Action Unit Generation through Dimensional Emotion Recognition from Text

TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network

To address the challenges of traffic diversity and data imbalance in network intrusion detection, we propose GraphACGAN, a novel detection framework that integrates auxiliary classifier generative adversarial networks (ACGANs) with graph neural networks (GNNs). In this architecture, the GNN is embedded in the ACGAN discriminator to exploit the latent graph structures inherent to network traffic, thereby improving the model's capacity to distinguish between benign and malicious behaviors. Simultaneously, the ACGAN generator was leveraged to synthesize minority‐class attack traffic, effectively mitigating class imbalances and enhancing generalization. Comprehensive experiments conducted on three benchmark datasets, namely, NF‐BoT‐IoT‐v2, NF‐ToN‐IoT‐v2, and NF‐UNSW‐NB15‐v2, demonstrate that GraphACGAN consistently outperforms all baselines, including E‐GraphSAGE, GCN, ACGAN, LSTM, and KNN in terms of accuracy, precision, recall, and F 1 ‐score. An evaluation of resource‐limited computing platforms further demonstrates GraphACGAN's inference efficiency for real‐world deployment.

Convolutional neural networks (CNNs) have been widely used for gas‐insulated switchgear (GIS) partial discharge (PD) pattern recognition due to their powerful feature extraction ability. However, there is commonly a scarcity of fault samples due to low insulation failure rate of GIS equipment, which degrades the diagnostic performance of these CNN networks when directly applied to small and unbalanced datasets. Therefore, we propose a novel auxiliary classifier generative adversarial network for GIS PD pattern recognition for small and unbalanced samples. First, we propose using synchrosqueezed wavelet transform to extract time‐frequency characteristics of PD pulses and obtain a time‐frequency image with high energy aggregation and time‐frequency distribution rate. Then, we propose an improved generative adversarial network with an auxiliary classier and self‐attention mechanism, which can generate high‐quality PD samples for situations with few classes. Experiments show that our proposed method can reach 95.75% recognition accuracy for small datasets, which is the highest among several comparable methods. Furthermore, the proposed method has excellent and stable recognition performance for various unbalanced datasets.

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods.

Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.

Effective response strategies to earthquake disasters are crucial for disaster management in smart cities. However, in regions where earthquakes do not occur frequently, model construction may be difficult due to a lack of training data. To address this issue, there is a need for technology that can generate earthquake scenarios for response training at any location. We proposed a model for generating earthquake scenarios using an auxiliary classifier Generative Adversarial Network (AC-GAN)-based data synthesis. The proposed ACGAN model generates various earthquake scenarios by incorporating an auxiliary classifier learning process into the discriminator of GAN. Our results at borehole sensors showed that the seismic data generated by the proposed model had similar characteristics to actual data. To further validate our results, we compared the generated IM (such as PGA, PGV, and SA) with Ground Motion Prediction Equations (GMPE). Furthermore, we evaluated the potential of using the generated scenarios for earthquake early warning training. The proposed model and algorithm have significant potential in advancing seismic analysis and detection management systems, and also contribute to disaster management.

With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training.

Deep neural networks require a large amount of data for supervised training and learning, but it is often difficult to obtain a large amount of label data in practical applications. Since semi-supervised learning can reduce the dependence of deep networks on label data, the generative confrontation network based on semi-supervised learning can improve the classification effect. Although researchers have made progress in semi-supervised learning, small-scale, fully-supervised tasks have not been solved well, because even unlabeled data cannot be obtained in large quantities in such tasks. Therefore, we propose a new GAN model EAC-GAN based on the auxiliary classifier generative adversarial network (ACGAN), which uses genetic algorithms and semi-supervised algorithms to improve classification under fully supervised conditions. Our method utilizes ACGAN to generate artificial data for supplementing supervised classification. More specifically, we attached an external classifier to the original ACGAN generator, so it was named EAC-GAN instead of sharing an architecture with the discriminator. Our experiments show that the performance of EAC-GAN is far superior to standard methods based on data augmentation and regularization, and it is effective on a small amount of real data sets.KeywordsImage synthesisData enhancementGenerative adversarial networkSemi-supervised learning

Accurate classification of brain tumors is often limited by the scarcity of labeled medical images, particularly for complex tumor subtypes. This study proposes an auxiliary classifier generative adversarial network (ACGAN) to generate class-conditioned synthetic MRI images and to enhance the performance of tumor-classification models. The ACGAN was trained using the publicly available Figshare Brain MRI dataset, which contains four categories: glioma, meningioma, pituitary tumor, and healthy tissue. After training, the model produced high-fidelity synthetic MRI images that were quantitatively evaluated using the Fréchet inception distance (FID) as a distribution-level proxy metric, achieving a score of 4.6 and indicating close alignment between the real and synthetic data distributions. In addition to generative quality, the classification network trained with ACGAN-augmented data achieved 94.8% accuracy, 95.3% sensitivity, 93.6% specificity, and 94.1% F1-score, outperforming models trained solely on real data. Comparative analysis demonstrates that the proposed framework provides superior class separability and lower FID values than commonly used generative methods, including DCGAN and standard cGAN approaches. These findings highlight the potential of ACGAN-based synthetic augmentation to support medical-imaging tasks under limited-data conditions and to improve the reliability of brain-tumor classification systems.

B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models require large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work, we exploit auxiliary classifier generative adversarial network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.

In order to improve students’ learning effect, more and more universities favor foreign language teachers who are native speakers of English. Based on the analysis and summary of the research status of emotion recognition, this paper proposes that, in college English classroom teaching, foreign language teachers can reduce the communication barriers with Chinese students through emotion recognition. Based on literature review and actual situation investigation, this study identified four influencing factors on emotion recognition of foreign language teachers, namely, interactive action, facial expression, vocal emotion, and body posture. In our opinion, in the teaching process, teachers can adjust the four factors of emotion recognition to achieve better teaching effect. Further, improve students’ learning efficiency. Analytic Hierarchy Process (AHP) is chosen as the research method in this study. After building the analysis model, we collected the questionnaire using the Questionnaire Star, and finally got 12 valid data. After determining the importance of different factors by pairwise comparison, we draw the following conclusions: the influence degree of emotion recognition factors of foreign language teachers is in descending order, interactive action (43%), facial expression (28%), vocal emotion (21%), and body posture (9%). Our research adds to the body of knowledge on emotion recognition among college English teachers. Furthermore, this research assists students in improving their grasp of course content based on the emotions of foreign English lecturers. Based on the findings, we recommend that foreign language teachers in college English classrooms alter their interactive behaviors, facial expressions, and vocal emotions in response to various instructional materials and emphases.

This work presents the design and analysis of an Adaptive User Interface (AUI) for a desktop application that uses a novel solution for the recognition of the emotional state of a user through both facial expressions and body posture from an RGB-D sensor. Six basic emotions are recognized through facial expressions in addition to the physiological state, which is recognized through the body posture. The facial expressions and body posture are acquired in real-time from a Kinect sensor. A scoring system is used to improve recognition by minimizing the confusion between the different emotions. The implemented solution achieves an accuracy rate of above 90%. The recognized emotion is then used to derive an Automatic AUI where the user can use speech commands to modify the User Interface (UI) automatically. A comprehensive user study is performed to compare the usability of an Automatic, Manual, and a Hybrid AUI. The AUIs are evaluated in terms of their efficiency, effectiveness, productivity, and error safety. Additionally, a comprehensive analysis is performed to evaluate the results from the viewpoint of different genders and age groups. Results show that the hybrid adaptation improves usability in terms of productivity and efficiency. Finally, a combination of both automatic and hybrid AUIs result in significantly positive user experience compared to the manual adaptation.

Humans have traditionally found it simple to identify emotions from facial expressions, but it is far more difficult for a computer system to do the same. The social signal processing subfield of emotion recognition from facial expression is used in a wide range of contexts, particularly for human-computer interaction. Automatic emotion recognition has been the subject of numerous studies, most of which use a machine learning methodology. The recognition of simple emotions like anger, happiness, contempt, fear, sadness, and surprise, however, continues to be a difficult topic in computer vision. Deep learning has recently drawn increased attention as a solution to a variety of practical issues, including emotion recognition. In this study, we improved the convolutional neural network technique to identify 7 fundamental emotions and evaluated several preprocessing techniques to demonstrate how they affected the CNN performance. This research focuses on improving facial features and expressions based on emotional recognition. By identifying or recognising facial expressions that elicit human responses, it is possible for computers to make more accurate predictions about a person's mental state and to provide more tailored responses. As a result, we examine how a deep learning technique that employs a convolutional neural network might improve the detection of emotions based on facial features (CNN). Multiple facial expressions are included in our dataset, which consists of about 32,298 photos for testing and training. The preprocessing system aids in removing noise from the input image, and the pretraining phase aids in revealing face detection after noise removal, including feature extraction. As a result, the existing paper generates the classification of multiple facial reactions like the seven emotions of the facial acting coding system (FACS) without using the optimization technique, but our proposed paper reveals the same seven emotions of the facial acting coding system.

Summary Deep learning have led to a certain extent breakthrough in seismic facies analysis(SFA). The greatest challenge in the SFA domain is how to cope with large seismic datasets and the limited amount of annotated samples, especially when employing supervised machine learning algorithms that require labelled data and larger training sample. we develop a supervised seismic facies analysis via an improved Auxiliary Classifier Generative Adversarial Network(ACGAN).To solve the problem of imbalanced seismic facies class distribution, and limited training data with labels, we used an improved ACGAN to generate synthetic seismic data sets, and then train Convolutional Neural Networks(CNN) using theses generated synthetic data. Fault data and seismic facies data test demonstrated that generated seismic data via the improved ACGAN can be used for synthetic data augmentation as for increased the performance of CNN and avoided overfitting compared to original seismic data training set. Therefore, we believe that the proposed method can effectively alleviate the problems of insufficient and imbalanced seismic data in SFA, and also can generalize to other seismic data classification applications.

Traditional Steganography need to modify the carrier image to hide information, which will leave traces of rewriting, then eventually be perceived by the enemy. In this paper, an information hiding scheme based on Auxiliary Classifier Generative Adversarial Networks (AC-GANs) model is proposed for Steganography. This method designs and trains the networks model based on AC-GANs by constructing a dedicated dictionary and image database. The sender can map the secret information into the category labels through the dictionary, and then use the labels generate the real looking images to be sent through the model. On the contrary, the receiver can identify the image label through the model and obtain the secret information. Through experiments, the feasibility of this method is verified and the reliability of the algorithm is analyzed. This method transmits secret messages by generating images without overwriting the carrier images. It can effectively solve the problem of modification of carrier images in traditional information hiding.