Digital Commons Network^™

Coupling Category Alignment For Graph Domain Adaptation, Nan Yin, Xiao Teng, Zhiguang Cao, Mengzhu Wang

Research Collection School Of Computing and Information Systems

Graph domain adaptation (GDA), which transfers knowledge from a labeled source domain to an unlabeled target graph domain, attracts considerable attention in numerous fields. However, existing methods commonly employ message-passing neural networks (MPNNs) to learn domain-invariant representations by aligning the entire domain distribution, inadvertently neglecting category-level distribution alignment and potentially causing category confusion. To address the problem, we propose an effective framework named Coupling Category Alignment (CoCA) for GDA, which effectively addresses the category alignment issue with theoretical guarantees. CoCA incorporates a graph convolutional network branch and a graph kernel network branch, which explore graph topology in implicit and explicit …

Detecting Defi Fraud With A Graph-Transformer Language Model, Wei Ma, Junjie Shi, Jiaxi Qiu, Cong Wu, Jing Chen, Lingxiao Jiang, Shangqing Liu, Yang Liu, Yang Xiang

Research Collection School Of Computing and Information Systems

With the rapid development of blockchain technology, the widespread adoption of smart contracts—particularly in decentralized finance (DeFi) applications—has introduced significant security challenges, such as reentrancy attacks, phishing, and Sybil attacks. To address these issues, we propose a novel model called TrxGNNBERT, which combines Graph Neural Network (GNN) and the Transformer architecture to effectively handle both graph-structured and textual data. This combination enhances the detection of suspicious transactions and accounts on blockchain platforms like Ethereum. TrxGNNBERT was pre-trained using a masked language model (MLM) on a dataset of 60,000 Ethereum transactions by randomly masking the attributes of nodes and edges, thereby …

Map As A By-Product: Collective Landmark Mapping From Imu Data And User-Provided Texts In Situated Tasks, Ryo Yonetani, Kotaro Hara

Research Collection School Of Computing and Information Systems

This paper presents Collective Landmark Mapper, a novel map-as-a-by-product system for generating semantic landmark maps of indoor environments. Consider users engaged in situated tasks that require them to navigate these environments and regularly take notes on their smartphones. Collective Landmark Mapper exploits the smartphone's IMU data and the user's free text input during these tasks to identify a set of landmarks encountered by the user. The identified landmarks are then aggregated across multiple users to generate a unified map representing the positions and semantic information of all landmarks. In developing the proposed system, we focused specifically on retail applications and …

Towards Multimodal Emotional Support Conversation Systems, Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, Richang Hong

Research Collection School Of Computing and Information Systems

The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth and nuance of human conversations. Despite the potential, the current landscape of conversational AI is markedly limited by its reliance on single-modal data, constraining the systems’ ability to empathize and provide effective emotional support. This limitation stems from a paucity of resources that encapsulate the multimodal nature of human communication essential for therapeutic counseling. To address this gap, we introduce the Multimodal Emotional Support Conversation (MESC) dataset, a first-of-its-kind resource enriched with comprehensive annotations across text, …

Conv4rec: A 1‑By‑1 Convolutional Autoencoder For User Profiling Through Joint Analysis Of Implicit And Explicit Feedbacks, Antoine Ledent, Petr Kasalický, Rodrigo Alves, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Second, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as ”implicit feedback”). It can also make separate predictions for the probability of consuming content and the likelihood of granting it a high rating …

Finding Safety Violations Of Ai-Enabled Control Systems Through The Lens Of Synthesized Proxy Programs, Jieke Shi, Zhou Yang, Junda He, Bowen Xu, Dongsun Kim, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input signal that causes the control system to violate a formal safety specification using optimization algorithms. However, applying falsification to AI-enabled control systems poses two significant challenges: (1) it requires the system to execute numerous candidate test inputs, which can be time-consuming, particularly for systems with AI models that have many parameters, and (2) multiple safety requirements are typically defined as a conjunctive specification, …

Ponzilens+: Visualizing Bytecode Actions For Smart Ponzi Scheme Identification, Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen, Jun Sun, Feida Zhu, Yong Wang

Research Collection School Of Computing and Information Systems

With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive …

Full Issue, Volume 20, Issue 3

Journal of Youth Development

No abstract provided.

Assessing The Effectiveness Of Crawlers And Large Language Models In Detecting Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the influence of these attacks on data collection by web crawlers, which famously elude conventional detection techniques using large language models (LLMs). Empirically, we find some vulnerabilities in the current crawler mechanisms and large language model detection, especially in code inspection, and propose enhancements that will help mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, …

Cysteine Oxidation Of A Redox Hub Within Complex I Can Facilitate Electron Transport Chain Supercomplex Formation, Runtai Chen, Seyed Amirhossein Tabatabaei Dakhili, Rokas Gerulskis, Yuan Yuan Zhao, Steven Lockhart, Lusine Tonoyan, Arno G. Siraki, Guocheng Huang, Adam Kinnaird, Darren H. Freed, Shelley D. Minteer, Evangelos D. Michelakis, John R. Ussher

Chemistry Faculty Research & Creative Works

The mitochondrial electron transport chain (ETC) is a four complex unit that could be considered the most essential infrastructure within the mitochondria, as it primarily functions to generate the mitochondrial membrane potential (ΔΨm), which can then be utilized for ATP synthesis or heat production. Another important aspect of ETC function is the generation of mitochondrial reactive oxygen species (mtROS), which are essential physiologic signaling mediators that can be toxic to the cell if their levels become too high. Currently, it remains unresolved how a highly utilized and functioning ETC can sense excessive mtROS generation and adapt, to enhance ΔΨm. Here, …

A Difficult Act To Maintain, J.G. Allen

Research Collection Yong Pung How School Of Law

In "Where is Singapore's AI regulation headed?" (Issues, Summer 2025), Manoj Harjani starts by recounting its delicate balancing act in governing artificial intelligence. The city-state is able to maintain credibility on the global stage while remaining pragmatically grounded in technical experimentation, maintaining policy realism without overcommitting to rigid legal frameworks. The choice to adopt a regulatory “light touch” is often framed as a way to maintain flexibility, avoid overregulation, and enable innovation. But this obscures the extent to which this posture is itself a political and economic settlement—one that reinforces Singapore’s position in the global digital economy by facilitating capital …

Bridging The Great Wall: China’S Evolving Cross-Border Data Flow Policies And Implications For Global Data Governance, Sheng Zhang, Henry S. Gao

Research Collection Yong Pung How School Of Law

Despite the rapid expansion of the digital economy, the global regulatory framework for data flows remains fragmented, with countries adopting divergent approaches shaped by their own regulatory priorities. As a key player in the Internet economy, China’s approach to cross-border data flows (CBDF) not only defines its domestic digital landscape but also influences emerging global norms. This paper takes a comprehensive view of the evolution of China’s CBDF regime, examining its development through both domestic and international lenses. Domestically, China’s regulation of CBDF has evolved from a security-first approach to one that seeks to balance security with economic development. This …

Pine Fruit Activated Carbon Modified With Cuco2se4 For The Application In High-Performance Battery-Type Supercapacitor, Farshad Tavakoli, Ali A. Ensafi, Behzad Rezaei

Chemistry & Biochemistry Faculty Publications and Presentations

Biomass has attracted considerable interest in the field of energy storage because of its outstanding characteristics, such as superior charge transport ability, affordability, eco-friendly, structural versatility, and renewability. Moreover, biomass-derived activated carbons, combined with pseudocapacitive materials, have recently gained popularity as efficient electrochemical components for energy storage devices. In this study, activated carbon was prepared from pinecone flowers as the biomass precursor through a process involving alkaline treatment and pyrolytic carbonization in an inert gas environment. Subsequently, CuCo2Se4 nanoparticles (CCS) were synthesized on the Pine Fruit Activated Carbon (PFAC) surface via a hydrothermal method. CCS serves as a pseudocapacitive material …

A Consensus Guide To Preclinical Indirect Calorimetry Experiments, Alexander S Banks, David B Allison, Thierry Alquier, Ansarullah, Steven N Austad, Johan Auwerx, Julio E Ayala, Joseph A Baur, Stefania Carobbio, Gary A Churchill, Morten Dall, Rafael De Cabo, Jose Donato, Nathalia R V Dragano, Carol F Elias, Anthony W Ferrante, Brian N Finck, Jose E Galgani, Zachary Gerhart-Hines, Laurie J Goodyear, Justin L Grobe, Rana K Gupta, Kirk M Habegger, Sean M Hartig, Andrea L Hevener, Steven B Heymsfield, Corey D Holman, Martin Hrabě De Angelis, David E James, Lawrence Kazak, Jae Bum Kim, Martin Klingenspor, Xingxing Kong, Sander Kooijman, Louise Lantier, K C Kent Lloyd, James C Lo, Irfan J Lodhi, Paul S Maclean, Owen P Mcguinness, Gema Medina-Gómez, Raghavendra G Mirmira, Christopher D Morrison, Gregory J Morton, Timo D Müller, Yoshihiro Ogawa, David Pajuelo-Reguera, Matthew J Potthoff, Nathan Qi, Marc L Reitman, Patrick C N Rensen, Jan Rozman, Jennifer M Rutkowsky, Kei Sakamoto, Philipp E Scherer, Gary J Schwartz, Radislav Sedlacek, Mohammed Selloum, Saame Raza Shaikh, Shuai Chen, Gerald I Shulman, Vojtěch Škop, Alexander A Soukas, John R Speakman, Bruce M Spiegelman, Gregory R Steinberg, Katrin J Svensson, John P Thyfault, Tony Tiganis, Paul M Titchenell, Nigel Turner, Licio A Velloso, Antonio Vidal-Puig, Christopher S Ward, Ashley S Williams, Christian Wolfrum, Allison W Xu, Ying Xu, Juleen R Zierath, International Indirect Calorimetry Consensus Committee (Iiccc)

Children’s Nutrition Research Center Staff Publications

Understanding the complex factors influencing mammalian metabolism and body weight homeostasis is a long-standing challenge requiring knowledge of energy intake, absorption and expenditure. Using measurements of respiratory gas exchange, indirect calorimetry can provide non-invasive estimates of whole-body energy expenditure. However, inconsistent measurement units and flawed data normalization methods have slowed progress in this field. This guide aims to establish consensus standards to unify indirect calorimetry experiments and their analysis for more consistent, meaningful and reproducible results. By establishing community-driven standards, we hope to facilitate data comparison across research datasets. This advance will allow the creation of an in-depth, machine-readable data …

Exploring The Use And Misuse Of Large Language Models, Hezekiah Paul D. Valdez, Faranak Abri, Jade Webb, Thomas H. Austin

Faculty Research, Scholarly, and Creative Activity

Language modeling has evolved from simple rule-based systems into complex assistants capable of tackling a multitude of tasks. State-of-the-art large language models (LLMs) are capable of scoring highly on proficiency benchmarks, and as a result have been deployed across industries to increase productivity and convenience. However, the prolific nature of such tools has provided threat actors with the ability to leverage them for attack development. Our paper describes the current state of LLMs, their availability, and their role in benevolent and malicious applications. In addition, we propose how an LLM can be combined with text-to-speech (TTS) voice cloning to create …

Deep Learning-Based Drowsiness Detection System For Driver’S Safety, Sindhu Vidyanathan Dixith, Shrikant Jadhav, Youngsoo Kim, Naveenkumar Jayakumar

Faculty Research, Scholarly, and Creative Activity

Driver drowsiness is a leading contributor to road accidents, accounting for over 100,000 crashes and approximately ~1000 fatalities each year in the United States alone, as per National Safety Council (NSC). To mitigate this urgent public-safety risk, we propose a real-time Driver Drowsiness Detection System that achieves both high accuracy and fast inference on standard hardware. Our key idea is to combine two complementary deep-learning strategies: 1) a custom Convolutional Neural Network (CNN) paired with a Support Vector Machine (SVM) classifier, and 2) a lightweight transfer-learning model built upon a pre-trained convolutional backbone. We evaluate these approaches on two datasets: …

Bone Quality Response To Lifestyle Intervention In Older Adults With Obesity (Limb-Q Trial): A Randomised Controlled Trial, Giulia Gregori, Sanjay Mediwala, Michael Liebschner, Daeseung Kim, Mon S Bryant, Nina Klonis, Reina Armamento-Villareal, Clifford Qualls, Dennis T Villareal

Faculty, Staff and Students Publications

Background: Lifestyle interventions for weight loss might exacerbate age-related bone loss and osteoporosis. However, there is limited knowledge about their effects on bone quality. We examined whether lifestyle intervention can preserve or enhance bone quality, despite reductions in bone mineral density.

Methods: The Lifestyle Intervention to Improve Bone Quality (LIMB-Q) study was a randomised controlled trial conducted at Baylor College of Medicine and the Michael E DeBakey VA Medical Center (Houston, TX, USA) including older adults (aged 65-85 years) with obesity (BMI ≥30 kg/m2). Participants were randomly assigned to receive either an intensive lifestyle intervention (intensive lifestyle group, consisting of …

Impacts Of Climate Disruption On Mobility Aircraft Performance In The Pacaf Region, Hannah M. Dauterman

Theses and Dissertations

This thesis investigates the projected impacts of climate disruption on the performance and fuel management of the C-17 Globemaster III, a critical mobility aircraft in the Pacific Air Forces (PACAF) region. As rising global temperatures reduce air density, the performance of aircraft is compromised, resulting in increased fuel consumption, as well as the potential for extended runway requirements and diminished cargo capacity. Using climate projection data from Coupled Model Intercomparison Project Phase 6 (CMIP6), this research analyzes future air temperature trends and their implications for C-17 fuel consumption. Results suggest that by 2049, the U.S. Air Force may incur an …

Electrical Characterization Of Germanium Tin Alloys And Devices For Space Reliability, Kevin K. Choe

Theses and Dissertations

GeSn (germanium tin) alloys are potentially well suited for near-mid infrared space optoelectronic applications. Alloys of GeSn have similar properties to group III-V and mercury-cadmium-telluride semiconductors and are compatible with cost-effective complementary metal oxide semiconductor (CMOS) manufacturing technology. Recent progress in non-equilibrium remote plasma-enhanced chemical vapor deposition (RPECVD) has enabled the crystalline growth of GeSn with Sn concentrations of up to 10% without Sn surface segregation. Several experimental studies in previous literature report CVD- or molecular beam epitaxy (MBE)-grown GeSn alloys achieving a direct bandgap with 6%-9% Sn content. This novel growth technique opens opportunities for a cost-effective, next-generation optical …

Automation Of Lcmc Logistics Processes: A Delphi Approach, Kline M. Alt

Theses and Dissertations

As the U.S. Air Force confronts growing complexity in system acquisition, the implementation of digital models in system design and logistics process management allows the incorporation of digital tools and the possibility for automation of portions of logistics processes. This thesis investigates where these technologies can be most effectively integrated within the Air Force Life Cycle Management Center logistics enterprise (AFLCMC). Using a three round Delphi study, AFLCMC logistics subject matter expert (SME) opinions were solicited from program-level senior logisticians, program managers to identify high-need areas, key success factors, and potential barriers to adoption. Quantitative consensus from Likert-scale and ordinal …

Designing An Experiment To Create And Evaluate Behavioral Changes In Air Force Pilots' Fuel Efficiency, Jackson Macias

Theses and Dissertations

Fuel represents over 70% of the logistical resupply demand within the Department of Defense, with the United States Air Force accounting for most of that consumption. While prior fuel efficiency efforts have primarily focused on technical upgrades, this research explores how behavioral interventions can influence pilot decision-making and operational energy outcomes. Using the Theory of Planned Behavior (TPB) as a guiding framework, this study investigates the effects of targeted behavioral strategies on pilots’ attitudes, subjective norms, perceived behavioral control, intentions, and actual fuel-efficient behaviors. A quasi-experimental design was applied through a controlled pilot study using simulator flights. Six participants were …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Low-Cost Transfers For Cislunar Liaison Navigation, Ka'eo J. Swartzmiller

Theses and Dissertations

CubeSats have an inherent limitation on volume due to their small form factor. Naturally, this extends to the capacity of on-board ΔV, thus limiting the maneuverability of the satellites. This limited maneuverability makes it extremely important to be intentional when planning out mission trajectories. The cislunar region subjects these trajectories to the gravitational forces of both the Earth and the Moon, making it all the more necessary to deliberately plan out maneuvers. The Circular Restricted Three Body Problem (CR3BP) provides a simpler model for estimating the motion of spacecraft within the cislunar region, allowing for the gravitational forces to be …

Investigating The Impact Of Forest Vegetation On Nuclear Weapon Prompt Radiation Energy Deposition, Paul A. Clement

Theses and Dissertations

This work seeks to answer the unstudied questions “Does a layer of vegetation impact the prompt radiation energy deposition in the soil from an atmospheric nuclear detonation?” and “If yes, by how much?” A new methodology was developed to repeatably automate and streamline a process of generating a three-dimensional geometry for subsequent radiation transport simulations and statistical analysis. The methodology streamlined three diverse applications: a three-dimensional mesh generator, a radiation transport simulation code, and a sensitivity analysis & uncertainty quantification software. The methodology was tested by simulating the quantitative influence that vegetation has on changing the energy deposition into the …

Spin State Of Positronium In Two Dimensions, Emille Bryant

Theses and Dissertations

We investigate the spin state of the electron-positron system positronium to determine experimental criteria that can identify a positronium anyon. Anyons have been posited to be the basis for a truly fault-tolerant quantum computer, but they have not been indisputably observed in experimental systems. Through the analysis of the fully relativistic symmetry group of fermions and bosons in three dimensions, we investigate the symmetry of its subgroups to determine that positronium in two dimensions behaves as a scalar with respect to rotations. We conclude that positronium in two dimensions has an arbitrary relative spin orientation of annihilation photons and only …

How The Inter-Nucleon Force Shapes Emergent Collectivity And Imparts Uncertainties In Ab Initio Nuclear Observables, Kevin Becker

LSU Doctoral Dissertations

The importance of collective motion in atomic nuclei has been well-established for many decades. Even before the advent of the nuclear shell model and its much-needed answers to surprising empirical phenomena, collectivity and surface deformation have played a vital role in broadening our knowledge. Reconciling the collective and single-particle camps into a unified description required tremendous strides in the application of group theory and advances in high-performance supercomputing, and thanks to these efforts the symmetry-adapted no-core shell model, or SA-NCSM, has developed into a reliable model for the study of deformed light and medium-mass nuclei. State-of-the-art calculations reveal remarkably simple …

High-Fidelity Finite Element Modeling Technique To Improve Sensitivity To Bone Tissue Changes Of Older Adults With Obesity Undergoing Intensive Lifestyle Intervention, Michael A K Liebschner, Daeseung Kim, Nina Klonis, Giulia Gregori, Reina Armamento-Villareal, Clifford Qualls, Dennis T Villareal

Faculty, Staff and Students Publications

Introduction: Obesity presents a significant health risk for the aging population. Research shows that weight loss and regular exercise can greatly improve the functional status of older adults who are obese. However, weight loss may also result in a decrease in bone mass. To properly assess changes in fracture risk due to lifestyle interventions, a direct biomechanical evaluation of bone strength and fracture risk at metabolically active sites is essential.

Methods: Computed tomography scans taken at two different time points of ten human volunteers provided the foundation for this study. A high-fidelity segmentation and modeling approach was taken to generate …

Enhanced Key Generation Method Using Deep Q-Networks Algorithm With Chaotic Maps, Ali A. Mahdi, Mays M. Hoobi

Mesopotamian Journal of CyberSecurity

In contemporary digital environments, exponential cyber threat growth has made cryptographic key generation a critical security challenge. Traditional Pseudo-Random Number Generators (PRNGs) and existing chaos-based methods often exhibit insufficient entropy, limited randomness quality, and inadequate resistance to statistical attacks. Current implementations frequently produce suboptimal entropy values and fail to meet modern cryptographic security standards and rigorous randomness testing protocols. This paper aims to design and implement an advanced cryptographic key generation system that combines Deep Q-Networks (DQN) algorithms with multiple chaotic maps to produce cryptographically secure stream key bits with high randomness and strong resistance to cryptanalytic attacks. The proposed …

Enhancing Internet Of Things (Iot) Network Security: A Machine Learning-Driven Framework For Real-Time Intrusion Detection And Anomaly Classification, Loiy Alsbatin, Firas Zawaideh, Basem Mohamad Alrifai, Tareq A. Alawneh, Tareq A. Alawneh

Mesopotamian Journal of CyberSecurity

The rapid proliferation of IoT devices presents serious IoT network cybersecurity threats; hence, advanced IDSs are necessary. Signature and rule-based IDS mechanisms cannot address novel attacks, generate excessive alarms, and are computationally inefficient. Therefore, in response, in this paper, a machine learning IDS for IoT network real-time intrusion detection and anomaly categorization is proposed via black widow optimization (BWO) for optimal feature and hyperparameter selection. The IDS employs standard machine learning models, such as random forest and support vector machines (SVMs), and deep models, such as long short-term memory (LSTM), to address IoT environment nuances. The framework is evaluated on …

Priflex: A Secure Federated Learning Framework For Evaluating Privacy Leakage And Defense In Cross-Modal Medical Data, Mohammad Othman Nassar, Feras Fares Al-Mashagba

Mesopotamian Journal of CyberSecurity

Federated learning (FL) enables decentralized, privacy-preserving machine learning by training models across distributed data without sharing raw patient information. However, most FL frameworks focus on unimodal data and overlook critical challenges in multimodal healthcare settings, such as privacy risks, fairness disparities, and reduced model interpretability. We present PRIFLEX (Privacy-Resilient Integration Framework for Learning Exchange), a novel FL framework designed for secure integration of structured and unstructured medical data. PRIFLEX combines 12-lead electrocardiograms (ECG) from the PhysioNet PTB-XL dataset and clinical notes from the Medical Information Mart for Intensive Care IV (MIMIC-IV), supporting early, late, and hybrid data fusion strategies. To …