Facial expression recognition plays a key role in human-computer emotional interaction. However, human faces in real environments are affected by various unfavorable factors, which will result in the reduction of expression recognition accuracy. In this paper, we proposed a novel method which combines Fine-tuning Swin Transformer and Multiple Weights Optimality-seeking (FST-MWOS) to enhanced expression recognition performance. FST-MWOS mainly consists of two crucial components: Fine-tuning Swin Transformer (FST) and Multiple Weights Optimality-seeking (MWOS). FST takes Swin Transformer Large as the backbone network to obtain multiple groups of fine-tuned model weights for the homologous data domains by hyperparameters configurations, data augmentation methods, etc. In MWOS a greedy strategy was used to mine locally optimal generalizations in the optimal epoch interval of each group of fine-tuned model weights. Then, the optimality-seeking for multiple groups of locally optimal weights was utilized to obtain the global optimal solution. Experiments results on RAF-DB, FERPlus and AffectNet datasets show that the proposed FST-MWOS method outperforms various state-of-the-art methods.

Alzheimer’s Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer’s Disease through speech and text information is of significant importance. However, the multimodal data is often complex and inconsistent, which leads to inadequate feature extraction. To address the problem, We propose a model for early diagnosis of Alzheimer’s Disease based on multimodal attention(EDAMM). Specifically, we first evaluate and select three optimal feature extraction methods, Wav2Vec2.0, TF-IDF and Word2Vec, to extract acoustic and linguistic features. Next, by leveraging self-attention mechanism and cross-modal attention mechanisms, we generate fused features to enhance and capture the inter-modal correlation information. Finally, we concatenate the multimodal features into a composite feature vector and employ a Neural Network(NN) classifier to diagnose Alzheimer’s Disease. To evaluate EDAMM, we perform experiments on two public datasets, i.e., NCMMSC2021 and ADReSSo. The results show that EDAMM improves the performance of Alzheimer’s Disease diagnosis over state-of-the-art baseline approaches on both datasets.

Human–machine interface (HMI) electrodes enable interactions between humans and bioelectronic devices by facilitating electrical stimulation and recording neural activity. However, reconciling the soft, hydrated nature of living human tissues with the rigid, dry properties of synthetic electronic systems is inherently challenging. Overcoming these significant differences, which is critical for developing compatible, effective, and stable interfaces, has become a key research area in materials science and technology. Recently, hydrogels have gained prominence for use in HMI electrodes because these soft, hydrated materials are similar in nature to human tissues and can be tuned through the incorporation of nanofillers. This review examines the functional requirements of HMI electrodes and highlights recent progress in the development of polyphenol‐mediated multifunctional hydrogel‐based HMI electrodes for bioelectronics. Furthermore, aspects such as mussel‐inspired and polyphenol‐mediated adhesion, underlying mechanisms, tissue‐matching mechanical properties, electrochemical performance, biocompatibility, biofouling resistance, stability under physiological conditions, anti‐inflammatory, and antioxidant properties are discussed. Finally, applications in bioelectronics and further perspectives are outlined. Advances in HMI hydrogel electrodes are expected to facilitate the unprecedented integration of biological systems and electronic devices, potentially revolutionizing various biomedical fields and enhancing the capabilities and performance of bioelectronic devices.

Permanent control of environment by using remote sensing images requires effective techniques. Two new methods for remote sensing image change detection are proposed. The first method is based on the...

Applying prediction models to software engineering is an interesting research area. There have been many related studies which leverage prediction models to achieve good performance in various software engineering tasks. With more and more researches in software engineering leverage prediction models, there is a need to sort out related studies, aiming to summarize which software engineering tasks prediction models can apply to and how to better leverage prediction models in these tasks. This article conducts a comprehensive taxonomy on prediction models applied to software engineering. We review 136 papers from top conference proceedings and journals in the last decade and summarize 11 research topics prediction models can apply to. Based on the papers, we conclude several big challenges and directions. We believe that the comprehensive taxonomy will help us understand the research area deeper and infer several useful and practical implications.

The purpose is to build a better intelligent transport platform and improve the performance of surveillance video abnormal behavior detection systems under rapid progress of science and technology, to process large-scale traffic surveillance video data. Autoencoder (AE) can detect abnormal behavior by using reconstruction error information. However, it cannot decode some abnormal codes well, so an AE based on memory needs improvement. The objective of this research is to propose a model where abnormal surveillance video can be handled. Therefore, a self-coding method based on memory enhancement is proposed. The steps are as follows: different abnormal behavior detection system algorithms are analyzed at first. The characteristics of three different methods, namely, the original autoencoder (AE), recurrent neural network, and convolutional neural network, are compared. Then, a memory module is proposed to enhance the automatic encoder to reduce the reconstruction error of normal samples and increase the reconstruction error of abnormal samples. The effect image is obtained by Laplace transform and convolution for the image with low definition, and the image with noise is processed by guided filtering. Finally, different methods are used for experimental comparison. Experiments show that, on the dataset Avenue, the frame-level result of the method proposed is about 2% higher than that of the optimal ConvLSTM in the comparison method; on the Ped1 and Ped2 datasets, it is also about 3% higher than ConvLSTM. The comparison of different methods shows that the effect of the method proposed is the best. The self-coding traffic surveillance video abnormal behavior detection system based on memory enhancement is designed with a modular structure and it uses the self-coding method based on memory enhancement. The effectiveness of the proposed method in the real scene is verified by comparing the performance of different methods in the same data set (Xia and Li, 2021).