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This paper proposes a lightweight facial expression recognition model based on an improved Mini-Xception algorithm to address the issue of deploying existing models on resource-constrained devices. The model achieves lightweight facial expression recognition, particularly for elder-oriented applications, by introducing depthwise separable convolutions, residual connections, and a four-class expression reconstruction. These designs significantly reduce the number of parameters and computational complexity while maintaining high accuracy. The model achieves an accuracy of 79.96% on the FER2013 dataset, outperforming various other popular models, and enables efficient real-time inference in standard CPU environments.

Accurately estimating story points in Agile Scrum environments remains a challenging task, as traditional models often struggle to capture the complex relationships between user stories and their corresponding effort estimations. In this study, we leverage Gemini’s embedding representations to enhance the modeling of user stories within a story point estimation dataset. To improve prediction performance, we propose GemSP, an ensemble regression model that integrates two complementary regression techniques applied to the Gemini embeddings. Our approach aims to exploit the rich semantic representations of user stories while benefiting from the robustness of ensemble learning. Experimental results show that, when instantiated with Gemini embeddings, the proposed GemSP framework achieves lower prediction error than selected baseline models (GPT-2, Deep-SE, and GPT2SP) under cross-project evaluation on JIRA datasets. These results illustrate the practical benefit of decoupling semantic representation learning from regression, enabling effective integration of stronger embedding models within lightweight ensemble predictors.

Reading speed is hypothesized to have distinct neural signatures across orthographically diverse languages, yet cross-linguistic evidence remains limited. We investigated this by classifying speed readers versus regular readers among Sinhalese and Japanese adults ($n=142$) using task-based fMRI and 35 supervised machine learning classifiers. Functional activation was extracted from 12 reading-related cortical regions. We introduced Fuzzy C-Means (FCM) clustering for data augmentation and Shapley additive explanations (SHAP) for model interpretability, enabling evaluation of region-wise contributions to reading speed classification. The best model, an FT-TABPFN network with FCM augmentation, achieved 81.1% test accuracy in the Combined cohort. In the Japanese-only cohort, Quadratic SVM and Subspace KNN each reached 85.7% accuracy. SHAP analysis revealed that the angular gyrus (AG) and inferior frontal gyrus (triangularis) were the strongest contributors across cohorts. Additionally, the anterior supra marginal gyrus (ASMG) appeared as a higher contributor in the Japanese-only cohort, while the posterior superior temporal gyrus (PSTG) contributed strongly to both cohorts separately. However, the posterior middle temporal gyrus (PMTG) showed less or no contribution to the model classification in each cohort. These findings demonstrate the effectiveness of interpretable machine learning for decoding reading speed, highlighting both universal neural predictors and language-specific differences. Our study provides a novel, generalizable framework for cross-linguistic neuroimaging analysis of reading proficiency.