This study evaluated the ability of ChatGPT to generate simulated data for theory replication and validation, focusing on the Computational Thinking Scale (CTS). The CTS model was replicated ten times, resulting in 10 trial models and 4,427 simulated responses. Each trial was analyzed using Structural Equation Modeling (SEM) to evaluate validity, reliability, and consistency. The results showed that ChatGPT-simulated data performed comparably to the original models. Key metrics—including Composite Reliability (simulated: 0.767–0.816; original: 0.84–0.88), Average Variance Extracted (simulated: 0.455–0.563; original: 0.57–0.67), Cronbach’s Alpha (simulated: 0.595–0.702; original: 0.74–0.83), and R-squared (simulated: 0.215–0.33; original: 0.38–0.43)—were lower but aligned. Similar patterns were observed in structural path coefficients (simulated: 0.015–0.504; original: 0.20–0.43), further supporting comparability. These findings highlight the potential of ChatGPT as a generative tool for research simulations, enabling cost-effective, scalable methodological testing and a pathway for future investigation.

This study explores ChatGPT’s role as a teaching aid in algorithmic education, focusing on its ability to generate and evaluate algorithmic questions and solutions. Qualitative analysis shows strong performance in Sensibleness, Readiness, and Topicality, though Novelty remains an area for improvement. ChatGPT also demonstrated self-improvement in criteria like Efficiency and Robustness, with over 60% enhancement. A comparison of AI and teacher grading revealed that ChatGPT provided accurate assessments, closely aligning with expert evaluations. The findings highlight ChatGPT’s potential in educational assessment and call for further exploration of student perceptions and ethical considerations.

Brain serves as a critical cornerstone of human intelligence, which involves a series of complex neuropsychological activities that lead to the coordination of various functions in the brain network. In recent years, brain network analysis methods based on graph neural networks (GNNs) have attracted increasing attention for the identification of brain disorders. However, these methods generally assume that the brain network is a homogeneous graph while ignoring its heterogeneity among human brain activities, which is reflected in both the complex connectivity of the brain network and distinctive brain functions. To overcome this problem, we propose a heterogeneous subdivision GNN (HSGNN), which captures the heterogeneous connections and functions of the brain network simultaneously. Specifically, we first employ two fundamental brain connectivity patterns to capture both statistical dependency and directional information flow among different brain regions and construct a heterogeneous brain connectivity network for each subject. Then, we develop a functional subdivision method that encodes brain networks into multiple latent feature subspaces corresponding to heterogeneous brain functions and extracts features of brain networks accordingly. Considering the intricate interactions of brain functions to facilitate cognitive activities within the brain network, we further employ the self-attention mechanism to obtain comprehensive representations of brain networks in a joint latent space. Finally, we propose a composite loss function to train the model for obtaining the heterogeneous brain network representation, which can be utilized for disease classification. The experimental results in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets demonstrate that our method outperforms several state-of-the-art (SOTA) methods to identify different types of brain cognitive-related disorders.

Depression is a serious mental disorder with complex etiology, exhibiting strong heterogeneity in clinical manifestations such as various subtypes. Research on depression subtypes may deepen the understanding of the disease, contributing to the diagnosis and prognosis. While brain functional network and graph neural networks (GNNs) provide such a means, the task is still challenged by limited feature encoding from the informative fMRI data, ineffective information fusion of brain functional network, and small size of the recruited subjects. Therefore, we propose a hierarchical encoding and fusion framework of brain functions. First, we pre-train a model to extract the features from individual brain regions, which signify nodes in the brain functional network. Then, distinct graphs are constructed to link the nodes within each subject, resulting in multi-view graphs of the brain functional network. We further develop a graph fusion strategy to integrate the multi-view information, by referring to the local encoding of the nodes and their interactions across multiple graph instances. Finally, we attain the classification of depression subtypes based on the fused graph representation. The experimental results demonstrate that our method can superiorly distinguish major depression subtypes and outperform the state-of-the-art methods.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

Recent years have witnessed a growing number of location-based service providers (LBSPs) outsourcing their points of interest (POI) datasets to third-party cloud service providers (CSPs), which in turn answer various data queries from mobile users on their behalf. A main challenge in such systems is that the CSPs cannot be fully trusted, which may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. As an important type of queries, location-based skyline queries (LBSQs) ask for the POIs that are not spatially dominated by any other POI with respect to some query position. In this paper, we propose three novel schemes that enable efficient verification of any LBSQ result returned by an untrusted CSP by embedding and exploring a novel neighboring relationship among POIs. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.