Digital Commons Network^™

Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1—an open-source reasoning model—against OpenAI’s GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning curves. Our experiments show DeepSeek-R1 achieves a 91.39% F1 score on 5-class sentiment and 99.31% accuracy on binary tasks with just 5 shots, an eightfold improvement in few-shot efficiency over GPT-4o. Architecture-specific distillation effects emerge, where a 32B Qwen2.5-based model outperforms the 70B Llama-based variant by 6.69 percentage points. While its reasoning …

Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

Towards Goal-Oriented Intelligent Tutoring Systems In Online Education, Yang Deng, Zifeng Ren, An Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Interactive Intelligent Tutoring Systems (ITSs) enhance the learning experience in online education byfostering effective learning through interactive problem-solving. However, many current ITS models do notfully incorporate proactive engagement strategies that optimize educational resources through thoughtfulplanning and assessment. In this work, we propose a novel and practical task of Goal-oriented IntelligentTutoring Systems (GITS), designed to help students achieve proficiency in specific concepts through a tailoredsequence of exercises and evaluations. We introduce a novel graph-based reinforcement learning framework,named Planning-Assessment-Interaction (PAI), to tackle the challenges of goal-oriented policy learningwithin GITS. This framework utilizes cognitive structure information to refine state representation andguide the selection …

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.

Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao

Dissertations and Theses Collection (Open Access)

Graph perturbation, rooted in classical perturbation theory, studies how small topology edits, i.e., adding or deleting edges, affects graph properties (e.g., density, centrality). This fundamental problem underpins applications like bioinformatics, privacy preservation and system defense. While much prior work targets perturbations that influence global graph statistics or model outputs, comparatively little addresses robustness for knowledge discovery and information retrieval. In these settings, graphs are attributed: nodes carry real-world semantics (e.g., locations, people) and edges encode interactions or relationships. This thesis proposes new formulations and algorithms that generate and leverage graph perturbations to make knowledge discovery and retrieval more robust. Specifically, …

When It Comes To Scientific Information Extraction And Llms, Less Is More, Sameer Shaik

Theses and Dissertations from DePaul University

The scientific literature continues to expand rapidly, making manual extraction of structured scientific facts increasingly impractical. Traditional Machine Learning and Natural Language Processing (NLP) pipelines require large expert-annotated datasets, which are costly to produce. Novel Large Language Models (LLMs) face challenges in long-context scientific reasoning, hallucinations, and entity linking. This thesis investigates ELSIE-Blob, a domain-aware preprocessing method that segments scientific articles into compact text “blobs” containing components of entity relations (here, polymer names, melting point indicators, and numerical values). We test whether blob-based input allows lightweight, consumer-hardware-accessible LLMs to extract polymer–melting point (polymer–Tm) pairs accurately without training data. Experiments using …

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

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Attorneys And Ai: How Lawyers Use Artificial Intelligence And Analyze Its Impacts, Matthew I. Hall, Christian Turner, Eddie A. Gomez Schieber, Nathaniel Kite, Ari Schlesinger

Scholarly Works

AI systems are testing lawyers' professional ethics obligations of competence, confidentiality, and candor. In the legal profession, the widespread availability of AI systems presents opportunities, like improving the review of documents during the discovery stage of a lawsuit, and challenges, illustrated by the handful of high-profile incidents where lawyers submitted legal briefs in court citing and describing fictitious cases based on AI-generated output. We conducted interviews with 44 legal professionals in the U.S. to understand how attorneys are making sense of AI technology and the impacts these technologies are having on their profession, legal ethics, and legal institutions. We describe …

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.

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

Startup Success Forecasting Through Machine Learning: A Comprehensive Analysis Of It Startups, Khaled Abdulla Alhassani

Theses

Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success?

This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), …

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 …

Oil Spill Pollution In The Caspian Sea In Case Of Azerbaijan : Evaluating Spill Patterns And Governance Gaps In Azerbaijan’S Marine Oil Pollution Response, Ulviyya Hasanova

World Maritime University Dissertations

No abstract provided.

The Role Of Marine Spatial Planning (Msp) In Managing Conflicts Between Marine Resource Users In Tanzania, Ramadhani Juma Shausi

World Maritime University Dissertations

No abstract provided.

Harnessing Coherent-Wave Control For Sensing Applications, Pablo Jara, Arthur Goetschy, Hui Cao, Alexey Yamilov

Physics Faculty Research & Creative Works

Imaging techniques such as functional near-infrared spectroscopy and diffuse optical tomography (DOT) achieve deep, noninvasive sensing in turbid media; however, they are constrained by the photon budget, as most of the injected light is lost to scattering before reaching the detector. Wavefront shaping (WFS) can enhance signal strength via interference at specific locations within scattering media, enhancing light-matter interactions and potentially extending the penetration depth of these techniques. Interpretation of the resulting measurements relies on knowing the optical sensitivity - the relationship between changes in the detected signals and perturbations at a specific location inside the medium; however, conventional diffusion-based …

A Fake Friend? Ai Companions Are Exactly That, Seow Hon Tan

Research Collection Yong Pung How School Of Law

In a commentary, SMU Associate Professor of Law Tan Seow Hon discussed how AI companions, which promise emotionally intelligent companionship, have blurred the line between human and machine relationships by mimicking empathy, memory, and affection. She suggested that while such technologies may ease loneliness, they risk fostering narcissism, diminishing real human connection, and replacing authentic friendship with comforting illusions that erode the capacity for love and community.

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 …

Final Draft Report - Mapping Pfas In Groundwater Of The West Plains, Spokane County, Washington, 2023-2025, Chad Pritchard

EWU PFAS Research Collection

Since 2017, per- and polyfluoroalkyl substances (PFAS) have been detected in West Plains groundwater. Sampling conducted by Fairchild Air Force Base (FAFB) and the Environmental Protection Agency (EPA) have measured PFAS concentrations in private wells that exceed drinking water standards. Two known sources of PFAS have been identified on the West Plains: FAFB and Spokane International Airport (SIA). Washington Department of Ecology (Ecology) awarded the City of Medical Lake an area-wide groundwater grant to develop a groundwater model of PFAS contamination in the West Plains, the grant was originally written by Eastern Washington University (EWU) and Spokane County Water Resources …

Generative Ai And Empirical Software Engineering: A Paradigm Shift, Christoph Treude, Margaret-Anne Storey

Research Collection School Of Computing and Information Systems

The widespread adoption of generative AI in software engineering marks a paradigm shift, offering new opportunities to design and utilize software engineering tools while influencing both developers and the artifacts they create. Traditional empirical methods in software engineering, including quantitative, qualitative, and mixed-method approaches, are well established. However, this paradigm shift introduces novel data types and redefines many concepts in the software engineering process. The roles of developers, users, agents, and researchers increasingly overlap, blurring the distinctions between these social and technical actors within the field. This paper examines how integrating AI into software engineering challenges traditional research paradigms. It …

International Workshop On Multimodal Generative Search And Recommendation (Mmgensr@Cikm 2025), Yi Bin, Haoxuan Li, Haokai Ma, Yang Zhang, Wenjie Wang, Yunshan Ma, Yang Yang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Recent breakthroughs in generative Artificial Intelligence (AI) have ignited a revolutionary wave across information retrieval and recommender systems. This workshop serves as a premier interdisciplinary platform to explore how generative models, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), are transforming multimodal search and recommendation paradigms [3, 6, 9, 10, 12-14]. We aim to convene researchers and practitioners to discuss innovative architectures, methodologies, and evaluation strategies spanning generative document retrieval [5, 8] generative image retrieval [ 7, 16], grounded answer generation [17], generative recommendation [2, 4, 11], and related tasks involving multiple modalities [1,15]. The workshop will facilitate …

From Personas To Talks: Revisiting The Impact Of Personas On Llm-Synthesized Emotional Support Conversations, Shenghan Wu, Yimo Zhu, Wynne Hsu, Mong-Li Lee, Yang Deng

Research Collection School Of Computing and Information Systems

The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can …

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 …

The Ai-Powered Learning Loop In Higher Education, Oualid Abidi, Vladimir Dzenopoljac, Aleksandra Dzenopoljac

All Works

Purpose – This study examines how generative AI tools affect business students’ academic performance by investigating whether flexible AI policies promote deeper learning, enhance self-efficacy and facilitate tacit knowledge acquisition in a Middle Eastern context, while ensuring efficiency and academic integrity. Design/methodology/approach – A qualitative, exploratory study observed 20 final-year business students in Kuwait during five in-class activities using generative AI tools. Semi-structured interviews complemented the researcher’s observations. Thematic analysis revealed patterns in benefits, challenges and learning processes, leading to the development of the AI-powered learning loop framework to explain academic performance outcomes. Findings – The study indicates that generative …

Versatile Nitration Of Bodipy Dyes Using No₂Bf₄: Effects Of Nitro Substituents On Spectroscopic And Self-Assembly Properties, Caroline Sarange Gwaro

LSU Master's Theses

This study explores the regioselective nitration of BODIPY dyes at the 2, 3- and 2,6-positions using nitronium tetrafluoroborate (NO₂BF₄), offering a high-yielding and mild synthetic route. The introduction of nitro groups significantly alters the dyes’ photophysical and self-assembly properties. Mono-nitrated BODIPYs exhibit hypsochromic shifts in absorption and emission spectra, increased dipole moments, enhanced Stokes shifts, and reduced molar absorptivity. In contrast, di-nitrated analogs show bathochromic shifts and decreased dipole moments in both ground and excited states. Spectroscopic characterization was performed using UV-vis and fluorescence spectroscopy, supported by density functional theory (DFT) calculations. Self-assembly behavior was investigated in polar (acetonitrile) and …

Establishing A Framework Of Best Management Practices For Sustainable Water Quality: Integration Of Technology And Remote Sensing, Mason L. Marcantel

LSU Master's Theses

Water quality security is a complex management issue that is constantly challenged by factors of seasonal variability in climate change, urban population growth, and agricultural intensification. These factors warrant the need for innovative technological adaptations to management practices that integrate remote monitoring, predictive modeling, and sustainable resource utilization. This study establishes a framework for sustainable water quality practices through the integration of remote sensing technology for urban wastewater treatment and agricultural irrigation systems. This dual-site research study explores seasonal water quality dynamics and technological interventions to create a more proactive approach to water quality security.

In the first study, water …

Quantum Leap: Harnessing Quantum–Ai Synergy For Resilient Supply Chains And Predictive Routing Under Tariff Shocks, Andrew Burnstine, Raouf Ghattas

Faculty and Staff Publications & Presentations

No abstract provided.

Persepsi Mahasiswa Ilmu Perpustakaan Terhadap Penggunaan Perangkat Ai Llm Dalam Pencarian Informasi, Danisya Laila Zahra, Muhamad Prabu Wibowo

Jurnal Ilmu Informasi, Perpustakaan, dan Kearsipan

The increasing use of generative artificial intelligence (AI) among university students is driving changes in the way they seek and manage information, including in academic contexts. ChatGPT and DeepSeek AI are two AI platforms based on Large Language Models (LLMs) that are increasingly utilized as tools to support information seeking processes. This study aims to analyze the preferences of students from the Library and Information Science Program, Faculty of Humanities, Universitas Indonesia (FIB UI), in using these two platforms. The research employs a case study method with a qualitative approach, involving in-depth interviews with ten students. This study explores their …

Computational Nanoscale Investigations Of Structure, Dynamics, And Reactivity At Solid–Liquid Interfaces, Golam Azom

LSU Doctoral Dissertations

Interfaces where two or more condensed phases meet govern phenomena central to catalysis, electrochemical energy conversion, separations, and environmental remediation. Their structural heterogeneity and dynamic nature make them challenging to probe experimentally, but computer simulations provide molecular-level insights into processes that remain inaccessible to experiments. This dissertation employs ab initio molecular dynamics (AIMD), classical molecular dynamics, and machine-learned interatomic potentials to investigate the structure, dynamics, and proton transport at graphene oxide (GO)–liquid interfaces.

The first project explores aqueous GO interfaces across a spectrum of oxidation states that extends from highly oxidized sheets to fully reduced graphene. Deep neural network force …

Enzymes Of Calendula Officinalis L. As Affected By Foliar Application Of Nano-Nitrogen And Potassium Fertilizers, Under Water Stress Conditions, Aqeel Abdulabbas Alsudani, Qais Hussain Abbas Al-Semmak

Karbala International Journal of Modern Science

Water stress is a major environmental factor that limits the growth and productivity of Calendula officinalis L. To alleviate its negative effects, recent approaches have increasingly focused on nano-fertilizers that enhance plant antioxidant defenses. This study therefore aimed to evaluate the effect of foliar application of nano-nitrogen (0, 2, and 4 mL L⁻¹) and nano-potassium (0, 2, and 4 g L⁻¹) fertilizers under two irrigation regimes (100% and 50% of field capacity) on the activity of key antioxidant enzymes, including catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD). The results revealed that irrigation at 50% field capacity significantly increased CAT, …