Griffith Research Online

Griffith Research Online (GRO) is a digital archive of research and scholarship from Griffith University, Brisbane, Australia.

GRO delivers free online full-text versions of journal articles, conference papers, and more, where this is possible with the appropriate permissions of copyright owners. GRO increases the impact and influence of Griffith research and scholarship by ensuring it is visible, discoverable and accessible via search engines like Google and discovery services like the National Library’s Trove.
 

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Journal article
Community music for critical positive youth development: A cross-disciplinary literature review of implications for addressing social inequity
Heard, Emma; Bartleet, Brydie-Leigh (Journal of Applied Arts & Health, 2025)

Critical positive youth development (CPYD) is a promising framework for supporting young people in strengths-based ways to both thrive individually and address structural foundations of social inequity. This literature review explores community music for operationalizing CPYD. Database searches were supplemented with expert consultation and handsearching of identified articles and key journals. The authors used a purposive sampling strategy within a critical interpretive synthesis methodology to select 50 articles exploring outcomes of community music programmes with young people. They synthesized findings from these 50 cross-disciplinary studies to illuminate outcomes across the CPYD framework: competence, confidence, compassion, connection, character, contribution and critical consciousness. This study affirms the use of community music in contexts of youth development and implications for health and arts practitioners and researchers are discussed in relation to using music as a potential approach for CPYD to address social inequity with young people. Findings highlight the need for increased art-based research that explores how community music can support equity.

Journal article
MedShield: A Fast Cryptographic Framework for Private Multi-Service Medical Diagnosis
Wang, F; Ouyang, J; Liu, X; Pan, L; Zhang, LY; Doss, R (IEEE Transactions on Services Computing, 2025)

The substantial progress in privacy-preserving machine learning (PPML) facilitates outsourced medical computer-aided diagnosis (MedCADx) services. However, existing PPML frameworks primarily concentrate on enhancing the efficiency of prediction services, without exploration into diverse medical services such as medical segmentation. In this paper, we propose MedShield, a pioneering cryptographic framework for diverse MedCADx services (i.e., multi-service, including medical imaging prediction and segmentation). Based on a client-server (two-party) setting, MedShield efficiently protects medical records and neural network models without fully outsourcing. To execute multi-service securely and efficiently, our technical contributions include: 1) optimizing computational complexity of matrix multiplications for linear layers at the expense of free additions/subtractions; 2) introducing a secure most significant bit protocol with crypto-friendly activations to enhance the efficiency of non-linear layers; 3) presenting a novel layer for upscaling low-resolution feature maps to support multi-service scenarios in practical MedCADx. We conduct a rigorous security analysis and extensive evaluations on benchmarks (MNIST and CIFAR-10) and real medical records (breast cancer, liver disease, COVID-19, and bladder cancer) for various services. Experimental results demonstrate that MedShield achieves up to 2.4×, 4.3×, and 2× speed up for MNIST, CIFAR-10, and medical datasets, respectively, compared with prior work when conducting prediction services. For segmentation services, MedShield preserves the precision of the unprotected version, showing a 1.23% accuracy improvement.

Journal article
Spatiotemporal Pre-Trained Large Language Model for Forecasting with Missing Values
Fang, L; Xiang, W; Pan, S; Salim, FD; Chen, YPP (IEEE Internet of Things Journal, 2025)

Spatiotemporal data collected by sensors within an urban Internet of Things (IoT) system inevitably contains some missing values, which significantly affects the accuracy of spatiotemporal data forecasting. However, existing techniques, including those based on Large Language Models (LLMs), show limited effectiveness in forecasting with missing values, especially in scenarios involving high-dimensional sensor data. In this article, we propose a novel spatiotemporal pre-trained large language model dubbed SPLLM for forecasting with missing values. In this network, we seamlessly integrate a specialized spatiotemporal fusion Graph Convolutional Network (GCN) module that extracts intricate spatiotemporal and graph-based information, for generating suitable inputs to the SPLLM. Furthermore, we propose a Feed-Forward Network (FFN) fine-tuning strategy within the LLM and a final fusion layer to enable the model to leverage the pre-trained foundational knowledge of the LLM and adapt to new incomplete data simultaneously. The experimental results indicate that SPLLM outperforms state-of-the-art models on real-world public datasets. Notably, SPLLM exhibits a superior performance in tackling incomplete sensory data with a variety of missing rates. A comprehensive ablation study of key components is conducted to demonstrate their efficiency.

Journal article
Unmanned Aerial Vehicle-Aided Intelligent Transportation Systems: Vision, Challenges, and Opportunities
Telikani, Akbar; Sarkar, Arupa; Du, Bo; Santoso, Fendy; Shen, Jun; Yan, Jun; Yong, Jianming; Yap, Emily (IEEE Communications Surveys & Tutorials, 2025)

With their inherent attributes such as mobility, flexibility, and adaptive altitude, Unmanned Aerial Vehicles (UAVs) can potentially enable Intelligent Transportation Systems (ITS) to be more efficient by playing the role of aerial base stations for data collection, data analysis, and communication networks. Their ability to access hard-to-reach locations makes UAVs helpful in emergencies to search for and assess damages/accidents and support communication relays in disrupted infrastructures. Although there is a rising interest in the application of UAVs in the realm of ITS, a comprehensive survey that consolidates existing contributions is noticeably absent. This paper fulfils this research gap by investigating the contributions of UAVs in diverse domains of ITS. We divide existing works into three main categories: Functionality Level, Application Level, and Planning Level. For each category, this paper explores the tasks that UAVs enable in ITS and describes the related proposed schemes. Furthermore, various simulation testbeds and datasets used for the implementation and evaluation of UAV-enabled ITS are described. Based on the identified challenges, we provide recommendations for future research.

Journal article
Physiology and Performance Research in Female Athletes: Bridging the Gap Between Opportunity and Evidence-Based Support
de Jonge, Xanne AK Janse; Minahan, Clare (International Journal of Sports Physiology and Performance, 2025)