Digital Commons Network^™

Teachers’ Perceptions Of Students’ Ict Skills For Online Assessments (Naplan): Influences And Impact, Cindy Carboni, Susan Main

Research outputs 2022 to 2026

This convergent mixed-methods study investigated Australian teachers’ perspectives on the computing competencies required for online National Assessment Program – Literacy and Numeracy (NAPLAN). A total of 107 teachers completed surveys examining how socio-educational backgrounds and classroom practices shape students’ readiness for high-stakes online assessments. Quantitative analysis revealed significant associations between socio-educational advantage and students’ proficiency in seven essential NAPLAN ICT online skills, including typing, navigation, and on-screen manipulation. Qualitative findings highlighted teachers’ concerns about limited exposure to computing tools and insufficient practice time, which many believed hindered student performance in online assessments. Teachers also noted that inequities in skill development …

The Monstrous-Digital: Data Necropolitics, Social Media, And The Assassination Of Brian Thompson, Laura Glitsos

Research outputs 2022 to 2026

This research article examines the assassination of UnitedHealthcare CEO Brian Thompson and the ensuing social media attention on the alleged shooter Luigi Mangione. I argue that the incident can be read as a cultural moment that captures the essence of an emergent sensibility that I call the “monstrous-digital” because of its layered and complex interconnections with the digital apparatus as a space of control and contestation. Specifically, I connect it to the biopolitical weaponization of computational systems, McQuillan’s (2015) algorithmic states of exception, and Pele and Mulholland’s (2023) data necropolitics. I approach this research using a qualitative case study method, …

The Teacher Wellbeing Turn: Neuropolitics, Education And The Psy-Complex, Andrew W. Wilkins, Antonio Olmedo, Brad Gobby, Saul Karnovsky

Research outputs 2022 to 2026

Across the globe, governments are responding to a ‘teaching crisis’ marked by high attrition rates and labour shortages. This crisis can be attributed to macro changes in the economy (intensified by the shift towards high inflation, low growth economies post-COVID) and micro changes to the organisation and funding of schools with teachers experiencing stagnating wages and unmanageable workloads in punishing accountability environments. In this paper we examine how government and other authorities in England seek to manage the teaching crisis through diagnostic tools and skills training designed to measure and improving teacher wellbeing. This includes inciting new teacher subjectivities, namely …

Integrated Sensing And Covert Communications With Ris Adaptive And Non-Adaptive Modes, Jia Zhang, Dengfeng Zhang, Ke Liu, Jiande Sun, Min Li, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider an integrated sensing and covert communication (ISACC) system with a finite blocklength L aided by a reconfigurable intelligent surface (RIS) with N elements. Specifically, with the aid of RIS, a transmitter Alice is to sense the potential existence of a target, and she is also probabilistically trying to send information to a receiver Bob covertly (i.e., trying to hide her transmissions from a warden Willie). Meanwhile, Willie is to detect whether Alice is sensing the target only or conducting ISACC. We consider two RIS operation modes, i.e., a non-adaptive mode, where RIS employs a common …

Improving Hpv Vaccine Rates For Aboriginal Young People Through A Social Media Campaign: An Interrupted Time Series, Natalie A. Strobel, Jocelyn Jones, Kim Gates, Simon L. Turner, Joanne E. Mckenzie, Daniel R. Mcaullay

Research outputs 2022 to 2026

Human papillomavirus (HPV) is a common sexually transmitted infection that can lead to HPV-related cancers such as cervical cancer and other cancers such as anal, vaginal, and penile cancer. HPV rarely produces symptoms and cannot be cured or treated; therefore, vaccination is essential to protect against HPV and HPV-related diseases. However, young Aboriginal and Torres Strait Islander (hereafter, respectfully, ‘Aboriginal’) people are not receiving their HPV vaccine dose, resulting in missed opportunities to be protected from HPV-related cancers. Health promotion is a critical way to empower people to take ownership over and control their health and is a core function …

Seagrasses Are Most Vulnerable To Marine Heatwaves In Tropical Zones: Local-Scale And Broad Climatic Zone Variation In Thermal Tolerances, Nicole Said, Chanelle Webster, Simone Strydom, Natasha Dunham, Matthew P. Adams, Kathryn Mcmahon

Research outputs 2022 to 2026

Under a changing climate, it is imperative that we understand how species may respond to temperature impacts, which can differ among populations of the same species due to local drivers. Thermal tolerance data, which can be used to assess an organism's upper thermal limits, is valuable to identify species and/or populations' susceptibility to thermal stress. This study assessed the variation in thermal tolerance of six seagrass species at both broad latitudinal (c. 500–1000 km) and local scales (c. 25 km). Photosynthesis–temperature curves (15–45°C) were conducted, by measuring oxygen in closed incubation chambers, and thermal optima (Topt) was extracted. We found …

Work-Integrated Learning As A Landscape Of Practice, Denise Jackson, Karsten E. Zegwaard, David Drewery

Research outputs 2022 to 2026

This article proposes that work-integrated learning (WIL) is a Landscape of Practice (LoP) within the wider higher education ecosystem. It applies concepts from the LoP literature to present a new perspective on WIL programs and ecosystems. It further emphasizes that students are central within the WIL LoP, developing knowledge as they traverse individual communities including industry partners, professional accrediting bodies, careers services, academics, curriculum, and support staff connected to WIL. Additionally, the article highlights the role of higher education institutions in addressing tensions of difference between communities comprising the WIL LoP. The article contributes to the literature on WIL and …

A Climate Justice Analysis Of Western Australian Government Funding For Collie’S Transition From Coal, 2019-2024, Emma O’Donovan, Naomi Joy Godden

Research outputs 2022 to 2026

The WA Government will close its State owned coal-fired power stations in Collie by 2030, and has committed AU$692 million for Collie’s energy transition. At the request of Wilman and Kaneang Elders and community members, Edith Cowan University’s Centre for People, Place and Planet conducted desktop research about how Collie’s transition funding was allocated between January 2019 and July 2024. This report is a climate justice analysis of publicly-available funding announcements for Collie’s transition during this period to assess whether the distribution of transition resources is equitable and just.

Can The Ability To Infer Relevance Account For Dimensional Psychoticism? An Exploration Of A Representative Community Sample, Adam Schwarz, Frank D. Baughman, Evan Dauer, Ruby Koyllor Alarcon Gleeson, Jennifer Haywood, Bo Huei Huang, Karen H. Heslop, Barbara A. Mullan, Susan L. Rossell, Nicolas H. Hart, Darren Haywood

Research outputs 2022 to 2026

Purpose: Psychoticism is a common feature of psychological disorders. Deficits in perceptual inference have been associated with the development of psychotic symptoms. Traditional testing of perceptual inference has had low ecological validity, limiting the ability to infer functional relationships. The aims of this study were to investigate the relationship between the capacity to infer relevance in dynamic environments and dimensional psychoticism, as well as to investigate the association between inferring relevance and dimensional psychoticism. Method: Four hundred participants, representative of the general population of the USA, completed an online questionnaire consisting of the Brief Symptom Inventory, as well as demographic …

Metadata Makes Big Data Useful For Ai/Ml-Ready, Equitable Pandemic Response, Sudarshan Anand, Miae Lee, Seshadri S. Vasan

Research outputs 2022 to 2026

While genomic data helps understand the pathogen and its evolution, patient metadata is critical to get the host side of the picture, so that the pandemic response is effective and equitable.

Clinical Relevance: We highlight the importance of patient stratification and metadata to help develop diagnostics, therapies and vaccines as the pathogen (virus) evolves.

Geopolitical Changes And Futures Of Ccamlr, Nengye Liu

Research Collection Yong Pung How School Of Law

The Convention on the Conservation of Antarctic Marine Living Resources (CAMLR Convention) was negotiated in the late 1970s in the shadow of the Soviet Union’s expanding interest in Antarctic krill fisheries. The development of a management regime for the krill fishery in the remote Southern Ocean vividly reflects geopolitical changes of the world at that time. The Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) is similarly being affected by geopolitical forces from the wider international system today. This chapter therefore first reviews the historical background for the establishment of the CCAMLR. It then provides an overview of …

Human Rights At Sea: Analyzing States' Responses To Cruise Ships During The Covid-19 Pandemic, Lixin Chen

Research Collection Yong Pung How School Of Law

In the early stages of the COVID-19 pandemic, the treatment of cruise ships by coastal states was inconsistent, with some ships being allowed to dock while others were not. To that end, this Note focuses on the obligations that a coastal state owes to the individuals onboard the cruise ships in the context of the COVID-19 pandemic, including the rights to life and health. It further considers whether and how such rights are to be balanced with other countervailing considerations of such states, such as the risk of transmission to the local communities. This author concludes with the view that …

Mapping Biomedical Ontology Terms To Ids: Effect Of Domain Prevalence On Prediction Accuracy, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

This study evaluates the ability of large language models (LLMs) to map biomedical ontology terms to their corresponding ontology IDs across the Human Phenotype Ontology (HPO), Gene Ontology (GO), and UniProtKB terminologies. Using counts of ontology IDs in the PubMed Central (PMC) dataset as a surrogate for their prevalence in the biomedical literature, we examined the relationship between ontology ID prevalence and mapping accuracy. Results indicate that ontology ID prevalence strongly predicts accurate mapping of HPO terms to HPO IDs, GO terms to GO IDs, and protein names to UniProtKB accession numbers. Higher prevalence of ontology IDs in the biomedical …

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT) which is subsequently spatiotemporally imaged with an infrared (IR) camera. As all antennas have spatial variation in their radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT. This nonuniform heating causes uncertainty in defect detection and has the potential to lead to false positives and/or negatives. To this end, thermographic signal reconstruction (TSR), a well-established thermographic signal …

Aperture-Based Fss For Dielectric Thickness Sensing, Alexander Hook, Gage Donahue, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are planar arrays of patch- or aperture-based elements that have a particular transmissive or reflective response. As FSS performance is affected by changes in the local (to the FSS) environment, FSSs may be used to detect changes in strain, temperature, or nearby material (substructure) thickness, amongst other parameters. To this end, an aperture-based FSS can be considered as a sensor for substructure thickness monitoring for surface mounted sensing scenarios. An aperture-based design was selected due to its ability to operate in reflection mode (and hence a one-sided measurement) without the need for a conductive backplane. In …

Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

frequency selective surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters, such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This article presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature (23 ◦C–223 ◦C). The sensor integrates two-unit cells: 1) a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity (~16–18 MHz/0.1%) and 2) a loop-based cell with a temperature-sensitive …

Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT). The inspection surface of the SUT is imaged with an infrared (IR) camera over the inspection time. As the thermal excitation originates from a spatially varying radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT that is directly related to this power density. This nonuniform heating causes uncertainty in defect detection and has the potential to lead …

Balanced Benchmarking Of Zero-Shot And Rag Approaches For Biomedical Term Normalization, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Normalization of medical concepts to an ontology is a key aspect of the natural language processing of biomedical text. It enables the mapping of medical expressions to standardized ontology terms and their identifiers, thereby enhancing the interoperability and computability of medical concepts. Although large language models (LLMs) can identify and standardize medical terms, they may struggle to accurately map ontology terms to their corresponding ontology identifiers. These challenges arise from the stochastic nature of LLMs, their limited exposure to uncommon ontology identifiers during training, and their lack of an integrated lookup mechanism. We generated test sets of synthetic terms to …

Centralized And Federated Heart Disease Classification Using Uci Dataset: A Benchmark With Interpretability Analysis, Mario Padilla Rodriguez, Eyiara Oladipo, Mohamed Nafea

Electrical and Computer Engineering Faculty Research & Creative Works

Cardiovascular disease (CVD) is a leading cause of global mortality, highlighting the need for accurate diagnostic methods. This study benchmarks centralized and federated learning (FL) algorithms for heart disease binary classification using the UCI dataset, which includes 920 patient records from four hospitals in the USA, Hungary, and Switzerland. Our benchmark is supported by Shapley-value as well as Local Interpretable Model-agnostic Explanations (LIME) interpretability analyses to quantify feature importance for classification. In the centralized setup, various classification algorithms are trained on pooled data, with the Naive Bayes classifier achieving the highest test accuracy of 81.1%. Further, FL algorithms with four …

Benchmarking Deep Learning Architectures For Ecg-Based Multi-Label Heart Disease Prediction Using Mimic-Iv Database, Eyiara Oladipo, Sarwar Nazrul, Mohamed Nafea

Electrical and Computer Engineering Faculty Research & Creative Works

Cardiovascular disease (CVD) is a leading cause of global mortality, accounting for an estimated 17.9 million deaths annually. CVD is broadly defined as a group of medical conditions influenced by modifiable or non-modifiable risk factors that affect the heart's ability to function properly. Machine learning (ML) has emerged as a powerful tool for analyzing complex medical data, aiding in early detection and accurate diagnosis of CVD and improving patient outcomes. Recent studies proposed various deep learning (DL) architectures for detecting CVD, yet there is a lack of robust benchmarks for comparing their performance on large-scale databases. In this work, we …

Honey-Reram Enabled Sustainable Edge Ai System For Iot Applications, Jinhui Wang, Feng Zhao, Mohammad Rafeeq Khan, Md Mehedi Hasan Tanim, Zoe Templin, Harshvardhan Uppaluru

Electrical and Computer Engineering Faculty Research & Creative Works

This paper is toward a promising solution to address the environmental sustainability challenge in computing by building brain-inspired and green non-Von Neumann systems with Resistive Random-Access Memory (ReRAM) made from natural organic materials, honey, for energy-efficient operation, renewable material resources, sustainable device manufacturing, and environmentally-friendly disposal. In this paper, honey-ReRAM and its arrays are firstly manufactured and tested. The resistance modulation mechanism of honey-ReRAM is analyzed and investigated. Then a Computing-in-Memory (CIM) architecture based on honey-ReRAM for edge AI and IoT applications is proposed and evaluated. The experimental results indicate that the proposed edge AI systems with the VGG8 and …

Spatial Analysis And Machine Learning Integration For Nutritional Status Mapping Using Ann And Random Forest Models, Desi Anis Anggraini, Fachrul Kurniawan, Fresy Nugroho, Meidya Koeshardianto, Mohammad Iqbal Bachtiar

Knowledge Engineering and Data Science

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify …

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana

Knowledge Engineering and Data Science

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …