Publications

This study examines the intersection between social media and mainstream television (TV) news with an aim to understand how social media content amplifies its impact through TV broadcasts. While many studies emphasize social media as a primary platform for information dissemination, they often underestimate its total influence by focusing solely on interactions within the platform. This research examines instances where social media posts gain prominence on TV broadcasts, reaching new audiences and prompting public discourse. By using TV news closed captions, on-screen text recognition, and social media logo detection, we analyze how social media is referenced in TV news. Our …

Towards Trust And Reputation As A Service In Society 5.0, Stephan Olariu, Ravi Mukkamala, Meshari Aljohani

Computer Science Faculty Publications

Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government which seeks to create a sustainable human-centric society by putting to work recent advances in technology. One of the key challenges in implementing Society 5.0 is providing trusted and secure services for everyone to use. Motivated by this challenge, this paper makes three contributions that we summarize as follows: Our first main contribution is to propose a novel blockchain and smart contract-based trust and reputation service design to reduce the uncertainty associated with buyer feedback in marketplaces that we expect to see in Society 5.0. Our …

Retrogressive Document Manipulation Of Us Federal Environmental Websites, Lesley Frew, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

Changes made to webpages can affect their retrievability. Often this is done with the intention of increasing the page's search engine ranking to improve overall access to information on the page. The Environmental Data and Governance Initiative (EDGI) created a dataset that describes changes on US federal environmental webpages between 2016 and 2020. EDGI noted that many environmental terms were deleted from the pages, but without user data, claims that page retrievability and public information access were lowered are only anecdotal. The Open Resource for Click Analysis in Search (ORCAS) dataset was created during the same time frame, from 2017 …

Autonomous Strike Uavs For Counterterrorism Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

UAVs are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and potential solutions for successful implementation of an autonomous UAV mission.

Building Datasets To Support Information Extraction And Structure Parsing From Electronic Theses And Dissertations, William A. Ingram, Jian Wu, Sampanna Yashwant Kahu, Javaid Akbar Manzoor, Bipasha Banerjee, Aman Ahuja, Muntabir Hasan Choudhury, Lamia Salsabil, Winston Shields, Edward A. Fox

Computer Science Faculty Publications

Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and …

Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong

Computer Science Faculty Publications

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2015 And 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive’s (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt’s (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based …

Quantification Of Landside Congestion In Ports: An Analysis Based On Gps Data, Kumushini Thennakoon, Namal Bandaranayake, Senevi Kiridena, Asela K. Kulatunga

Computer Science Faculty Publications

Hinterland transport is a critical segment in maritime cross-border logistics, which links the end-users of global supply chains to the maritime segment. Truck-based hinterland transport is known to cause congestion in and around ports. This study aimed to quantify the congestion caused by trucks at the Port of Colombo, which has not been a subject of a systematic study. To this end, the study makes use of GPS data. In addition to revealing heavy congestion within the port, the study also reveals significant variations in congestion during different times of the day with the duration of journeys peaking from 1200hrs …

Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

757transfamily: Empowering Parents To Raise Their Transgender Child Through Knowledge, Connection, And Joy, Jessica Gurley

Institute for the Humanities Master's Papers, Projects, and Capstones

Parents of transgender and non-binary children often want to support their children, but are not sure how. Giving these parents the support and resources they need will increase their ability to support their children, further increasing the chance that their children will live a long, healthy, authentic life.

I built a website, and organized a workshop that parents can attend in order to access information and resources about the transgender community. These parents will receive the support they need to be better equipped to support their gender-diverse children. I will be collecting information about the parents' feelings and experience towards …

The Underrepresentation Of Black Females In Cybersecurity, Makendra Latrice Crosby

Cybersecurity Undergraduate Research Showcase

The significance of cybersecurity methods, strategies, and programs in protecting computers and electronic devices is crucial throughout the technological infrastructure. Despite the considerable growth in the cybersecurity field and its expansive workforce, there exists a notable underrepresentation, specifically among Black/African American females. This study examines the barriers hindering the inclusion of Black women in the cybersecurity workforce such as socioeconomic factors, limited educational access, biases, and workplace culture. The urgency of addressing these challenges calls for solutions such as education programs, mentorship initiatives, creating inclusive workplace environments, and promoting advocacy and increased awareness within the cybersecurity field. Additionally, this paper …

Lip(S) Service: A Socioethical Overview Of Social Media Platforms’ Censorship Policies Regarding Consensual Sexual Content, Sage Futrell

Cybersecurity Undergraduate Research Showcase

The regulation of sexual exploitation on social media is a pressing issue that has been addressed by government legislation. However, laws such as FOSTA-SESTA has inadvertently restricted consensual expressions of sexuality as well. In four social media case studies, this paper investigates the ways in which marginalized groups have been impacted by changing censorship guidelines on social media, and how content moderation methods can be inclusive of these groups. I emphasize the qualitative perspectives of sex workers and queer creators in these case studies, in addition to my own experiences as a content moderation and social media management intern for …

The Analysis And Impact Of Artificial Intelligence On Job Loss, Ava Baratz

Cybersecurity Undergraduate Research Showcase

This paper illustrates the analysis and impact of Artificial Intelligence (AI) on job loss across various industries. This paper will discuss an overview of AI technology, a brief history of AI in industry, the positive impacts of AI, the negative impacts of AI on employment, AI considerations that contribute to job loss, the future outlook of AI, and employment loss mitigation strategies Various professional source articles and reputable blog posts will be used to finalize research on this topic.

"I Think There Is A Place For Small Programs:" Advocating, Implementing, And Sustaining Tpc Programs In Small Us Institutions, Martha Lynn Russell

English Theses & Dissertations

Technical and Professional Communication (TPC) programs in small institutions compose of over a third of all programs in the US, yet this space has been understudied by most scholars. To fill this gap, this dissertation presents findings from one-hour interviews with twenty-six TPC program directors in small US institutions with undergraduate populations of less than six thousand. The results of this dissertation include the ways that small institutions are advocating, implementing, and sustaining their TPC program in unique ways with implications for how any TPC programs regardless of size can learn from these findings.

Knowing Just Enough To Be Dangerous: The Sociological Effects Of Censoring Public Ai, David Hopkins

Cybersecurity Undergraduate Research Showcase

This paper will present the capabilities and security concerns of public AI, also called generative AI, and look at the societal and sociological effects of implementing regulations of this technology.