In recent years, generative transformers have become increasingly prevalent in the field of artificial intelligence, especially within the scope of natural language processing. This paper provides a comprehensive overview of these models, beginning with the foundational theories introduced by Alan Turing and extending to contemporary generative transformer architectures. The manuscript serves as a review, historical account, and tutorial, aiming to offer a thorough understanding of the models’ importance, underlying principles, and wide-ranging applications. The tutorial section includes a practical guide for constructing a basic generative transformer model. Additionally, the paper addresses the challenges, ethical implications, and future directions in the study of generative models.

Background: Parkinson’s disease (PD), a progressive neurodegenerative disorder prevalent in aging populations, manifests clinically through characteristic motor impairments including bradykinesia, rigidity, and resting tremor. Early detection and timely intervention may delay disease progression. Spiral drawing tasks have been established as effective auxiliary diagnostic tools. This study developed a hybrid deep learning model to analyze motion data from finger drawings of spiral and wave lines on smartphone screens, aiming to detect early Parkinson’s disease. Methods: We recruited 58 age-matched participants (28 early idiopathic PD patients: 68.4 ± 5.7 years; 30 healthy controls: 68.0 ± 4.5 years) for two smartphone-based drawing tasks (spiral and wave). A custom-developed app recorded finger touch coordinates, instantaneous movement speed, and timestamps at a sampling frequency of 60 Hz. Our hybrid model combined multi-scale convolutional feature extraction (using parallel 1D-Convolutional branches) with bidirectional temporal pattern recognition (via gated recurrent unit [GRU] networks) to analyze movement abnormalities and detect the disease. Results: The proposed model demonstrated robust diagnostic performance, achieving a cross-validation accuracy of 87.93% for spiral drawings (89.64% sensitivity, 86.33% specificity). Wave drawings yielded 87.24% accuracy (86.79% sensitivity, 87.67% specificity). The integration of both tasks achieved 91.20% accuracy (95% CI: 89.2%−93.2%) with balanced sensitivity (91.43%) and specificity (91.00%). Conclusion: This study establishes the technical feasibility of a hybrid deep learning framework for early PD detection using smartphone-captured finger motion dynamics. The developed model effectively combines one-dimensional convolutional neural networks with bidirectional GRUs to analyze drawing tasks. Distinct from existing approaches that rely on clinical rating scales, neuroimaging modalities, or stylus-based digital assessments, this telemedicine-compatible method requires only bare-finger interactions on consumer-grade smartphones and enables operator-independent assessments. Furthermore, it facilitates cost-effective and convenient PD assessment in remote healthcare and patient monitoring, particularly in resource-limited settings.

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others.

Wearable hand gesture recognition has attracted considerable attention in the field of smart sensors, human-computer interaction etc. In this paper, we propose a portable device for gesture recognition based on dual-view wrist-worn cameras. Our device is easy to wear and will not impede hand movement. The main challenges we face are the sever occlusion as well as diverse lighting conditions, which significantly hinder the accuracy of gesture recognition. To mitigate these issues, we propose a novel framework based on attentive feature fusion. The overall framework mainly consists of three components, including an adaptive feature amplification module, an attentive feature enhancing as well as a cross-view attention module. Our intuition is that the detailed features from palm and dorsal hand regions can complement each other and can be regarded as strong cues for inferring hand gestures. Moreover, by adaptively adjusting the brightness of the images, the proposed method can be adaptable to diverse lighting conditions. Our method is efficient and quite effective. By comparing our work with other state-of-the-art methods, we achieve superior performance under various experiment configurations. Extensive experimental results validate that our proposed framework surpasses existing state-of-the-art works by a significant margin.

Classroom behavior detection is essential for smart education. Enhance teaching effectiveness, promote personalized learning, and support holistic student development. Existing methods that rely on single-feature input are susceptible to noise interference, which can lead to misjudgments in classroom behavior detection. This paper proposes an efficient classroom behavior detection through end-to-end multiscale feature fusion. Initially, the original YOLOv7 input module was replaced with a multi-feature information input module to enhance spatial feature utilization efficiency. Then, a feature weight attention mechanism was incorporated to allow the main network to weigh feature information on various scales comprehensively. This approach has improved the accuracy of the algorithm’s identification and reduced the number of network parameters. Finally, including a focal loss function suppressed background interference, improving recognition accuracy. Experimental results demonstrate that the improved algorithm attains 94.1% accuracy in the RizeHand dataset and effectively recognizes student behaviors in real-world scenarios.