Kevin Wong

Explainable Artificial Intelligence methods have gained significant traction for their ability to elucidate the decision-making processes of black-box models, particularly in high-stakes fields such as healthcare and finance. Among these, Local Interpretable Model-agnostic Explanations (LIME) stands out as a widely adopted post-hoc, model-agnostic approach that interprets black-box predictions by constructing an interpretable surrogate model on perturbed instances to approximate the local behavior of the original model around a given instance. However, the effectiveness of LIME can depend on the quality of the training data used by the black-box model. When trained on limited or low-quality data, the black-box model may yield inaccurate predictions for perturbed samples, resulting in poorly defined local decision boundaries and consequently unreliable explanations. This limitation is especially problematic in data-scarce settings. To overcome this challenge, we propose TransLIME, a novel end-to-end explainable transfer learning framework that improves the local fidelity and stability of LIME on limited tabular datasets by transferring relevant explainability knowledge from a related auxiliary source domain with a shifted distribution. Also, in TransLIME, only representative source prototype explanations obtained through clustering are transferred to the target domain, thereby reducing cross-domain exposure of both data and explanatory information during transfer. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed framework in improving explanation quality in target domains with limited data.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) serves as a key tool for the test of lipophilic substances in laboratory medicine and is widely employed in the analysis of coenzyme Q10 (CoQ10) and 25-hydroxyvitamin D (25OHD). In this paper, fuzzy concept was applied to improve the LC-MS/MS methods used for CoQ10 and 25OHD detection. The focus was placed on selecting the optimal mobile phase for CoQ10 analysis and examining the differences between LC-MS/MS and chemiluminescence immunoassay (CLIA) methods for 25(OH)D measurement. Through screening various organic phase combinations and employing fuzzy inference, the optimal mobile phase ratio for CoQ10 test is determined to be methanol and isopropanol at a ratio of 8:2. Additionally, fuzzy logic was employed to analyze the variations in 25OHD concentrations across different sexes and age groups. The results showed that women aged 30–40 exhibited greater differences in 25(OH)D levels compared to other groups. This study shows that the use of fuzzy concepts can enhance the adaptability and accuracy of LC-MS/MS detection, offering a novel approach to the analysis of lipophilic substances.

Vaccine hesitancy is still a significant barrier to achieving widespread immunity in many communities. In this paper, we evaluated a serious game fo-cusing on vaccination against COVID-19. This study investigates the potential of virtual reality (VR) as an innovative educational tool to address this issue. Focusing on the serious game " Spike Force " , which simulates the mechanisms of the mRNA COVID-19 vaccine, this research evaluates the game's effectiveness in enhancing participants' understanding, altering attitudes, and influencing behaviours related to vaccination. Participants engaged with " Spike Force, " and their knowledge, attitudes, and behaviours were assessed through pre-and post-gameplay questionnaires. The findings show that immersive VR experiences can significantly improve vaccine literacy, increase confidence in vaccine-related discussions, and promote positive behavioural changes toward vaccination. These results suggest that VR could play an effective advocacy role for public health education, particularly in combating vaccine hesitancy.

While Large Language Models (LLMs) have shown remarkable proficiency in text-based tasks, they struggle to interact effectively with the more realistic world without the perceptions of other modalities such as visual and audio. Multi-modal LLMs, which integrate these additional modalities, have become increasingly important across various domains. Despite the significant advancements and potential of multi-modal LLMs, there has been no comprehensive PRISMA-based systematic review that examines their applications across different domains. The objective of this work is to fill this gap by systematically reviewing and synthesising the quantitative research literature on domain-specific applications of multi-modal LLMs. This systematic review follows the PRISMA guidelines to analyse research literature published after 2022, the release of OpenAI’s ChatGPT
3.5. The literature search was conducted across several online databases, including Nature, Scopus, and Google Scholar. A total of 22 studies were identified, with 11 focusing on the medical domain, 3 on autonomous driving, and 2 on geometric analysis. The remaining studies covered a range of topics, with one each on climate, music, e-commerce, sentiment analysis, human-robot interaction, and construction. This review provides a comprehensive overview of the current state of multi-modal LLMs, highlights their domain-specific applications, and identifies gaps and future research directions.

Integration of renewable energy sources like solar and wind power into the power network has increased significantly in recent years. However, these sources are inherently variable and intermittent, which leads to challenges in maintaining grid stability and reliability. A promising solution to these challenges is the strategic deployment of battery energy storage systems (BESS). The BESS can support improving system voltage and frequency stability and increase system reliability because it can rapidly charge and discharge the grid when needed. To fully explore the advantages of BESS in power systems, it is crucial to determine their optimal allocation. Therefore, this paper presents a technique for optimal allocation of BESS in weak grids to bolster system voltage and frequency stability and enhance system reliability. The proposed method uses the recent adaptive grey wolf optimisation (AGWO) algorithm to identify the optimal capacity and placement of the BESS. The AGWO algorithm is a metaheuristic optimisation algorithm that uses a population of wolves to explore the solution space for the best outcome. The outcomes from the AGWO method are validated using grey wolf optimisation (GWO), beluga whale optimisation (BWO), and sparrow search algorithm (SSA). The efficacy of the proposed methodology is validated in a high renewable distributed generation (DG) penetrated weak IEEE-39 bus system using DIgSILENT PowerFactory software. Simulation findings demonstrate that integrating BESS at the optimal location and size can significantly improve the voltage and frequency stability of the grid and increase its reliability. The proposed methodology can help grid operators and system planners make informed decisions on integrating BESS into the grid.

Explainable Artificial Intelligence (XAI) methods, such as Local Interpretable Model-Agnostic Explanations (LIME), have advanced the interpretability of black-box machine learning models by approximating their behavior locally using interpretable surrogate models. However, LIME's inherent randomness in perturbation and sampling can lead to locality and instability issues, especially in scenarios with limited training data. In such cases, data scarcity can result in the generation of unrealistic variations and samples that deviate from the true data manifold. Consequently, the surrogate model may fail to accurately approximate the complex decision boundary of the original model. To address these challenges, we propose a novel Instance-based Transfer Learning LIME framework (ITL-LIME) that enhances explanation fidelity and stability in data-constrained environments. ITL-LIME introduces instance transfer learning into the LIME framework by leveraging relevant real instances from a related source domain to aid the explanation process in the target domain. Specifically, we employ clustering to partition the source domain into clusters with representative prototypes. Instead of generating random perturbations, our method retrieves pertinent real source instances from the source cluster whose prototype is most similar to the target instance. These are then combined with the target instance's neighboring real instances. To define a compact locality, we further construct a contrastive learning-based encoder as a weighting mechanism to assign weights to the instances from the combined set based on their proximity to the target instance. Finally, these weighted source and target instances are used to train the surrogate model for explanation purposes.

This paper introduces a novel method for the registration of color fundus photographs, featuring a new descriptor named Haar-Optimized Local Binary Descriptor (HOLBD). HOLBD is a fast-to-compute and match descriptor, highly optimized to uniquely describe retinal bifurcation and crossover points, which are crucial landmarks for fundus image registration. It utilizes four patterns reminiscent of Haar basis functions, optimized to define these bifurcation and crossover points. These patterns perform pixel intensity tests to form a 340-bit binary vector. Before computing the HOLBD descriptor, the overall image orientation and scaling factors are estimated, and images are normalized, making HOLBD robust against rotation and scaling. Experiments were conducted on both publicly available and private retinal image registration datasets, comprising a total of 484 retinal images (i.e., 242 pairs). The proposed method was compared with state-of-the-art techniques, including Generalized Dual-Bootstrap Iterative Closest Point, Hernandez-Matas et al., Saha et al., and Chen et al.'s methods. Results show that the proposed method outperforms the best performing method. On private dataset, the proposed method achieves 1-3% higher accuracy than the best-performing method for error thresholds up to 15 pixels. It significantly outperforms other methods by 4-30% for error thresholds up to 10 pixels. On the public dataset, the proposed method marginally outperforms the best reported method. It significantly outperforms GDP ICP, Hernandez-Matas et al., and Chen et al. by a margin of 10-40%.

This study initially undertook a scoping review of mobile Computer-Supported Collaborative Learning (mCSCL) to determine the characteristics, and methodologies of the reviewed studies, and its benefits and challenges in resource-constrained nations. Online databases yielded no related articles on mCSCL in resource-constrained countries from 2007 to 2023. Nevertheless, 32 papers centred on mobile learning (m-learning) in resource-constrained countries were identified. Findings from the research questions encompass the theoretical framework used, the study context, sample size, and research design of the reviewed articles; and the benefits, and challenges of m-learning in resource-constrained countries. Implications for future m-learning and mCSCL studies in resource-constrained countries are considered. These include focussing on the benefits of m-learning as a basis to foster its implementation in the region, while providing adequate measures to mitigate its challenges, potential areas for investigation, such as research situated in resource-constrained countries in South American and Eastern Europe and research on aspects of m-learning such as mCSCL, more strategically focussed research interventions utilising available mobile infrastructure in developing countries, studies grounded in established theoretical frameworks and seminal studies on research methodology to inform research design.

The transmission of African swine fever (ASF) could be influenced by temperature and rainfall, particularly through the transmission of wild boars. Australia's ASF risk assessment capabilities can be further enhanced by analyzing the impact of temperature and precipitation on ASF. As there are currently no cases of ASF in Australia, this study utilized Poland's ASF-wild boar cases between 2018 and 2021 to establish a risk assessment model for Australia. Two methods were adopted to model the risk by analyzing the correlation between the number of ASF-wild boar cases, and the temperature and rainfall. The two methods used were linear regression and fuzzy inference systems. The aim is to develop a risk assessment analysis that can estimate the seasonal risk of ASF in Australia. The results from the two models showed that there is a significant relationship between the number of cases and the changes in the temperature, but has shown no prominent association with the amount of rainfall. To the best of our knowledge, this is the first model that conducts a seasonal assessment of ASF risk in Australia. The proposed technique used in modelling the Australia’s risk assessment is leading and can handle the incompleteness of data, making this a novel approach that can be used to build models for other countries or regions and also for different infectious diseases.