Digital Commons Network^™

Concept Drift Detection For Streaming Data Using One-Class Classification, Poorna Sandamini Senaratne

Graduate Studies Theses and Dissertations 2026

Modern machine learning systems are increasingly deployed in streaming environments where data arrive sequentially and the underlying data-generating process may evolve over time. This phenomenon, known as concept drift, can significantly degrade model performance if not detected and addressed in a timely manner. This dissertation proposes a principled framework for concept drift detection based on one-class classification, integrating neural network embeddings with Support Vector methodologies.

The proposed approach leverages neural networks to learn compact and informative embeddings of input data, capturing complex nonlinear structures in a lower-dimensional latent space. These embeddings are then used to construct a statistical description of …

Volumetric Fluorescence Microscopy For High-Throughput And High-Sensitivity Imaging: From Single Molecules To Tissues, Le-Mei Wang

Graduate Studies Theses and Dissertations 2026

Fluorescence microscopy is an indispensable tool in the biological sciences, enabling researchers to investigate intricate subcellular structures, particularly for volumetric studies. However, conventional optical microscopy for volumetric imaging remains fundamentally constrained by imaging speed and throughput. To bypass traditional serial z-scanning, we introduce an axially scan-free method using a phase layer cake to modulate the system's point spread function. This approach projects volumetric information onto a 2D plane in a single shot, offering high flexibility in tuning axial depth alongside simultaneous multicolor imaging with high spatial resolution and sensitivity. This dissertation divides these technical advancements into cellular and tissue imaging …

Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article studies the practical predefined-time synchronization (PPTS) for complex networks (CNs) under deception attacks based on the asynchronously intermittent event-triggered control (AIE-TC). Notably, AIE-TC effectively integrates the advantages of asynchronously intermittent control (AIC) and event-triggered control, where AIC provides each subsystem node with independent control and rest intervals. Besides, all synchronization errors of the CNs converge to an adjustable neighborhood within the predefined time by designing a bounded time-varying function into the controller. Moreover, this article considers that the transmission network is subjected to stochastic deception attacks modeled by a Markov process, which captures the state-driven dynamic transition characteristics …

A General Algorithm For Assortment Optimization Under Random Utility Choice Models, Tien Mai, Andrea Lodi

Research Collection School Of Computing and Information Systems

This work concerns the assortment optimization problem that refers to selecting a subset of items that maximizes the expected revenue in the presence of the substitution behavior of consumers specified by a random utility choice model. The key challenge lies in the computational difficulty of finding the best subset solution, which often requires exhaustive search. The literature on constrained assortment optimization lacks a practically efficient method that is general to deal with different types of customer choice models (e.g., the multinomial logit, mixed logit or general multivariate extreme value models). In this work, we propose a new approach that allows …

Integrating Symbolic And Waveform Music Into Large Language Models, Teng Tu, Xiaohao Liu, Yunshan Ma, Ji Qi, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Music, as a unique and integral element of human life, is characterized by its complex structures, intricate details, and the fusion of multimodal information. Recent study advance music understanding by leveraging knowledge and reasoning capabilities derived from Large Language Models (LLMs). However, they often lack compatibility and fail to fully utilize the complementary strengths of diverse representations (e.g., ABC, MIDI, Waveform). To address these limitations, we propose a unified music-language model framework, named UniMuLM, transitioning from single-representation approaches to the integration of multiple music representations for LLM. Unifying different music representation formats poses challenges such as patch integrity and boundary …

Decentralized Linear Solvers: Communication Cost And Privacy, Nelson G. Brasil, Vinay A. Vaishampayan

Publications and Research

We consider the problem of multiple parties iteratively solving a system of linear equations in a decentralized manner. Specifically, we solve for $\negr{x} \in \R^n$, the $n \times n$ system of linear equations $M\negr{x} = \negr{b}$ when each party only knows their row of the matrix $M$ and a single component of $\negr{b} \in \R^n$. Our objective is to determine the tradeoff between the accuracy of the solution and the total communication cost measured in bits. A fully connected, reliable mesh network is assumed to connect the different parties. We develop a general formulation that applies to a large class …

Evaluation Of Chromium-Crosslinked Amps-Hpam Copolymer Gels: Effects Of Key Parameters On Gelation Time And Strength, Maryam Sharifi Paroushi, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Controlling CO₂ channeling in heterogeneous reservoirs remains a major challenge for both enhanced oil recovery (EOR) and secure geological storage. AMPS-HPAM copolymers exhibit high-temperature resistance and brine tolerance compared with conventional HPAM gels, making them well suited for the harsh environments associated with CO₂ injection. Chromium-based crosslinkers (CrAc and CrCl₃) were investigated because sulfonic acid groups in AMPS can coordinate with trivalent chromium ions, enabling dual ionic crosslinking and the formation of a robust gel network. While organic crosslinked AMPS-HPAM gels have been widely studied, the behavior of chromium-crosslinked AMPS-containing systems, particularly their gelation kinetics under …

Effect Of Ionic Strength On Gelation Time And Strength Of Amps-Based Polymer Gels, Maryam Sharifi Paroushi, Xuyang Tian, Baojun Bai, Thomas P. Schuman, Yin Zhang, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Polymer gel treatment has been widely applied for improving sweep efficiency and controlling excessive water and gas production. Their performance depends on gelation time and final gel strength. In most studies, brine salinity is used to describe the effect of formation water on gel behavior. However, changing salinity also changes ionic strength and ion composition at the same time. Because of this coupling, it is difficult to identify the mechanisms controlling gelation, which has led to inconsistent trends in the literature. Increasing salinity has been reported to either slow or accelerate gelation and to weaken or strengthen gels depending on …

Large Language Model-Assisted Research Question Development In Public Health: A Case Study In The Special Supplemental Nutrition Program For Women, Infants, And Children (Wic), Qi Zhang, Bidusha Neupane, Priyanka Patel, Futun N. Alkhalifah, Yi He, Leslie Hodges

Health Behavior, Policy & Management Faculty Publications

Objective:

To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages.

Design:

We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the USDA summary of the final public comments on the WIC revision. Five questions weekly for three weeks were generated using LLMs under two conditions: fed with or without relevant literature. The experiment generated 90 questions, which were evaluated using the FINER criteria (Feasibility, Innovation, Novelty, Ethics, …

Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To …

Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria

Computer Science Faculty Research & Creative Works

Mining industry is rapidly transforming into an AI-driven cyber-physical ecosystem where safety and operational reliability depend on robust perception, resilient communication, trustworthy distributed intelligence and continuous monitoring of miners and equipment. Real-world mining environments impose severe constraints like poor illumination, dust, occlusion, GPS-denied conditions, irregular underground topologies, and intermittent connectivity. These factors degrade perception quality, disrupt situational awareness, impair trajectory prediction and weaken the reliability of distributed learning systems. Emerging cyber-physical threats, including backdoor triggers, sensor spoofing, label-flip attacks and poisoned model updates, further jeopardize operational safety, particularly as mines increasingly adopt autonomous vehicles, humanoid assistance, and federated learning for …

Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which …

Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network …

Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …

You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …

Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes …

Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma

Computer Science Faculty Research & Creative Works

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

Aim5b: Ai Integrated Semantic Framework For 5g And Beyond Network Management, Thanveer Sulthana, Ava Sharif Jourabchi, Venkat Rao Manavarthi, Jayadithya Nalajala, Ankitha Srirama Reddy, Baek Young Choi, Sejun Song

Computer Science Faculty Research & Creative Works

Scalable, interpretable, and intelligent network monitoring and management are critical for 5 G and future networks. This paper introduces Aim5B, an AI-integrated semantic framework for 5 G and beyond network management to address these challenges. Aim5B processes unstructured logs from key 5G core network functions, and transforms them into a knowledge graph aligned with the semantic structure of control-plane events. Leveraging a large language model (LLM), Aim5B enables natural language queries to be translated into Cypher graph queries, facilitating precise log retrieval, event analysis, temporal correlation, and statistical summarization-without relying on static parsing rules or predefined dashboards. Integrated on a …

Escher: Efficient And Scalable Hypergraph Evolution Representation With Application To Triad Counting, S. M. Shovan, Arindam Khanda, Sanjukta Bhowmick, Sajal K. Das

Computer Science Faculty Research & Creative Works

Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as triad counts is essential, as hypergraphs can reveal intricate group interaction patterns that conventional graphs fail to capture. In realworld scenarios, these networks are often large and dynamic, introducing significant computational challenges. Due to the absence of specialized software packages and data structures, the analysis of large dynamic hypergraphs remains largely unexplored. Motivated by this gap, we propose ESCHER, a GPU-centric parallel data structure for Efficient and Scalable Hypergraph Evolution Representation, designed to manage largescale hypergraph dynamics efficiently. We also design …

Digital Twin Freshness Maximization In Edge Computing, Jing Li, Jianping Wang, Weifa Liang, Quan Chen, Sajal K. Das, Xiaohua Jia

Computer Science Faculty Research & Creative Works

Mobile Edge Computing (MEC) shifts powerful computing resource provisioning from remote powerful data centers to the edge of core networks. Meanwhile, Digital Twin (DT) has surfaced as a promising technology to provide comprehensive and dynamic descriptions of physical objects in cyberspace with bidirectional and real-time interactions. Moreover, Internet of Things (IoT) devices have contributed abundant, heterogeneous and continuous data from interconnected devices to the explosion of DTs. With technologies evolution, there is an increasing necessity to address the freshness of both DT states and DT data, through timely synchronizations between DTs and their objects in a highly dynamic IoT environment. …

Mapping Multiclass-Targeted Hate Speech In Online Discourse: An Open Dataset, Sanaa Kaddoura, Sumaia Al-Kohlani

All Works

Online social networks have become central spaces for public discourse, where hostile and discriminatory language toward social groups can cause psychological and social consequences for marginalized communities. Although multiple public hate speech datasets are available, many rely on binary categorization practices that obscure linguistic, cultural, and contextual variation across targeted groups. As a result, minority and less visible forms of hate speech remain insufficiently documented and analyzed. This discussion paper examines methodological limitations in existing hate speech annotation schemes and presents a re-annotation framework applied to the HatEval2019 dataset. The proposed framework introduces target-specific multiclass labels that distinguish subcategories of …

The Occurence Of Expansive Clay Soils In Illinois And Their Effects On Shallow Lightly Loaded Foundations, Greg James Gollaher

Masters Theses

"The damage caused to shallow foundations and slabs on grade by the shrink-swell activity of clay soils is well documented across the United States. There are local and national resources available that delineate areas that are problematic and areas that are not. These resources paint Illinois as an undemanding foundation environment. However, our experience differs from this. Our experience tells us that damage related to the shrink-swell activity of clay soils is widespread, and commonly severe causing not only cosmetic damage to structures and hard improvements but also affecting the serviceability and longevity of structures. My goal is to accurately …

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO₂, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO₂-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and ¹H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

Machine Learning For Wearable Sensor-Based Human Movement Rehabilitation: A Five-Year Systematic Review, Yassine Benachour, Farid Flitti, Lina Maloukh, Aicha Beya Far, Elhocine Boutellaa, Mohamed Bentoumi, Marwa Chendeb El Rai, Nour Aburaed, Khaled Ali, Moez Rehman, Sultan Mosleh, Rania Dghaim, Sadok Bouamama

All Works

Wearable-sensor-based human movement analysis is an increasingly important component of digital health and rehabilitation, enabling objective monitoring and data-driven personalization of therapy. In parallel, machine learning (ML) methods have rapidly expanded for interpreting multimodal movement signals, yet the evidence base remains heterogeneous and difficult to benchmark. This PRISMA-guided systematic review synthesizes recent ML approaches for wearable human motion analysis in rehabilitation-oriented health applications. We searched IEEE Xplore, PubMed, and Scopus for English-language studies published from 2021 to 2025 and extracted information on sensor modalities, ML task formulations and model families, dataset characteristics, validation protocols, and reported performance metrics, together with …

Sadqn-Based Residual Energy-Aware Beamforming For Lora-Enabled Rf Energy Harvesting For Disaster-Tolerant Underground Mining Networks, Hilary Kelechi Anabi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a …

Gui Test Migration Via Abstraction And Concretization, Yakun Zhang, Chen Liu, Xiaofei Xie, Yun Lin, Jin Song Dong, Dan Hao, Lu Zhang

Research Collection School Of Computing and Information Systems

GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widget-mapping paradigm that maps widgets from source apps to target apps. However, since different apps may implement the same functionality in different ways, direct mapping may result in incomplete or buggy test cases, thus significantly impacting the effectiveness of testing the target functionality and the practical applicability of migration approaches.In this article, we propose a new migration paradigm (i.e., the abstraction-concretization paradigm) that first abstracts the test logic for the target functionality and …

A Case Study Of Gender And Online Team Communication In Software Engineering Education, Rita Garcia, Christoph Treude

Research Collection School Of Computing and Information Systems

Collaboration is crucial in Software Engineering (SE), yet factors like gender bias can shape team dynamics and behaviours. This descriptive case study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub projects. Focusing on gender, we used a mixed-methods approach to analyse Slack communications, identifying gender differences in how students respond to initiated communications and comparing how students’ communications influenced other aspects of students’ performance, including learning gains. We found higher help-seeking and leadership behaviours in the all-woman team involved in this case study, while men responded more slowly. Although communication did not directly affect …

Gig Worker Social Referrals On An On-Demand Food Delivery Platform, Hai Wang, Hao Sun, Peter Zhang

Research Collection School Of Computing and Information Systems

Social referral programs are commonly used by online labor platforms to incentivize labor supply by rewarding existing workers for successful referrals. This study investigates the impact of such programs on gig workers' labor supply in online labor platforms using data from an on-demand food delivery platform in Singapore. In particular, we analyze how gig workers' past labor supply and referral behavior influence the generation and value of social referrals. This research offers insights into the mechanisms that drive labor supply dynamics in the gig economy and highlights the effectiveness of social referral programs for shaping worker behavior and enhancing platform …

Generalization Bounds For Semi‑Supervised Matrix Completion With Distributional Side Information, Antoine Ledent, Mun Chong Soo, Minh Hieu Nong

Research Collection School Of Computing and Information Systems

We study a matrix completion problem where both the ground truth R matrix and the unknown sampling distribution P over observed entries are low-rank matrices, and share a common subspace. We assume that a large amount M of unlabeled data drawn from the sampling distribution P is available, together with a small amount N of labeled data drawn from the same distribution and noisy estimates of the corresponding ground truth entries. This setting is inspired by recommender systems scenarios where the unlabeled data corresponds to ‘implicit feedback’ (consisting in interactions such as purchase, click, etc. ) and the labeled data …