Digital Commons Network^™

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Private Set Intersection: A Systematic Review, Yunbo Yang, Defan Zhu, Jianting Ning, Qi Feng, Xiaoguo Li, Yuejia Cheng, Guomin Yang, Kui Ren

Research Collection School Of Computing and Information Systems

Various services, such as search engines, are increasingly deployed in cloud-based and distributed systems. However, data are typically managed by trusted servers, making user privacy and data security critical concerns. Private set intersection (PSI) is a powerful cryptographic primitive that enables multiple parties to compute the intersection of their datasets without revealing private inputs. It has been extensively studied over the past two decades, leading to significant gains in computational and communication efficiency. Yet, in many real-world scenarios, revealing the raw intersection may still leak sensitive information. To address this, numerous PSI variants have been developed to meet different application …

Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study, Benjamin Lee, Wolfgang Garn, Masoud Fakhimi, Nick F. Ryman-Tubb

Research Collection School Of Accountancy

Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics …

In What Style Shall I Confront Them? The Role Of Social Relationships In Social Correction Of Misinformation Among The Uk And Arab Social Media Users, Muaadh Noman, Mohamed B. Almourad, Ala Yankouskaya, Firoj Alam, Raian Ali

All Works

This study investigates how social factors influence the likelihood of employing direct or indirect communication styles when correcting misinformation on social media in two different cultural contexts, the United Kingdom (UK) and the Arab Gulf Cooperation Council (GCC) countries. We conducted an online survey, supported by vignettes, that involved 686 participants, 367 from the UK and 319 from the Arab GCC countries. Participants were presented with a misinformation scenario and asked about their likelihood of using direct or indirect communication styles to correct their acquaintances. The survey captured variations in gender similarity (same vs. different gender), social status (lower vs. …

Navigating Ethical Considerations And Implications Of Ai Chatbots In Higher Education: A Systematic Review, Ons Al-Shamaileh, Ramy Hammady, Mahmoud Abdelrahman, Omar Mubin

All Works

This systematic review explores the ethical challenges associated with the use of AI-based chatbots in higher education, focusing on their implications for students, educators, institutions, and administrative stakeholders. Following PRISMA guidelines, peer-reviewed literature published between 2014 and 2024 was systematically identified across eight major academic databases, yielding a total of 109 eligible studies. A thematic analysis of the included literature indicates that concerns related to academic integrity are most frequently discussed, alongside recurring issues involving data privacy and security, algorithmic bias, overreliance on automated systems, and the risk of inaccurate or misleading outputs. The findings further demonstrate considerable variation in …

A High-Fidelity Multimodal Synthetic Dataset Generation Framework For Off-Road Unstructured Terrain Navigation Training Of Autonomous Robots, Liyana Wijayathunga, Dulitha Dabare, Alexander Rassau, Douglas Chai, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

The success of deep learning methods in a wide range of application areas has inspired many recent developments in the urban and off-road autonomous navigation domain. In particular, techniques for semantic scene understanding, a key aspect of the navigation pipeline, have been researched extensively, resulting in many real-world and synthetic datasets. However, in comparison to urban semantic segmentation datasets, the availability of datasets for off-road environments remains sparse. In this paper, we aim to overcome this challenge by introducing a methodology capable of efficiently generating photorealistic synthetic datasets for off-road environments with support for multiple sensor modalities. The developed approach …

Explainable Artificial Intelligence Models For Detecting Suspicious Bank Transactions, Narasimha Kumar Narasapuram, Syed Afaq Ali Shah, Mohd Fairuz Shiratuddin, Ferdous Sohel

Research outputs 2022 to 2026

Detecting financial crime is a complex challenge due to evolving criminal strategies and fragmented detection systems, particularly in the areas of money laundering and fraud. While it is easy to implement, traditional rule-based approaches lack adaptability to new threats, and machine learning models, though more effective, often function as opaque "black boxes," limiting their practical use in regulated domains like banking, where interpretability and accountability are essential. This research presents a novel framework that combines intrinsic and post-hoc XAI techniques to detect suspicious bank transactions. Intrinsic methods provide model-inherent transparency, while post-hoc methods offer behavior-level explanations, enabling robust cross-verification of …

Graph Convolution Neural Network And Deep Q-Network Optimization-Based Intrusion Detection With Explainability Analysis, Kelvin Mwiga, Mussa Dida, Leandros Maglaras, Ahmad Mohsin, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a …

Optimizing And Fortifying Ai Software Through The Lens Of Artifact Synthesis, Jieke Shi

Dissertations and Theses Collection (Open Access)

Artificial Intelligence (AI) has transformed the software landscape, ushering in a new era of intelligent systems that increasingly shape our daily lives. This transformation is evident in various domains, including Software Engineering (SE), where Large Language Models (LLMs) support many development tools, and control systems, where self-driving cars and autonomous drones rely on deep learning models for real-time decision-making. These AI systems are collectively referred to as AI software, with the former categorized as AI4SE software (AI for Software Engineering) and the latter as AI4Control software (AI for Control). As AI software becomes central to modern computing infrastructure, its reliability …

Student Mental Health: Screening For Stress, Anxiety And Depression Using Fitbit Data, Rebecca Lopez, Avantika Shrestha, Ml Tlachac, Kevin Hickey, Xingting Guo, Shichao Liu, Elke Rundensteiner

Information Systems and Analytics Department Faculty Conference Proceedings

College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited concerning the variety of psychological instruments administered, physiological modalities, and time series parameters. In this research, we collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic. We assess the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities. Our findings indicate potential in physiological modalities such as heart …

How Agile Became The Design Philosophy Of Ai Fishbowl Under Real-World Constraints, Jad Saad

University Honors Theses

This capstone review examines the development of AI Fishbowl, a public-facing, interactive artificial intelligence system, as a case study in how Agile methods evolve from a project management tool into a design philosophy under real-world constraints. Although the project adopted an Agile workflow early on through a Kanban-style task management approach, the initial system design and architecture were still shaped by a largely plan-first mindset. This created a mismatch between flexible process and rigid design assumptions, which became increasingly apparent as the team moved from high-level architecture into implementation.

A critical turning point occurred when early architectural plans proved difficult …

Codeultrafeedback: An Llm-As-A-Judge Dataset For Aligning Large Language Models To Coding Preferences, Martin Weyssow, Aton Kamanda, Xin Zhou, Houari Sahraoui

Research Collection School Of Computing and Information Systems

Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavor that requires a deep assessment of LLMs' outputs. Existing methods and benchmarks rely primarily on automated metrics and static analysis tools, which often fail to capture the nuances of user instructions and LLM outputs. To address this gap, we introduce the LLM-as-a-Judge evaluation framework and present CodeUltraFeedback, a comprehensive dataset for assessing and improving LLM alignment with coding preferences. CodeUltraFeedback consists of 10,000 coding instructions, each annotated with four responses generated from a diverse pool of 14 LLMs. These responses are annotated using GPT-3.5 …

The Application, Construction, And Validation Of Hidden Markov Model Profiles For Carbonic Anhydrase Enzymes, Samuel F. Kaplan

Master's Theses

Carbonic anhydrases (CAs) catalyze the reversible hydration of CO2 and have evolved independently at least eight times, resulting in structurally distinct enzyme families (α, β, γ, δ, ζ, η, θ, ι). Traditional sequence alignment methods struggle to classify these convergently evolved proteins because their sequential similarity does not reliably indicate functional or evolutionary relationships. Many CA sequences in public databases are annotated generically without family assignments, and prior computational approaches have focused predominantly on the three well characterized families (α, β, γ), leaving the five recently discovered classes without robust classification tools. Family level assignment is often a prerequisite for …

Designing For Trust In Chat-Based Question Answering Systems: An Exchange-Based Retrieval Approach, Nathan Mccutchen

Master's Theses

Community chat platforms such as Discord and Slack support spontaneous, collaborative communication but make it difficult to retrieve previously discussed information. As conversations accumulate, valuable exchanges become buried, leading to repeated questions and sustained burden on experienced community members.

This work contributes a set of design requirements for question-answering systems operating over unstructured chat data, a Discord bot prototype implementing those requirements named Echo, and an empirical evaluation of how such a system affects user trust. Rather than encoding discrete question-answer pairs or generating synthetic responses with a language model, Echo indexes conversation topics for semantic retrieval and presents results …

Stock Market Price Prediction Using Big Data Models Comparison Analysis, Vibhor Pal

Shelby Hall Graduate Research Forum Posters

The stock market consists of complex financial datasets, and achieving stock price real time prediction needs an efficient big data framework for processing. This paper compares big data distributed data processing frameworks for forecasting stock prices using Graph Neural Networks (GNNs) - Apache Flink and Apache Spark. We analyze 70 publicly traded companies’ monthly data for the last 5 years from Yahoo Finance, ranked by Price-to-Earnings (P/E). In the companies’ datasets, there may be a connection or similarity between companies, and this can lead to similar stocks’ price behavior. These interfirm relationships are maintained by GNNs models, and their output …

Text Corpus Combined Method And Tools For Music Textual Analysis, Yuwei Lu

Shelby Hall Graduate Research Forum Posters

While there has been a great deal of research conducted on how to search images and video using text, there has been less focus on how to retrieve music using multiple facets such as mood and lyrical content. or its features This is due in part to the historical lack of available musical corpuses. A musical corpus is a specialized text corpus specifically designed to capture information about music. Recently, several musical corpora for music information retrieval have been built and made available. However, these corpora and the tools designed to exploit them are typically designed to facilitate only one …

Deconstructing Digital Disinformation: Social Media Data Preparation And Analysis For Healthcare Research, Russell W. Cantrell, Matt Campbell

Shelby Hall Graduate Research Forum Posters

The spread of medical misinformation poses significant threats to public health, healthcare system stability, and the quality of patient care. Our research examines misinformation targeting the U.S. healthcare system. It uses a mixed-methods approach that includes social media data analysis, surveys of practicing nurses, and agent-based simulation. This poster focuses on the initial phase, which attempts to detect potential misinformation and disinformation by analyzing patterns in social media posts and user account behaviors. A detailed account of the data preparation process lays the groundwork for examining how disinformation operates online. This phase draws on the Pushshift repository, which offers historical …

Detecting Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton Purdy

Shelby Hall Graduate Research Forum Posters

From around the globe, malicious actors continually probe critical infrastructure assets for weaknesses. Their backgrounds, goals, and motives may vary, but the purpose of their attacks is the same: to damage, undermine, or exploit the functionality of these assets [2]. At a fundamental level, critical infrastructure is any essential system and asset vital to national security. Critical infrastructure includes assets such as power, transportation, telecommunications, water and wastewater systems (WWS), and many more [3].

Improving Consensus In Blockchain, Nelson Navas

Shelby Hall Graduate Research Forum Posters

Called the 4th industrial revolution, Industry 4.0 is the latest paradigm for implementing industrial applications. This new approach relies heavily on increased automation, smart machines, human-machine interaction, AI, and telecommunications. Industry 4.0 applications introduce the idea of the smart factory. The integration of information technology (IT) and operational technology (OT) is a key factor that promotes efficiency in the supply chain. All this is predicated in the generation, sharing, and storage of large quantities of data and transactions to facilitate management, traceability, and control of industrial processes. Increased reliance on interconnectedness causes cybersecurity challenges. Access to machinery, infrastructure, IT systems, …

Generative Ai For Text-To-Video Generation: Recent Advances And Future Directions, Kadhim Hayawi, Sakib Shahriar

All Works

Text-to-video (T2V) generation has recently emerged as a transformative technology within the field of generative AI, enabling the creation of realistic, temporally coherent videos based on natural language descriptions. This paradigm provides significant added value in many domains such as creative media, human-computer interaction, immersive learning, and simulation. Despite its growing importance, systematic discussion of T2V is still limited compared with adjacent modalities such as text-to-image and image-to-video. To alleviate the scarcity of discussions in the T2V field, this paper provides a systematic review of works published from 2024 onward, consolidating fragmented contributions across the field. We survey and categorize …

Driver Behavior Analyzer 2.0: A Modular Framework For Interpretable Driver Safety Analysis From Obd-Ii And Gps Telemetry, Sangwhan Cha, Venkata Sundar Kamesh Durvasula

Harrisburg University Other Works

Driver behavior analysis plays a central role in advancing road safety and enabling data-driven driver feedback. Although commercial telematics platforms offer sophisticated analytics, they are frequently expensive, proprietary, and optimized for enterprise-scale use. At the same time, low-cost On-Board Diagnostics II (OBD-II) adapters make telemetry collection widely accessible, but they typically do not provide higher-level behavioral interpretation.

In this paper, we present Driver Behavior Analyzer 2.0 (DBA 2.0), an offline-first, modular analytics framework that converts OBD-II and GPS telemetry into interpretable safety insights. DBA 2.0 supports ingestion of telemetry logs in CSV and JSON formats, data normalization, rule-based detection of …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission In A Homogeneous Syringe-Sharing Network, Seun Ale, Que Thi Nguyet Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model assumes homogeneous mixing among syringe-sharing agents, without any form of heterogeneity in the agents interactions or syringe-sharing attitude. All syringe-sharing PWID are treated as identical in terms of their interaction frequency and syringe-sharing probability. Interactions are generated dynamically using proximity-based sampling at each timestep (one day), allowing agents to form syringe-sharing interactions based on spatial closeness. The number of daily interaction events is fixed at the population level, and each syringe-sharing agent has the same probability …

An Odd Protocol For An Agent-Based Model Of Hepatitis C Virus Transmission With Inter-Group Structural And Behavioural Heterogeneous Syringe-Sharing Networks (M4), Seun Ale, Que Nguyen Dr., John D. Kelleher Prof., Elizabeth Hunter Dr.

Reports

The model described in this ODD is an agent-based model of hepatitis C virus (HCV) transmission among people who inject drugs (PWID). The model incorporates structural heterogeneity through a three-group interaction framework and behavioural heterogeneity through group-specific syringe-sharing rates. The syringe-sharing population in the model is divided into core, inner, and outer circle groups representing individuals with high, moderate, and low levels of syringe-sharing interaction intensity, respectively. In addition to differences in the number of daily interaction opportunities across groups, agents in each group are assigned distinct syringe-sharing probabilities, reflecting variation in risk-taking behaviour across structural groups within the syringe-sharing …

Navigation Beyond Wayfinding: Robots Collaborating With Visually Impaired Users For Environmental Interactions, Shaojun Cai, Nuwan Janaka, Ashwin Ram, Janidu Shehan, Yingjia Wan, Kotaro Hara, David Hsu

Research Collection School Of Computing and Information Systems

Robotic guidance systems have shown promise in supporting blind and visually impaired (BVI) individuals with wayfinding and obstacle avoidance. However, most existing systems assume a clear path and do not support a critical aspect of navigation—environmental interactions that require manipulating objects to enable movement. These interactions are challenging for a human–robot pair because they demand (i) precise localization and manipulation of interaction targets (e.g., pressing elevator buttons) and (ii) dynamic coordination between the user’s and robot’s movements (e.g., pulling out a chair to sit). We present a collaborative human–robot approach that combines our robotic guide dog’s precise sensing and localization …

Compositions Of Variant Experts For Integrating Short-Term And Long-Term Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user’s immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommendations. Through an empirical study conducted on diverse real-world datasets, we have observed and quantified the existence and impact of both short-term (immediate and transient) and long-term (enduring and stable) preferences on users’ …

Addressing Graph Heterogeneity And Heterophily From A Spectral Perspective, Kangkang Lu, Yanhua Yu, Ruopei Guo, Nan Cheng, Zhiyong Huang, Yunshan Ma, Meiyu Liang, Yuling Wang, Xiting Qin, Yimeng Ren, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) face two key challenges, heterogeneity and heterophily, which often degrade performance. Existing approaches either focus narrowly on specific meta-paths, limiting their expressiveness, or are expressive but cannot effectively leverage higher-order neighbors. In this paper, we propose the Heterogeneous Heterophilic Spectral Graph Neural Network (H2SGNN), which combines local independent filtering to adaptively handle meta-path subgraphs with varying homophily ratios, and global hybrid filtering to capture high-order neighbor interactions with linear computational complexity. On five heterogeneous graph benchmarks—DBLP, ACM, IMDB, AMiner, and Yelp—H2SGNN consistently outperforms strong baselines, for example, achieving +1.0% Macro-F1 and +1.3% Micro-F1 on IMDB. It …

Using Large Language Models To Analyze Political Texts Through Natural Language Understanding, Kenneth Benoit, Scott De Marchi, Conor Laver, Michael Laver, Jinshuai Ma

Research Collection School of Social Sciences

Large language models (LLMs) offer scalable alternatives to human experts when analyzing political texts for meaning, using natural language understanding (NLU). Qualitative NLU methods relying on human experts are severely limited by cost and scalability. Statistical text-as-data methods are scalable but rely on strong and often unrealistic assumptions. We propose a systematic, scalable, and replicable method that can extend existing qualitative and quantitative approaches by using LLMs to interpret texts meaningfully rather than as mere data. Our ensemble means of LLM-generated estimates of party positions on six key issue dimensions correlate highly with equivalent mean ratings by country specialists. When …

An Explainable Transformer-Based Model For Phishing Email Detection: A Large Language Model Approach, Mohammad Amaz Uddin, Md Mahiuddin, Iqbal H. Sarker

Research outputs 2022 to 2026

Phishing email is a serious cyber threat that tries to deceive users by sending false emails with the intention of stealing confidential information or causing financial harm. Attackers, often posing as trustworthy entities, exploit technological advancements and sophistication to make the detection and prevention of phishing more challenging. Despite extensive academic research, phishing detection remains an ongoing and formidable challenge in the cybersecurity landscape. In this research paper, we present a fine-tuned transformer-based masked language model, RoBERTa (Robustly Optimized BERT Pretraining Approach), for phishing email detection. In the detection process, we employ a phishing email dataset and apply the preprocessing …

Stard-Net: Spatiotemporal Attention For Robust Detection Of Tiny Airborne Objects From Moving Drones, Hasibur Rahman, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

The rapid adoption of drones across various domains, alongside advancements in computer vision, has driven growing interest in vision-based airborne object detection from moving aerial platforms. However, this task remains challenging due to the small scale of objects, camouflage within cluttered backgrounds, and occlusions. To address these challenges, we introduce an end-to-end detection framework that integrates a Drone Receptive Field Block (DRFB) to extract multiscale and geometrically diverse features, specifically designed to enhance the detection of small and camouflaged airborne objects. To model motion patterns over time while preserving spatial structure, particularly for detecting camouflaged, cluttered and occluded objects with …