Despite the development of computer vision techniques, the micro-expression (ME) recognition task still remains a great challenge because MEs have very low intensity and short duration. However, the ME recognition is of great significance since it provides important clues for real affective states detection. This paper proposes a novel Block Division Convolutional Network (BDCNN) with the implicit deep features augmentation. In detail, BDCNN learns from four optical flow features computed by the onset and apex frames of each video. It innovatively divides each image into a set of small blocks in the deep learning model, then the convolution and pooling operations are performed on these small blocks in sequence. To handle the small sample size problem in the micro-expression data, this study uses the improved implicit semantic data augmentation algorithm in the deep features space. Experiments are conducted on three publicly available databases, viz, CASME II, SMIC, and SAMM. Experimental results show that our model outperforms the state-of-the-art methods by attaining the accuracy of 84.32% and F1-score of 82.13% on the 3-class datasets, and the accuracy of 81.82% and F1-score of 75.46% on the 5-class datasets, respectively. Our source code is publicly available for non-commercial or research use at https://github.com/MLDMXM2017/BDCNN.

Electromyography (EMG) signals find wide applications in the fields of medicine, sports, and rehabilitation. However, the collection of EMG signals is a laborious process constrained by ethical limitations, resulting in a scarcity of available data and limiting its applicability in various tasks. This study aims to address this limitation by employing generative techniques to synthesize high-quality and diverse EMG signal samples and evaluating their classification accuracy through gesture recognition tasks. We propose an improved approach based on Diffusion Probabilistic Models (DDPM), specifically tailored to the characteristics of EMG signals and the requirements of low-data setting. Our approach employs a conditional generative method, leveraging diffusion models and introducing a patch-based training strategy. Additionally, we have improved the network architecture of DDPM to better suit the task of few-shot EMG signals generation. We evaluate the performance of the generated EMG signals in gesture recognition tasks using the Ninapro DB4, DB5, BioPatRec DB2, and DB3 datasets. The results indicate that when using 20% of the available data for training and generating the same amount of synthetic samples, the average classification accuracy of the generated data reached 94.31% of that of the real data. These findings demonstrate the effectiveness of our approach in modeling the distribution of real data and generating usable synthetic data. Moreover, the addition of synthetic data to the training set significantly enhances classification performance, further validating the efficacy of the generated data. Our proposed approach based on diffusion models exhibits promising performance in few-shot EMG signal generation task. This research contributes to the field of EMG signal synthesis and offers new possibilities for data augmentation, enabling more robust and accurate analysis of EMG signals in various applications.

With the development of Internet of vehicles, the information exchange between vehicles and the outside world results in a higher risk of external network attacks to the vehicles. The attack modes to the most widely used vehicle-mounted CAN bus are complex and diverse, but most of the intrusion detection approaches proposed by now can only detect one type of attack at a time. Aiming at detecting multi-types of attacks using a single model, we proposed a detection method based on the Mosaic-coded convolution neural network for intrusions containing various combinations of attacks with multi-classification capability. A Mosaic-like two-dimensional data grid was created from the one-dimensional CAN ID for the CNN to effectively extract the data features and maintain the time connections between the CAN IDs. Four types of attacks and all possible combinations of them were used to train and test our model. The autoencoder was also used to reduce the dimensionality of the data so as to cut down the model’s complexity. Experimental results showed that the proposed method was effective in detecting all types of attack combinations with high and stable multi-classification ability.

Estimating joint moment using surface electromyography (sEMG) signals is vital in industrial and rehabilitation area. Current prediction methods mostly focused on extracting single-scale temporal features. However, the spatial information between channels is also essential in joint moment estimation. Therefore, we introduced a trans-scale hybrid (TranSEMG) model that has two streams. The spatial-feature-net module was designed for one stream, which can extract the spatial features according to different effects of muscles corresponding to different channels on joint moment. Another stream used the temporal convolutional network (TCN) module to learn temporal features. Then, we utilized the designed fusion module (FM) for temporal and spatial features fusion. The model was tested on experimental data collected from nine healthy subjects walking on treadmill, level ground, and ramp. The root-mean-square error (RMSE), the coefficient of determination (R2), and variance accounted for (VAF) between the predicted and inverse dynamics moment are used to evaluate the prediction accuracy. Compared with the state-of-the-art models, the trans-scale hybrid model can predict joint moments with lower RMSE, higher R2, and higher VAF values. Furthermore, the model also has good versatility and showed good performance in other walking environments, such as ramp and level ground. In conclusion, this study provided a new model to get more features from sequences to predict the hip joint moment and achieved a good prediction accuracy.

Introduction: There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process. Related work: Decision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings. Method: This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST). Experiment: The two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics. Conclusion: The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.