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Sentiment analysis has a wide application prospect in business, medicine, security and other fields, which provides a new perspective for the development of education. Students' sentiment data play an important role in the evaluation of teachers' teaching quality and students' learning effect, and provide a basis for the implementation of effective learning intervention. However, most of the research is to obtain the real-time learning status of students in the classroom through teachers' naked eye observation and students' text feedback, which will lead to some problems such as incomplete feedback content and delayed feedback analysis. Based on the mini-Xception framework, this article implements the real-time identification and analysis of student sentiment in classroom teaching, and the degree of student engagement is analyzed according to the teaching events triggered by teacher to provide reasonable suggestions for subsequent teaching progress. The experimental results show that the mini-Xception model trained by FER2013 data sets has high recognition accuracy for the real-time detection of seven student sentiments, and the average accuracy is 76.71%. Compared with text feedback, it can assist teachers in understanding student learning states in time so that they can take corresponding actions, and realize the real-time performance of wisdom classroom teaching information feedback, the high efficiency of information transmission, and the intelligence of information processing.

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Privacy protection, high labeling cost, and the varying characteristics of seizures among patients and at different times are the main obstacles to building seizure detection models. In light of these, we propose a novel Mentor-Student architecture for Patient-Specific seizure detection (MS4PS). It contains a new way of knowledge transferring named mentor-select-for-student, which exploits the knowledge of a Mentor model by using this model to select data for training a Student model, making it possible to avoid transferring patients' data and the negative influence of transferring parameters/structures of pretrained models. It also contains a new way of active learning, which uses both an experienced Mentor model and a quick-learning Student model to select high-quality samples for doctors to label. Each of the two models is coupled with a particular sample selection strategy that combines the uncertainty/certainty and the distance between unlabeled samples and labeled seizure samples. The proposed method could quickly train a suitable detector for a patient at his/her first epilepsy diagnosis with the help of: (1) an experienced Mentor model that chooses the most category-certain electroencephalography (EEG) data segments; (2) a Student model (detector itself) that chooses the most category-uncertain EEG data segments; (3) doctors who label these data segments selected by both the Mentor model and Student model. By replacing or improving the Mentor model and refining the historical models of patients when they come next time, the MS4PS system could be sustainedly promoted. The proposed method is tested on CHB-MIT and NEO datasets and the results demonstrate its effectiveness and efficiency.

Summary Person re‐identification (Re‐ID) aims at identifying the same person across multiple non‐overlapping camera views. A number of existing methods have been presented for this task in a fully‐supervised manner that requires a large amount of training annotations. However, obtaining high quality labels is extremely time consuming and expensive. In this article, we focus on the semi‐supervised person Re‐ID and propose a one‐shot stepwise learning method to address the above issue. It exploits only one labeled data along with additional unlabeled samples to gradually but steadily improving the discriminative capability of the feature representation. Specifically, we first construct labeled data portion to train Re‐ID model. Then we fine‐tune the overall system by the following two steps iteratively: (1) assigning the estimated labels to the unlabeled portion; (2) updating the network parameters according to the selected data. During the propagation process, different from conventional sampling method, we propose a novel dynamic sampling strategy to enlarge the pseudo‐labeled subset step by step to make the pseudo labels more reliable. On Market‐1501, DukeMTMC‐ReID and MARS datasets, we conducted extensively experiments to demonstrate that our proposed method contributes indispensably and achieves a very competitive Re‐ID performance.