Digital Commons Network^™

Representative National Survey On Drug Use During The Covid-19 Stay-At-Home Order In The Usa, Soo-Hoon Lee, Bernadette Cullen, Alidon Athey, Calliope Holingue, Michelle R. Kaufman, Paul Nestadt, Jack Samuels, Tracy Vannorsdall, Phillip Phan

Management Faculty Publications

Objectives This study explores the drug use behaviour in the US general population in the early days of the COVID-19 pandemic with a focus on the relationship between sociopsychological factors, mobility restrictions from March to June 2020 and mental health conditions.

Design A retrospective anonymous online survey representing a cross-section of the US population in 2020.

Setting A qualified panel of 500 000 Qualtrics participants stratified by gender, race, age and geographical region to represent the US population.

Participants 3340 participants voluntarily consented to respond.

Measures Outcome measure for illicit and non-medical use of prescription drugs based on the National …

A Different Risk-Return Relationship, Aydin Selim Oksoy, Matthew R. Farrell, Shaomin Li

Management Faculty Publications

We challenge the widely accepted premise that the valuation of an early-stage firm is simply the capital invested (USD) divided by the equity received (%). Instead, we argue that this calculation determines the break-even point for the investor; for example, investing USD 1.0 in exchange for a 10% equity sets a firm-level free cash flow target of USD 10.0, resulting in a 0% return for the investor. The design of our study is that of a descriptive analysis of the phenomenon, based on three assumptions: that angel investing is a two-issue negotiation, that negotiation positions are communicated sequentially from capital …

Not Exactly "Perfect," But Pretty Darn Good: A Review Of Michael Schur's Book- How To Be Perfect: The Correct Answer To Every Moral Question, Justin Pepe

Management Faculty Publications

How to Be Perfect: The Correct Answer to Every Moral Question provides a quick-start guide to addressing ethical challenges by using humor to make complex philosophical concepts accessible. Based on the premise that traditional ethics pedagogy is often stodgy, esoteric, and depressing, the book appeals to students and professionals who find conventional texts unapproachable. By blending humor with moral philosophy, it offers a fresh way to develop ethical reasoning in students and employees. This book can help address the urgent need for managers and professionals to build the ethical capabilities necessary to tackle grand challenges.

Problem-Centered Post-Secondary Computer Science Education: A Study Of The Private Artificial Intelligence Curriculum, Golnoush Haddadian, Prajwal Panzade, Daniel Takabi, Min Kyu Kim

School of Cybersecurity Faculty Publications

In response to the demand for Artificial Intelligence (AI) experts, this study introduced a curriculum development initiative. The aim was to design and implement a Private AI curriculum to understand the computer science (CS) students’ evaluations of the curricular activities and their levels of interest and motivation. Twenty-five students, a mix of undergraduates and graduates, were recruited and a scaled-down version of the curriculum was implemented. A parallel mixed-methods approach was employed. The results reinforced the significance of problem-centered curricula in CS context. Students rated the curricular activities highly and demonstrated strong motivation; however, graduates expressed more favorable view of …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …

A Trust-By-Learning Framework For Secure 6g Wireless Networks Under Native Generative Ai Attacks, Md Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Trinidad Mario Dena, Walid Saad, Zhu Han, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Sixth-generation (6G) wireless networks will become vulnerable due to native generative AI (GenAI)-driven intelligent poisoning attacks in both the radio unit and the core network. In particular, network parameters and metrics in cross-layer design pose fundamentally uncertain conditions and can be compromised through the native GenAI mechanism, which leverages data augmentation and reconstruction capabilities. This work investigates the capabilities of native GenAI to create novel poisoning attacks in wireless networks, while investigating their impact through uncertainty-informed root analysis. Then, detected attacks are mitigated by developing a trustworthy service aggregation in the wireless network. First, a joint decision problem is formulated …

Empowering Customer Service With Generative Ai: Enhancing Agent Performance While Navigating Challenges, Charles Costa, Souvick Ghosh

Faculty Research, Scholarly, and Creative Activity

Introduction. As large language models (LLMs), such as GPTs, become more intelligent, a key area of exploration is how these technologies can improve the customer experience. Contrary to common belief, many consumers, including Gen Z, prefer human-provided customer service, illustrating the importance of human-AI collaboration in the space. Method. By leveraging the author’s real-world knowledge of enterprise knowledge management and customer service delivery, we reviewed numerous literature about AI, knowledge management, and service design and synthesised practical insights for industry professionals to build a successful AI strategy. Analysis. We examined the gap between academic research on generative AI and how …

Layover Start Timing Predicts Layover Sleep Quantity And Timing On Long-Range And Ultra-Long-Range Trips, Michael J. Rempe, Ian Rasmussen, Kevin Gregory, Cheyenne Johnson, Matthew Hsin, Erin Flynn-Evans, Amanda Lamp, Cassie J. Hilditch

Faculty Research, Scholarly, and Creative Activity

Study Objectives: Airline transport pilot sleep during layover is an important factor for alertness on subsequent flights. Assessing pilots' sleep on layover is an important first step in helping them obtain the most recuperative sleep possible on layover. Here, we investigate the quantity and timing of sleep during layovers and determine predictors for layover sleep. Methods: Sleep was assessed in 256 pilots flying a total of 473 long-range (LR; flight time 12-16 hours) or ultra-long-range (ULR; flight time > 16 hours) trips. Sleep was assessed using actigraphy. We employed linear mixed-effects models with layover sleep characteristics as the outcomes. The predictor …

Using A Sober Curious Framework To Explore Barriers And Facilitators To Helping Sexual Minority Women Reduce Alcohol-Related Harms: Protocol For A Descriptive Study, Tonda L. Hughes, Lauren Bochicchio, Laurie A. Drabble, Belinda Lunnay, David Whiteley, Jillian R. Scheer, Beth Meadows, Paul Ward, Carol Emslie

Faculty Research, Scholarly, and Creative Activity

Background: Globally, women consume less alcohol than men, but alcohol consumption among women has declined less in recent years than among men. Drinking rates and alcohol-related harms vary substantially across population groups of women, and sexual minority women (eg, lesbian, bisexual, and queer) are at notably high risk. An emerging body of literature suggests that in addition to minority stress (eg, stigma, discrimination), drinking norms and drinking cultures likely influence sexual minority women’s drinking. Almost no research has explored these factors as possible targets of interventions. Sober curiosity is a rapidly growing wellness movement that may be particularly salient for …

Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach, Franck B.N. Tonle, Henri E.Z. Tonnang, Milliam M.Z. Ndadji, Maurice Tchoupe Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy

All Peer-Reviewed Publications

Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key …

Morphometric Characterization Of Invasive Diaphorina Citri Infesting Citrus And Orange Jasmine In Ghana, Kodwo Dadzie Ninsin, Maxwell Kelvin Billah, Bernard Agyeman Boateng, Jonathan Osei-Owusu, Fred Kormla Ablormeti, Samuel Opuni-Basoa, Aboagye Kwarteng Dofuor, George Edusei, Steve B.S. Baleba, Owusu Fordjour Aidoo

All Peer-Reviewed Publications

Morphometric analysis plays a crucial role in the accurate identification and classification of insect pests, which is essential for implementing effective management strategies. The Asian citrus psyllid (Diaphorina citri Kuwayama; Hemiptera: Liviidae), an invasive species and a known vector of ‘Candidatus Liberibacter asiaticus’ and ‘Ca. Liberibacter americanus,’ the bacterial pathogens associated with Huanglongbing (HLB), has recently emerged as a pest of concern in Ghana. Despite its significance, little is known about the biological traits of field populations in the region. In this study, we examined morphometric variation in D. citri populations infesting citrus and orange jasmine (Murraya paniculata) across different …

A Review On Knowledge And Information Extraction From Pdf Documents And Storage Approaches, Salvador D. Atagong, Henri Tonnang, Kennedy Senagi, Mark Wamalwa, Komi M. Agboka, John Odindi

All Peer-Reviewed Publications

Introduction: Automating the extraction of information from Portable Document Format (PDF) documents represents a major advancement in information extraction, with applications in various domains such as healthcare, law, or biochemistry. However, existing solutions face challenges related to accuracy, domain adaptability, and implementation complexity. Methods: A systematic review of the literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine approaches and trends in PDF information extraction and storage approaches. Results: The review revealed three dominant methodological categories: rule-based systems, statistical learning models, and neural network-based approaches. Key limitations include the rigidity of rule-based …

Predicting Food Prices In Kenya Using Machine Learning: A Hybrid Model Approach With Xgboost And Gradient Boosting, Benard O. Ogol, Evans Omondi, John Olukuru, Betsy Muriithi, Kennedy Senagi

All Peer-Reviewed Publications

Introduction: Food price volatility continues to be a significant concern in Kenya's economic development, posing challenges to the country's economic stability. Methodology: This study examines the application of machine learning methods, employing a hybrid approach that combines XGBoost and gradient boosting, to predict food prices in Kenya. The food prices data from the World Food Programme, covering the period from January 2006 to September 2024, as well as currency exchange rates data from the Central Bank of Kenya in US dollars (USD) and inflation rates data, were collated and preprocessed to be ready for analytics and machine learning. The augmented …

The Effectiveness Of Tech Support Fraud In Damaging Older Individual’S Financial Security, Vanessa Perera

Theses : Honours

This study discovers the tactics employed to create detrimental effects upon older people impacted from fraudulent tech-support incidents. It examines social engineering, and financial confusion of older people. This is significant considering adaptations towards digital banking and payment management. This research looked at online and active over 65s. Using largely qualitative approaches over 65s were interviewed and responses validated against cyber-professionals’ responses. This identified three key findings: older adults feel confused and misunderstand tech support scams; threat actors build trust by pretending to offer technical help but use this to deceive their victims; and older adults face serious social and …

Swatting Flies: Biting Insects As Non-Invasive Samplers For Mammalian Population Genomics, Anubhab Khan, Ryan Carter, Chengetai D. Mpamhanga, Daniel Masiga, Manun Channumsin, Marc Ciosi, Oliva Manangwa, Furaha Mramba, Umer Zeeshan Ijaz, Harriet Auty, Barbara K. Mable

All Peer-Reviewed Publications

Advances in next-generation sequencing have allowed the use of DNA obtained from unusual sources for wildlife studies. However, these samples have been used predominantly to sequence mitochondrial DNA for species identification while population genetics analyses have been rare. Since next-generation sequencing allows indiscriminate detection of all DNA fragments in a sample, technically it should be possible to sequence whole genomes of animals from environmental samples. Here we used a blood-feeding insect, tsetse fly, to target whole genome sequences of wild animals. Using pools of flies, we compared the ability to recover genomic data from hosts using the short-read sequencing (Illumina) …

Cochlear Implantation With Sporadic Inner Ear Schwannomas: Outcomes In 106 Patients From An International Multi-Institutional Study, John P. Marinelli, Torsten Rahne, James R. Dornhoffer, Christine M. Lohse, Justin Cottrell, J. Thomas Roland, Nicholas J. Thompson, Kevin D. Brown, Jacob C. Lucas, Seilesh C. Babu, Nathan R. Lindquist, Elizabeth L. Perkins, Donald Tan, John I. Lane, Jacob B. Hunter, Erin Harvey, Nicholas L. Deep, Samantha Y. Cerasiello, Matthew L. Kircher, Mana Espahbodi, Evan L. Tooker, Niels Cramer West, Per Caye-Thomasen, Matthew L. Carlson, Stefan K. Plontke

Department of Otolaryngology - Head and Neck Surgery Faculty Papers

OBJECTIVE: To evaluate cochlear implant speech perception among patients with sporadic inner ear schwannoma who underwent ipsilateral implantation.

STUDY DESIGN: Retrospective multi-institutional cohort study.

SETTING: Eleven tertiary academic medical centers across Germany, Denmark, and the United States.

PATIENTS: One hundred six patients with sporadic inner ear schwannoma who received an ipsilateral cochlear implant from 2011 to 2022.

INTERVENTIONS: Ipsilateral cochlear implantation with observation, stereotactic radiosurgery, or microsurgery for tumor management.

MAIN OUTCOME MEASURES: Monosyllabic speech perception testing scores and rates of open-set speech perception.

RESULTS: Among 106 patients studied, 101 (95%) achieved open-set speech perception with a median of 24 …

Modelling The Impacts Of Recreational Catch-And-Release Variability On Fish Stock Dynamics, Brett Crisafulli

Theses: Doctorates and Masters

Recreational fisheries often lack sufficient data for robust stock assessments, and post-release mortality (PRM) of discarded fish, particularly demersal species, poses a significant threat to sustainability. Because demersal fish often experience higher PRM (often due to barotrauma and stress), the substantial global interest in their recreational fisheries (e.g., Australia, Gulf of Mexico, Western Baltic Sea) raises conservation concerns. This study employs three distinct modelling approaches, applied to four reef-dwelling demersal species, to support effective recreational fisheries management by addressing data uncertainty, unintended consequences of management changes, and PRM effects. Specifically, this thesis (1) evaluates variability in average weights of recreationally-caught …

The First International Study Into The Characteristics And Statistical Effectiveness Of Treatment For Indigenous And First Nations People Who Have Sexually Offended: A Meta-Analysis And Survey Of Routine Practice, James Finney

Theses: Doctorates and Masters

Psycho-criminological literature has continued to examine post-treatment recidivism outcomes homogeneously for people who have sexually offended. Because results indicate clear differences between Indigenous and non-Indigenous people who have sexually offended, this project set out to understand the characteristics and effectiveness of treatment for Indigenous and First Nations people from four jurisdictions: Australia, New Zealand, Canada and the United States. A strategic three study strategy was implemented and resulted in a systematic narrative synthesis, meta-analysis and survey of sexual offender treatment organisations in Australia. The systematic review used a comprehensive and scientifically rigorous approach to identify studies (k = 14) that …

Early, Standardised, And Precise Treatment Of Copd Exacerbations And Covid-19, Sanjay Ramakrishnan

Theses: Doctorates and Masters

Background: Background Chronic Obstructive Pulmonary Disease (COPD) is a common chronic condition causing significant morbidity and mortality. Patients with COPD report sudden episodes of deteriorating respiratory symptoms called exacerbations, often presumed to be precipitated by infections. COPD is in the top 5 causes of death globally. COPD exacerbations drive a large proportion of these deaths. Despite this, there is no standardised model of care for the sickest patients who need hospitalisations for exacerbations. Meanwhile, treatment for COPD exacerbations continues to be guided by symptoms, instead of underlying biology. Most patients are treated with unnecessary and harmful systemic corticosteroids. During the …

Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang

Research Collection School Of Computing and Information Systems

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …

Bi-Objective Dynamic Tugboat Scheduling With Speed Optimization Under Stochastic And Time-Varying Service Demands, Xiaoyang Wei, Hoong Chuin Lau, Zhe Xiao, Xiuju Fu, Xiaocai Zhang, Zheng Qin

Research Collection School Of Computing and Information Systems

With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage …

Constitutional Comparisons And Language, De Maartje Visser

Research Collection Yong Pung How School Of Law

Language can be seen as an intuitive challenge for those who embark on comparative constitutional research: its intricate entanglement with law is often overlooked and not engaged with explicitly. This chapter argues that this is a missed opportunity at best, and arguably presents a hazard for the collective quality of the scholarly discourse. It discusses four ways in which language matters when engaging in constitutional comparisons: the problem of literal translation; the problem of legal-conceptual translation; a multilingual discourse that is marked by fragmentation; and expectations in communication with a foreign or international readership. The approach taken is constructive, with …

Ai Characterisations And Their Legal Implications, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

This chapter examines the difficult legal characterisation problems that artificially intelligent systems raise and explores how different characterisations of artificial intelligence (AI) shape practical legal outcomes. Three reasons are offered for the legal difficulty with characterising AI. First, answers to characterisation problems are inherently subjective and perspective-driven, particularly when the subject is an intangible technological system. Second, AI technology is especially difficult to define since the field typically proceeds on inexact anthropomorphic metaphors. Third, AI characterisation problems raise difficult sub-problems, particularly in determining how autonomous an AI system is. The chapter thus argues that a range of plausible AI characterisations …

Attribute-Based Encryption With Searchable Encryption, Yang Yang

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) with searchable encryption is a notion that inherits the advantages of both ABE (Goyal et al., 2006) and searchable encryption (SE) (Boneh et al., 2004; Boneh and Waters, 2007) mechanisms to realize secure share and search for the outsourced data. ABE creates fine-grained access control system to prevent sensitive data from being accessed by unauthorized user or external attacker (Bethencourt et al., 2007). SE generates encrypted index for outsourced data such that it can be searched by a keyword trapdoor (or token) (Shi et al., 2007; Naveed et al., 2014), where the outsourced content and search keyword(s) …

The Effectiveness Of Local Updates For Decentralized Learning Under Data Heterogeneity, Tongle Wu, Zhize Li, Ying Sun

Research Collection School Of Computing and Information Systems

We revisit two fundamental decentralized optimization methods, Decentralized Gradient Tracking (DGT) and Decentralized Gradient Descent (DGD), with multiple local updates. We consider two settings and demonstrate that incorporating local update steps can reduce communication complexity. Specifically, for  $\mu$-strongly convex and $L$-smooth loss functions, we proved that local DGT  achieves communication complexity {}{$\tilde{\mathcal{O}} \Big(\frac{L}{\mu(K+1)} + \frac{\delta + {}{\mu}}{\mu (1 - \rho)} + \frac{\rho }{(1 - \rho)^2} \cdot \frac{L+ \delta}{\mu}\Big)$}, where $K$ is the number of additional local update}, $\rho$ measures the network connectivity and $\delta$ measures the second-order heterogeneity of the local losses. Our results reveal the tradeoff between communication and …

She Speaks: Amplifying Women's Voices For Wellbeing, Narelle Lemon

Reports accepted as research outputs

Executive Summary

The SheSpeaks project successfully transformed how 223 Western Australian women understand and practice self-care, demonstrating  that targeted wellbeing literacy interventions can create lasting change  in women’s relationship with their own wellbeing. Funded by the Women’s Grants for a Stronger Future program, this innovative initiative combined evidence-based workshops with authentic podcasting to challenge the pervasive cultural narrative that positions women’s self-care as selfish  or impossible.