Computer Sciences Commons^™

Ethical Decision-Making In Older Drivers During Critical Driving Situations: An Online Experiment, Amandeep Singh, Sarah Yahoodik, Yovela Murzello, Samuel Petkac, Yusuke Yamani, Siby Samuel

Psychology Faculty Publications

The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios. 204 participants from North America, grouped into two age groups (18–30 years and 65 years and above), were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem. Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment. Bayesian hierarchical models were used to analyze …

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 …

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, …

Trust: The Feature That Vending Machines And Atms Share, But Simplygo Lacks, Sun Sun Lim

Research Collection College of Integrative Studies

The article discussed the intricacies of trust in the SimplyGo debacle and highlighted how the design of physical interfaces like vending machines and ATMs and digital interfaces from apps like Grab, Parking.sg and ShopBack have critical features to instil trust. People need to be reassured that their transactions have proceeded as they should, and thay have not been short-changed.

Evaluating Pre-Trial Programs Using Interpretable Machine Learning Matching Algorithms For Causal Inference, Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Faculty Scholarship

After a person is arrested and charged with a crime, they may be released on bail and required to participate in a community supervision program while awaiting trial. These ‘pretrial programs’ are common throughout the United States, but very little research has demonstrated their effectiveness. Researchers have emphasized the need for more rigorous program evaluation methods, which we introduce in this article. We describe a program evaluation pipeline that uses recent interpretable machine learning techniques for observational causal inference, and demonstrate these techniques in a study of a pre-trial program in Durham, North Carolina. Our findings show no evidence that …

The Right To A Glass Box: Rethinking The Use Of Artificial Intelligence In Criminal Justice, Brandon L. Garrett, Cynthia Rudin

Faculty Scholarship

Artificial intelligence (“AI”) increasingly is used to make important decisions that affect individuals and society. As governments and corporations use AI more pervasively, one of the most troubling trends is that developers so often design it to be a “black box.” Designers create AI models too complex for people to understand or they conceal how AI functions. Policymakers and the public increasingly sound alarms about black box AI. A particularly pressing area of concern has been criminal cases, in which a person’s life, liberty, and public safety can be at stake. In the United States and globally, despite concerns that …

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 …

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 …

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 …

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 …

Attitudes And Perceptions Towards Privacy And Surveillance In Australia, Aleatha J. Shanley

Theses: Doctorates and Masters

Understanding attitudes towards privacy and surveillance technologies used to enhance security objectives is a complex, but crucial aspect for policy makers to consider. Historically, terrorism-related incidents justified the uptake of surveillance practices. More recently however, biosecurity concerns have motivated nation-states to adopt more intrusive surveillance measures. There is a growing body of literature that supports the public’s desire to maintain privacy despite fears of biological or physical threats.

This research set out to explore attitudes towards privacy and surveillance in an Australian context. Throughout the course of this endeavour, the COVID-19 pandemic emerged bringing with it a variety of track …

Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …

Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram

Graduate Theses, Dissertations, and Problem Reports (ETD)

Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …

Integrating Arcgis And Redux Using Middleware, Vishnu Vardhan Reddy Rapuru

Dissertations, Master's Theses and Master's Reports

The integration of ArcGIS with Redux through middleware presents a novel approach to managing state in geospatial applications. This report outlines the process and benefits of combining ArcGIS’s robust mapping and analytics capabilities with Redux’s predictable state container for JavaScript apps. It begins with an introduction to both technologies, followed by a detailed discussion on the architecture design, focusing on the role of middleware as the linchpin in this integration[1]. The paper highlights the benefits, such as improved state management and application performance, and addresses the challenges encountered during the integration process. Implementation details are provided, including the setup of …

Opportunities And Challenges Posed By Disruptive And Converging Information Technologies For Australia's Future Defence Capabilities: A Horizon Scan, Pi-Shen Seet, Anton Klarin, Janice Jones, Mike Johnstone, Helen Cripps, Jalleh Sharafizad, Violetta Wilk, David Suter, Tony Marceddo

Research outputs 2022 to 2026

Introduction: The research project's objective was to conduct a comprehensive horizon scan of Network Centric Warfare (NCW) technologies-specifically, Cyber, IoT/IoBT, AI, and Autonomous Systems. Recognised as pivotal force multipliers, these technologies are critical to reshaping the mission, design, structure, and operations of the Australian Defence Force (ADF), aligning with the Department of Defence (Defence)’s offset strategies and ensuring technological advantage, especially in the Indo-Pacific's competitive landscape.

Research process: Employing a two-pronged research approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding …

Enhancing Students’ User Experience With A Code Critiquer, Laura E. Albrant

Dissertations, Master's Theses and Master's Reports

This thesis explores the role of human factors in the realm of code critiquers and students’ experiences with them. Across three studies, the work utilized Design Thinking to improve the user experience of WebTA for introductory engineering students learning MATLAB. The first two studies gathered observational and interview data to empathize, define, and ideate a new user interface (UI). Said UI was prototyped and then tested with the third study. Overall, the surveys’ data suggests that most students found the new design to be more appealing, useful, and purposeful; however, there is still plenty of room for improvement. Additionally, analysis …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Guilty Machines: On Ab-Sens In The Age Of Ai, Dylan Lackey, Katherine Weinschenk

Critical Humanities

For Lacan, guilt arises in the sublimation of ab-sens (non-sense) into the symbolic comprehension of sen-absexe (sense without sex, sense in the deficiency of sexual relation), or in the maturation of language to sensibility through the effacement of sex. Though, as Slavoj Žižek himself points out in a recent article regarding ChatGPT, the split subject always misapprehends the true reason for guilt’s manifestation, such guilt at best provides a sort of evidence for the inclusion of the subject in the order of language, acting as a necessary, even enjoyable mark of the subject’s coherence (or, more importantly, the subject’s separation …

E-Governance: The Implication Of Next Social Generation Welfare Information System, Yaya Mulyana Abdul Aziz, Andre Ariesmansyah

Smart City

Accelerating bureaucracy can be performed by e-governance present in order to improve the quality of government administration in the world. E-governance draft is closely related to the development of information and communication technology (ICT) globally. One form of embodiment of e-governance is the implementation of a smart city. smart cities are expected to be able to become a liaison between the demands of the community in appropriate, effective, and efficient services from the city government, by utilizing ICT. There are various related definitions of smart city in this world. One of them is as explained by Nam and Pardo who …

Exploring The Nexus Of Cybersecurity Leadership, Human Factors, Emotional Intelligence, Innovative Work Behavior, And Critical Leadership Traits, Sharon L. Burton, Darrell Norman Burrell, Calvin Nobles, Laura A. Jones

Publications

Data shows that 12% of leaders are rated as 'very effective' at leadership. This research emphasizes the importance of understanding human behavior and its impact on leadership effectiveness, innovative work behavior (IWB), and the ability to respond to complex cyber threats, particularly in the realm of cybersecurity leadership. Emotional intelligence (EI), a key human factor, is highlighted as a crucial element that can stimulate cognitive absorption, leading to innovative work behavior and improved innovation efficiency (IE). This underscores the need for leaders to not only be technically proficient but also emotionally intelligent to effectively manage their teams and respond to …

The Socialization Of Social Media: Examining The Impact Of Social Media Use On Interpersonal Skills During Face-To-Face Interaction, Tenille Thomas

Masters Theses & Specialist Projects

Research has well established that the use of social networking sites (SNS) has increased accessibility and connectivity to people with limitless boundaries. SNS have progressively become the preferred source of communication. However, often overlooked is the impact SNS use has on face-to-face interactions, specifically on interpersonal skills. The purpose of this study was to examine the relationship between SNS use and face-to-face interaction. Specifically, this study examines the participant’s ability to recognize and interpret nonverbal cues with increased SNS use. This was a correlational study utilizing a quantitative design of self-reported questionnaires. A total of 178 participants, ranging in age …