Digital Commons Network^™

Let’S Make The Change: Addressing The Knowledge Gaps And Building Confidence In Teaching Aboriginal Perspectives For Pre-Service Teachers, Sarah Booth, Libby Jackson-Barrett, Mary Anne Macdonald, Renae Isaacs-Guthridge, Sian Bennett

Research outputs 2022 to 2026

Teaching Aboriginal perspectives and Aboriginal students by non-Indigenous teachers can feel daunting; however, this need not be the case. This study focuses on two frameworks that can be used to motivate pre-service teachers to feel more confident in teaching Aboriginal perspectives even when starting with trepidation in this area. To explain further, this study explored the impact an Aboriginal Contexts in Education unit had on pre-service teachers’ readiness to teach Aboriginal students and Aboriginal curriculum. Through two frameworks pre-service teachers were exposed to a range of Aboriginal perspectives through Aboriginal educators, authors, and resources, and spoke of their appreciation of …

Text Messaging Programs To Support Family Literacy Practices: A Scoping Review, Lennie Barblett, Leanne Lavina, Nicola Johnson, Caroline Barratt-Pugh

Research outputs 2022 to 2026

Using bulk digital text messaging has been shown to be effective in many different fields such as health program interventions and supporting parents/carers. Evaluations of these interventions indicate positive changes in practice; however, it appears the potential of digital messaging through short message services (SMS) have not been fully taken advantage of in early childhood education. This scoping review using Arksey and O’Malley’s (2005) approach investigates the current practices of text messaging programs to support families in the development of preschool aged children’s language and literacy learning. Searches of five databases identified 16 articles that fitted our criteria. Results indicate: …

Feeling Giggity: Ambiguous Emojis In Teaching And Learning Communications, Mary Kynn, Nicole Reinke, Eva Hatje

Research outputs 2022 to 2026

There is an increase in the number of digital platforms used in higher education classrooms that promote using emojis to capture students’ emotions. However, there is little evidence to suggest that interpretation of emojis is reliable in this context. This pilot project explored if emojis can reliably signify the emotions expressed by first-year university students. Students were asked to record their emotions by selecting a representative emoji and using free text to describe its meaning. A total of 72 responses were recorded in an anonymous online survey. Happy face emojis were used more frequently than emojis representing neutral or unpleasant …

Systematic Literature Mapping: Integrated System Of Logistic Distribution For Island, Eka Suswaini, Mochammad Agung Wibowo, Ferry Jie

Research outputs 2022 to 2026

Purpose: The study aims to identify and compare various methods used to optimize supply chain distribution, with a focus on uncovering novel approaches. It particularly addresses the gap in logistics distribution models tailored for island environments, proposing the development of an integrated information system using genetic and memetic algorithms to handle uncertainties like weather and vessel availability. Design/methodology/approach: A Systematic Literature Mapping (SLM) approach was adopted. The K-Chart method was applied to categorize conceptual strategies and identify research gaps. A case study was also included to analyze logistical challenges in island settings, incorporating real data variables like transportation schedules and …

ℵ-Ipomdp: Mitigating Deception In A Cognitive Hierarchy With Off-Policy Counterfactual Anomaly Detection, Nitay Alon, Joseph M. Barnby, Stefan Sarkadi, Lion Schulz, Jeffrey S. Rosenschein, Peter Dayan

Research outputs 2022 to 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called ℵ-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the ℵ-mechanism’s …

Advancing Sustainable Tourism Behavioural Research: A Metacognitive Perspective, Arghavan Hadinejad, Kourosh Esfandiar, Liubov Skavronskaya

Research outputs 2022 to 2026

The current scholarly inquiry on sustainable tourism behaviour research predominantly relies on cognitive behavioural approaches, particularly exemplified by the widespread application of the theory of planned behaviour among other theories. Given recent advancements in psychology underscoring the necessity to expand beyond conventional cognitive frameworks, the current research aims to integrate metacognitive perspectives in sustainable tourism behaviour scholarship. This perspective explores the reflective awareness and control individuals exert over their cognitive processes which can uncover nuanced factors shaping sustainable tourism behaviour that may be overlooked in traditional cognitive models. Using a critical review approach, this research proposes the adoption of alternative …

Drafting Policy To Address Academic Integrity: The Other Ai In Education, Elizabeth Burns

STEMPS Faculty Publications

The article focuses on the role of school librarians in drafting policies to address academic integrity amid the increasing use of artificial intelligence (AI) in K-12 education. It defines AI, including generative AI (GenAI), and discusses both the challenges and opportunities AI presents for teaching and learning, emphasizing the need for clear guidelines on ethical use. The article outlines three common AI policy models—permissive, hybrid, and restrictive—and stresses the importance of collaboration between librarians and teachers to establish consistent expectations, promote critical thinking, and ensure proper attribution of AI-generated content. School librarians are positioned as key facilitators in guiding students …

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, …

Effects Of Short-Term Exposure To Environmentally Relevant Pesticides Mixture On Morphological Alterations, Oxidative-Nitrative Stress Biomarkers, Cellular Apoptosis, And Antioxidant Expression In Kidneys Of Goldfish, Esmirna Cantu, Md Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Chemical stressors are pervasive, affecting both terrestrial and aquatic environments. The continual influx of these toxins is damaging ecosystems and the organisms that inhabit them. The abundance of environmental toxins makes aquatic habitats inhospitable for aquatic life. These chemical stressors consistently disrupt the life processes of aquatic organisms, particularly their physiological functions. This study examined on the effects of environmentally relevant pesticides mixture (low-dose and high-dose: S-metolachlor: 2.4 go/L, 12 μg/L; linuron: 2.0 μg/L, 10 μg/L; isoproturon: 1.2 μg/L, 6.0 μg/L; tebucanozole: 1.2 μg/L, 6.0 μg/L; aclonifen: 0.8 μg/L, 4.0 μg/L; atrazine: 0.4 μg/L, 2.0 μg/L; pendimethalin: 0.4 μg/L, 2.0 …

Biomarkers Of Foraging And Reproduction In Captive Adult Female Hawksbill Sea Turtles (Eretmochelys Imbricata), Joslyn Blessing Kent, Kari Renee Dawson, Shingo Fukada, Masae Makabe, Isao Kawazu, Ken Maeda, Roldán A. Valverde

School of Earth, Environmental, & Marine Sciences Faculty Publications

Hawksbill sea turtles (Eretmochelys imbricata) are listed as critically endangered by the International Union for the Conservation of Nature (IUCN). To implement best conservation practices for this species, its biology should be well understood. Attempting to characterize the foraging physiology of free-ranging hawksbill sea turtles is complicated by the fact that sampling is typically limited to nesting females during the reproductive season. Without data from non-reproductive periods, it is difficult to determine whether observed physiological values reflect baseline conditions or are specific to the energetically demanding nesting season. Accordingly, in this study, we described the physiology of foraging in …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

David Bancroft Johnson Personal Papers - Accession 2, David Bancroft Johnson

Manuscript Collection

This collection consists of the personal correspondence and papers of Dr. David Bancroft Johnson, founder and first president of Winthrop.  The letters address various subjects and people, both personal and professional, concerning issues from the original funding of Winthrop in 1886 to the minutiae of ordering flour and cotton seed, as well as personal letters to family, friends, and acquaintances. This collection also includes news clippings, brochures, programs, and various documents belonging to Dr. Johnson.  The letters and documents are by organized by each item chronologically.

Packet Scheduling In Mixed Traffic Networks: A Simulation Study Using Ns-3, Landon Mohr

Annual Research Symposium

Packet scheduling determines the order in which packets are transmitted when multiple flows compete for a network link. Schedulers directly affect key performance metrics including latency, jitter, packet loss, and throughput. While scheduling algorithms have been widely studied, fewer evaluations examine their behavior under mixed traffic workloads representative of real networks. This study uses the NS-3 network simulator to evaluate how different packet scheduling algorithms behave under congestion across several representative traffic profiles.

Post-Quantum Cryptography Secure Communication, Iot, And Blockchain, Nidhish Bhanse, Mark Spanier

Annual Research Symposium

Modern public-key cryptography, such as RSA and Elliptic Curve Cryptography (ECC), plays a crucial role in securing data. However, the development of quantum computing threatens the security of data encrypted with these methods. Data encrypted today might be decrypted in the future due to the increased power of quantum computers. To combat this, the National Institute of Standards and Technology (NIST) has developed new standards for post-quantum cryptography. The following research aims to provide an analysis of these NIST post-quantum cryptographic algorithms and their potential for use in various secure communication protocols and platforms.

A Context-Driven Framework For Fairer Ranking Across Real-World Systems, Pawan Chaudhary, Justin Schroeder

Annual Research Symposium

Classical ranking methods each rely on a single input: Massey uses point differentials, Colley uses win/loss records, and PageRank uses win proportions. None incorporates the full context of a matchup. This research develops a general-purpose ranking framework built on graph theory and linear algebra. Competitors are modeled as nodes, and each interaction produces a 10-dimensional feature vector that is mapped to an edge weight via a sigmoid function. The resulting linear system 𝑀 𝑥=−1 is solved to produce ratings with provable mathematical guarantees. The framework is validated on UEFA Champions League 2025–26 data and designed to generalize to student evaluation …

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 …

Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients 'selfish'. This work proposes a Fair and Robust strategy for aggregation in the Federated Learning (FL) server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel …

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 …

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

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 …

Corrigendum To "Human Umbilical Cord Mesenchymal Stem Cells Secretome And Nanoemulsion Propolis Combination Ameliorate Osteoclastogenesis In Lipopolysaccharide-Induced Osteolysis In Hyperglycemia Rats"

Journal of Dental Sciences

No abstract provided.