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.

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.

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.

The main contribution of this paper is to introduce a framework for integrating Machine Learning (ML), Human, and Virtual Reality (VR) into one platform to promote a collaborative visualisation environment that can assist in better analysis and improve the human-machine teaming capability. This platform was demonstrated using a case study in ana-lysing road traffic conditions. The ‘Ab-normal Machine Learning Road Traffic Detection in VR (AbnMLRTD-VR)’ prototype system was developed to assist the human analyst. The proposed system has two main integrative components: a data-driven ML model and a 3D real-time visualisation in a VR environment. An unsupervised ML model was built using real traffic data. The AbnMLRTD-VR system highlights the outliers in the road sections in actual road contexts of a road traffic network. This gives the human analyst a 3D real-time immersive visualisation in a VR environment to evaluate road conditions. The AbnMLRTD-VR system demonstrated that it could help minimise the need for human pre-labelling of the data. It enables the visualisation of the road traffic conditions more meaningfully and to understand the context of the road traffic conditions of road sections at any given time.

To improve users’ tendency towards shopping in Virtual Reality (VR), en-hancing the User Experience (UX) of the VR shopping environments is of primary importance. Product viewability, reachability, and personalisation are some of the primary UX factors in a shopping environment. This paper proposes and discusses three factors for a Personalised Adaptive Aisle (PAA) in a VR shopping environment to improve the shopping experience. They are 1) Shelf placement for viewability and reachability, 2) User view-point in VR, and 3) Personalised Product placement.

Gated Recurrent Neural Networks (RNNs) such as LSTM and GRU have been highly effective in handling sequential time series data in recent years. Although Gated RNNs have an inherent ability to learn complex temporal dynamics, there is potential for further enhancement by enabling these deep learning networks to directly use time information to recognise time-dependent patterns in data and identify important segments of time. Synonymous with time series data in real-world applications are missing values, which often reduce a model’s ability to perform predictive tasks. Historically, missing values have been handled by simple or complex imputation techniques as well as machine learning models, which manage the missing values in the prediction layers. However, these methods do not attempt to identify the significance of data segments and therefore are susceptible to poor imputation values or model degradation from high missing value rates. This paper develops Cyclic Gate enhanced recurrent neural networks with learnt waveform parameters to automatically identify important data segments within a time series and neglect unimportant segments. By using the proposed networks, the negative impact of missing data on model performance is mitigated through the addition of customised cyclic opening and closing gate operations. Cyclic Gate Recurrent Neural Networks are tested on several sequential time series datasets for classification performance. For long sequence datasets with high rates of missing values, Cyclic Gate enhanced RNN models achieve higher performance metrics than standard gated recurrent neural network models, conventional non-neural network machine learning algorithms and current state of the art RNN cell variants.

•A simple lipid extraction of chicken egg yolks was developed for LC-MS/MS analysis.

•937 lipid species from 20 major lipid subclasses were characterized in egg yolk.

•Statistical modeling was used to classify the types of conventional chicken eggs.

•Cage, barn, and free-range eggs can be differentiated based on lipid profile.

•Eggs from caged birds can be accurately predicted based on the lipidomic signature.

Free-range eggs are ethically desirable but as with all high-value commercial products, the establishment of provenance can be problematic. Here, we compared a simple one-step isopropanol method to a two-step methyl-tert-butyl ether method for extracting lipid species in chicken egg yolks before liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The isopropanol method extracted 937 lipid species from 20 major lipid subclasses with high reproducibility (CV < 30 %). Machine learning techniques could differentiate conventional cage, barn, and free-range eggs using an external test dataset with an accuracy of 0.94, 0.82, and 0.82, respectively. Lipid species that differentiated cage eggs were predominantly phosphocholines and phosphoethanolamines whilst the free-range egg lipidomes were dominated by acylglycerides with up to three fatty acids. The lipid profiles were found to be characteristic of the cage, barns, and free-range eggs. The lipidomic analysis together with the statistical modeling approach thus provides an efficient tool for verifying the provenance of conventional chicken eggs.