Digital Commons Network^™

Context-Aware Hierarchical Taxonomy Generation For Scientific Papers Via Llm-Guided Multi-Aspect Clustering, Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin

Research Collection School Of Computing and Information Systems

The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models (LLMs), often lack coherence and granularity. We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering. Our method leverages LLMs to identify key aspects of each paper (e.g., methodology, dataset, evaluation) and generates aspect-specific paper summaries, which are then encoded and clustered along each aspect to form a coherent hierarchy. In addition, we introduce a new evaluation benchmark of 156 expert-crafted taxonomies encompassing 11.6k …

Why Stop At One Error? Benchmarking Llms As Data Science Code Debuggers For Multi-Hop And Multi-Bug Errors, Zhiyu Yang, Shuo Wang, Yukun Yan, Yang Deng

Research Collection School Of Computing and Information Systems

LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases. LLMs’ capabilities to autonomously find and fix runtime logical errors in complex data science code remain largely unexplored. To address this gap, we introduce DSDBench: the Data Science Debugging Benchmark, the first benchmark for systematic evaluation of LLMs on multi-hop error tracing and multi-bug detection in data science code debugging. DSDBench adapts datasets from existing data science task benchmarks, such as DABench and MatPlotBench, featuring realistic data science debugging tasks with automatically synthesized multi-hop, multi-bug code snippets. …

Temporal And Heterogeneous Graph Neural Network For Remaining Useful Life Prediction, Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu

Research Collection School Of Computing and Information Systems

Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time-series sensory data from such systems, deep learning (DL) models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modeled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal …

Does Generative Ai Facilitate Investor Trading? Early Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao

Research Collection School Of Accountancy

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading …

Global Cooling Watch 2025, Omar Abdelaziz, Ray Gluckman, Ian Hamilton, Radhika Khosla, Lily Riahi

Research Collection College of Integrative Studies

The second edition of UNEP’s Global Cooling Watch Report takes a deep dive into one of the decade’s most urgent challenges: surging heat, soaring cooling demand, and stark inequalities in access. Produced by the UNEP-led Cool Coalition, the report provides a comprehensive assessment of the rapidly growing global demand for cooling and the need for climate-friendly solutions to the issue. The 2025 report builds on the first Global Cooling Watch (2023), which established the Sustainable Cooling Pathway framework. The new edition deepens analysis of intensifying extreme heat and its effect on cooling demand, improved assessment of passive cooling, and coverage …

Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth’s surface, each with a specific profit and multiple time windows. The objective is to maximize the collected profit of all observations completed under some operational constraints. The problem can be modeled as a variant of the Team Orienteering Problem with Time Windows (TOPTW). The key differences with the regular TOPTW are twofold: first, a time-dependent transition time is required for each pair of consecutive observations to adjust the camera’s look angles. Second, the time windows of each target vary during different observation cycles, …

Draft 2026 Interim Site-Wide Surface Water Monitoring Quality Assurance Project Plan, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Butte Priority Soils Operable Unit Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp) Revision 0, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Catch Me If You Can: Microplastics Hidden In Plain Sight, Karin Mattsson, Pere Masque, Thomas Maes, Marc Metian

Research outputs 2022 to 2026

Microplastic (MP) pollution is widely recognised as a pressing environmental issue, yet certain major sources remain under-addressed. This paper focuses on three significant but often overlooked contributors: paint particles, textile fibres, and tire wear particles (TWP). Together, these sources account for over 25 % of global MP emissions, nearly 3 million tonnes annually, yet they are rarely singled out in research or regulation. Each presents unique analytical and regulatory challenges due to their complex compositions, diffuse emissions, and strong ties to daily life. We enumerate current knowledge on their environmental pathways, emission estimates, and detection methods, highlighting the need for …

Carbon–Neutral Granular Filtration For Sustainable And Safe Rainwater Harvesting In Urban Malaysia, Mohammad Nur-E-Alam, Md Helal Uddin, Saurav Dixit, Boon Kar Yap, Mohammad Tariqul Islam, Nowshad Amin

Research outputs 2022 to 2026

This work aims to design a granular filter-based carbon–neutral rainwater treatment system. Granular filters are capable of cleaning rainwater while leaving a carbon–neutral trace to promote environmentally friendly rainwater collection techniques by examining the filtration performance and impact on the environment. The elimination of pollutants and impurities from collected rainwater through granular filters highlights the significance of meeting water quality requirements. The system uses three filters (F1, F2, and F3) for the treatment process where palm (oil) fruit shell-activated carbon, coconut shell-activated carbon, coarse sand, and fine sand are used as raw materials. The physicochemical quality of rainwater (harvested at …

A Century Of Sediment Metal Contamination Of Mar Menor, Europe's Largest Saltwater Lagoon, Irene Alorda-Montiel, Valentí Rodellas, Ariane Arias-Ortiz, Albert Palanques, Andrea G. Bravo, Júlia Rodriguez-Puig, Aaron Alorda-Kleinglass, Carlos Green-Ruiz, Marc Diego-Feliu, Pere Masqué, Javier Gilabert, Jordi Garcia-Orellana

Research outputs 2022 to 2026

Coastal enclosed ecosystems, such as lagoons, are vulnerable to anthropogenic impacts because they favor the accumulation of contaminants from the surrounding watersheds, particularly in their sediments. Europe's largest saltwater lagoon, the Mar Menor (SE, Iberian Peninsula), is a highly impacted ecosystem and the first in the continent to be granted personhood rights. Based on a high-resolution spatial and temporal dataset, we present the historical reconstruction of metal contamination in this ecosystem during the last century. Our results highlight that sediment metal contamination has been mainly driven by the development of the mining industry in the nearby Sierra Minera de Cartagena-La …

Adaptive Guard Band And Power Control For Resource Allocation In Mobile And Fixed Mission-Critical Iout Networks, Walid K. Hasan, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

The Internet of Underwater Things (IoUT) is transforming underwater communication by enabling essential mission-critical applications such as precise navigation, emergency response coordination, diver safety, robust security and surveillance systems, and real-time environmental monitoring. However, Underwater Acoustic Communication (UAC), which serves as the primary communication medium for IoUT, experiences substantial challenges, including limited bandwidth availability, severe signal attenuation and Doppler-induced frequency shifts, especially pronounced in mobile underwater environments. These challenges degrade throughput and increase latency, making it difficult to meet the strict delay and reliability demands of mission-critical IoUT applications. Without adaptive solutions, real-time underwater communication remains unreliable and inefficient. This …

Clinician-Led Development And Feasibility Of A Neural Network For Assessing 3d Dental Cavity Preparations Assisted By Conversational Ai, Mohammed El-Hakim, Haitham Khaled, Amr Fawzy, Robert Anthonappa

Research outputs 2022 to 2026

Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT^® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700^® and …

Analysis Of Modern Research In The Field Of Automation And Digitalization Of The Dairy Industry, N.A Kabulov, U.K. Sabirov, A.K. Oqilov

Chemical Technology, Control and Management

This article presents a systematic analysis of modern research aimed at the advancement of automation and digitalization technologies in the dairy industry, with particular emphasis on the processes of product sorting and quality control. The main directions of digital solution implementation are examined, including the use of robotic sorting lines, computer vision systems, spectral analysis sensors, and artificial intelligence tools for the automatic verification of product compliance with established standards. A comparative analysis of existing approaches to the implementation of intelligent control systems within the technological cycle — from raw material intake to packaging and finished product logistics — is …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

Development Of Fire Prediction And Prevention Digital System Algorithms, Oybek Zokirovich Koraboshev

Chemical Technology, Control and Management

This research work is devoted to the development of algorithms for a digital system aimed at early detection, prediction and prevention of fire hazards. In the work, the process of fire hazard assessment is modeled on the basis of modern information technologies and artificial intelligence tools. The main focus is on collecting data in real time, analyzing it and creating algorithms that determine the level of danger. In the process of research, methods of data cleaning, normalization and determination of correlation between variables were used to process multidimensional data streams obtained from various sensors (temperature, smoke, gas concentration and humidity …

The Problem Of Identification Of Linear Stationary Objects With Distributed Parameters By Their Experimental Transient Characteristics, Miraziz Vorisovich Sagatov

Chemical Technology, Control and Management

A wide class of control system elements can be described with reasonable accuracy by the concept of a linear stationary dynamic object. Several mathematical descriptions of such an object are known. The traditional mathematical model is a high-order ordinary linear differential equation. In the Laplace image space, this corresponds to a fractional-rational transfer function. The latter can be decomposed into elementary fractions. Then, using the convolution theorem and tables of elementary Laplace transform functions, one can access the originals. It is crucial to ensure precise alignment of the parameters of the mathematical model of the object used in the corrector …

Spatio-Temporal Gcn With Softmax Classifier For Skeleton-Based Human Action Recognition, Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov

Chemical Technology, Control and Management

Skeleton-based human action recognition is an important research area with many practical applications. Most existing methods rely on single representations of skeletal sequences, which cannot totally obtain all the complex features of human movements. This paper presents LFHAR (Latent Features for Human Action Recognition), a new framework that uses multiple spatio-temporal latent representations to improve the extraction of action features. Our method captures how skeletal poses change over time and combines motion information from both individual joints and connected body parts. The proposed approach applies graph-based processing to each skeleton frame in a sequence, then arranges the resulting graph features …

More Alike Than Not: The Open Access Preferences Of Humanities Scholars, Rachel E. Scott, Ana Dubnjakovic

Faculty and Staff Publications – Milner Library

Several studies have noted that humanists have not been as quick or enthusiastic in their adoption of Open Access (OA) as their colleagues in other disciplines. This article leverages the Ithaka S+R US 2021 Faculty Survey to provide contextualized analysis of the OA and Open Education Resources (OER) preferences of humanities scholars, as well as some practices related to OA and OER, relative to their colleagues in other disciplines. Findings suggest that although humanists do stand apart in many OA preferences, the small effect sizes render these differences less important than previously suspected. The implications of these findings are considered …

Pfaffian Solution For Dark–Dark Soliton To The Coupled Complex Modified Korteweg–De Vries Equation, Chenxi Li, Xiaochuan Liu, Bao-Feng Feng

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we study the coupled complex modified Korteweg–de Vries (ccmKdV) equation by combining the Hirota’s method and the Kadomtsev–Petviashvili (KP) reduction method. First, we show that the bilinear form of the ccmKdV equation under nonzero boundary condition is linked to the discrete BKP hierarchy through Miwa transformation. Based on this finding, we construct the dark–dark soliton solution in the pfaffian form. The dynamical behaviors for one- and two-soliton are analyzed and illustrated.

(Un)Just Transitions For Youth And The Disabled? The Exclusionary Pressures Facing Science, Technology, Engineering And Mathematics (Stem) Education, Elizabeth Done, Helen Knowler, Irina Lazar, Sylvia Hayes, Joshua Lait, Andrea Perez-Porres

School of Law, Humanities and Social Sciences

This paper highlights the under-representation of young people with disabilities in STEM subjects and career pathways, with particular reference to the engineering sector given its prominent role in the design of energy systems related to net-zero policies. We consider the role of physical infrastructure, and pedagogic and representational practices, in facilitating the marginalisation and under-representation of young people with disabilities. Ultimately, we seek a broader understanding of the ‘just transition’ that acknowledges the role of education in ensuring the active participation of all young people, regardless of learning difficulty or disability or social identity, in that transition. The research is …

Image Captioning Through The Lens Of The Gricean Maxims: Generating Meaningful And Relevant Image Descriptions, Annika Lindh

Doctoral

Image captioning models enable us to automatically generate natural language image descriptions for previously unseen images. It combines the two fields of computer vision and natural language generation, allowing models to interpret the con tent of an image and communicate that knowledge through natural language text.

Research into image captioning has the potential benefit of reducing the gap in digital information availability between fully sighted individuals and those who are visually impaired. However, automatically generated captions often fail to provide the required level of detail and specificity to achieve this goal. Furthermore, current standard evaluation methods are insufficient at measuring …

Model Fatigue: Debugging Hector Through Climate Computational Aesthetics, Helen Pritchard, Guillemette Legrand

School of Art, Design and Architecture

In this paper, we document three computational features of the climate model Hector by translating the modalities through which the model predicts climate futures in a game engine. We build upon the computational aesthetics of M. Beatrice Fazi and Matthew Fuller (2016) and suggest additional characteristics of computation to their proposed list. We argue that these computational aesthetics are essential to climate modelling. The three features are time series, couplings and cosmograms, which are based on our practical and theoretical inquiry into Hector’s computation and literacy. We develop a framework that employs two processual methods: translating the model’s operations into …

Multicomponent One-Dimensional Quantum Droplets Across The Mean-Field Stability Regime, I. A. Englezos, P. Schmelcher, S. I. Mistakidis

Physics Faculty Research & Creative Works

The Lee-Huang-Yang (LHY) energy correction at the edge of the mean-field stability regime is known to give rise to beyond mean-field structures in a wide variety of systems. In this work, we analytically derive the LHY energy for two-, three-and four-component one-dimensional bosonic short-range interacting mixtures across the mean-field stability regime. For varying intercomponent attraction in the two-component setting, quantitative deviations from the original LHY treatment emerge being imprinted in the droplet saturation density and width. On the other hand, for repulsive interactions an unseen early onset of phase-separation occurs for both homonuclear and heteronuclear mixtures. Closed LHY expressions for …

Defects4c: Benchmarking Large Language Model Repair Capability With C/C++ Bugs, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Jiongchi Yu, Jiaolong Kong, Yi Li

Research Collection School Of Computing and Information Systems

Automated Program Repair (APR) plays a critical role in enhancing the quality and reliability of software systems. While substantial progress has been made in Java-based APR, largely facilitated by benchmarks like Defects4J, there remains a significant gap in research on C/C++ program repair, despite the widespread use of C/C++ and the prevalence of associated vulnerabilities. This gap is primarily due to the lack of high-quality, open-source benchmarks tailored for C/C++. To address this issue, we introduce Defects4C, a comprehensive and executable benchmark specifically designed for C/C++ program repair. Our dataset is constructed from real-world C/C++ repositories and includes a large …

Mmlu-Prox: A Multilingual Benchmark For Advanced Large Language Model Evaluation, Weihao Xuan, Et. Al.

Research Collection School Of Computing and Information Systems

Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves …

Interaction2code: Benchmarking Mllm-Based Interactive Webpage Code Generation From Interactive Prototyping, Jingyu Xiao, Yuxuan Wan, Yintong Huo, Zixin Wang, Xinyi Xu, Wenxuan Wang, Zhiyao Xu, Yuhang Wang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance on the design-to-code task, i.e., generating UI code from UI mock-ups. However, existing benchmarks only contain static web pages for evaluation and ignore the dynamic interaction, limiting the practicality, usability and user engagement of the generated webpages. To bridge these gaps, we present the first systematic investigation of MLLMs in generating interactive webpages. Specifically, we formulate the Interaction-to-Code task and establish the Interaction2Code benchmark, encompassing 127 unique webpages and 374 distinct interactions across 15 webpage types and 31 interaction categories. Through comprehensive experiments utilizing state-of-theart (SOTA) MLLMs, evaluated via both automatic …

Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Automated audio captioning (AAC) benefits from incorporatingexternal context to interpret complex sounds, but doing so withretrieval-augmented generation (RAG) at inference is sometimesinfeasible due to data availability or incurs significant latency andcomplexity. We propose DistillCaps, a novel training-time frame-work that leverages RAG to guide knowledge distillation for im-proved audio-language alignment, while lessening the relianceon retrieval during inference. In our framework, a RAG-equippedteacher model retrieves relevant textual information (e.g., simi-lar captions) for each audio clip and uses it for training to gener-ate context-enriched captions. Simultaneously, a student model istrained to imitate this teacher, learning to produce high-qualitycaptions from audio alone. We further …

Designing For Novice Debuggers: A Pilot Study On An Ai-Assisted Debugging Tool, Oka Kurniawan, Erick Chandra, Christopher M. Poskitt, Yannic Noller, Kenny T.W. Choo, Cyrille Jegourel

Research Collection School Of Computing and Information Systems

Debugging is a fundamental skill that novice programmers must develop. Numerous tools have been created to assist novice programmers in this process. Recently, large language models (LLMs) have been integrated with automated program repair techniques to generate fixes for students' buggy code. However, many of these tools foster an over-reliance on AI and do not actively engage students in the debugging process. In this work, we aim to design an intuitive debugging assistant, CodeHinter, that combines traditional debugging tools with LLM-based techniques to help novice debuggers fix semantic errors while promoting active engagement in the debugging process. We present findings …

Instructors’ Strategies In Creating And Implementing Constructivist Llm-Based Learning Activities, Emily Aurelia, Shun Yi Yeo, Michelle Lui, Effie Lai-Chong Law, Anthony Tang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being integrated into educational settings, enabling more adoption of constructivist teaching and learning approaches in classrooms. This paper explores the strategies instructors are currently using to incorporate LLMs into learning activities that align with constructivist principles, which emphasize that learners actively construct their own knowledge. Through interviews with nine instructors who have designed eleven distinct LLM-based activities and using reflexive thematic analysis, this study identifies various types of learning activities with respect to four different aspects of the constructivist learning theory. The strategies employed and challenges faced to foster constructivist student-LLM interaction were also …