Computer Sciences Commons^™

Reproducibility Debt In Scientific Software, Zara Hassan, Christoph Treude, Graham Williams, Michael Norrish, Alex Potanin

Research Collection School Of Computing and Information Systems

Reproducibility Debt (RpD) refers to accumulated technical and organisational issues in scientific software that hinder the ability to reproduce research results. While reproducibility is essential to scientific integrity, RpD remains poorly defined and under-addressed. This study introduces a formal definition of RpD and investigates its causes, effects, and mitigation strategies using a mixed-methods approach involving a systematic literature review (214 papers), interviews (23 practitioners), and a global survey (59 participants). We identify seven categories of contributing issues, 75 causes, 110 effects, and 61 mitigation strategies. Findings are synthesised into a cause-effect model and supported by taxonomies of team roles and …

Viewsrd: 3d Visual Grounding Via Structured Multi-View Decomposition, Ronggang Huang, Haoxin Yang, Yan Cai, Xuemiao Xu, Huaidong Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

3Dvisual grounding aims to identify and localize objects in a 3Dspacebasedontextualdescriptions. However, existing methods struggle with disentangling targets from anchors in complex multi-anchor queries and resolving inconsisten cies in spatial descriptions caused by perspective variations. To tackle these challenges, we propose ViewSRD, a frame work that formulates 3D visual grounding as a structured multi-view decomposition process. First, the Simple Rela tion Decoupling (SRD) module restructures complex multi anchor queries into a set of targeted single-anchor state ments, generating a structured set of perspective-aware de scriptions that clarify positional relationships. These de composed representations serve as the foundation for the Multi-view …

A Comprehensive Review Of Financial Knowledge Graphs, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang

Research Collection School Of Computing and Information Systems

Knowledge Graphs (KGs) are increasingly used in finance to manage complex, interconnected data and support advanced analytics. This survey provides an overview of how KGs are applied across various financial areas, such as fraud detection, credit risk assessment, anti-money laundering, and regulatory compliance. We examine key techniques for building and using KGs in finance, including graph construction, embedding methods, and machine learning models. The survey also discusses challenges specific to finance, like handling private data, ensuring interpretability, and managing real-time data. Additionally, we explore the emerging combination of KGs with large language models and generative AI, which offers new possibilities …

Information Provision And Search Frictions: Evidence From The Taxi Industry In Singapore, Sumit Agarwal, Shih-Fen Cheng, Jussi Keppo, Long Wang, Yang Yang

Research Collection School Of Computing and Information Systems

Search frictions and misallocation are common in decentralized transportation markets. Using novel trip-level data of taxis in Singapore, this paper examines the impactof real-time demand information at airport terminals on search frictions. The information reduces taxi supply misallocation, increasing deadheading speed by 16.3% and decreasing deadheading time by 10.77%, benefiting both passengers and drivers. It raises daily earnings by $3.70 USD and adds 6.2 minutes of operational time per airport-trip taxi. Spatial spillovers are primarily observed among drivers in adjacentdistricts. Taxis from the Budget Terminal and drivers with fewer prior airport pickups benefit more from this information.

Filterfl: Knowledge Filtering-Based Data-Free Backdoor Defense For Federated Learning, Yanxin Yang, Ming Hu, Xiaofei Xie, Yue Cao, Pengyu Zhang, Yihao Huang, Mingsong Chen

Research Collection School Of Computing and Information Systems

As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the lack of data auditing for untrusted clients, FL is vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned data for local training or directly changing the model parameters, attackers can easily inject backdoors into the model, which can trigger the model to make misclassification of targeted patterns in images. To address these issues, we propose a novel data-free trigger-generation-based defense approach based on the two characteristics of backdoor attacks: i) triggers are learned …

Stable Score Distillation, Haiming Zhu, Yangyang Xu, Chenshu Xu, Tingrui Shen, Wenxi Liu, Yong Du, Jun Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

Text-guided image and 3D editing have advanced with diffusion-based models, yet methods like Delta Denoising Score often struggle with stability, spatial control, and editing strength. These limitations stem from reliance on complex auxiliary structures, which introduce conflicting optimization signals and restrict precise, localized edits. We introduce Stable Score Distillation (SSD), a streamlined framework that enhances stability and alignment in the editing process by anchoring a single classifier to the source prompt. Specifically, SSD utilizes Classifier-Free Guidance (CFG) equation to achieve cross-prompt alignment, and introduces a constant term null-text branch to stabilize the optimization process. This approach preserves the original content's …

Seeing 3d Through 2d Lenses: 3d Few-Shot Class-Incremental Learning Via Cross-Modal Geometric Rectification, Tuo Xiang, Xuemiao Xu, Bangzhen Liu, Jinyi Li, Yong Li, Shengfeng He

Research Collection School Of Computing and Information Systems

The rapid growth of 3D digital content necessitates expandable recognition systems for open-world scenarios. However, existing 3D class-incremental learning methods struggle under extreme data scarcity due to geometric misalignment and texture bias. While recent approaches integrate 3D data with 2D foundation models (e.g., CLIP), they suffer from semantic blurring caused by texture-biased projections and indiscriminate fusion of geometric-textural cues, leading to unstable decision prototypes and catastrophic forgetting. To address these issues, we propose Cross-Modal Geometric Rectification (CMGR), a framework that enhances 3D geometric fidelity by leveraging CLIP’s hierarchical spatial semantics. Specifically, we introduce a Structure-Aware Geometric Rectification module that hierarchically …

Omnivton: Training-Free Universal Virtual Try-On, Zhaotong Yang, Yuhui Li, Shengfeng He, Xinzhe Li, Yangyang Xu, Junyu Dong, Yong Du

Research Collection School Of Computing and Information Systems

Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised in-the-wild methods, which improve adaptability but remain constrained by data biases and limited universality. A unified, training-free solution that works across both scenarios remains an open challenge. We propose OmniVTON, the first training-free universal VTON framework that decouples garment and pose conditioning to achieve both texture fidelity and pose consistency across diverse settings. To preserve garment details, we introduce a garment prior generation mechanism that aligns clothing with the body, followed by continuous boundary stitching technique to achieve …

Cross-Subject Mind Decoding From Inaccurate Representations, Yangyang Xu, Bangzhen Liu, Wenqi Shao, Yong Du, Shengfeng He, Tingting Zhu

Research Collection School Of Computing and Information Systems

Decoding stimulus images from fMRI signals has advanced with pre-trained generative models. However, existing methods struggle with cross-subject mappings due to cognitive variability and subject-specific differences. This challenge arises from sequential errors, where unidirectional mappings generate partially inaccurate representations that, when fed into diffusion models, accumulate errors and degrade reconstruction fidelity. To address this, we propose the Bidirectional Autoencoder Intertwining framework for accurate decoded representation prediction. Our approach unifies multiple subjects through a Subject Bias Modulation Module while leveraging bidirectional mapping to better capture data distributions for precise representation prediction. To further enhance fidelity when decoding representations into stimulus images, …

Search Trajectory Network-Enhanced Multi-Objective Dynamic Algorithm Configuration, Robbert Reijnen, Zaharah Bukhsh, Hoong Chuin Lau, Yaoxin Wu, Yingqian Zhang

Research Collection School Of Computing and Information Systems

Deep reinforcement learning (DRL) has emerged as an effective technique for dynamic algorithm configuration, particularly in evolutionary computation, enabling adaptive parameter updates during algorithmic execution. DRL-based methods have shown broad applicability across different problem domains and are designed to configure algorithms without problem-specific information, making them highly transferable across problem variants and scalable to different problem sizes. This paper proposes a novel graph neural network-based approach that learns representations of Search Trajectory Networks (STNs) to track the convergence behavior of multiple objectives and dynamically reconfigures multiobjective evolutionary algorithms during execution. By capturing how solutions evolve and interact over time, the …

Memad: Structured Memory Of Debates For Enhanced Multi-Agent Reasoning, Shuai Ling, Lizi Liao, Dongmei Jiang, Weili Guan

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) demonstrate remarkable in-context learning capabilities but often struggle with complex, multi-step reasoning. Multi-Agent Debate (MAD) frameworks partially address these limitations by enabling iterative agent interactions. However, they neglect valuable historical insights by treating each new debate independently. In this paper, we propose Memory-Augmented MAD (MeMAD), a parameter-free memory-augmented MAD framework that systematically organizes and reuses past debate transcripts. MeMAD stores structured representations of successful and unsuccessful reasoning attempts enriched with self-reflections and peer feedback. It systematically retrieves them via semantic similarity at inference time to inform new reasoning tasks. Our experiments on challenging mathematical reasoning, scientific …

Mitigating Cross-Modal Representation Bias For Multicultural Image-To-Recipe Retrieval, Qing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Existing approaches for image-to-recipe retrieval have the implicit assumption that a food image can fully capture the details textually documented in its recipe. However, a food image only reflects the visual outcome of a cooked dish and not the underlying cooking process. Consequently, learning cross-modal representations to bridge the modality gap between images and recipes tends to ignore subtle, recipe-specific details that are not visually apparent but are crucial for recipe retrieval. Specifically, the representations are biased to capture the dominant visual elements, resulting in difficulty in ranking similar recipes with subtle differences in use of ingredients and cooking methods. …

Art4math: Handwritten Mathematical Expression Recognition Via Multimodal Sketch Grounding, Yang Zhou, Jin Wang, Yuxiao Zhang, Kaixiang Huang, Guodong Lu, Jingru Yang, Shengfeng He

Research Collection School Of Computing and Information Systems

Handwritten Mathematical Expression Recognition (HMER) remains a challenging task due to the structural complexity of mathematical notation and the ambiguity of handwritten symbols-e.g., ''ρ'' vs. ''p'' or ''B'' vs. ''β''. While stroke-based models offer disambiguation via temporal cues, most existing methods are constrained by coarse modality fusion and a lack of fine-grained cross-modal alignment, further hindered by limited annotated data. We introduce Art for Math (Art4Math), a novel framework that leverages the structural richness of human sketches to enhance HMER through fine-grained, modality-aware learning. Art4Math follows a two-stage training paradigm: Art Grounding (A-Grd) and Math Decoding (M-Dec). In A-Grd, the …

Fine-Grained Abnormality Prompt Learning For Zero-Shot Anomaly Detection, Jiawen Zhu, Yew‑Soon Ong, Chunhua Shen, Guansong Pang

Research Collection School Of Computing and Information Systems

Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained visionlanguage models to detect anomalies in a target dataset without using any dataset-specific training or demonstration. However, these methods often focus on crafting/learning prompts that capture only coarse-grained semantics of abnormality, e.g., high-level semantics like ‘damaged’, ‘imperfect’, or ‘defective’ objects. They therefore have limited capability in recognizing diverse abnormality details that deviate from these general abnormal patterns in various ways. To address this limitation, we propose FAPrompt, a novel framework designed to learn Fine-grained Abnormality Prompts for accurate ZSAD. To this end, a novel Compound Abnormality Prompt …

Website Owner Identification Through Multi-Level Contrastive Representation Learning, Cheng Tu, Yunshan Ma, Yang Li, Min Zhang, Miao Hu, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable …

Unsupervised Visual Chain-Of-Thought Reasoning Via Preference Optimization, Kesen Zhao, Beier Zhu, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing ap proaches focus on text CoT, limiting their ability to lever age visual cues. Visual CoT remains underexplored, and the only work [35] is based on supervised fine-tuning that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoTperforms preference comparisons between model generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for …

Auxiliary Prompt Tuning Of Vision‑Language Models For Few‑Shot Out‑Of‑Distribution Detection, Wenjun Miao, Guansong Pang, Zihan Wang, Jin Zheng, Xiao Bai

Research Collection School Of Computing and Information Systems

Recent advancements in CLIP-based out-of-distribution (OOD) detection have shown promising results via regularization on prompt tuning, leveraging background features extracted from a few in-distribution (ID) samples as proxies for OOD features.However, these methods suffer from an inherent limitation: a lack of diversity in the extracted OOD features from the few-shot ID data.To address this issue, we propose to leverage external datasets as auxiliary outlier data (i.e., pseudo OOD samples) to extract rich, diverse OOD features, with the features from not only background regions but also foreground object regions, thereby supporting more discriminative prompt tuning for OOD detection. We further introduce …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Ai-Enabled Multi-Layer Security Operations: A Combined Siem, Ids, And Threat Intelligence Model For Adaptive Cyber Defense, Mohamad Khayat

Dissertations

This dissertation presents a comprehensive framework for the evolution of Security Operation Centers (SOCs) through the integration of advanced artificial intelligence (AI), blockchain, and optimization techniques. Motivated by the increasing complexity of cyber threats and the limitations of traditional reactive SOC strategies, this work begins with a systematic literature review that identifies critical gaps in current SOC operations. Based on these insights, a reference architecture is proposed to guide the integration of intelligent components into SOC environments. To address the challenge of secure and trustworthy information sharing, a blockchain-based threat intelligence platform is developed, leveraging Byzantine Fault Tolerance and Zero-Knowledge …

Explainabledetector: Exploring Transformer-Based Language Modeling Approach For Sms Spam Detection With Explainability Analysis, Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today's modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult …

Better Digital Contracts With Prosocial Friction-In-Design, Brett Frischmann, Moshe Y. Vardi

Faculty Publications

Contract law is supposed to enable people to reach genuine agreements and cooperate. If this ideal was ever a reality, the rise of mass market contracts and boil­erplate rendered it pure fiction. Modern consumer contracts are incomprehensible to most people. No one reads them anyway.

Digital contracting involves design features that amplify traditional boilerplate harms and create others. For example, digital contracting is too cheap; low marginal costs lead to overexpansion in scale and scope. To make matters worse, the loss of autonomy from repeat engagement with digital contracting systems is pernicious. People become increasingly predictable and programmable as digital …

Building Career Pathways: Investigating The Potential For A Cooperative Education Program For Graduate Itm Students At Central Washington University, Liam J. E Holland

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Susan Rivera, PhD

This research examines the feasibility of introducing a cooperative education (co-op) program at the master’s level in the Central Washington University (CWU) ITM Department. Methods included a review of existing co-op programs, analysis of interviews with past co-op students, and an examination of survey data on student interest in graduate-level cooperative education. The study aims to identify key elements of effective co-op programs and assess demand for such programs. The analysis of current co-op models offers insights into program structure, industry engagement, and student success. The 2019 NACE Internship & Co-op Survey found that 50.2% …

Cwu's Electronic Waste Management: A Success Story In Sustainable Practice, Srijita Gurung

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Susan Rivera, PhD; Kevin Lomax

Purpose: This research examines how Central Washington University (CWU) manages electronic waste (e-waste) to promote economic, environmental, and equitable sustainability practices. In February 2024, Computer Support Services (CSS) started a computer reconciliation project, which is vital due to the Windows 10 operating system going end of life after October 14, 2025, and no longer receiving software support. This project tracks and documents the life of electronic assets, with a focus on the role of Surplus and IT Asset Disposal (ITAD) companies. Method: The primary research methods involve interviews, and online research of active …

Optimizing Cybersecurity Through Ai Predictive Analytics And Human Expertise, Cathy Mae C. Dutong

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

As cybersecurity threats evolve in complexity and scale, the reliance on artificial intelligence (AI) has become increasingly prevalent across both public and private sectors. This study examines the dual role of AI-driven predictive analytics in strengthening organizational cybersecurity, while addressing the ongoing need for human oversight. Through a mixed-method approach, combining survey data from cybersecurity professionals with an extensive literature review, this research analyzes AI's capacity to detect emerging threats, the systemic challenges associated with AI integration, and the indispensable role of human expertise in interpreting AI outputs. Findings indicate that while AI enhances proactive …

Global Trends In Ai-Driven Product Development: A Cross-Country Analysis, Shilpa Dhananjayan

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Hideki Takei, DBA

Artificial Intelligence (AI) is transforming industries and accelerating global innovation, yet its benefits remain unevenly distributed. A nation’s AI readiness—its capacity to adopt and implement AI technologies—plays a crucial role in economic growth and technological advancement. Key determinants of AI readiness include digital infrastructure, data accessibility, government policies, research and development (R&D) investment, and workforce development. This study examines the relationship between AI readiness, AI adoption, innovation, R&D investment of a nation, Digital Infrastructure Index (DII) and Human Capital Index (HCI) using a Random Forest regression model. Findings reveal a strong correlation between AI adoption …

Innovation Within Sustainable Technologies: The Future Of Energy Conserving Hardware In Data Centers, Brooke Cruz

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Susan Rivera, PhD

The US is confronting a growing energy demand crisis, with analysts projecting a 2.4% rise in data center energy consumption by 2030. Data centers are major energy consumers, with primary usage concentrated in IT equipment and extensive cooling systems. While Power Usage Effectiveness (PUE) remains the leading metric for assessing energy efficiency, it is limited to assessing total facility energy and IT energy use without accounting for environmental and social impacts. Supplementary metrics of data center energy consumption include Water Usage Effectiveness (WUE) and Carbon Usage Effectiveness (CUE), which measure water use and carbon emissions, …

Synthesis Of Nitrogen-Enriched 3d Graphene Foam For Electrochemical Sensing Of Hydrogen Peroxide (H₂O₂), Bhargavi Dronavalli, Madhava Rao Vallabhaneni, Jatla Murali Prakash, Deepti Kolli, Dandamudi Srilaxmi

Karbala International Journal of Modern Science

In this work, A simple hydrothermal process was utilized to create 3D-foam-type Nitrogen-doped graphene (NDG). The synthesized material was characterized using a range of physicochemical characterization techniques. These techniques confirm that nitrogen is successfully incorporated into the carbon network, resulting in NDG. Cyclic voltammetry (CV) investigation demonstrated that NDG-modified glassy carbon electrode (NDG/GCE) displayed finite charge transfer properties against typical redox systems. NDG/GCE is identified as a simple, facile, and efficient electrocatalyst material for the estimation of hydrogen peroxide (H₂O₂). From CV analysis, it is revealed that NDG/GCE can produce a signal for electrochemical quantification of …

A Feature Engineering Technique For Enhancing The Generalization Of Machine Learning Models In Estimating Crop Evapotranspiration, Gaku Yokoyama, Sohta Harigai, Shigehiro Kubota, Koichi Nomura, Gregory R. Goldsmith, Daisuke Yasutake, Tomoyoshi Hirota, Masaharu Kitano

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Accurate and precise estimation of evapotranspiration (ET) is crucial for understanding the terrestrial carbon, water, and energy cycles. While process-based models of ET, such as the Penman–Monteith model offer robust generalization capabilities, they are limited by the need for detailed parameters (e.g., stomatal conductance,) that are challenging to measure continuously. On the other hand, machine learning models can estimate ET by capturing relationships between ET and environmental variables without experimentally measuring model parameters. However, machine learning models face the challenge of limited generalizability. This issue is particularly significant given the uncertainty introduced by changing climatic …

Fundamentals Of The New Neutrosophic Matrices, Adebisi Sunday Adesina, Ogunmuyiwa Sodiq Damilola

Neutrosophic Systems with Applications

The New Neutrosophic Matrices provides a mathematical extension of classical and fuzzy matrix theory that incorporates the element of indeterminacy alongside truth and falsity. Neutrosophic logic, pioneered by FlorentinSmarandache, provides a richer framework for dealing with uncertainty and vagueness in real-world data. This study explores the definitions, classifications, and algebraic operations onneutrosophic matrices, including addition, multiplication, scalar operations, and the formation of identities.A comparative analysis is presented to highlight the distinctions between classical, fuzzy, and neutrosophic matrices. From the concepts, potential applications could be proposed most especially, for more problem solving as well as for future exploration.Findingsaffirmthat neutrosophic matrices offer …

Solution Of Fractional Order Diffusion Equations With Clique Neural Network, Merve Zeynep Kaya, Mesut Karabacak, Ercan Çelik

Mathematical Modelling and Numerical Simulation with Applications

In this paper, the clique artificial neural network method is used to solve the fractional diffusion equation, which is a subclass of partial differential equations. The clique neural network architecture is constructed using input, hidden, and output layers. Several degrees of clique polynomials were used as activation functions, and the output layer was obtained by multiplying them with weight coefficients. Subsequently, the optimization equation was derived, and the exact solution, numerical solution, and error function graphs were obtained using a specialized algorithm. Analysis of the results demonstrates that the clique artificial neural network method provides quicker and more accurate results …