Purpose – The purpose of this study is to investigate the effect of Malaysian public universities’ service quality on international student satisfaction, institutional image and loyalty. Design/methodology/approach – A total number of 400 questionnaires were distributed to international students, selected using convenience sampling technique, at three public Malaysian university campuses in Kuala Lumpur. Of this, 241 were deemed fit for analysis (60 per cent response rate). Partial Least Squares Structural Equation Modeling was used to analyze the collected data, assess the model and test hypotheses. Findings – The findings show that all the five dimensions of higher education service quality influence student satisfaction which in turn influences institutional image, and together, they influence student loyalty. Research limitations/implications – There are a number of limitations associated with this study. First, the findings of the study are based on data from international students at only three Malaysian public university campuses. Second, this study focuses on a relatively small sample of international students. Besides, this study uses HEdPERF to assess higher education service quality which might exclude some factors that may influence international student satisfaction. On the other hand, it highlights a number of implications for the management of Malaysian universities. Originality/value – This study validates the HEdPERF scale in the context of Malaysian public universities with regard to the perceptions of international students. Furthermore, this study extends the HEdPERF scale and examines its effects on student satisfaction, institutional image and loyalty.

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Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model, where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions. However, in real applications, the distribution of a material is often not Gaussian. In this paper, we use Gaussian mixture models (GMM) to represent endmember variability. We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives). The first perspective originates from random variable transformations and gives a conditional density function of the pixels given the abundances and GMM parameters. With proper smoothness and sparsity prior constraints on the abundances, the conditional density function leads to a standard maximum a posteriori (MAP ) problem which can be solved using generalized expectation maximization. The second perspective originates from marginalizing over the endmembers in the GMM, which provides us with a foundation to solve for the endmembers at each pixel. Hence, compared to the other distribution based methods, our model can not only estimate the abundances and distribution parameters, but also the distinct endmember set for each pixel. We tested the proposed GMM on several synthetic and real datasets, and showed its potential by comparing it to current popular methods.

Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promising approach to identify biomarkers is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, i.e. brain networks constructed by fMRI. One way to interpret important features is through looking at how the classification probability changes if the features are occluded or replaced. The major limitation of this approach is that replacing values may change the distribution of the data and lead to serious errors. Therefore, we develop a 2-stage pipeline to eliminate the need to replace features for reliable biomarker interpretation. Specifically, we propose an inductive GNN to embed the graphs containing different properties of task-fMRI for identifying ASD and then discover the brain regions/subgraphs used as evidence for the GNN classifier. We first show GNN can achieve high accuracy in identifying ASD. Next, we calculate the feature importance scores using GNN and compare the interpretation ability with Random Forest. Finally, we run with different atlases and parameters, proving the robustness of the proposed method. The detected biomarkers reveal their association with social behaviors and are consistent with those reported in the literature. We also show the potential of discovering new informative biomarkers. Our pipeline can be generalized to other graph feature importance interpretation problems.

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