Emotions are pivotal in influencing students’ learning experience, academic achievement, and general well-being. Fear and anxiety, especially, are hindrances to cognitive processes like memory, concentration, and problem-solving, usually evoked by high-stakes academic contexts. Conventional self-reported questionnaires and psychological tests employed for fear measurement are plagued by subjectivity, response bias, and a lack of ability to reflect real-time emotional fluctuations. To overcome these constraints, this research presents PsyVisionNet, a multi-modal deep learning platform that incorporates computer vision-based facial emotion analysis and psychological parameters (heart rate, stress level, and anxiety scores) for real-time fear recognition among students. The proposed model utilizes a pre-trained ResNet-18 to extract facial features associated with fear and a Multi-Layer Perceptron (MLP) to process physiological information. These characteristics are integrated in a decision-making layer for categorizing students into “Phobic” or “Non Phobic” classes. PsyVisionNet yields 92.5
In this paper, a new adversarial imitation learning algorithm is proposed based on Weighted Wasserstein distance (WWDAIL). This algorithm employs an enhanced proximal optimization strategy to improve the efficiency and stability of parameter updates. Furthermore, the weighted Wasserstein distance is utilized as a metric for assessing policy differences, thereby increasing the flexibility and dynamism of the measurement. Furthermore, by refining the reward function derived from the discriminator's output, the reward collection capability and efficiency are significantly enhanced. Experimental results in the Mujoco simulation environment have demonstrated that the proposed algorithm exhibits substantial stability in continuous control tasks and markedly improves the agent's reward collection ability and efficiency.
A quantum multi-classification network based on parameterized quantum circuits is proposed for handwritten digit image classification tasks. Classical 28 × 28 pixel images are downsampled to 4 × 4 resolution, and the resulting 16 classical data points encoded into probability amplitudes of 4-qubit quantum states. These quantum states are input into a quantum state preparation circuit. The parameterized quantum circuit, composed of rotation and entanglement layers with trainable parameters, is designed to process the input quantum states. Projective measurements are performed on the output quantum states to obtain measurement vectors, which are transformed into output value vectors corresponding to One-hot labels through the Softmax function. The quantum 4-class classification network is taken as an example to introduce in detail. We also extended to 5–10 classification tasks.
Stock price fluctuations are characterized by randomness and complexity, making accurate prediction a challenging task in financial markets. This paper proposes a dual-modal stock price change rate forecasting framework based on pretrained models (DMFPre) to improve the accuracy of stock price change rate forecasting. The framework consists of two main modules: on one hand, it utilizes a pretrained language model combined with company-related textual data to predict the weekly stock price change rate range. The prediction accuracy is closely related to the design of the prompts, and manually designed prompts not only have significant room for optimization but also involve time-consuming and labor-intensive adjustments. To address this, the paper introduces an automatic prompt optimization method that automatically updates the prompts through algorithms. On the other hand, the framework employs a pretrained time series forecasting model (PTSFM) to predict the daily price change rate trend based on historical stock trading data. Finally, by integrating the prediction results from both modules, the framework generates more accurate stock price change rate forecasting. Experimental results show that on the CSI 100 constituent stock dataset, the DMFPre framework significantly outperforms traditional single-modal time series forecasting models in terms of prediction accuracy.
In this paper, we propose adaptive control schemes for the universal function projective synchronization of delayed Chen system and delayed Lorenz system with unknown parameters. We first consider the situation where a vector is used as the reference system. Adaptation control scheme is used for the unknown parameters. Then, we consider the situation where an uncertain Liu system is used as the reference system. We add switching control terms in the control laws for external disturbances. To alleviate the chattering, fuzzy systems are used in the controllers. Simulation results demonstrate the effectiveness of the proposed methods.
